Business analysts (BAs) have long been an important but frequently unseen part of the banking industry. In highly regulated environments, we translate needs, manage change, and make sure nothing goes wrong by sitting between business, operations, compliance, and technology. Although many banking operations have been automated, the BA position has remained primarily manual, time-consuming, and document intensive.

That is now shifting.

Business analysts’ work is being subtly transformed by generative AI (GenAI), which enhances their effect, speed, and ability to think. GenAI is no longer an experiment in 2026. For BAs working in banking and financial services, it is turning into a useful co-pilot.

The Traditional BA Challenge in Banking: – A typical BA in banking handles:

• Complicated requirements for compliance and regulations
• A lot of documentation, including traceability matrices, process flows, FRDs, and BRDs
• Several stakeholders in the areas of operations, risk, law, and IT
• Tight delivery deadlines, data transfers, and legacy systems

GenAI can help with this.

How the BA Role Is Being Enhanced by GenAI

Although GenAI cannot “understand banking” on its own, it can be a useful tool when directed by a BA’s subject-matter expertise.

A faster, smarter demand document: – GenAI is capable of:

• Use the meeting notes to organize BRDs or user stories
• Create rough drafts of functional requirements
• Reword the standards so that they are more understandable and consistent with stakeholders

As a result, business analysts can move more instantly from documentation to validation and decision-making.

1. Determine the TO-BE and AS-IS procedures: – GenAI can analyse inputs such as SOPs, process documentation, tickets, and past changes

• List the current protocols (AS-IS)
• Draw attention to gaps, redundancies, and manual handoffs
• Help build optimal TO-BE flows

GenAI accelerates analysis and discovery while the BA maintains control over the logic.

Improved stakeholder communication: – Misalignment in banking programs can be costly.

GenAI helps by

• Translating technical information into business-appropriate language
• Developing executive summaries for leadership
• Conducting impact evaluations for risk and compliance teams

This reduces rework and builds stakeholder trust.

2. Assistance with Data Migration and Modernization Programs: – GenAI can help BAs with core banking modernization, data migration, and cloud transformation.

• Assist with source-to-target mapping drafts
• Determine data quality rules and reconciliation scenarios
• Develop test case ideas based on requirements

This is particularly useful for mainframe-to-cloud or legacy platform conversions.

Switching to agent-based support from automation

Agentic AI, which enables GenAI-powered bots to carry out tasks independently while being supervised by humans, is the next stage.

This suggests for BAs:

• Agents oversee monitoring modifications to requirements and updating documentation
• Agents who are responsible for identifying consequences related to regulations or reliance
• Monitoring discrepancies between system performance and business objectives
• The BA becomes a decision orchestrator instead of a document manager

Why does human judgment still matter?

GenAI has limitations despite its abilities, especially in the banking industry.

• To understand the purpose of the rule, advice is needed
• There is no accountability
• Has no risk tolerance or understanding of organizational politics

As a result, the BA job gains value rather than loses it.

The future BA combines:

• Expertise in the domain
• Demonstrates analytical thinking
• Demonstrating ethical judgment
• AI-assisted productivity

Conclusion

GenAI is not meant to replace Business Analysts in banking.

Its purpose is to remove friction, reduce repetition, and improve insight.

In a world of increasing regulation, complexity, and transformation, banks will need business analysts who can combine human judgment and AI intelligence. The future belongs to analysts who can think critically, communicate effectively, and use GenAI responsibly.

About the Author :

Ms. Swati Sharma 
Senior Industry Consultant – Financial Market,
IBM

Ms. Swati Sharma is an experienced Business Analyst specializing in the financial markets sector, with a focus on market intelligence, risk analysis, and financial product strategy. With over 11+ years in the investment banking sector. Ms. Swati Sharma‘s background spans business analysis, solution development, and securities services. Ms. Swati Sharma is analysing and refining financial processes, products, services and systems.

Ms. Swati Sharma has lead the front-to-back digital transformation of investment banking, securities trading, clearing, settlement & portfolio management platforms.

Ms. Swati Sharma’s area of expertise is capital markets technology modernization.

Ms. Swati Sharma areas of expertise include risk frameworks, regulatory compliance, and using AI, cloud-native architectures, and advanced analytics to streamline processes and open up new revenue streams.

Ms. Swati Sharma is working with investment banks, asset managers, custodians, and fintech companies.

Ms. Swati Sharma assist businesses in future-proofing their business models by striking a balance between client expectations, regulatory requirements and new market opportunities.

Ms. Swati Sharma is Accorded with the following Honors & Awards : 

https://www.linkedin.com/in/swati-sharma-53574897/details/certifications/

Ms. Swati Sharma can be contacted at:

LinkedIn

Also read Ms. Swati Sharma‘s earlier article:

The post GenAI for Business Analysts in Banking appeared first on Industry4o.com.

Wealth Management (WM) represents a holistic and highly personalized framework to oversee and optimize the complex financial solutions of high-net-worth individuals and family offices. A diverse set of stakeholders with multiple roles, multi-layered activities, and a broad spectrum of services create multiple opportunities to leverage digital automation across advisory, operations, compliance, and reporting functions.

Globally, the wealth management industry is undergoing rapid digital transformation as firms compete to manage expanding pools of assets. For example, a single advisory platform such as Ameriprise Financial reported more than $1.7 trillion in client assets under management, administration, and advisement in 2026, reflecting the scale and competitive intensity of modern wealth ecosystems.

This scale sets the ecosystem for a level playing field for modern digital technologies and disruption enhancing the capabilities of wealth management firms.

Through this article we explore workflow solutions, patterns, and trends shaping wealth management’s aggregated landscape. We touch upon functions where technology enriches customer visualization experience such as AR/VR, key back-office and middle-office roles linked to AI-orchestrated autonomous workflow of NAV calculation, and uplifted reporting.

The immense contribution and multi-fold influence of Agentic AI and GenAI pairing is emphasized. Agentic AI transforms passive tools into proactive, autonomous financial agents, elevating their capabilities beyond traditional algorithmic management. Generative AI (GenAI) creates new content such as text, images, code, audio, or video by learning patterns from existing data.

According to McKinsey, generative AI alone could unlock between $200 billion and $340 billion in annual value across the global banking sector, primarily through productivity improvements and workflow automation—highlighting its transformative relevance for wealth operations and advisory functions.

Collaboratively, Agentic AI and GenAI ensure seamless, effective, and efficient AI adaptation across wealth ecosystems.

Technology Transformation Across Core Wealth Functions

Adopting path-breaking technology is becoming a game changer for key WM functions such as:

Digital investment advisory platforms are already reshaping portfolio construction at scale. Global robo-advisor assets under management are projected to reach approximately $2.06 trillion in 2025 and grow to $2.38 trillion by 2029, demonstrating rapid adoption of automated portfolio intelligence across investor segments.

In parallel, the global robo-advisory platform market itself is expected to expand from $6.61 billion in 2023 to over $41.83 billion by 2030, reflecting strong enterprise investment in automated advisory infrastructure.

AR and VR Customer Experience Transformation

Customers are operating in a digital age of “Netflix-level expectations.” Digital technologies empower wealth investors by offering real-time insights, personalized experiences, and seamless accessibility. These tools enhance portfolio visibility while strengthening transparency and collaboration with advisors.

Hyper-Personalization

AI analyses user behaviour to customize AR/VR content in real time. A VR-enabled portfolio visualization environment can allow investors to explore asset allocation risk exposures interactively.

Collaborative Advisor Sessions

Shared VR war-rooms enable advisors and clients to jointly restructure portfolios using 3D analytics.

Conversational Analytics

AI explains complex holdings using simplified analogies, helping investors interpret asset correlations faster.

Contextual Benchmarking

Risk heatmaps displayed in immersive environments help investors understand volatility intuitively.

Future Evolution

Biometric integration may allow VR interfaces to dynamically adjust portfolio visualization during stress conditions such as market volatility.

This shift toward immersive advisory environments aligns with a broader industry trend toward hyper-personalized engagement, where McKinsey estimates nearly 75% of generative AI value creation across industries is concentrated in customer engagement and knowledge-synthesis use cases.

Non-Touch NAV Valuation Transformation

There is a paradigm shift toward NTN No-Touch NAV operations.

Non-Touch NAV refers to a fully automated end-to-end process for calculating, validating, and reporting fund NAV without manual intervention. It leverages:

To ingest market feeds, reconcile discrepancies instantly, and detect anomalies proactively.

Agentic AI orchestrates synchronization of:

Deep learning compares NAV against historical trends and peer funds.

Financial institutions are rapidly moving toward automation-led operations. Surveys indicate that nearly 65% of financial services organizations are already actively deploying AI solutions across workflows, with most reporting measurable improvements in revenue growth and cost optimization.

Agentic AI and GenAI provide meaningful results to largely unstructured and non-indexed data often stored across fragmented enterprise systems. In multi-agent architectures, specialized agents perform subtasks and coordinate execution through orchestration layers.

Straight-Through Processing becomes achievable through integration of:

Robo-Advisory and Autonomous Portfolio Intelligence

Agentic AI strengthens robo-advisory platforms through real-time decision intelligence.

The global robo-advisor user base is expected to reach more than 34 million users by 2029, demonstrating rapid adoption of AI-assisted investment decision platforms among both retail and affluent investors.

Hybrid advisory models combining human expertise with AI-driven analytics already account for nearly 64% of global robo-advisory platform revenues, reinforcing the importance of augmented advisory workflows.

Agentic AI Supporting Intelligent Wealth Operations

Agentic AI enables orchestration across:

Industry surveys indicate that approximately 42% of financial institutions are already exploring or deploying AI agents in operational workflows, signaling early movement toward agentic enterprise architectures in financial services.

These developments mark a transition from workflow automation toward autonomous workflow coordination.

The Next Frontier of Intelligent Wealth Management

Peering beyond the horizon, the future appears profoundly transformative. The diverse spectrum of wealth management clients mirrors the intricate systems underpinning services, market dynamics driving sovereign capital flows, and digital technologies shaping innovation.

Meeting rising expectations and orchestrating this complex ecosystem will require Agentic AI.

Enterprise adoption of generative AI is accelerating across financial institutions, with surveys showing over 40% of organizations actively experimenting with GenAI initiatives primarily to enhance customer experience and reduce operational costs—two central pillars of wealth-management transformation.

This evolution should resemble disciplined growth—progressing deliberately through stages of maturity, fuelled by learning and structured capability building.

Until one day, it stands fully evolved as a guiding beacon charting a secure course for technological advancement within defined governance boundaries rather than drifting into unstructured experimentation.

About the Authors :

Mr. Sudeep Mukherjee 
Senior Industry Consultant – Financial Market,
IBM

Mr. Sudeep Mukherjee is a senior consulting professional working in a renowned technology MNC. Mr. Sudeep Mukherjee  has collaborated with global financial services sector firms worldwide. Mukherjee  holds Financial Risk Manager (FRM) certification from Global Association of Risk Professional (GARP) and has Master’s degree in business management with specialisation in finance, complimented by Bachelor’s degree in computer science.

Mr. Sudeep Mukherjee has rich experience in digital transformation and  strong client relationship leading to enhanced customer experience and value creation.

Mr. Sudeep Mukherjee has front-to-back project implementation experience in Investment & Private Banking, Wealth Management, Fixed Income (Interest Rate Derivatives) and OTC Derivatives, Regulatory and Trade Reporting Services, Credit & Liquidity risk, Data harmonization and Data Migration.

Mr. Sudeep Mukherjee is an evangelist for agile method and devops, thereby bringing changes to projects via continuous improvement and collaboration.

Mr. Sudeep Mukherjee can be contacted at:

E-mail |  LinkedIn

Also Read Mr. Sudeep Mukherjee‘s earlier article:

Mr. Raja Basu

Client Delivery & GTM – Financial Services, IBM

Mr. Raja Basu is a Senior Consulting professional in a leading MNC.

Mr. Raja Basu works as a business architect and helps global banking and financial markets clients to enable their digital transformation journey.

Mr. Raja Basu has special interest in responsible use of AI and sustainability.

Mr. Raja Basu is also pursuing his doctoral studies (PhD) from XLRI Jamshedpur.

Mr. Raja Basu is based out of Kolkata, India.

Mr. Raja Basu is an experienced leader in both technology and business, he has a proven track record of defining and implementing technology-driven transformations for clients in the global banking and financial markets.

Mr. Raja Basu focus lies in automation, particularly artificial intelligence (AI), and its impact on climate and sustainability (SCR).

Mr. Raja Basu possess a deep understanding of value-driven advisory practices, which have played a significant role in building strong client relationships. Throughout his career, he has actively contributed to numerous transformation programs involving complex applications for international clients across the United States, Canada, Europe, and Singapore.

Mr. Raja Basu is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/basuraja/details/certifications/

Mr. Raja Basu is Volunteering in the following International Associations & Institutions :

https://www.linkedin.com/in/basuraja/details/volunteering-experiences/

Mr. Raja Basu is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/basuraja/details/honors/

Mr. Raja Basu can be contacted at :

E-mail | LinkedIn

Also read Mr. Raja Basu’s earlier article :

The post Fintech Innovation appeared first on Industry4o.com.

1. Abstract: Rise of AI in Financial Services

The financial services industry is experiencing a significant transformation driven by artificial intelligence (AI) and financial technology (fintech). One of the most promising developments is Agentic AI, a paradigm where autonomous AI agents can perceive, reason, and act to accomplish tasks on behalf of users. In retail banking, fintech initiatives are leveraging agentic AI to deliver highly personalized services, automate complex processes, and improve overall customer experience. This white paper explores how fintech innovations are enabling agentic AI in retail banking, highlighting key technologies, use cases, benefits, and implementation challenges. The paper concludes with insights into the future of agentic banking ecosystems.

2. Implementing Agentic AI backed applications in Retail Banking

Agentic AI refers to intelligent software agents capable of independent decision-making and task execution. These agents integrate machine learning, large language models (LLMs), Retrieval Augmented Generation (RAG) algorithms or frameworks and financial data systems to autonomously assist customers and banks. In the banking environments, agentic AI systems often operate as multi-agent architectures, where different specialized agents handle tasks such as payments, compliance checks, and customer interactions. These agents collaborate and exchange information to complete complex workflows.

