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.

What if your appliances could haggle over the electricity bill so you don’t have to? Think of a following scenario: your EV needs a charge immediately, but the dishwasher you just loaded can easily run later tonight. Instead of both drawing heavy power at the same time and straining the system, the EV essentially tips the dishwasher a digital energy token to hold off for a few hours. It all happens in milliseconds, completely behind the scenes, without a central hub directing traffic and without any human intervention.

This is not an imaginary  scenario. It is the core premise of a new energy management framework that combines Multi-Agent Reinforcement Learning (MARL) with a blockchain-enforced token economy to bring truly autonomous, fair, and privacy-preserving energy distribution to the smart home.

The Problem with the Existing Smart Home Solution

In today’s world, home energy systems are generally managed by a central controller. The central controller can be remote devices co-ordinating between different appliances through individual instructions. This kind of architecture has an inherent flaw – single point of failure e.g. with the absence of a remote, you may not be able to turn on a dishwasher remotely. Imagine central controller going offline during a heatwave or grid emergency. This can have catastrophic effect as the entire system will fail in the absence of a controller.

Also a centralised controller usually processes requests from dozens of appliances sequentially, introducing decision latency of 200–500 milliseconds per cycle. In a world where grid-frequency events demand sub-second responses, this is simply too slow. As appliances passively wait for commands, the system misses the flexible value they inherently hold e.g. an EV that could defer charging for thirty minutes or a water heater coasting on thermal mass has no mechanism to express that flexibility.

“Each appliance becomes an economic agent — not a passive switch, but an autonomous negotiator with its own wallet, its own urgency, and its own strategy.”

— MARL-PoN Framework Design Principles

Every Appliance Becomes an Agent

Instead of a central controller giving instructions to other devices , every device acts as its own independent agent, constantly reading the room—checking real-time electricity prices, grid frequency, and what else is running in the house. Based on that data, the appliance calculates how badly it needs power using a straightforward formula:

A medical ventilator, with a safety priority of 10, always scores highest — it receives energy unconditionally. An EV with a depleted battery scores high on urgency. A dishwasher mid-cycle scores low on time sensitivity. These scores drive an encrypted auction, run every sixty seconds, in which appliances bid for energy tokens using zero-knowledge proofs — mathematical techniques that allow an appliance to prove its bid is valid without revealing the amount, preserving the occupant’s privacy completely.

This paradigm — known as Centralised Training, Decentralised Execution (CTDE) — means agents are trained on rich global data but execute their policies independently at runtime. No server, no cloud dependency, no single point of failure.

What the Published Evidence Shows

A growing body of peer-reviewed research supports the performance case for MARL-based energy management. A 2025 study spanning more than 1,200 buildings across three metropolitan areas found that MARL coordination reduced peak demand loads by approximately 23% and cut energy consumption costs by nearly 19% compared to traditional centralised control [C1]. On the standardised CityLearn benchmark — the field’s primary evaluation platform — the attention-based AAC-MADRL algorithm achieved energy cost reductions of up to 18% and improved self-sufficiency by up to 10.5% during periods of limited solar generation, while deploying 40% faster than decentralised deep RL alternatives [C2].

Adversarial robustness is equally critical for real-world deployment. A 2022 study found that the Robust Adversarial MARL framework (RAMARL-DR) recovered approximately 39% of grid-ramping performance degraded by cyber-attacks [C3] — a result directly relevant here. The Proof-of-Necessity consensus tolerates up to 33% Byzantine (malicious) nodes by slashing the staked tokens of any appliance found misrepresenting its priority, achieving equivalent resilience through economic incentive rather than trusted hardware.

Blockchain: Not Hype — Hard Mathematics

The blockchain layer does something that very few software policy can.  It can make budget violation mathematically impossible, not just unlikely. Every day, the household receives exactly B energy credit tokens from the utility company, where each token corresponds to a watt-hour . Appliances spend tokens to activate. Since the ledger enforces the constraint Σ(active allocations) ≤ B through distributed consensus, there is no race condition, no double-spending, and no way for a software bug to allow simultaneous overconsumption.

Tokens have a time decay property so that if a household or agent tries to hoard them, the value decays exponentially, which in turn ensures energy credits circulate like actual energy rather than being stockpiled by greedy agents. But what is more important is that this creates a cryptographic audit trail. Households participating in demand-response programmes can prove to utilities that they did indeed curtail demand by the promised amount. Thus unlocking incentive payments that soft-enforcement systems cannot access.

What This Means for Smart Homes

The broader significance reaches beyond the home. The same architecture can be scaled for neighbourhood microgrids. Households with rooftop solar can sell surplus tokens to neighbours, enabling peer-to-peer energy trading without an intermediary. As building electrification accelerates in terms of EVs, heat pumps, grid-connected batteries, etc.,  the number of negotiating agents grows, but so does the available flexibility. A 2020 study estimated that demand response programmes hold the potential to cut electricity peaks by around 20% in the building sector alone.

This opens new avenues of opportunity for different kinds of stakeholders. For appliance manufacturers, this can mean a new agent-enabled appliance that ships with a reinforcement learning policy, a cryptographic wallet, and a smart-contract execution environment built into its firmware.

When it comes to  grid operators, it is a population of millions of autonomous demand-response participants who can react in under 100 milliseconds without a single manual override.

Last but not least, for the residents, it’s a home that optimises itself, proves its fairness, and never shares its data.

The self-negotiating home is not a distant vision. The mathematics are settled, the benchmarks are encouraging, and the architecture is ready for pilot deployment. The only remaining question is how quickly the industry is prepared to stop thinking of appliances as passive loads — and start treating them as agents.

About the Authors:

Ms. Madhusmita Patil is an IBM Distinguished Engineer, an IBM Master Inventor & an open group certified Distinguished IT Architect.

Ms. Madhusmita Patil as a Chief Technical officer of a few elite clients for IBM, leads their technology strategy & defines their technical architecture.

Ms. Madhusmita Patil is known as an innovative technical leader focused on client- centered solutions.

Ms. Madhusmita Patil  is a member of IBM Open Innovation Community

Ms. Madhusmita Patil has more than twenty patents & thirty publications to her credit.

Ms. Madhusmita Patil is a Bronze recipient of ISG Digital Innovation Award 2025.

Ms. Madhusmita Patil is an eminent speaker.

Ms. Madhusmita Patil is associated with multiple Universities / Academics.

Ms. Madhusmita Patil can be contacted at:

LinkedIn

Mr. Soumya Bhattacharya is a Senior Architect working in a leading Software Development/ Service Company with over two decades of rich experience with expertise in Banking domain.

Mr. Soumya Bhattacharya has led complex multi-year programs across multiple industries from vision to analysis, to implementation to application go-live, including accommodations for business and technical requirements and impact.

Mr. Soumya Bhattacharya can be contacted at :

LinkedIn

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 Decentralized Energy Intelligence appeared first on Industry4o.com.

They say failures are pillar of success. Now consider a scenario where everything in a project goes right from requirement gathering to documentation, design, implementation, testing and change management. But in the final hurdle during production deployment something goes wrong, and it fails horribly due to lack of proper planning. This kind of scenario undo whatever great job project has done earlier and makes this mistake all too glaring and visible to important stake holders. This shakes the confidence of client on project team as well. This is where a proper Project Cutover Plan comes into picture. A project cutover plan incorporates all the risk, issues and scenarios that can go wrong from past experiences and provides necessary mitigation plans and includes best practices from multiple projects.

There are numerous examples where improper planning resulted in massive failure. Let us go back to April 2018. TSB Bank, in an ambitious move, decided to upgrade its computer systems while shifting over a billion customer records. Sounds like a bold leap forward, right? Unfortunately, what was meant to be a technological triumph turned into a nightmare.

The result? A full-blown disaster of TSB’s internet and mobile banking systems. Customers were deeply impacted. Some couldn’t log in, others found their balances all wrong, and surprisingly, a few even landed up inside someone else’s account. It was chaos, the kind that shakes customer trust to the core. This incident serves as a textbook case of how critical cutover planning and risk mitigation are, especially when the stakes involve millions of end users and their financial data.