For example, conversational banking platforms powered by AI agents allow customers to conduct transactions through natural language interactions. In some implementations, multiple agents coordinate functions such as user intent detection, payment execution, and regulatory verification within a single conversational interface.

The goal is to transition from static banking interfaces to intelligent financial assistants that actively manage financial tasks for users.

3. Key Fintech Initiatives in retail Banking

4. Benefit and Incentives

5. Challenges and Future Outlook

Despite its potential, implementing agentic AI in retail banking presents several challenges.

Addressing these challenges requires collaboration between fintech companies, banks, regulators, and technology providers.

Agentic AI is expected to become a foundational component of next-generation banking. Fintech innovations in open banking, digital identity, real-time payments, and cloud infrastructure will further accelerate the adoption of autonomous financial agents.

Future retail banking platforms may feature AI financial copilots capable of managing daily finances, optimizing investments, and conducting secure transactions autonomously. As these technologies mature, banking will shift from reactive service models to intelligent, proactive financial ecosystems.

6. Conclusion

Fintech initiatives are playing a critical role in enabling agentic AI within retail banking. By combining advanced AI technologies with modern financial infrastructures, fintech innovators are transforming how banks interact with customers and manage operations. Agentic AI introduces a new paradigm where intelligent systems actively assist customers in financial decision-making, automate complex banking processes, and provide highly personalized services. While regulatory, technological, and ethical challenges remain, the continued evolution of fintech ecosystems will likely make agentic AI a central pillar of the future retail banking experience.

About the author:

Mr. Sudhir Kumar Ghosh
Senior Business Analyst & Industry Consultant – Financial Services, IBM

Mr. Sudhir Kumar Ghosh is a seasoned Business Analyst with 17 years of experience in helping Banks and Financial Institutions improve customer experience and engagement through the implementation of innovative digital journeys.

Mr. Sudhir Kumar Ghosh takes interests in conceptualizing technology based financial solutions that can be easily accessible to the less privileged section of the society.

Mr. Sudhir Kumar Ghosh has a strong expertise in digital transformation, business process engineering & data driven decision making.

Mr. Sudhir Kumar Ghosh is also skilled at translating complex business requirements into scalable enterprise technology solutions that improve operational efficiency, customer experience & financial performance.

Mr. Sudhir Kumar Ghosh has proven ability to end-to-end manage & deliver technology-based solutions.

Mr. Sudhir Kumar Ghosh lead’s cross-functional teams for delivering strategic transformation initiatives.

Mr. Sudhir Kumar Ghosh can be contacted at :

LinkedIn

Also read Mr. Sudhir Kumar Ghosh‘s earlier article :

The post Fintech Led Agentic AI appeared first on Industry4o.com.

Abstract

AI and machine learning are fundamentally transforming capital markets beyond automation across three critical areas: market microstructure analysis, portfolio construction, and advanced risk modelling. Real-world implementations by JPMorgan’s LOXM, BlackRock’s Aladdin, and Morgan Stanley’s Portfolio Risk Platform demonstrate measurable improvements in trade execution, risk assessment, and optimization. These technologies enable real-time processing of vast data and complex market relationship identification, achieving 25-40% performance improvements while managing trillions through AI-enhanced platforms.

Core Applications in Market Operations

Market Microstructure Analysis

Advanced algorithmic systems have become powerful platforms in processing vast quantities of data simultaneously, revealing concealed market behaviours and emerging profit opportunities. JPMorgan Chase exemplifies this technological evolution through their groundbreaking LOXM (Liquidity Optimization Execution Machine), which leverages Azure Machine Learning to optimize client order execution. This intelligent system continuously analyses historical trade patterns while dynamically adjusting to evolving market conditions. The results demonstrate remarkable performance improvements: a 40% reduction in processing time and 25% improvement in successful trade execution, underscoring the transformative impact of AI-driven market analysis on modern trading infrastructure.

Portfolio Construction Evolution

Advanced intelligent systems are revolutionizing portfolio management through three key innovations:

Dynamic Real-Time Rebalancing Unlike conventional portfolio management with predetermined rebalancing schedules, intelligent systems enable continuous optimization. These platforms monitor market volatility, asset correlations, and macroeconomic developments in real-time, automatically adjusting allocations to capture opportunities while minimizing risk. This eliminates human delays and maintains optimal distribution without manual intervention, particularly valuable for evolving markets.

BlackRock’s Aladdin (Asset Liability Debt and Derivative Investment Network) exemplifies this advancement, utilizing machine learning to analyse thousands of portfolios daily. The platform’s growth reflects its effectiveness: investment advisory and administration fees from Aladdin-based assets under management surged from $355.5 million in 2023 to $926.5 million in 2024.

As of 2020, Aladdin oversees more than $21.3 trillion in assets, with technology services revenue reaching a record $1.6 billion in 2024.

Fig 1: Aladdin Technology Services Revenue (2018-2024)

Alternative Data Integration Modern portfolio systems extend beyond conventional financial metrics like earnings reports and price movements, incorporating diverse data sources including satellite imagery, ESG sustainability indicators, consumer transaction patterns, geospatial intelligence, and supply chain analytics. This comprehensive data integration enables AI-driven platforms to identify subtle market trends, consumer behaviour shifts, and geopolitical risks, facilitating more strategic and anticipatory investment decisions.

BlackRock’s Aladdin Climate demonstrates this capability by quantifying climate-related risks and opportunities in financial terms, combining climate science, policy projections, asset information, and financial modelling to generate climate-adjusted valuations and risk assessments.

Personalized Risk Management Intelligent systems customize portfolio strategies based on individual investor characteristics, including risk tolerance, investment objectives, and timeline preferences. These platforms analyse behavioural patterns and financial goals to dynamically modify asset allocations, implement protective hedging strategies, and recommend personalized approaches—whether targeting aggressive growth, steady income generation, or capital preservation.

JPMorgan’s Athena platform illustrates this personalization capability, delivering comprehensive cross-asset risk assessment, pricing, and trade management solutions. The system processes billions of daily risk calculations while achieving a 30% reduction in processing time and approximately 80% decrease in hourly calculation costs.

Risk Modelling Advancement

Modern computational approaches have transformed risk assessment by:

1. Identifying Complex, Non-Linear Relationships Between Market Variables

Traditional linear risk models miss complex market correlations. Advanced machine learning—neural networks and decision trees—captures non-linear relationships conventional models overlook. JPMC’s AI research demonstrates this through risk-sensitive decision trees using reinforcement learning for trade execution, helping investors make better risk-adjusted decisions by understanding underlying price dynamics.

The application of these technologies has achieved impressive results and inspired broader uses of deep neural networks for agent representation in trading applications. Deep Q-trading systems have emerged as effective solutions for algorithmic trading, while LSTM (Long Short-Term Memory) representations of reinforcement learning trading agents have demonstrated significant potential in market applications.

2. Enhancing the Accuracy of Tail Event Predictions

Extreme market events like crashes are difficult to predict using traditional models that inadequately weigh risks. AI-driven deep learning and Bayesian networks improve tail risk forecasting by analysing vast datasets. Morgan Stanley’s Portfolio Platform, combined with BlackRock’s Aladdin Risk Engine, helps foresee potential impacts of market shocks on portfolios.

These advanced systems simulate a wider range of market scenarios, utilizing thousands of potential risk factors to help investors and their financial advisors understand how various risks may affect their holdings, including both internal investments and external positions. Morgan Stanley has further strengthened this capability through their patent (US8370241B1) for systems and methods that utilize probabilistic networks—essentially Bayesian networks—to analyse financial models.

This patented approach enables dynamic modelling of financial variables and their interdependencies, significantly aiding in the assessment of complex risk scenarios and tail events. By incorporating these sophisticated AI-driven techniques, the platform enhances portfolio resilience through proactive risk mitigation strategies, allowing investors to better prepare for and respond to extreme market conditions that could otherwise cause substantial losses.

3. Adapting to Shifts in Market Regimes

Financial markets undergo structural shifts from monetary policy, geopolitical events, or liquidity crises, altering dynamics. Traditional models struggle with real-time adjustment, while intelligent systems use adaptive learning algorithms to detect regime shifts early through pattern recognition.

S&P’s Kensho AI platform NERD identifies 142 million entities—companies, people, organizations, events, places—across market regimes, connecting them to S&P Capital IQ data . This entity recognition monitors multiple information streams simultaneously, providing early warning signals for market changes and enabling dynamic investment strategy recalibration before risks materialize.

AI-driven platforms continuously learn from market data and adapt to regime shifts, unlike static traditional models, ensuring risk strategies remain effective and protecting investors against unexpected transitions.

Conclusion

The next evolution in intelligent financial systems will likely centre on quantum computing integration, federated learning across institutions, and autonomous trading ecosystems that require minimal human intervention. Financial institutions should prioritize three strategic imperatives: investing in scalable cloud infrastructure to handle exponentially growing data volumes, developing cross-functional AI expertise through targeted talent acquisition and training programs, and establishing robust governance frameworks to ensure ethical AI deployment while maintaining regulatory compliance. Organizations that proactively build these capabilities today will be positioned to capitalize on emerging technologies like quantum-enhanced portfolio optimization and real-time regulatory reporting systems.

The organizations that will thrive in this intelligent finance era are those that view AI not merely as a technological upgrade, but as a fundamental reimagining of how financial markets can operate more efficiently, transparently, and effectively for all participants.

The dawn of intelligent financial systems is not a distant future—it is happening now, reshaping capital markets one algorithm at a time.

About the Authors :

Mr. Bhushan Joshi
Competency Leader for Banking ISV, Financial Markets and Wealth Management
IBM

Mr. Bhushan Joshi is Competency Leader of Financial Markets and Wealth in IBM India.

Mr. Bhushan Joshi has 25+ years of experience in Driving Business Transformation for clients in Banking, Capital Markets and Wealth Management Industry.

Mr. Bhushan Joshi  has played variety of roles including setting up new practices, working with business units and ISV ecosystem partners to drive growth, in program management and working on strategic consulting engagements for CxO level clients at Global Banks in Legacy Modernization with Microservices and Cloud, Business Architecture, Process Strategy and Optimization, Package Implementations, Vendor selection and Innovation.

Mr. Bhushan Joshi Specialties are Consulting, Digital Banking, Capital Markets and Wealth Management

Mr. Bhushan Joshi is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/joshibhushan/details/certifications/

Mr. Bhushan Joshi can be contacted at:

LinkedIn

Mr. Saravanakumar Dinakaran
Senior Managing Consultant
IBM

Mr. Saravanakumar Dinakaran is a Managing Consultant at IBM with over 23
years of experience leading business transformation initiatives across Financial Markets and Investment Banking.

Mr. Saravanakumar Dinakaran brings deep expertise in front-office trading support, risk management, regulatory reporting, and end-to-end business analysis.

Mr. Saravanakumar Dinakaran with strong domain knowledge across all major asset classes—including Equities, Debt, Derivatives, and OTC products—he has successfully partnered with leading global banks and  regulatory bodies to deliver complex, high-impact programs.

Mr. Saravanakumar Dinakaran recent work focuses on applying AI-driven capabilities to modernize capital markets, enhance risk controls, and accelerate digital transformation across the financial ecosystem.

Mr. Saravanakumar Dinakaran is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/saravanad/details/certifications/

Mr. Saravanakumar Dinakaran can be contacted at:

LinkedIn

References and Sources

https://www.linkedin.com/pulse/ai-powered-trading-financial-revolution-driven-guy-massey-8wole/

https://thefinancialtechnologyreport.com/blackrocks-powerful-aladdin-platform-over-20-trillion-and-counting/

https://s24.q4cdn.com/856567660/files/doc_financials/2025/ar/BLK_AR24.pdf

https://www.blackrock.com/aladdin/products/aladdin-climate

https://www.efinancialcareers.com/news/2022/05/jpmorgan-technology-jobs-and-spending

https://advisor.morganstanley.com/the-stalwart-group/articles/investing/portfolio-risk-management-prepare-unexpected?

https://patents.google.com/patent/US8370241B1/en

https://kensho.com/nerd

The post How AI is Revolutionizing Capital Markets appeared first on Industry4o.com.

Executive Summary

Today’s fraud looks different than it did in the past. Since millions of digital payments are made every minute, avoiding fraud is more difficult than simply detecting it. Once reliable, traditional rule-based systems are now too hard to handle the speed and skill of existing fraud. This landscape is being remodelled by artificial intelligence (AI) and machine learning (ML), which provide a more intelligent, immediate, and manageable method of identifying differences as they occur. To avoid fraud in real time rather than counter to it after it has already happened, financial institutions are using artificial intelligence (AI). This article explains why this change is so important for protecting their reputation and customers.

“AI is transforming fraud detection from a reactive safeguard into a proactive defence system.”

1. From Reactive to Proactive Fraud Management

A shady transaction happened, the system flagged it, and an analyst investigated it. This was the simple pattern of fraud management for many years. It was real—until defrauders determined how to beat the system. New attack patterns were too quick for the rules to change. AI flips this strategy on its head. AI learns from real-time data all the time, changing to new behaviors as they happen instead of just using set thresholds.

This means that AI can find patterns like:

2. Real-Time Anomaly Detection: Fast with Smart

AI-driven fraud systems combine observed and unverified learning to quickly look at huge amounts of transactions. Unverified models discover atypical or infrequent patterns absent prior labelling, whereas observed learning recognizes established fraud categories. Organizations can find risks that they know about and those that they don’t.

Some of the main benefits are:

AI systems offer real-time detection, enabling immediate responses to threats. They are scalable, handling large transaction volumes without proportional increases in resources. AI improves accuracy, reducing false positives and enhancing customer trust. Additionally, AI systems lower operational costs by automating fraud detection tasks, freeing human analysts to focus on high-risk cases.

For instance, companies like PayPal have improved real-time fraud detection by 10% using AI, while American Express enhanced fraud detection by 6% with advanced AI models.

Companies that use AI-based detection often get 30–50% more false alerts and respond up to 60% faster, which directly boosts production and customer satisfaction.

3. Using predictive analytics to stop threats

AI doesn’t just find fraud; it also helps stop it from happening in the first place.  Predictive analytics lets systems figure out what debts will happen before they happen. AI immediately changes the level of risk and starts taking steps to stop problems by linking transaction context and behavioural trends.