All projects generally have some kind of cutover plan depending on the type and complexity of the project. A cutover plan can vary from a new complex system implementation to something where systems are enhanced to include new features or modify existing system to fix some bugs.  So now we need to know how a cutover plan looks like and what are the factors or parameters that influences a typical project cutover plan. We can analyze this from real world IT experience of numerous transformational projects from its inception to delivery and then subsequently transitioning it to Business as Usual (BAU) or maintenance activities.

Plan Early

They key to successful cutover plan is to start thinking about it early during the inception stages of the project. Ideally there should be some kind of preliminary approach to the cutover activities documented in Solution Design document e.g. type of deployment (Rolling Update, Blue-Green, Canary etc.), type of infrastructure (Cloud / On-premises) required etc. The type of deployment approach which is required as part of cutover plan depends on the type of application to be deployed and corresponding production down time that is acceptable as part of client Business Continuity Plan. These kind of broad approaches as part of conceptual solution document often requires going through Architecture review Board approval. While the detailed cutover plan or checklist will be elaborated towards the end of a project, time needs to be allocated to what cutover plan might look like much earlier. This type of thought process will lead to cutover plan being envisioned as part of Solution Design Document during early phases of project.

Involve all the stake holders

Actual cutover planning process requires involvement of all the stake holders of project delivery team, not just the architects. Different sets of people will have different perspective towards the cutover plan. Project managers will see it from perspective of resources, who is doing what and who are on standby mode.  Testing team will see whether the functional and integration testing are approved by business users. Performance testing team will see whether the outcome of load testing results is approved   by   concerned business authority and all   NFR (non-functional requirement) are met and aligned with the customer. Security team will look at the reports for security scan of code (e.g., Veracode/Prisma/Checkmarx etc.) are clean and approved by customer’s security team. Change Managers might look at a cutover plan through the lens of who needs to be notified and trained. The cutover plan should identify specific roles for each task and, ideally, nominate secondary resources should the primary ones not be available during the cutover period.

Dress Rehearsal

 There is a saying that tomorrow belongs to the people who prepare for it today. The dress rehearsal of an actual cutover plan addresses just that. The actual cutover plan and checklist needs to be validated so that it is exhaustive and there is no room for new surprise. This scenario reminds us of famous scene from Apollo 13 movie where the astronauts needed to figure out a way of using carbon dioxide scrubbers meant for command module to the lunar module as well. It was a typical case of square peg in a round hole. The ground control stuff sitting in Houston command and control center worked diligently to find out a solution, rehearsed each step of the solution in the control center at earth and then provided astronauts with step-by-step process once they are confident with the results from the rehearsals at their end. The dress rehearsal or mock cutover is generally carried out in pre-production environment which closely simulates production environment in terms of infrastructure and code.  The lessons learnt during dress rehearsal on pre-production environment is incorporated into production runbook and cutover plan to make it more exhaustive. We can implement some of the best practices during mock cutover in pre-prod environment itself e.g. like total code freeze, migrating / archiving data center (if applicable), involving same set of users doing both mock cutover and actual production cutover.  We don’t want people doing a cutover task for the very first time in production. One of the major objectives of mock cutover is to establish time and sequence of each of the activities documented in Production Runbook and cutover plan.

Fig 1: Schematic representation of Prod and Pre-Prod activities for a bank

Stakeholder communication & Checkpoints

 The communications plan is critical for banks for aligning stakeholder expectations with respect to project objectives and transition considerations.  The plan considers current and targeted levels of understanding of program objectives among various stakeholder groups. The frequency of communication and checkpoints depends on the complexity and duration of the cutover process, the nature of the cutover tasks, and the number of stakeholders.  Typically, high-level communications regarding the cutover will be distributed widely, while more granular communications will be tailored for project participants.

Communication plans are generally developed by the Change Managers who will work closely with Project Managers and steering committee members to define and agree with checkpoints and make stake holders aware about it. When cutover activities involve some production downtime, open and effective communication both internally and externally with business leadership and customers becomes even more important.