Example of how to use it:

Using natural language processing (NLP) to find phishing or social engineering attempts and stop them before they can do any harm makes it less likely that known users will need help.

While traditional fraud systems detect incidents after they occur, predictive analytics helps anticipate them. By analysing historical fraud data, seasonal patterns, and user behaviour, predictive models assign risk scores to transactions and entities. Banks and e-commerce companies use these insights to proactively block high-risk activities before they escalate.

Predictive models also change constantly, retraining themselves as new fraud routes surface, making them unique for adaptive fraud prevention across sectors.

“Real-time anomaly detection allows threats to be identified and eliminated in milliseconds rather than hours.”

4. Developing a Framework for Adaptive, Transparent AI

AI-powered fraud detection requires the stabilization of automation and accountability. Explainable AI (XAI) offers clarity, enabling agents and regulators to understand the decision-making process.

An adjustable, reasonable model must have the following essential components:

“Thanks to explainable AI, every decision is understood rather than just executed.”

5. Prospects for the Future: Cooperation and Conscientious Innovation

As financial backgrounds develop into established and distributed finance, fraud detection must adapt. In order to enhance detection without losing privacy, the next generation of systems will combine blockchain validation, AI with mutual learning, and reliable data cooperation.

Some latest developments are linked learning models for safe inter-institutional understanding sharing and blockchain-based transaction verification for data integrity.

Future developments in fraud security will be shaped by collaboration and information sharing, which will allow the sector to identify and rule out risks faster than any one organization could alone.

“The future of fraud detection lies in collaborative intelligence and responsible innovation.”

Conclusion

AI has transformed the field of fraud detection into one that is dynamic, intelligent, and active. By using real-time change detection, analytical analytics, and visible decision-making, financial institutions can stay ahead of fast-changing fraud techniques. Technology, human expertise, and data ethics will work together to create a secure and reliable digital.

About the Author :

Ms. Swati Sharma 
Senior Industry Consultant – Financial Market,
IBM

Ms. Swati Sharma is an experienced Business Analyst specializing in the financial markets sector, with a focus on market intelligence, risk analysis, and financial product strategy. With over 11+ years in the investment banking sector. Ms. Swati Sharma‘s background spans business analysis, solution development, and securities services. Ms. Swati Sharma is analysing and refining financial processes, products, services and systems.

Ms. Swati Sharma has lead the front-to-back digital transformation of investment banking, securities trading, clearing, settlement & portfolio management platforms.

Ms. Swati Sharma’s area of expertise is capital markets technology modernization.

Ms. Swati Sharma areas of expertise include risk frameworks, regulatory compliance, and using AI, cloud-native architectures, and advanced analytics to streamline processes and open up new revenue streams.

Ms. Swati Sharma is working with investment banks, asset managers, custodians, and fintech companies.

Ms. Swati Sharma assist businesses in future-proofing their business models by striking a balance between client expectations, regulatory requirements and new market opportunities.

Ms. Swati Sharma is Accorded with the following Honors & Awards : 

https://www.linkedin.com/in/swati-sharma-53574897/details/certifications/

Ms. Swati Sharma can be contacted at:

LinkedIn

Also read Ms. Swati Sharma‘s earlier article:

The post Fraud Detection with AI appeared first on Industry4o.com.

A virtual payment card is a digital card, non-physical version of a credit or debit card that can be used for online and in-app purchases.

The first virtual payment card was launched in 1995 by Citibank in partnership with the Mastercard network. This product was initially designed to address the growing need for more secure and convenient online payments during the early days of e-commerce.

The Virtual payment cards market size is expected to reach USD 60.06 billion by 2030, expanding at a CAGR of 20.7% from 2022 to 2030, according to a new study conducted by Grand View Research, Inc. The increasing usage of digital payment platforms is creating the demand for virtual cards worldwide, which is anticipated to drive the growth of the market. In addition, various types of virtual cards offer the consumer a particular set of benefits, such as efficiency and convenience, which also bodes well for the growth of the market.

The global payment processing solutions market size is expected to reach USD 98.01 billion by 2027, registering a CAGR of 14.5% from 2020 to 2027, according to a new report by Grand View Research, Inc. The market growth can be attributed to the emergence of digital technology and customers’ demand for immediacy of transactions. The customers are looking for faster and convenient money transfer options, thereby driving the adoption of payment processing solutions.

A new study from Juniper Research has found the number of virtual payment cards transactions will exceed 121 billion globally by 2027; increasing from 28 billion in 2022. This represents growth of 340%. It predicts rising integration of virtual cards to existing digital wallets services, such as Apple Pay and Google Pay, will drive this growth by exploiting existing substantial user bases of these digital wallets.

The research found the global number of virtual payment card transactions through mobile payment methods will grow from 5 billion in 2022 to 53 billion in 2027, as adoption of virtual cards in contactless payments accelerates. It identified the inclusion of virtual cards as a key market driver of consumer adoption of contactless payments in rapidly developing economies, such as India. This allows them to meet market demand for convenient payment options during checkout amongst unbanked populations.

Virtual payment cards face tough competition from other payment methods, such as traditional contactless cards and QR codes. However, the report anticipates that the secure nature of virtual cards will drive demand in an increasingly security-conscious market.

Additionally, the report forecasts that the US will account for $71 billion in B2B virtual payment card revenue by 2027; representing 72% of global revenue!. It predicts that the growing use of virtual cards in the healthcare sector will be key to driving this growth, with virtual cards increasingly used to pay for supplies and equipment due to the speed of transactions and inherent security of this payment method.

The safe ‘swipe’

VPC offer enhanced security features and is often linked to a user’s existing card (Virtual cards are typically linked to a user’s existing credit or debit card), generating a unique card number and other details for each transaction or a set period. They provide a temporary card number, expiration date, and CVV, which are different from the user’s physical card details. This unique information is used for a specific transaction or a set period, adding an extra layer of security against fraud.

When a user initiates a transaction, a unique card number, CVV, and expiration date are generated in form of Virtual Payment Card. User can generate VPC immediately from their bank. This temporary card information is used to complete the online transaction.

The virtual card details may expire after a single use, after a specific time, or when the user chooses to deactivate it.  Virtual cards help protect against fraud and data enhanced by using unique, temporary card details for each transaction.

 Types of VPCs:-

So what makes them special?

Create a one time Virtual Card number     Account info. Not stored           Payment Info. Unusable

• Convenience and Speed: Can be used immediately after generation.

Some Key Players in Virtual Cards Market

• American Express Company
• BTRS Holdings, Inc.
• Wise Payments Limited
• JPMorgan Chase & Co.
• Marqeta, Inc.
• Mastercard
• Skrill USA, Inc.
• Stripe, Inc.
• WEX, Inc.
• Adye
• Axis Bank
• Niche players like – Privacy.com, Airbase, Soldo, Divvy (Bill.com) etc.

Summary

1. Card Information

2. Transaction History

3. Card Limits and Usage

4. Security Features

5. Payment Summary

6. Access and Management

About the Author :

Mr. Avirup Bhattacharya,
Senior Banking Industry Consultant – IBM

Mr. Avirup Bhattacharya is seasoned Senior Banking Industry Consultant with  13 years of experience in helping banks and financial institutions in improving Core Banking systems as well as improve customer experience and engagement through the implementation of Digital Banking.

Mr. Avirup Bhattacharya is analysing and redefining Core banking solution, products, services and systems.

Mr. Avirup Bhattacharya is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/avirup-bhattacharya-9a107016/details/certifications/

Mr. Avirup Bhattacharya can be contacted at :

Linkedin

The post From Plastic to Digital: The Rise of Virtual Payment Cards appeared first on Industry4o.com.

“AI is poised to reshape corporate treasury by enabling greater automation, precision, and insight in forecasting, risk management, and decision-making.”
— Deloitte, “AI and the future of Treasury,” 2023

In January 2008, internal risk teams noticed suspicious trades in French bank, Société Générale. Jérôme Kerviel, mid-level trader in the bank’s Delta One trading desk (derivatives & equity futures), made unauthorized directional bets on European stock market futures. He concealed his true trading exposure by booking fictitious offsetting hedge trades, which were cancelled prior to settlement, thereby bypassing reconciliation and position-monitoring controls. Weaknesses in treasury back-office reconciliation and confirmation controls enabled the manipulation due to bank’s slack internal controls, disconnected front-to-back systems, heavy reliance on human intervention and his insider back-office knowledge to avoid triggering red flags in risk reports. The losses were to the tune of $7bn.

Between 2008-2015 period, Toshiba Corp (Japan), overstated profit exploiting the gaps in treasury monitoring and accounting manipulation. The scandal involves treasury-type mechanisms, like – Revenue recognition manipulation via financing techniques, Misreporting financial health by manipulating cash flow projections and investment returns, and Misuse of hedging and forward contracts to hide losses.

Fraud is omnipotent and omnipresent. This article sheds light on treasury fraud vulnerabilities and demonstrates how AI can transform remediation efforts. The usecases expose vulnerability of treasury department to multi-faceted frauds. Since treasury work together with multiple business unit link to internal and external eco-system, its susceptible to various kind of frauds. The numerous functions of treasury department create entry point for fraudsters to wreck the financial and reputation havoc of an organisation. The lack of user controls, particularly segregation of duties, approval layers, user access controls and undefined irregular audits adds fuel to the fire. We use a looking glass to deep-seek treasury division’s fraud detection and mitigation.

Based on the primary functions of Treasury departments, Frauds of key Treasury function can be bucketed into :

Traditional Fraud Detection

Like Santiago in Hemingway’s novel, most of existing legacy system to detect fraud is like catching the marlin with a fishing rod. Despite being entrenched in most treasury operations, traditional fraud surveillance systems face persistent challenges:

• Rule-based Rigidity – Static, pre-defined logic can’t adapt to emerging fraud typologies.
• High False Positives – Legitimate customer transactions are frequently flagged, impacting CX.
• Manual Dependency – Analysts must continually tune rules to stay relevant.
• Limited Pattern Recognition – Complex, cross-account fraud often escapes detection.

As fraud tactics evolve, these gaps become wider. And the consequences—financial loss, compliance risk, and reputation damage—grow exponentially.

AI – Game Changer

Machine learning doesn’t just find fraud; it predicts and prevents it.” – Forrester Research

AI is growing in depth and coverage and empower Financial Fraud detection and remediation. AI introduces adaptive, context-aware, and predictive capabilities to fraud detection. It not only learns from past events but anticipates future ones—offering a level of foresight that traditional systems simply can’t match.

AI-powered fraud analytics can:

– Detect evolving fraud patterns using deep learning

– Analyze behavior across accounts and channels

– Perform real-time scoring on massive transaction volumes

– Leverage both structured and unstructured data for better insights

This is where AI moves from being a tool to a trusted partner in safeguarding financial integrity.

 Thought Provoking Shots: Training AI for Treasury Fraud Resilience

Fraudsters innovate fast, but AI-powered defences innovate faster.

Treasury sits at the junction of high-value flows, privileged access, and fragmented systems—an ideal target for modern fraud. Legacy, rules-based controls catch yesterday’s patterns but struggle with cross-channel schemes and insider abuse. AI changes the physics: few-shot learning adapts to new typologies with minimal labels, CoT makes alerts auditable, RAG grounds decisions in policy and prior cases, and periodic fine-tuning keeps models aligned with shifting behaviour. Combined with strong segregation of duties and model governance, banks can cut false positives, reduce detection latency, and materially lower loss given fraud. Since the AI is continuous learning and improvement, Machine Learning takes centre stage.

That’s where right adoption is critical. It can be achieved with advanced AI training techniques like Few-shot and Zero-shot Learning, Chain-of-Thought (CoT) reasoning etc.

Why Few-shot Learning + Chain-of-Thought Reasoning is the Best Approach for Financial Fraud Detection?

1. Few-shot Learning for Rapid Adaptation

Fraud tactics rarely stand still. By the time a traditional system is retrained, a new scheme is already in motion. Few-shot learning allows AI to adapt to these shifts quickly, using just a handful of confirmed cases. Few-shot learning enables AI systems to:

• Adapt to new fraud types quickly using a small number of labelled cases.
• Reduce operational cost by avoiding constant large-scale retraining.

Example:

New Scam Pattern in Wire Transfers

• A bank notices a new mule account scam where stolen funds are funnelled through low-balance accounts within minutes.
• By feeding these few examples into the model, along with indicators like transaction velocity, IP patterns, and receiving account behaviour, the system can start spotting similar incidents almost immediately, instead of monthly feedback loop.

2. Chain-of-Thought (CoT) Reasoning for Explainability — Seeing the Why Behind the Flag

One of the biggest frustrations for fraud teams is getting an alert with no explanation. Chain-of-Thought reasoning changes that. It encourages the model to walk through its thinking step-by-step before making a decision — exactly the kind of transparency regulators and analysts expect.

This is essential for:

• Regulatory compliance — Providing a traceable decision process for auditors and regulators.
• Analyst trust — Giving investigators insight into why a transaction was flagged.
• Complex signal integration — Linking signals like geolocation anomalies, device changes, velocity spikes, and unusual transaction behaviour.

Example:

A Chain-of-Thought approach model identifies the transaction as a likely fraud since it traces its origination from a new device, used an IP address in a high-risk region, exceeded typical spending limits, followed multiple failed login attempts, and matched a known high-risk cluster.

3. Periodic Fine-Tuning for Sustained Accuracy — Keeping the Edge Sharp

Even with Few-shot adaptability, periodic fine-tuning is necessary to:

• Incorporate confirmed fraud cases into the core model.
• Address model drift caused by changing customer behaviours and evolving fraud patterns.
• Refresh the underlying detection logic while retaining domain-specific knowledge.

 It’s about folding confirmed fraud cases back into the system, correcting any drift, and ensuring the model’s detection logic stays sharp without losing the domain expertise it’s already built.

Recommended cadence: Quarterly fine-tuning with high-quality, labelled datasets.

4. Using RAG for Context without Full Retraining — Smarter Context, Less Guesswork

Retrieval-Augmented Generation (RAG) allows the model to reference trusted external fraud knowledge bases in real time without retraining, which:

• Reduces hallucinations by grounding outputs in verified information.
• Speeds up pattern matching against historical fraud cases and analyst notes.
• Minimizes compute cost by avoiding full model retraining for every new fraud type.

Example:

New Scam Keyword Detection

• Chat logs in a bank’s customer support system contain messages about a “prize redemption” scheme.
• RAG retrieves recent fraud analyst reports describing similar scams and their markers.
• AI uses this retrieved knowledge to immediately detect and warn customers in ongoing conversations — without retraining the full model.

5. The Power of All Four Together – Combined Benefits

Individually, these (Few-shot Learning, CoT Reasoning, Periodic Fine-Tuning, and RAG) techniques are strong. Together, they create a fraud detection system that is:

New Fraud Defence Paradigm – Intelligent, Adaptive & Context-Aware Defence

AI shifts gear from reactive to proactive, intelligent, and integrated fraud risk management—especially relevant in modern treasury operations, where fraud is increasingly sophisticated and fast-evolving.

With fraudsters outsmarting the systems and controls, Fraud possesses challenges best described

– Fraud today is multi-faceted, multi-channel, and adaptive—static rules can’t keep up.

– Few-shot and zero-shot learning unlock rapid response to new fraud methods.

– Chain-of-Thought reasoning brings transparency and traceability to decisions.

– Retrieval-Augmented Generation reduces hallucination and improves auditability.

– KPIs must evolve to reflect both effectiveness and customer sensitivity.

Shot for Thought: The AI-Driven Future of Fraud Risk Management

AI isn’t just a tool—it’s the new teammate reshaping how we think, decide, and act.

AI in fraud detection isn’t just a tech upgrade—it’s a strategic transformation. With a hybrid of Few-shot learning, Chain-of-Thought reasoning, and Retrieval-Augmented Generation, banks and financial institutions can achieve higher detection rates, lower false positives, and greater compliance-readiness.

AI will, henceforth, pitted against the darker side of its mirror ecosystem, which will in a perpetual contest try to outclass each other. In the battle of supremacy and continuous effort to strengthen the citadel of treasury functions, AI is necessity for strategic transformation, and longer a choice. Fraud will keep on mutating to metamorphose from one method to another. Hence, AI-ML with alliance with cloud infrastructure, blockchain, Big Data and Analytics, Social Engineering and data security need to detect and remediate fraud.

Species that thrives are the ones that best adapt to the changing environment. In this is the era of Digital Revolution of adaptability and fusion, nothing adapt like Gen AI.

About the Authors :

Mr. Sudeep Mukherjee 
Senior Industry Consultant – Financial Market,
IBM

Mr. Sudeep Mukherjee is a senior consulting professional working in a renowned technology MNC. Mr. Sudeep Mukherjee  has collaborated with global financial services sector firms worldwide. Mukherjee  holds Financial Risk Manager (FRM) certification from Global Association of Risk Professional (GARP) and has Master’s degree in business management with specialisation in finance, complimented by Bachelor’s degree in computer science.

Mr. Sudeep Mukherjee has rich experience in digital transformation and  strong client relationship leading to enhanced customer experience and value creation.

Mr. Sudeep Mukherjee has front-to-back project implementation experience in Investment & Private Banking, Wealth Management, Fixed Income (Interest Rate Derivatives) and OTC Derivatives, Regulatory and Trade Reporting Services, Credit & Liquidity risk, Data harmonization and Data Migration.

Mr. Sudeep Mukherjee is an evangelist for agile method and devops, thereby bringing changes to projects via continuous improvement and collaboration.

Mr. Sudeep Mukherjee can be contacted at:

E-mail |  LinkedIn

Mr. Prasad Naik
Delivery Manager – Complex Programs,
IBM

Mr. Prasad Naik is a AWS Certified seasoned AI Practitioner and delivery leader with over 24 years of experience in global program management, specializing in payments, treasury, and security services within the financial services and insurance (FSI) sector. Mr. Prasad Naik‘s expertise spans AI-augmented program delivery, cloud transformation, and holistic payments solutions across global client locations. Mr. Prasad Naik decorates his career with professional accreditations in PMP®, CSM®, and PRINCE2®.

Mr. Prasad Naik can be contacted at :

LinkedIn 

Mr. Prasad Naik is bestowed with the following License & Certifications :

https://www.linkedin.com/in/prasad-n-6aa1a3139/details/certifications/

The post AI to Reshape Corporate Treasury appeared first on Industry4o.com.

Introduction

In recent decades, the diffusion of digital technology into nearly every business and industries, also known as “digitalization,” has been remaking the U.S. economy and the world of work. Digitization in North America, particularly in the United States, is advancing rapidly.

Zelle was established as clearXchange in 2011 by three large American banks, Bank of America, JPMorgan Chase, and Wells Fargo. It functioned as a digital payments network to facilitate person-to-person (P2P), business-to-consumer (B2C), and government-to-consumer (G2C) transactions. The company was acquired by Early Warning Services in 2016 and re-launched as Zelle in 2017.

Early Warning Services, LLC, (fintech) an innovator in payment and risk management solutions, Zelle makes it easy, fast and safe for money to move. Founded in 2017, Zelle is a digital payment network owned by a consortium of major U.S. banks.

Zelle has revolutionized person-to-person (P2P) payments in the U.S. banking ecosystem. In 2025, Zelle processed $1.92 trillion in transactions—a 20% increase from 2024.

In 2024, it crossed the $1 trillion mark for the first time, with 3.6 billion transactions sent by 151 million users.

In 2023, Zelle handled $806 billion across 2.9 billion transactions, up 28% over-year.

Zelle’s bank-backed model has propelled its user base to 145 million in 2025

What is Zelle?

Zelle is a digital payments network that allows users to send money directly from their bank account to another registered user’s bank account within the United States using email address or U.S. mobile number. Users can access Zelle through their mobile banking app or the banking portal. To send money, user need the recipient’s email address or U.S. mobile number. If the recipient is already enrolled with Zelle, the money will go directly into their bank account, typically within minutes. Zelle, is integrated into over 2,000 banking apps, making it widely accessible for users who have accounts with participating bank. In 2018, Zelle introduced the business account function, making it easy for consumers to pay businesses as well.

To use Zelle for your business, the user needs a business bank account. Although Zelle doesn’t offer business-specific features, it is helpful if you’d like to accept and make payments from a business account at a much lower (or no) cost, saving your company unwanted transaction fees.

P2P payments accounted for 52% of Zelle’s total transactions in 2025, maintaining its lead as the most used payment type.

B2B transactions rose 29% in 2025, powered by expanded bank partnerships and business integrations.

Recurring payments grew 22% in 2025, highlighting Zelle’s role in monthly rent, tuition, and bill payments.

Over 91% of users in 2025 rated Zelle as secure and user-friendly.

Fraud concerns remain a challenge, but Zelle has implemented extensive consumer education and security alerts

Zelle Key features

Small Business Zelle adoption

One of Zelle’s biggest strengths is its zero processing fees. Small businesses that are looking for zero-cost payment processing will find Zelle to be highly suitable. Although Zelle does not charge any processing fees, the customer’s bank may charge additional fees for using Zelle. Fast disbursements for services, supplies, or freelance work. Used for recurring expenses like employee wages or rent payments.

Impact

• 7 million+ small businesses enrolled as of 2025
• 35% increase in commercial transactions via Zelle in 2025
• Average payment received: ~$465 Average payment sent: ~$630

Zelle Security features

Bank-Level Encryption: Zelle operates within customer bank app, using the same security protocols as their financial institution

Minimal Info Sharing: Customers need to share an email or U.S. mobile number and not sensitive account numbers. Account details are not exposed.

Authentication & Monitoring: Banks use multi-factor authentication and fraud monitoring to detect suspicious activity

Firewall: Zelle has a firewall that acts as a 24/7 filter. It scans all data that attempts to enter a network and prevents anything suspicious from getting through.

Data encryption: Encryption converts plain text into cipher text within Zelle. This makes it unintelligible or unreadable to anyone who doesn’t have the precise decryption key. Therefore, even if hackers do gain access to a database, users data will not be of any use.

Device security feature: Zelle allows you to enable security features built into your device such as facial or fingerprint recognition.

Enrollment and Customer Trends

Zelle Growth rate over years

Zelle and AI

Artificial Intelligence plays a behind-the-scenes but powerful role in enhancing Zelle’s payment ecosystem. AI is mainly used for fraud detection.

Pattern Recognition: Zelle uses AI to spot unusual transaction patterns that may signal fraud. For instance, sudden large amount or number of transfers or activity from unfamiliar devices

Risk Profiling: AI assesses the risk level of email addresses and phone numbers used for transactions, helping banks flag suspicious accounts

Real-Time Alerts: AI enables instant fraud alerts and automated account lockdowns, especially useful for banks facing high scam volumes

Scams are designed to exploit trust and mimic legitimate behavior, which makes them especially challenging for automated systems to detect. Identifying scams relies much more on customer awareness, notifying users with as much information as possible significantly reduces risk. Zelle ultimately rely on a combination of customer vigilance and prevention measures to prevent scams.

Conclusion

About the Author :

Ms. Nandhini Balajee 
Senior Industry Consultant | QA Lead
IBM

Ms. Nandhini Balajee is a Software Quality Analyst with over 10+ years experience of both web-based and client server Applications in the Banking domain.

Ms. Nandhini Balajee is expertise in understanding and analyzing test requirements, Well acquainted with all phases of SDLC and STLC.

Ms. Nandhini Balajee is experienced in smoke testing, System testing, SIT, Regression and User Acceptance testing.

Ms. Nandhini Balajee is also expertise on testing REST API using SOAP UI and CA DevTest tool.

Ms. Nandhini Balajee can be contacted at:

LinkedIn

Reference :

https://www.zellepay.com/press-releases/zelle-soars-806-billion-transaction-volume-28-prior-year

https://www.bankrate.com/banking/checking/how-to-use-zelle-beginners-guide-to-digital-payments/

https://www.brookings.edu/articles/digitalization-and-the-american-workforce/

https://www.pymnts.com/earnings/2023/

https://coinlaw.io/zelle-statistics/

https://www.emarketer.com/content/zelle-growth-tear-1-trillion-volume

https://www.zellepay.com/security

https://paymentcloudinc.com/blog/is-zelle-safe/

https://fitsmallbusiness.com/how-to-use-zelle-for-business/#zelle

https://www.pymnts.com/earnings/2025/mastercard-q2-revenue-jumps-17percent-as-demand-for-ai-and-cybersecurity-grows/

https://www.zellepay.com/press-releases/zeller-reaches-five-year-milestone-more-five-billion-safe-secure-transactions

The post Revolutionizing Digital Payments appeared first on Industry4o.com.

Business Case

The Indian Banking and Finance Industry must introduce reforms in the lending products & services for the retail customers. Introduction of artificial intelligence-based solutions that are developed with robust models to ascertain risk, predict customer behaviour and market fluctuations is the need of the hour.

Banks and Finance companies often provide their commercial and corporate clients with tailor made products & services to manage debts consolidation and restructuring. However, there are no tailor-made solution available for the retail customers to manage their debts and get financial relief. The development of a solution for debt restructuring and retail customers pay-off is the focus of the article.

Problem Statement

Financial mismanagement is a major risk for retail customers. Since the beginning of recorded history there are incidences when people have lost their wealth due to bad financial decisions and mismanagement of both liquid and immovable assets. Also, retail customers when faced with adversities like natural disaster, crop failure, job loss, significant loses to income source due to spread of epidemics, or because of treatment of critical diseases like cancer or kidney failure, lose a lot of money and become dependent on multiple loans.

According to the 2022 study by PwC and Equifax*, smaller ticket size loans such as consumers and personal loans, had the highest rates of Delinquency while Covid outbreak. The higher delinquency among the rural and semi-urban population was attributed to the reduction in household income during lockdowns. The greatest burden of Non-Performing Assets (NPAs) fell upon NBFCs who targeted retail sectors with unsecured personal loans.

Figure 1.1 Below RBI data also proves a correlation between adversity such as COVID and percentage of NPAs in the retail segment. Due to lock down and job losses at the end of 2018-19, Gross and NPAs were highest in the Financial Year (FY) 2018-19. However, a gradual decline in NPAs was observed with unlock and improvement in market & health conditions and increase in the employment opportunities.

Retail customers initially rely on a single lender but as the situation become more extreme, they become dependent upon multiple loans for survival and attempt to bail out of their financial difficulties. Any mismanagement of funds/misses in EMI payments on account of limited knowledge of financial products, lack of/incorrect financial advice, eventually pushes them in a debt trap. Soon it becomes impossible to keep track of multiple loans, finances and life becomes a hellish experience where one is constantly harassed by threatening phone calls and visits from recovery/collection that the lenders have appointed as well as legal notice sent by their advocates.

Figure 1.2 Below RBI data shows impact of numerous factors upon growth of NPAs/Loan Defaults.

If the financial situation does not improve, a few retail customers become very distressful and may take desperate measures- start criminal activities, commit suicide, send/sell their women folks to prostitution and more to fulfil financial requirements.

What do retail customers who have failed to manage their debts may be looking for?

Most customers suffering the above-mentioned problem statement are looking for relief from the threatening follow ups for payments through home visits and over phone calls. They want to consolidate their debts by closing all the smaller loans with one single loan which offers them flexibility in terms of release of funds, tenure of payments and interest rates. Possibly rewards or incentives and some financial advice.

Existing financial products that may help retail customers to manage debt issues.

A few solutions for debt managements that are offered to retail customers are listed below for illustrative purposes.

1. Loan Balance Transfer: Help retail customers consolidate their loans; financial organizations offer balance transfer loan products. Idea here is to close multiple smaller loans with single large one. However, this is upselling of personal loans. Such products often fall short of a retail clients need as they do not provide any financial advice on debt restructuring and pay off. Moreover, customer having poor credit bureau scores are not eligible.

2. Payday loans: Payday loans are short-term loans that are having tenures ranging from 7 days to 35 days. These have high processing fees and interest rates, sometimes up to 30%. These are mostly provided by NBFCs and non-regulated lenders. Payday loans require minimum paperwork and are disbursed quickly; hence can be attractive to few retail customers. However, smaller tenure and excessive cost of funds can push customers to debt traps.