Fig 2: Typical Communication Plan for a bank during cut over process

 Contingency / Rollback Plan

 Imagine a team building a new bridge (the project) to replace a rickety old one (the legacy system). The cutover is like switching traffic to the new bridge, and the contingency / rollback is a plan to quickly switch back to the old bridge if something goes wrong during the switchover. The contingency plan may not be always about rollback plan, if something goes wrong during cutover process. The best way to handle when the things are not going on expected lines is to have an exception handling team ready who can help monitoring logs / queue for any issues. Assign issues (if any) to relevant SME (Subject Matter Expert). SME’s will then investigate and diagnose issues and identify solutions and then co-ordinate testing of the solution and authorize a work around, if possible. Rollback plan should be always kept as last resort to implement business continuity plan. A typical rollback plan might include several components or phases of rollback e.g., if cutover involves DB migration and archival for a bank then during Rollback first thing that we need to revert is DB migration related changes.

After a Rollback is done as part of contingency plan, testing team is involved to do one round of sanity testing before the Rollback is made available after cutover. At the end a detailed cutover plan is of no use unless it is followed diligently. A well-defined cutover plan, while crucial, is only as effective as it’s execution.  This means not just having a plan, but actively following it, including communication, issue resolution, and stakeholder engagement.

About the Authors:

Mr. Soumya Bhattacharya is a Senior Architect working in a leading Software development/ Services company with over two decades of rich experience with expertise in Banking domain. He has led complex multi-year programs across multiple industries from vision to analysis, to implementation, to application go-live, including accommodations for business and technical requirements and impact.

Mr. Soumya Bhattacharya can be contacted at :

LinkedIn

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 When Preparation Meets Production Reality appeared first on Industry4o.com.

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 :

LinkedIn

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 Digital Darwinism appeared first on Industry4o.com.

Thinking about how the fashion industry will progress in the future. Affordability plays as significant a role as creative innovation. To democratize fashion and make it accessible to everyone. It must cater to a broad spectrum of economic groups. Seems emphasizing service consumption over exclusivity. According to Price Elasticity of Demand says if a product is price-elastic, a decrease in price leads to a proportionally larger increase in the demand.

At the same time income effect teaches us when prices drop. Generally, consumers have more purchasing power then. It enables them to buy more of the goods or services. Now the point is can we think fashion or garment industry from service point of view. To be more precise can we apply ‘pay as you go’ model here. So that consumers can pay for garment services based on their actual usage. And that will help them to have more variations in selection. There will be no initial cost, maintenance cost and storage cost. It sounds interesting right?

Let us understand how we can make this a reality. As we know the principle that emphasizes the importance of increasing reach to make an industry popular aligns with the Network Effect. It also aligns with the concepts of Market Penetration. And accessibility in economics and business strategy principles apply for fashion industry the same way as well.

The network effect states that the value of service increases as more people use it. In the context of making an industry popular, expanding its reach ensures more widespread adoption. Which, in turn, enhances its attractiveness and perceived value.

Market penetration strategy focuses on increasing the accessibility of a service. This helps to gain a larger share of the market. An industry can grow its presence and popularity by targeting broader demographics, pricing competitively, and enhancing distribution.

Finally making a service widely available and affordable increases its reach and adoption. When barriers such as cost, or exclusivity are reduced, more people can engage. This is leading to greater popularity. In essence, a combination of these principles underscores that expanding reach. It is critical for making an industry widely recognized and popular through affordability, accessibility, and visibility.

A sustainable business model for fashion industry

Sources: UNEP, Ellen MacArthur Foundation, Global Fashion Agenda, Science Advances, The roundup.org

While the global fashion industry is valued over USD1.7 trillion, at the same time it produces over 92 million tons of textile waste annually, which accounts for almost 7% of landfills globally. Additionally, more than 85% of materials (approx. one truckload every second) used end up either in incinerators or in landfill. The staggering numbers call for a shift in both the production and use of clothing in a more sustainable manner. Circular economy model and democratizing access through an affordable service model are some of the business models that has the potential to achieve this. ‘Rent the Runway’, ‘The RealReal’, ‘Fashionphile’, ‘Ann’s Fabulous Finds’ are some of the companies that have popularised these concepts of fashion rental and resale. The ‘Goodwill’ stores also exemplify this concept of resale proving the viability of these business models for the mass public.