3. Gold loans: Gold loans are one of the oldest lending instruments that are useful to people in urgent need of funds. However, inflexible payment schedule and risk of losing the collateral that hold family emotions and sentiments make them not the preferable option for some retail customers.

4. Debt free consultancy services: Few companies provide debt advisory services to both business and retail customers at premium charges. These businesses hire expert financial counsellors and in-house lawyers who may negotiate with creditors on behalf of their clients to manage their debt challenges. The only drawback is that they themselves do not offer any loan/funds, and their solution would mostly depend upon the mercy/discretion of the lenders.

Proposed Solution

A solution that will employ recommendations from an Artificial Intelligence Assisted to ascertain an individual’s financial health and accordingly suggest loan products and customized path for debt relief is the need of the hour. Such a solution can be integrated with the lenders Loan Origination Software to identify, onboard and service retails customers especially those, who are poor in managing finances. The proposed solution should have an intelligence/capability to identify retail customers who have the capacity to payback loans yet are unable to secure debt consolidation due to the lack of financial knowledge and limitations of existing lending products and services.

The regulator should lay down guidelines for private sector, public sector banks and NBFCs to work on tailor made solutions for retail debt consolidation and restructuring.

How will such a solution work?

Roadmap: Onboarding & Servicing of debt -ridden retail customers with Artificial Intelligence based Technology solution for Debt consolidation, restructuring and pay off.

Benefit and Incentives

Benefits to the lenders

1. Once this line of business picks up new business opportunities will come to Banks and NBFCs in form of new customer segments or previously unexplored/untapped customer segments. Ensuring steady profits. Retail customers are safer than business and corporate customers because individuals have fixed income and their monthly income and spends can be easily tracked for any risk basis the account statements of their salary accounts.

2. Some of lenders may argue the retail segment would provide them low return but also there is lower risk.

Benefits to the retail customers:

Future Direction

Conclusion

Implementation of a financial product, which has the capability to ascertain customers credit profile and predict a tailor-made debt restructure plan basis artificial intelligence has become the need of the hour.

About the author:

Mr. Sudhir Kumar Ghosh
Senior Business Analyst – IBM

Mr. Sudhir Kumar Ghosh is seasoned Business Analyst with 16 years of experience in helping Banks and Financial Institutions improve customer experience and engagement through the implementation of innovative digital journeys.

Mr. Sudhir Kumar Ghosh takes interests in conceptualizing technology based financial solutions that can be easily accessible to the less privileged section of the society.

Mr. Sudhir Kumar Ghosh can be contacted at :

LinkedIn

Ms. Swati Sharma
Business Analyst – IBM

Ms. Swati Sharma is an experienced Business Analyst specializing in the financial markets sector, with a focus on market intelligence, risk analysis, and financial product strategy. With over 11 years in the investment banking sector.

Ms. Swati Sharma‘s background spans business analysis, solution development, and securities services.

Ms. Swati Sharma is analysing and refining financial processes, products, services, and systems.

Ms. Swati Sharma can be contacted at:

LinkedIn

The post AI Assisted Debt Consolidation & Pay off Solution appeared first on Industry4o.com.

What is Cyber Security in essence?

Cyber security in the banking industry refers to the methods, tools, and technologies used to defend financial organizations, their systems, and clients against online attacks. Among these dangers are ransomware, malware, phishing scams, identity theft, and more. Banks seek to preserve operational health, guard sensitive data, and stop financial losses by putting strong security frameworks in place.

A paradigm shifts in a more cohesive approach that reflects a better understanding and management of cyber threats is anticipated by the cyber security industry. This change pertains to the most recent technological revolution and adoption, as well as related liability, maturity, integration, regulatory, quantification, communication, and behavioural changes. Because of its handling of high-value transactions, sensitive personal data, and cross-border and global reach, the industry is a prime target for cybercriminals, making cybersecurity an essential. Also, digital transformation in banking predictably unleashes plethora of security loops holes. Online banking, mobile apps, APIs, cloud infrastructure, and fintech partnerships have transformed the banking arena, simultaneously expanding the attack surface.

Show me the money!

The sense of urgency is apparent in the world market of cybersecurity. It contributed to USD 172.24 billion in 2023 and is anticipated to raise to USD 562.72 billion by 2032 with a CAGR of 14.3%.

Cybercriminals reap enormous sums from banking-related fraud—measured broadly as the losses suffered by banks, businesses, and consumers. Some numbers to corroborate:

Lucrative High-Value Targets where banks and their customers hold large sums and sensitive data, low Risk/High Reward where automated tools (bots, credential-stuffing kits), AI-driven phishing, and “money-mule” schemes let attackers scale rapidly. And global reach where Cross-border transactions and diverse regulatory environments make detection and recovery difficult.

Forecast cybersecurity market revenue worldwide in 2024, by segment (in billion U.S. dollars) (Source: Statista’25)

The flip side of digital transformation

The convenience of digital banking comes with a significant increase in vulnerabilities and security loopholes that cybercriminals are quick to exploit. While the banking sector has embraced digital transformation through online platforms, mobile apps, APIs, cloud infrastructure, and fintech partnerships, this progress has also expanded the attack surface.

The canvas of threat landscape includes some common cyber threats:

• Phishing and Social Engineering Attacks
• Malware and Ransomware
• Advanced Persistent Threats (APTs)
• Distributed Denial of Service (DDoS)
• Insider Threats
• Credential Stuffing and Brute Force Attacks
• Third-Party and Supply Chain Risks

Among these, phishing remains one of the most significant threats. The financial sector is a top target for phishing campaigns that trick employees or customers into revealing sensitive data or downloading malicious software. With the advent of AI-generated phishing content, detection has become more difficult than ever.

Why should Cybersecurity matter?

In the ear of mammoth digital transformation, the importance of cybersecurity in the financial sector cannot be overstated for the following reasons:

The Legal and Regulatory Landscape

According to a Senior Cyber Security consultant from Deloitte: “The biggest deterrent for non-compliance is the financial penalty. But the long-term benefits of a disciplined approach to compliance far outweigh the cost.”

Banks have a legal obligation to safeguard customer information and prevent unauthorized access. Various global and national frameworks guide their cybersecurity efforts:

Global Standards & Frameworks:

Country-Specific Regulations:

To stay ahead of threats, banks are adopting advanced technologies:

Banks and financial institutions are obliged to safeguard sensitive data, ensure compliance. Despite the urgency, banks face several roadblocks, some of the challenges in implementing Cybersecurity are:

According to IBM’s IBV study- research with 1,000 executives across 21 industries and 18 countries (including 140 in banking) reveals a stark reality: banks juggle an average of 114 different security solutions from 42 vendors. This fragmented approach not only frustrates security professionals but also hinders overall effectiveness.

What Are Banks Doing Today?

To combat threats proactively, modern banks are deploying:

• Multi-Factor Authentication (MFA): Enhances security with layers of verification.
• Encryption: Protects data in transit and at rest.
• Security Audits: Regular reviews to detect and fix vulnerabilities.
• AI-Powered Fraud Detection: Identifies abnormal behaviour in real time.
• Secure Payment Gateways: Ensures secure online transactions.
• Employee Cyber Awareness Training: Reduces human error and insider threats.

As cybersecurity continues to be deeply integrated into every aspect of modern banking operations, embedding cybersecurity into every facet of operations, adopting forward-looking strategies, and fostering a culture of vigilance, financial institutions can safeguard their operations, build customer trust and help ensure a resilient financial ecosystem for the future.

While the cyber security file evolves, here are some emerging trends to watch:

According to an independent senior Cyber Security consultant has concerns over data handling- “Customized GenAI Security framework with open ends and no security to protect a bank’s data that is being used is a key concern”.

A continuous strategic imperative

Cybersecurity isn’t an ancillary or support function but a strategic imperative for modern banking. The convergence of technology, compliance, and customer trust demands continuous investment in people, processes, and innovation. It calls for continuous investment in people, processes, and technology is vital to stay ahead of threats.

As banks integrate cybersecurity into every layer of operation and embrace emerging technologies, they move closer to building a future-ready, resilient financial ecosystem. Thus, the future of cybersecurity in banking is poised for transformative advancements that can ensure a resilient financial ecosystem for the future.

IBM as a leader in Cybersecurity

IBM Cybersecurity Services is a trusted partner, delivering advisory, integration and managed security services, to offensive and defensive capabilities, we combine a global team of experts with proprietary and partner technology to co-create tailored security programs to provide vulnerability management and transform security into a business enabler.

Cybersecurity Consulting Services | IBM

IBM NCEE News Room – Case Studies

Security and fraud risks in banking and financial markets | IBM

Ms. Sudha Rawat Talwar is a Senior Consulting professional with a background in marketing and business development.

Ms. Sudha Rawat Talwar is keen on areas such as ESG and hyper personalization in banking and payments. She is based out of Bangalore.

Ms. Sudha Rawat Talwar can be contacted at:

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In today’s world where banking is not done only by traditional large banks but also by a set of new, digital only, challenger or non-banking players. While existing customers of traditional banks are not completely cutting off their relationship but increasingly shifting their financial activities to new players. This quiet attrition signals arrival of ‘Digital Darwinism’ in banking. In that context, adaptation of Generative AI with proper use cases will not only provide another competitive advantage but build the ground for survival.

The Great Banking Divide

Today’s banking landscape can be split into two broad species

Traditional Titans: Banks with burden of legacy despite some level of modernization, owning huge data, excess inertia, siloed operations and product-first mindset

Digital Disruptors: Fintech that are customer-centric, technology driven, agile mindset in execution. They challenge traditional banks in multiple ways to do banking differently.

As Darwin observed that it is not the strongest but those that are most adaptable survive. Similarly, in the banking ecosystem, while Gen-AI is coming with lot of potential either operational efficiency gain or outstanding customer service or completely different business for personalized banking, evolutionary pressure will be more. This will decide the fate of banking institutions, both new and old.

The Silent Customer Exodus

Studies show that account closure rates in traditional banks is almost stable however their transaction volume have declined over time while users started taking same services from fintech more.

Similar patterns emerge across the industry: customers maintain their primary accounts but increasingly use them as mere way stations for funds headed to specialized services.

“Banking customers today don’t necessarily break up with you – they just see other people on the side,” quips Brett King, founder of Moven and banking futurist. “And eventually, those relationships become the primary ones.”

This phenomenon is particularly pronounced among younger demographics. According to a 2024 Cornerstone Advisors study, 41% of Gen Z consumers and 38% of Millennials now consider a fintech app – not a traditional bank – their primary financial relationship.

Why Traditional Banks Struggle with Innovation

What explains this paradox where traditional banks, despite their resources, often lag in customer-centric innovation? The answer lies in fundamental structural and philosophical differences:

1. Product-First vs. Customer-First: Traditional banks organize around product lines, each with its own P&L, creating internal competition and fragmented customer experiences. Chase Manhattan Bank famously discovered that high-value customers were being contacted by seven different divisions with competing offers – sometimes in the same week.

2. Organizational Silos: In many established banks, compliance, risk, IT, and customer experience teams operate with separate and often conflicting KPIs. Compliance team of a large bank might require five forms of identification for regulatory reasons while their customer experience team aims for one-click onboarding – with no mechanism to resolve this contradiction.

3. Legacy Technical Debt: A few large banks e.g. JPMC, BoA still maintains critical systems written in COBOL – a programming language from 1959. Such technical foundations make rapid innovation nearly impossible.

4. Risk-Averse Culture: When large banks survey employees about barriers to innovation, “fear of compliance violations” ranks one of the tops– ahead of “technical limitations” or “budget constraints.”

The Fintech Advantage

By contrast, digital-native financial institutions approach the market fundamentally differently:

1. Customer Journey Centricity: Revolut helps people manage money easily. The app offers banking, budgeting, investing, and insurance in one place. Users get real-time alerts when they spend money. The app also has tools to help people save money (over 35 million users as of 2024). Everything is easy to use and well connected. Because of this, Revolut has grown quickly. Today, millions of people around the world use it.

2. Unified Experience Design: Nubank is a fintech company from Brazil. It offers credit cards, loans, and bank accounts in one app. The app is clean and easy to use. Customers can check balances, pay bills, and get help in the same place. This makes banking simple and stress-free. This company has redefined user experience design to a different level.

3. Agile Technical Foundations: Stripe uses ‘Agile Foundations’ to stay flexible and scale fast. They prioritize automated testing, continuous integration, and modular architecture. This helps their teams deploy new features quickly and safely. For instance, when launching Stripe Radar (a fraud detection tool), they used small, frequent updates to adapt quickly to user feedback and fraud trends.

Fundamental flow of a neobank

4. Experimentation Culture: PayPal encourages teams to test new ideas quickly. For example, they experimented with different checkout flows to reduce cart abandonment. Small user groups got different versions, and PayPal used the results to pick the best one.

Generative AI: Banking’s Evolutionary Catalyst :

Generative AI represents a potential reset moment. The enablers that could either amplify these differences or provide traditional banks with an opportunity to leapfrog ahead.

The following transformative applications already exist:

1. Hyper-personalized financial advice: Singapore’s DBS Bank deployed an AI system that analyzes transaction patterns. It works with real life events to provide tailored financial guidance. Early results show a moderate increase in advisory service adoption. It helps to achieve higher customer satisfaction.

2. Conversational banking interfaces: Capital One’s Eno and Bank of America’s Erica have evolved from simple chatbots to sophisticated virtual assistants. They can anticipate needs based on past behavior. They have the capabilities to structure unstructured customer requests into actionable steps.

3. Predictive financial wellness tools: Huntington Bank’s “Heads Up” feature uses generative AI to analyze spending patterns.  This proactively alerts customers to potential financial issues. It happens before the actual occurrence. It significantly reduces overdraft incidents

4. Automated regulatory compliance: HSBC implemented AI systems generating compliance documentation. It reduces compliance processing time while improving accuracy.

Potential Gen-AI value in banking worldwide 2025 by segment (Source: Statistia’25)

The Data Advantage Paradox

Traditional banks undoubtedly have huge advantage of rich historical and transactional data, which can be used for training for modelling for potential use cases. However, many incumbents also struggle to leverage this advantage due to data fragmentation across multiple systems including legacy systems, restrictive data governance, cultural resistance to change. Also, many gen-Z and millennials with strong AI background prefer to work in challenger banks, as opposed to traditional behemoths, to add their values where they are more welcome to implement their fresh ideas. At the same time, digital challengers are also building AI capabilities to entice customers.

• Acquiring customers at lower costs through superior digital experiences
• Forming strategic partnerships to enrich limited internal data
• Creating engagement loops that rapidly generate new behavioral data
• Implementing “data-first” architectural decisions from inception

Darwin’s Banking Prediction

As banking industry is undergoing evolution, here are the trends emerging:

1. The hybrid winners: Neither traditional bank not digital challengers have the ultimate mote to succeed in Gen-AI age. Those that can harness their rich data, shift to customer-centric mindset and deliver personalized products based on data analytics, operate very nimbly will acquire and grow customers with high stickiness.

2. Acquisition acceleration: Unable to transform internally fast enough, more traditional banks will acquire AI-native fintech not just for their technology but for their innovation culture. PNC’s acquisition of Lulo Bank and integration of its AI leadership into core operations shows this strategy in action.

3. AI-native banking: We’ll see the emergence of the first truly “AI-native” banks, where artificial intelligence isn’t just a feature but the foundational architecture. Early examples like Personetics’ Engagement Banking platform hint at this future.

4. Specialized symbiosis: Some traditional banks will strategically retreat from direct customer relationships, instead providing “banking-as-a-service” infrastructure to AI-powered customer-facing fintech, creating new symbiotic relationships in the financial ecosystem.

The Survival Imperative

For traditional banks, the Darwinian imperative is clear: evolve or face extinction. But evolution doesn’t mean abandoning strengths – it means adapting them to changing environments.The banks that survive will be those that leverage their unique advantages – customer trust, regulatory expertise, and rich historical data – while radically transforming how they organize, innovate, and engage customers through AI. As Charles Darwin observed, “It is not the strongest of the species that survives, nor the most intelligent; it is the one most adaptable to change.”

In banking’s new AI-driven landscape, adaptability will determine which institutions thrive and which become cautionary exhibits in the financial natural history museum. The clock is ticking. The quiet customer exodus continues. And in banking’s evolutionary race, Generative AI has just changed the pace from gradual evolution to revolutionary adaptation.The survivors will be those who recognize that in Digital Darwinism, there is no middle ground between evolution and extinction.

About the Author :

Mr. Suman Chandra is a Senior Technology Executive and Agile Transformation Advocate who consistently creates business value by delivering products that harness the power of Data, Analytics, and AI.

Mr. Suman Chandra is a trusted advisor and strategic partner to C-suite and leadership teams delivering high-impact digital programs and solutions in multiple industries including Financial Services.

Mr. Suman Chandra is keen on the strategic use of Generative AI in organizations.

Mr. Suman Chandra is based out of New York City – Metropolitan Area, USA.

Mr. Suman Chandra is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/sumankchandra/details/certifications/

Mr. Suman Chandra is Volunteering in the following International Industry Associations & Institutions :

https://www.linkedin.com/in/sumankchandra/details/volunteering-experiences/

Mr. Suman Chandra can be contacted at :

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Mr. Raja Basu is a Senior Consulting professional in a leading MNC.

Mr. Raja Basu works as a business architect and helps global banking and financial markets clients to enable their digital transformation journey.

Mr. Raja Basu has special interest in responsible use of AI and sustainability.

Mr. Raja Basu is also pursuing his doctoral studies (PhD) from XLRI Jamshedpur.

Mr. Raja Basu is based out of Kolkata, India.

Mr. Raja Basu is an experienced leader in both technology and business, he has a proven track record of defining and implementing technology-driven transformations for clients in the global banking and financial markets.

Mr. Raja Basu focus lies in automation, particularly artificial intelligence (AI), and its impact on climate and sustainability (SCR).

Mr. Raja Basu possess a deep understanding of value-driven advisory practices, which have played a significant role in building strong client relationships. Throughout his career, he has actively contributed to numerous transformation programs involving complex applications for international clients across the United States, Canada, Europe, and Singapore.

Mr. Raja Basu is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/basuraja/details/certifications/

Mr. Raja Basu is Volunteering in the following International Associations & Institutions :

https://www.linkedin.com/in/basuraja/details/volunteering-experiences/

Mr. Raja Basu is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/basuraja/details/honors/

Mr. Raja Basu can be contacted at :

E-mail | LinkedIn

Also read Mr. Raja Basu’s earlier article :

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Introduction – New Era of Taxation Begins

For over six decades, the Income Tax Act of 1961 has governed India’s taxation landscape, shaping the financial obligations of individuals and businesses alike. However, as economies evolve and financial structures become increasingly complex, the need for a streamlined and modernized tax system becomes imperative. Recognizing this, Finance Minister Nirmala Sitharaman introduced the Income Tax Bill 2025 in the Lok Sabha on February 13, ushering in a landmark reform that seeks to simplify and enhance clarity in taxation. This bill, spanning 2.6 lakh words and 536 sections across 23 chapters, is not just a legislative overhaul but a significant shift in the way India approaches taxation. With well-defined structures and a taxpayer-friendly orientation, the new law aims to make compliance easier and more transparent.

What’s Changing? Key Tax Reforms Unveiled

1. Rewriting the Calendar: The New ‘Tax Year’ Concept

One of the most prominent changes in the new bill is the introduction of the term ‘tax year’ replacing the older concepts of ‘previous year’ and ‘assessment ’ This change seeks to eliminate confusion and align India’s tax system with global norms. The previous system often led to misinterpretations and unnecessary complexity due to the dual terminology. By consolidating these concepts into a single ‘tax year,’ the bill ensures clarity and uniformity, making it easier for taxpayers to determine applicable tax rates and filing deadlines.

2. Structural Reforms: Streamlining Compliance and Recovery

A fundamental objective of the Income Tax Bill 2025 is to simplify compliance and improve tax recovery mechanisms. The bill introduces a well-structured system for tax assessment, modernized procedures for filing updated returns, and an expanded definition of Virtual Digital Assets under Section 2(111). A major reform in this domain is the consolidation of Tax Deducted at Source (TDS) and Tax Collected at Source (TCS) provisions. Under the previous regime, over 40 sections dictated different conditions and rates for TDS, often causing confusion among taxpayers.

The new bill merges these provisions into a single, clearly structured section with three  distinct tables, categorizing payees into residents, non-residents, and general taxpayers. Likewise, TCS provisions have been consolidated, ensuring that taxpayers no longer need to sift through multiple sections to determine their obligations.

3. Smarter Approach to Tax Deductions

The bill brings notable revisions to deductions, particularly in Sections 80C, 80G, 80TTA, and 80TTB. Section 80C, which serves as the backbone of tax- saving investments, has been restructured for better readability. Previously scattered provisions have now been compiled into a simplified schedule, allowing taxpayers to easily identify eligible savings instruments. Similarly, Section 80G, which pertains to deductions for charitable donations, has been refined to explicitly differentiate between contributions eligible for 100% deduction and those eligible for 50%. Meanwhile, the provisions of Sections 80TTA and 80TTB, which deal with deductions on interest earned from savings accounts, have been merged into a single, streamlined section with clear subsections, thereby reducing redundancy while maintaining tax benefits, particularly for senior citizens.

4. A New Perspective on Salary Taxation

Salaried employees stand to benefit significantly from the restructured provisions related to salary income. The bill consolidates all salary-related provisions under a single section, eliminating the need for taxpayers to cross-reference multiple chapters while filing returns. Deductions for gratuity, leave encashment, pension commutation, and voluntary retirement compensation, which were previously dispersed across various sections, have now been integrated into the salary chapter. Furthermore, allowances such as House Rent Allowance (HRA) have been listed separately in a dedicated schedule, ensuring better accessibility and comprehension for taxpayers. This restructuring aligns with the broader aim of the bill—enhancing readability and reducing redundancy.

5. Smooth Transitions: Stability Amidst Change

A significant concern surrounding such a comprehensive overhaul is the transition process. To address this, the government has ensured that individuals who have already opted for the new tax regime under the existing Income Tax Act of 1961 will not be required to opt in again under the revised framework.

Moreover, provisions related to capital gains taxation for charitable trusts, which had become redundant due to changes in asset acquisition norms, have been removed to prevent unnecessary complications. While certain sections—particularly those related to salary and house property—have seen minimal changes, this decision was intentional, ensuring stability in areas where provisions are already well-understood and widely accepted by taxpayers.

6. Retaining Due Dates: A Clear and Consistent Framework

To minimize disruptions, the bill retains the existing due dates for filing income tax returns. However, these due dates are now presented in a more structured tabular format, making them easier to locate and reference. Additionally, key tax slab revisions and rebates introduced in the most recent Union Budget have been seamlessly incorporated into the proposed legislation, ensuring clarity regarding their applicability. This structured approach eliminates any ambiguity and ensures that taxpayers can adhere to filing timelines without unnecessary confusion.

7. More Transparent Tax Assessment Process

The new bill brings a much-needed overhaul to tax assessment procedures, particularly in the areas of scrutiny, reassessment, and appeals. By integrating modern compliance mechanisms, the legislation enhances transparency, reduces litigation, and expedites resolution. Provisions relating to Minimum Alternate Tax (MAT) and tax refunds have also been clearly defined to prevent misinterpretations and disputes.

Key Takeaways from the Income-Tax Bill 2025

Here’s a quick look at the major changes that make tax rules clearer and easier to follow:

Conclusion :

The overarching theme of the bill is simplification— ensuring that taxpayers, whether individuals or businesses, no longer struggle with convoluted provisions. With its focus on transparency, efficiency, and taxpayer convenience, the bill represents a pivotal moment in India’s journey toward a modern, streamlined tax framework.

About the Author :

Ms. Subathra Mylsamy BA.MA.BL.LLM (UK)
Managing Partner
A.K.Mylsamy & Associates LLP 

Ms. Subathra is an experienced Partner with a demonstrated history of working in the legal services industry. Ms. Subathra is skilled in International Law, Legal Assistance,Legal
Research, and Legal Writing. Ms. Subathra has done her Master of Laws (LLM) focused in International Business law from The University of Manchester.

Brief Profile of Ms. Subathra Mylsamy :

Ms. Subathra Mylsamy can be contacted at :

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About A.K.Mylsamy & Associates LLP :

A.K.Mylsamy & Associates LLP [‘AKM’], is a distinguished Full-Service Corporate Law Firm celebrating its 60th year of practice.

At AKM, we offer a comprehensive range of services encompassing Litigation, Advisory, and Transactional expertise.Evolving our services to meet the  needs of our clients, we’ve pioneered in  corporate law as a one stop destination covering  Commercial, and Civil Law, with a keen focus on Mergers and Acquisitions, Structuring, Recoveries, Insolvency & Bankruptcy, Direct and Indirect Taxes, handled by a seasoned team of former ‘Indian Revenue Service’ officers. Additionally, we excel in Contracts, Intellectual Property Rights to Technology laws, Cyber security , Data Privacy, Energy and Sustainability laws and many more.

A.K.Mylsamy & Associates LLP is a strong foundation of quality, competency, commitment and ethics, which have translated into the firm’s exponential growth and high reputation. Our team of experts, who have enviable experience in their related fields, provides efficient and prompt delivery of legal solutions that works best for your business.

A.K.Mylsamy & Associates LLP set very high standards of professionalism for ourselves and achieve the same with a meticulous, scientific approach and a high degree of professional integrity. We seek long-term relationships with clients and provide the best comprehensive solutions.

Team A.K.Mylsamy & Associates LLP

A.K.Mylsamy & Associates LLP Partners Profile

A.K.Mylsamy & Associates LLP can be contacted at :

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A.K.Mylsamy & Associates LLP News Letters :

” The Lawtree “ is a News Letter brought out in a simple and creative manner bringing out Legal Updates for the public awareness. ” The Lawtree “ has recently Published there 100th Edition.

“QIkcee” one minute read is an exclusive Intellectual Property Newsletter.
Disclaimer: The above Article is for educational purposes only. Opinions or points of view expressed in this article represent the view of the author.

Also read Ms. Subathra Mylsamy’s earlier articles :

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The payments industry is undergoing a significant transformation. By and large it is driven by rapid technological advancements. However, the key driver behind this is ongoing consumer expectations. This transformation is not limited to a specific sector, most importantly it cuts across the entire ecosystem. It has the volume as starting from retail consumers to large institutional clients, all engage in managing complex financial transactions regularly. As a result of that payment has become an integral part of daily life. It calls for a seamless, secure, and efficient payment infrastructure that can enable delightful experience and enables scale exponentially if required. To address these critical needs, three fundamental aspects have emerged as critical pillars of future payment systems. These are security, usability, and convenience.

Security:

This is foremost requirement. As the volume of digital transactions are increasing it needs robust security measures. Fraud prevention, identity verification, biometric authentication, and AI-driven risk assessments are now standard requirements. These are essential to safeguard both retail and institutional customers.

Usability:

Payment platforms must be intuitive and user friendly. Banks are now putting effort to revamp the existing customer journey. This allows clients to process transactions with minimal friction. The need is more on designing the interfaces that cater to all types of users. And the intent is to make their experience delightful regardless of their technical expertise.

Convenience:

Customers now expect instant, hassle-free payments across multiple channels. This can be online, mobile, contactless. Now even emerging technologies like voice-activated payments and wearables are also used to enhance user’s convenience.

Traceability of Transaction:

Traceability in open banking ensures that every transaction is recorded, monitored, and auditable. This helps in enhancing security and compliance related issues. Banks and third-party providers use APIs to log transaction details. This helps regulators and customers to track financial activities transparently. This helps in fraud prevention, dispute resolution, and compliance with data protection laws like GDPR and PSD2.

To augment these capabilities, fintech companies are collaborating closely with traditional banks. In a way they are trying to expand service offerings and elevate the overall client experience. This synergy between established financial institutions and fintech is heralding the beginning of more sophisticated solutions. These include embedded finance, real-time payments, decentralized finance (DeFi), and AI powered financial assistants. To sum up payment systems are being redefined to create a faster, safer, and more customer-centric financial ecosystem.

In the paradigm of digital disruptions, Open Banking and API-Driven Payments has evolved as the one of the most emerging systems in the payments domain. Moreover, this takes care of the above three fundamental requirements. It leverages technology to create a decentralized, efficient, and accessible financial ecosystem.