Diagram: A sample process diagram of a sustainable business model for the fashion industry

Digital Technology can play a key role in driving sustainability and consumer education in global fashion industry.

AI/ML and GenAI enabled technologies offer the capabilities to improve process efficiency across the product lifecycle by gleaning intelligence at every stage. This intelligence can be leveraged to automate processes, monitor and manage demand across geographies by reallocating resources efficiently. Walmart, Amazon or e-bay’s efficient use of technology in their e-commerce platforms offer valuable insights and resale opportunities. As UNEP quoted, “The world’s consumers are buying more clothes and wearing them for less time than ever before, discarding garments as fast as trends shift”. “It is estimated that people are buying 60 per cent more clothes and wearing them for half as long.” This calls for promoting awareness on smart consumption, where social media platforms can play a significant role.

Conclusion

The fashion industry’s journey toward sustainability is complex and ongoing. It requires a collaborative effort that reimagines every aspect of the fashion value chain—from the extraction of raw materials to the end of a garment’s life and hinges on the commitment of all stakeholders. While fast fashion and high end-designer apparels has a huge middle income consumer market to capitalise on, it’s the mass lower income group that needs to be educated and mobilised. Cost efficiency in the entire process is a key factor, where digital technology has a key role to play through automation and intelligent recommendations. Educating consumers on smart consumption habits such as clothing-as-a-service is another key element that promotes reuse and recycle, thus reducing environmental impact. However, democratizing this concept of clothing-as-a-service needs innovation in business model, efficient use of e-commerce platforms and focus on ethical practices with an industry-wide focus on driving sustainability. While the challenges are significant, the opportunities for innovation, ethical growth, and environmental stewardship are immense. It’s our responsibility to act responsibly and protect the planet.

About the Authors:

Dr. Manas Panda Ph.D is a partner in a leading technology MNC advising banks and financial institutions implement their digital transformation strategies with focus on customer experience and operational efficiency. A Stanford LEAD alumnus, he talks about technology innovations in financial services.

With an analytical, attention to detail, & growth mindset, Dr. Manas Panda has designed and implemented multiple simultaneous programs and cross functional solutions, driving efficiency & experience for large global banks across continents.

Result oriented, Dr. Manas Panda thrives in solving problems and driving complex engagements.

Dr. Manas Panda builds perspectives on strategic opportunities by connecting the dots, tracks insights and manages communications with stake holders and leadership.

Dr. Manas Panda is highly committed and driven, resilient and optimistic at core.

Dr. Manas Panda is Bestowed with the following Licences & Certifications :

https://www.linkedin.com/in/manaspanda01/details/certifications/

Dr. Manas Panda can be contacted at:

E-mail | LinkedIn

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 Sustainability in Fashion appeared first on Industry4o.com.

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.

It’s a point of near consensus that the financial markets, as an industry, are exceptionally fluid due to the intricacies of emerging products, the scarcity of data, varied regulations, and the looming threat of cyber-crimes. These factors collectively offer substantial challenges for major investment organisations. Although the risks associated with financial markets are widely acknowledged, embracing innovative methods, models, and technologies could empower the banking industry to meet the growing demands of the evolving landscape. Moreover, optimized operations and advanced technology could effectively tackle the increasing requirements of regulatory authorities, ensuring meticulous compliance. For investors and profitability, a firm’s transparency and efficiency are of utmost importance. This article explores how the latest technologies, alongside robust operational frameworks, can adeptly manage Capital Market Operations. Characterised by AI, big data, and automation, Industry 4.0 signifies a revolutionary shift in the industrial sector. In the context of capital market operations, its impact is transformative, leading to automated trading, data-informed decision-making, more efficient risk management with real-time analytics, and profitable investment strategies to enhance customer satisfaction. Nonetheless, this necessitates cybersecurity measures and regulatory adaptations to address the challenges presented by these innovations.