These are secured, enables real time financial data sharing between banks, fintech companies, and third-party providers. Open Banking allows customers to access personalized financial services, by leveraging APIs. This makes the transactions seamless, and the users enjoy greater financial control. They do not have to rely on traditional banking systems. This enhances the convenience of regular and heavy transactions. This innovation fosters competition enhances customer experience. This healthy competition also reduces transaction costs by enabling direct bank-to-bank payments without intermediaries. In addition, Open Banking supports financial inclusion by providing alternative lending and payment solutions. This will be even more productive as and when regulatory framework becomes more sophisticated and data sharing requirements become mandatory. API-driven payments are set to redefine the global financial ecosystem. They are making transactions more efficient, secured, and customer centric in terms of usability.

Some Statistics on existing usage:

The below chart indicates the gradual increase of Open Banking users. This highlights the growing acceptance and trust in this technology. This steady rise reflects its expanding adoption among both customers and service providers. This is entirely driven by convenience and innovation. As more financial institutions and fintech companies integrate Open Banking, the ecosystem continues to evolve. This trend underscores the transformative impact of Open Banking on the payments and finance industry.

According to a recent Deloitte study, several countries, like India, Japan, Singapore, and South Korea, do not yet have formal or mandatory Open Banking frameworks. However, policymakers in these nations are implementing various initiatives to expedite the adoption of data sharing systems in the banking sector.

A Statista survey of payment system providers done in 2021 highlighted interesting results. Majority cited efficiencies as the primary motivation for implementing APIs. The report highlighted that efficiency is a key advantage of APIs. This is particularly for its ability to automate data retrieval. Enhanced efficiency invariably increases convenience for the users.

Open Banking and API-driven payments can play a critical role in augmenting Industry 5.0. It enables a seamless financial integration between humans and intelligent systems. As we know Industry 5.0 emphasizes human machine collaboration, hyper-personalization, and sustainability, this Open Banking APIs can provide real-time financial insights. It can automate transactions, and personalize financial solutions customised to businesses and individuals. These technologies enhance supply chain efficiency by enabling instant, frictionless B2B and B2C payments. This also reduces transaction costs and optimizes cash flow. Along with that, AI-driven financial analytics, can make data-driven decisions to improve risk management. Overall, it will enhance customer experience. As the industrial ecosystem moves towards greater automation and human-centric innovation, Open Banking and API-driven payments will serve as key enablers of financial agility, transparency, and sustainability in Industry 5.0.

Call to actions for Industry Leaders:

According to Ron Adner in “The Wide Lens: What Successful Innovators See that Others Miss” achieving success requires assessing the interconnections among all partners. It also requires creating strategic plans to navigate these dynamics. He stresses the importance of understanding the roles and power of the various players. At the same time how, their interactions influence the ecosystem.

Conclusion:

Finally, the rapid evolution of the payment ecosystem, driven by innovations such as Open Banking and API-driven solutions, is reshaping how financial services. As platforms become more integrated and user centric, the need for secure, usable and convenient payment systems will continue to grow. Industry leaders must prioritize collaboration, transparency, and proactive engagement with fintech. This will help to foster a dynamic ecosystem that benefits both businesses and consumers. Further it can enable driving greater financial inclusion and improving overall customer experiences in the sustainable, human-centric, and resilient landscape of Industry 5.0.

About the Authors:

Mr. Gautam Kumar is a Banking and Payment Industry veteran with over 25 years of experience.

Mr. Gautam Kumar works for a leading MNC.

Mr. Gautam Kumar leads multiple consulting engagements across the globe.

Mr. Gautam Kumar is into developing business model, business use case, solution roadmap and conducting design thinking workshops.

Mr. Gautam Kumar is also leading the Banking and Payment practice in India.

Mr. Gautam Kumar has published several white papers and PoVs on Payments and Banking.

Mr. Gautam Kumar can be contacted at :

LinkedIn | E-mail

Mr. Raja Basu is a Senior Consulting professional in a leading MNC.

Mr. Raja Basu works as a business architect and helps global banking and financial markets clients to enable their digital transformation journey.

Mr. Raja Basu has special interest in responsible use of AI and sustainability.

Mr. Raja Basu is also pursuing his doctoral studies (PhD) from XLRI Jamshedpur.

Mr. Raja Basu is based out of Kolkata, India.

Mr. Raja Basu is an experienced leader in both technology and business, he has a proven track record of defining and implementing technology-driven transformations for clients in the global banking and financial markets.

Mr. Raja Basu focus lies in automation, particularly artificial intelligence (AI), and its impact on climate and sustainability (SCR).

Mr. Raja Basu possess a deep understanding of value-driven advisory practices, which have played a significant role in building strong client relationships. Throughout his career, he has actively contributed to numerous transformation programs involving complex applications for international clients across the United States, Canada, Europe, and Singapore.

Mr. Raja Basu is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/basuraja/details/certifications/

Mr. Raja Basu is Volunteering in the following International Associations & Institutions :

https://www.linkedin.com/in/basuraja/details/volunteering-experiences/

Mr. Raja Basu is Accorded with the following Honors & Awards :

https://www.linkedin.com/in/basuraja/details/honors/

Mr. Raja Basu can be contacted at :

E-mail | LinkedIn

Also read Mr. Raja Basu’s earlier article :

The post APIs in Payments appeared first on Industry4o.com.

The Union Budget 2025-26, presented by Union Minister for Finance and Corporate Affairs Smt.Nirmala Sitharaman, is a comprehensive blueprint aimed at fostering sustainable economic growth, enhancing infrastructure, and improving the quality of life for all citizens. The budget is structured around four key engines of development: Agriculture, MSMEs, Investment, and Exports. Additionally, it introduces significant reforms in the financial sector and tax policies to simplify compliance, boost investment, and promote ease of doing business. Below is a detailed summary and analysis of the key highlights and initiatives announced in the budget.

1. Budget Estimates 2025-26

• Total Receipts (excluding borrowings): ₹34.96 lakh crore.
• Total Expenditure: ₹50.65 lakh crore.
• Net Tax Receipts: ₹28.37 lakh crore.
• Fiscal Deficit: 4.4% of GDP.
• Gross Market Borrowings: ₹14.82 lakh crore.
• Capital Expenditure (Capex): ₹11.21 lakh crore (3.1% of GDP).

Analysis:

2. Agriculture as the First Engine of Development

a. Prime Minister Dhan-Dhaanya Krishi Yojana:

b. Building Rural Prosperity and Resilience:

c. Aatmanirbharta in Pulses:

d. Comprehensive Programme for Vegetables & Fruits:

e. Makhana Board in Bihar:

f. National Mission on High Yielding Seeds

g. Fisheries

h. Mission for Cotton Productivity

i. Enhanced Credit through KCC

j. Urea Plant in Assam

Analysis:

3. MSMEs as the Second Engine of Development

a. Revision in Classification Criteria for MSMEs

b. Credit Cards for Micro Enterprises

c. Fund of Funds for Startups

d. Scheme for First-time Entrepreneurs

e. Focus Product Scheme for Footwear & Leather Sectors

f. Measures for the Toy Sector

g. Support for Food Processing

h. Manufacturing Mission

Analysis:

About the Author :

Ms. Subathra Mylsamy BA.MA.BL.LLM (UK)
Managing Partner
A.K.Mylsamy & Associates LLP 

Ms. Subathra is an experienced Partner with a demonstrated history of working in the legal services industry. Ms. Subathra is skilled in International Law, Legal Assistance,Legal
Research, and Legal Writing. Ms. Subathra has done her Master of Laws (LLM) focused in International Business law from The University of Manchester.

Brief Profile of Ms. Subathra Mylsamy :

Ms. Subathra Mylsamy can be contacted at :

LinkedIn | E-mail

About A.K.Mylsamy & Associates LLP :

A.K.Mylsamy & Associates LLP [‘AKM’], is a distinguished Full-Service Corporate Law Firm celebrating its 60th year of practice.

At AKM, we offer a comprehensive range of services encompassing Litigation, Advisory, and Transactional expertise.Evolving our services to meet the  needs of our clients, we’ve pioneered in  corporate law as a one stop destination covering  Commercial, and Civil Law, with a keen focus on Mergers and Acquisitions, Structuring, Recoveries, Insolvency & Bankruptcy, Direct and Indirect Taxes, handled by a seasoned team of former ‘Indian Revenue Service’ officers. Additionally, we excel in Contracts, Intellectual Property Rights to Technology laws, Cyber security , Data Privacy, Energy and Sustainability laws and many more.

A.K.Mylsamy & Associates LLP is a strong foundation of quality, competency, commitment and ethics, which have translated into the firm’s exponential growth and high reputation. Our team of experts, who have enviable experience in their related fields, provides efficient and prompt delivery of legal solutions that works best for your business.

A.K.Mylsamy & Associates LLP set very high standards of professionalism for ourselves and achieve the same with a meticulous, scientific approach and a high degree of professional integrity. We seek long-term relationships with clients and provide the best comprehensive solutions.

Team A.K.Mylsamy & Associates LLP

A.K.Mylsamy & Associates LLP Partners Profile

A.K.Mylsamy & Associates LLP can be contacted at :

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A.K.Mylsamy & Associates LLP News Letters :

” The Lawtree “ is a News Letter brought out in a simple and creative manner bringing out Legal Updates for the public awareness. ” The Lawtree “ has recently Published there 100th Edition.

“QIkcee” one minute read is an exclusive Intellectual Property Newsletter.
Disclaimer: The above Article is for educational purposes only. Opinions or points of view expressed in this article represent the view of the author.

Also read Ms. Subathra Mylsamy’s earlier articles :

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In the context of growing digital space and emerging technology, cybersecurity is vital and third-party risk is one of the growing threats in an organization. Cybersecurity incidents and data breaches are increasing across vendors, suppliers, and other businesses not limiting to the bank. In a digitally interconnected world, the cybersecurity strength of any organization is not measured by its own defense controls but by the weakest link in the third-party sources. Hence, digital banks need to consider their third parties cybersecurity posture to an equal degree as their own to build business resilience.

Although the banking sectors work hard on keeping their ecosystem strong, it doesn’t make or take cautious efforts to eliminate third-party risks. Often the third parties are not very vigilant about their own security which could, in turn, be putting an organization at risk of cybersecurity attacks, where substantial costs and reputational damage is involved. The importance of managing third-party risk cannot be overstated in the ever-evolving banking sector landscape. Banks rely on external vendors, suppliers, and service providers to enhance efficiency, cut costs, and offer a broader range of services. However, this dependency also exposes them to a myriad of risks.

This article delves on the challenges, strategies, and best practices for effectively managing third-party cyber risks in the banking sector with deeper study for a clear understanding. Working with third parties is primarily challenging but a necessary part of the business globally. So, it’s very important for the banks to make sure that the third party they are engaging with are secure, developed, and reliable to establish a connection. Third-party cyber risk is evolving day by day increasing the risk of leaving third party cybersecurity unaddressed. It primarily focuses on identifying, analyzing, and minimizing risks relating to cyber threats/data breaches. Many have faced cybersecurity attacks, phishing attacks, malware, data breaches and other cybercrimes majorly involving their third-party partners. To mitigate the cyber risks posed by third parties, banks need to take a proactive approach to build a robust cybersecurity posture that covers everything from end-to-end to protect the vendor environment.

If business resilience is the primary objective of the digital bank, it should take precautionary measures on its standards, its approach, processes, and metrics across different third parties to reduce cyber risks and increase the third-party risk resilience. For building a robust cybersecurity culture and implement security standards in a third-party environment, it is important to understand the prevailing risks and challenges associated with third parties.

Key challenges in the Third-Party Environment :

As the banks are evolving day by day in this digital world and adapting to changing technologies, it’s very important to have clear and concise policies/procedures that outlines the organization’s needs & expectations from the third-party service providers to mitigate the risks associated with regulatory uncertainty or in defending any cyber threats that may arise. The biggest challenge in enforcing end-to-end cybersecurity governance in a third-party environment are :

Usually, banks have a structured protocols to enforce the cybersecurity culture across inhouse business functions using their enterprise policies, procedures, and guidelines, but, when it comes to third parties, banks don’t have controls over their security standards and struggle to embed their cybersecurity culture. The main principle of third-party risk management is very clear and simple, if you’re not able to control it, you will not be able to secure it. This includes everything from identifying the right set of vendors, categorizing them, defining contractual obligations with clear visibility, conducting vendor risk assessments, mitigating the risks, continuous monitoring and implementing end-to-end encryption.

Best Practices to manage Cybersecurity and Data Protection Third Party Risks :

For managing third-party cyber risks, banks should take a proactive approach to define various business requirements, build business relationships, and risk factors for building a robust cybersecurity culture. Based on the size and criticality of the vendor, banks need to work on third-party risks to evaluate and do an in-depth assessment to understand the current maturity levels. Few important factors while doing an assessment are the regions where the vendor operates, spread of the vendor employees, practicing tools and technologies, their cloud strategies, ISO status, back up plans during a cyber incident/data breach, the insurances leveraged by the third party and incident response levels.

Overall compliance with regulatory requirements is non-negotiable in the banking sector. It’s very important to ensure the security controls within the organization and with their third parties are robust to face the emerging threats in this digital space. Failure to comply with legal and regulatory requirements can lead to substantial fines and reputational damage.

Managing third-party cyber risks in the banking sector is an ongoing process that requires a proactive approach to stay resilient in nature. By following best practices and adhering to legal & regulatory requirements, the banking sector can build a cyber-resilient environment to protect itself against the evolving third-party cyber risks.

About the Author :

Ms. Kavitha Srinivasulu
Global Head – Cyber Risk & Data Privacy – R&C BFSI
CCISO | DPO | CISM | CEH | CCSO | CCIO| PCSM | PDPP |

Tata Consultancy Services

Ms. Kavitha Srinivasulu has around 20+ years of experience focused on  Cybersecurity, Data Privacy & Business Resilience across BFSI, Financial  services, Retail, Manufacturing, Health care, IT Services and Telecom domains. She has demonstrated her core expertise in Risk Advisory, Business Consulting and Delivery assurance with diverse experience across corporate and Strategic Partners.She is a natural leader with versatility to negotiate and influence at all levels.

Ms. Kavitha Srinivasulu is a Board Member of Women in CyberSecurity (WiCyS) India

Ms. Kavitha Srinivasulu is an Executive Committee Member CyberEdBoard Community

Ms. Kavitha Srinivasulu is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/kavitha-srinivasulu-5619ab7/details/certifications/

https://www.linkedin.com/in/kavitha-srinivasulu-5619ab7/details/publications/

Ms. Kavitha Srinivasulu can be contacted at :

LinkedIn : https://www.linkedin.com/in/kavitha-srinivasulu-5619ab7/

Also read Ms. Kavitha’s earlier article:

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The Banking and Finance industry is one of the industry which is continuously evolving over the years and one of the fastest to adopt the technology to advance its reach and usage. In the past few years, line is slowly getting blurred between technology company and banks.

In the 21st century, businesses are no longer confined to implementing old-school or traditional ways, credit to the FinTech revolution! FinTech is known for its cutting-edge technologies of AI and blockchain. Lot of FinTech are growing at an exponential rate and giving tough competition to established institutions.

Every year, new innovations are taking BFSI industry to new heights and to a wider reach. Several central banks are launching CBDC while banning cryptocurrencies, it is yet to reach the critical mass adoption. Several countries have launched Crypto ATM however its usage is limited to only miniscule tech0savvy banking population.

Bank and state governments are exploring the usage of blockchain for record maintenance and other relevant use-cases. The widespread use of blockchain in Trade finance, cross-border payments, asset tokenization, KYC and settlement and clearing is visible.

Hyper-personalization is buzz-word for quite some time, but the customer is yet to get a feel of it and may become mainstream in coming years. Account aggregators are ‘IN’ thing and trying to compete with native apps. Super Apps are also another ‘Universal domain’ which banks are trying to build in a single mobile app and also attempting customer to use it.

Here, are some trends that will be noticed in 2024 and beyond:

1. Embedded Finance

Embedded finance seamlessly integrates financial services into non-financial platforms, enabling businesses in e-commerce, tech, or retail to offer financial products without direct interaction with a bank. For example, Shopify provides embedded financial services through Shopify Capital, streamlining the loan process for merchants based on their sales history. This allows businesses to access funding without traditional banking channels.

The popularity of Buy Now, Pay Later (BNPL) services in e-commerce platforms exemplifies embedded finance. Companies like Afterpay and Klarna seamlessly integrate installment payment options at checkout, aligning with users’ financial preferences.

Transportation is another sector adopting embedded finance. Ride-hailing apps like Uber integrate payment functionalities directly into their apps, providing a hassle-free experience for users to pay without cash or switching platforms.

The future of embedded finance holds great potential. With the proliferation of Internet of Things (IoT) devices, we may see embedded insurance where policies are automatically activated for specific products or services. The integration of embedded finance across various sectors enhances convenience, efficiency, and accessibility, revolutionizing the way businesses and consumers engage with financial products and services.

2. AL/ML in financial services

AI (GenAI) and ML are being extensively used in the banking and financial services industry, bringing numerous benefits and transforming various aspects of the sector. In coming days, AI/ML will not only help in taking informed investment decisions using robo-advisor, provide hyper-personalized portfolio management services and improve customer experience with AI-chatbots, but it will also be widely adopted in the following areas:

1. Reduction in operational costs and risk: AI and ML algorithms can automate manual processes, leading to cost savings and increased efficiency. They also help identify and mitigate potential risks by analyzing large volumes of data.

2. Financial monitoring and risk management: AI and ML models can analyze vast amounts of financial data in real-time, detecting anomalies and potential fraud. These technologies help banks and financial institutions in monitoring transactions, identifying suspicious activities, and managing risks effectively.

3. Regulatory compliance: AI and ML play a crucial role in assisting banks and financial institutions in meeting regulatory requirements. These technologies can analyze vast quantities of data to ensure adherence to compliance standards and detect any potential violations.

4. Fraud prevention: AI and ML models are employed to identify patterns and anomalies that indicate fraudulent activities. These technologies help in detecting and preventing financial fraud, protecting both customers and institutions.

Overall, AI and ML are revolutionizing the banking and financial services industry by improving operational efficiency, enhancing customer experiences, managing risks, and enabling data-driven decision-making.

3. AL/ML in Insurance services

AI and ML are revolutionizing the insurance industry by transforming various aspects of insurance services.

Here are some ways AI and ML are utilized and will see widespread adoption in coming year:

1. Risk assessment and underwriting: AI and ML algorithms analyze vast amounts of data to assess risks accurately and streamline the underwriting process. They help insurers make informed decisions by identifying patterns, correlations, and trends in historical data specific to the insurance domain.

2. Fraud detection and prevention: AI-powered technologies enable insurers to detect and prevent fraud by analyzing large volumes of data. These technologies can identify suspicious patterns, anomalies, and potential fraudulent activities, saving insurers significant costs.

3. Claims processing and automation: AI and ML algorithms automate claims processing, reducing the need for manual intervention. These technologies can extract relevant information from documents, analyze images, and predict claim outcomes, improving efficiency and accuracy.

4. Customer experience and personalization: AI-driven chatbots and virtual assistants provide personalized Customer experiences by answering queries, assisting with policy selection, and guiding customers through the insurance process. AI also helps insurers offer personalized recommendations and policies tailored to individual needs.

5. Predictive analytics and pricing: AI and ML models enable insurers to predict customer behavior using embedded IoT, calculate risk profiles, and refine pricing strategies, particularly for Auto-insurance. These technologies leverage data analysis to determine optimal coverage and premium rates for customers. Telematics and applications monitor real-time behavior, allowing adjustments in premiums based on driving habits. Acko, Progressive and Allstate determine premiums based on actual driving habits. Metromile’s pay-per-mile insurance benefits low-mileage drivers with dynamic pricing.

6. Loss prevention and risk management: AI-powered systems can analyze data from various sources, such as IoT devices, to monitor risks and identify potential hazards. Insurers can proactively mitigate risks, prevent losses, and offer risk management solutions to customers.

4. Open banking Platforms

Open Banking is a transformative practice that redefines the payment landscape by granting authorized third-party providers (TPPs) access to a customer’s banking data through secure application programming interfaces (APIs). This access is only permitted with explicit consent from the customer, giving them full control over their data-sharing preferences. This is leading to a surge in account-to-account (A2A) payments empowering customers to wield greater control over their financial data while unlocking a plethora of cutting-edge financial services apps.

Some of the top open banking platform providers are Mambu (cloud banking platform), Plaid (12k financial institutions), Tink (6000 integrations, 1BN API calls per month), Stripe, Token and True Layer.

The TPPs on behalf of bank customers offer two primary services: Account Information Services (AIS) that enables customers to access and consolidate information such as account balances and transaction history from all their bank accounts in one centralized platform. Payment Initiation Services (PIS) that helps customers to make online payments seamlessly without the need to manually input credit card details. Instead, they authorize secure payment transactions directly from their bank accounts.

Overall, open banking empowers customers with greater convenience, enhanced security, and control over their financial data, making it a game-changer in the payment landscape. Open Banking is already present and continues to evolve actively in coming years.

5. Metaverse banking

JPMorgan has become the first bank to establish a presence in the metaverse by opening a lounge in Decentraland, a blockchain-based virtual world. Users can create virtual avatars, explore the lounge, and access Ethereum-based services. This move highlights the growing trend of banks venturing into the metaverse to provide customers with immersive digital experiences.

In India, several banks have also launched their virtual branches in the metaverse. Punjab National Bank (PNB) offers a virtual environment where customers can explore various banking services, including deposits, loans, and government flagship schemes. Bank of Maharashtra has introduced Mahabank Metaverse, featuring a virtual humanoid that uses artificial intelligence, machine learning, and natural language processing to assist customers.

The metaverse provides an opportunity for banks to engage with customers in new ways and reach decentralized platforms and communities. As technology continues to advance, it is expected that more banks will establish their presence in the metaverse by 2025, offering immersive experiences and leveraging the potential of virtual environments for enhanced customer interactions.

With metaverse creating opportunities for ‘metonomics’, days are not far when metaverse branches will become mainstream in coming days like mobile banking.

6. Payment through Implant Micro-Chip

A human microchip implant is an electronic device inserted subcutaneously, often through injection. Individuals ranging from hobbyists to scientists and business personalities have chosen to have RFID microchips implanted in their hands or by others.

These subdermal implants typically contain a unique ID number that can be connected to an external database. This database can store various information, including identity documents, criminal records, medical history, medications, address book, and other potential uses. Among the developed use-cases, one prominent application is facilitating contactless payments, similar to contactless cards.

However, it is important to note that this technology is still in the prototype stage. There are several risks associated with implanting these chips in humans, such as corrosion, infection, cancer risks, and privacy intrusion. Despite these challenges, it is expected that in the coming years, the concept of embedded finance will gain wider acceptance and evolve into a more comprehensive and risk-free solution.

7. Sustainable Finance

The importance of Corporate Social Responsibility (CSR) in business is rapidly rising. Sustainable finance refers to the systematic incorporation of environmental, social, and governance (ESG) considerations into investment decision-making within the financial industry. Consequently, there is a notable surge in the allocation of resources towards sustainable economic activities and initiatives with longer-term horizons.

Changes in investment preferences, especially for millennials who stand to gain over the next 20 years from the most significant transfer of inherited riches in recorded human history, totalling $68 trillion, have been a major factor in the birth of sustainable finance. To Incorporating social governance and environmental considerations as essential elements inside their investment portfolios, institutional investors are also revising their long-term investment risk models.

The sustainable finance market may be categorized into two distinct subcategories, namely negative sustainable finance and positive sustainable financing also called Green finance. Green finance takes money out of current companies that are fuelling the climate crisis (negative/exclusive) or invests in new companies who are coming up with innovative ways to combat climate change (positive/integrated). Negative, or exclusionary, green finance often transfers capital from high-carbon to low-carbon companies (a process referred to as divesting) or funds companies whose objective is to reduce their carbon footprint.

With ESG ratings being published and ESG reporting being mandated, days are not far when the allocation of financial resources towards un-sustainable finance will dry up and investors will only fund sustainable industry and businesses.

In Nutshell The FinTech industry has thrived amid global challenges like the Covid-19 pandemic, benefiting customers with flexibility, opportunity, and stability. To fully embrace FinTech, it needs to advance further, with a collaborative ecosystem ensuring an open, just, and integrated financial system. The speed of technological advancements assures us that this progress won’t take long. Additionally, investors are heavily investing in the FinTech industry, making it a favorable time for ventures..

Mr. Rakesh Goyal, Mr. Mitra Wani, Mr. Chintan Oza & Mr. Biren Parekh has equally contributed to this article.

About the Authors :

Mr. Rakesh Chandra Goyal
Advisory Board Member – Agile Network India

Director – Sunmitra Education Technologies Limited

Mr. Rakesh Chandra Goyal is Passionate about Corporate management to embrace Agility & inculcate Agile culture in their team and people across all functions as Agility would help in enhancing business performance through alertness & vigilance.

Mr. Rakesh Chandra Goyal can be contacted at :

LinkedIn | Website | Twitter |  Facebook

Mr. Mitra Wani

Global Digital Transformation Leader | Business Technology Consultant | Enterprise AI & Automation Strategist

Mr. Mitra Wani can be contacted at :

Email | LinkedIn | Twitter

Mr. Chintan Oza

Advisory Board Member – CLAIRIFIER

Chief Advisor – India Startup Ecosystem, Toronto Business Development Centre

Mr. Chintan Oza is a Ecosystem enabler for Startups & Investors in G20 &
BRICS, he has helped to build startups from scratch to IPO or acquisitions.

Mr. Chintan Oza is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/chintanoza/details/certifications/

Mr. Chintan Oza can be contacted at :

LinkedIn | Website | Twitter

Mr. Biren Parekh
Director – CRISIL Limited

Mr. Biren Parekh is an Information Technology executive with over twenty-five years of experience managing and implementing complex BFSI digital transformation programs for top-tier global retail and corporate banks.

Currently, a Director at CRISIL Limited (as of June 2022), he is a certified corporate director with multiple global certifications like PMP, ACP, DASSM, PRINCE2, ITIL V3, PSM1, and CSM. He has also volunteered on the IEEE Innovation and entrepreneurship committee and the board of the PMI Mumbai chapter​ since April 2021.

Being one of the NEXT100 CIO 2020 winners, he is also a regular speaker at conferences and business schools like IIM, SJMSOM, NMIMS, PDPU, Delhi School of Economics and NITIE, where he conducts talks on project management, the Agile framework, Digital transformation and FinTech. He is also an angel investor in several Indian and international startups and even mentors for some of them.

He has been rated in “Top 25 thinkers on Project Management for April 2022” and “Top 50 thinkers in FinTech for Oct 2021 & Oct 2022” on the Thinkers360.com website.

Mr. Parekh has published a whitepaper titled ‘A Comparative Study of Vendor Selection Process in Global Outsourcing Industry with an Elucidated Scientific Approach’ in a reputed Scopus journal. He was also featured in the book ‘High Productivity Practices by Successful Leaders’ by Dr. Ravindran KA, whereby he shares tips on increasing productivity.

On the personal front, he is a fitness freak and a marathoner. He closely follows equity and enjoys reading, traveling, and exploring new cuisines. In his free time, he publishes articles on interesting topics on his website (birenparekh.com) and LinkedIn. His work has been featured in various magazines, blogs, and PMI journals.

He has published his book “Supercharge your Project Management skills” to share his experiences on his project management journey.

Mr. Biren Parekh Professional Experience :

https://www.linkedin.com/in/biren-parekh/details/experience/

Mr. Biren Parekh is Accorded with the following Honours & Awards :

https://www.linkedin.com/in/biren-parekh/details/honors/

Mr. Biren Parekh is Bestowed with the following Licenses & Certifications :

https://www.linkedin.com/in/biren-parekh/details/certifications/

Mr. Biren Parekh is Volunteering in the following Industry Associations & Institutions :

https://www.linkedin.com/in/biren-parekh/details/volunteering-experiences/

Public talk by Mr. Biren Parekh

Mr. Biren Parekh can be Contacted at :

Email | Website | Blog | LinkedIn | Twitter | Facebook  | Instagram

Also read  Mr. Biren Parekh’s earlier article :

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