Whilst the concept of Industry 4.0, denoting the fourth industrial revolution, is primarily focused on manufacturing and supply chain technologies and their applications. What sets Industry 4.0 apart is its potential applicability to financial markets industry as well. As the financial markets products and subsequent trading grow increasingly complicated, it has become even more imperative to simplify operations by adopting Industry 4.0 technologies. Apparently steering an investment bank may entail a straightforward approach, refining operations, target operating models and incorporating cutting-edge technologies could incur substantial implementation costs. Thus, any proposed technology transformation should undergo meticulous scrutiny and decision-making, with a specific focus on Industry 4.0 solutions that offer integrated platforms to efficiently manage various asset classes, elevate data-driven decision-making, and empower stake holders with adequate knowledge to fully harness the potential of these advanced technologies and effectively navigate unforeseen challenges. The adoption of Industry 4.0 principles has the potential to provide financial institutions with the means to secure a competitive advantage in an ever-changing arena, simultaneously meeting the rising demands of investors and market participants.

Up to now, our understanding has revolved around the importance of capital markets operations and the relevance of Industry 4.0. Yet, what if these operations were to experience interruptions due to manual errors or technical glitches? The critical consideration lies in whether the relevant technologies can adequately confront these challenges and reinstate the continuation of operations and restore the seamless flow. Let us take an example to expand our understanding:

Consequences of Operations Going Wrong:

The impact of operational errors resonates throughout the financial ecosystem. It erodes investor trust, undermines market integrity, results in financial losses, attracts regulatory scrutiny, and damages the firm’s reputation. Moreover, one event could have ripple effect on different areas of financial markets, affecting counterparties, clients, other banks, connected institutions, agencies, intermediaries, and even broader economic stability.

Trade Initiation: An infamous example is the “Flash Crash” of May 6, 2010, also known as crash of 2.45, happened when erroneous high-frequency trades caused the Dow Jones Industrial Average to plummet almost 1,000 points in a matter of minutes before recovering quickly. The incident exposed the vulnerability of automated trading systems to unexpected consequences and showcased the need for strict operational risk management. It was accepted that any automated system needs supervisions, testing and controlled implementation.  Monitoring the systems and automations also becomes important in many cases.

Trade Execution: Operational errors at this stage can lead to incorrect pricing, settlement delays, and failed trades. An American global financial services firm engaging in market making, faced unexpected event in 2012. A technical glitch caused the firm’s automated trading system to execute numerous unintended trades, resulting in a loss of $440 million within a matter of minutes. This event highlighted the significance of effective control mechanisms that needs to be in place to prevent trading algorithms issues.  This also highlights the need for post-trade reconciliation. This incident shows how important is the operation team’s efficiency and productivity in managing the systems and controlling the framework.

Risk assessment/Risk Management:  Although the exact cause of financial crisis of 2008 is a matter of discussion and there could be many stories around the matter, major reasons for the global crisis stands out to be lack of risk management and inadequate assessment of exposure.  While the pricing of structured products was not appropriate, the assessment of risk was also a major gap in this case.  From overnight rates to incorrect assessment of credit worthiness of the borrowers, incorrect pricing of credit derivatives, there were several risks that lead to the catastrophe.  Consequently, lenders and investors were worried about the risk and exposures of banks and other financial institutions which had multiple impact globally.  Most of the models fell apart due to liquidity problems created by this crisis. This also impacted the risk management strategies, as mismarked credit derivative exposed the bank’s inadequate risk management practices and lead to closure of many big institutions.  Risk management is integral part of operations, as it impacts market, credit, operational and counterparty risks.

Trade Settlements: Lack of good operations here could lead to, settlement failures, discrepancies in trade details, and delays in transferring ownership. An example is the T+2 trading settlement standard, where trades are expected to settle within two business days. Delays or errors in this process can result in liquidity problems, counterparty risk, and regulatory non-compliance. The collapse of Lehman Brothers in 2008 had a domino effect on trade settlements, as the bank’s bankruptcy led to chaos in the settlement process, causing considerable disruptions and financial losses.

Mitigation Strategies: To mitigate the risks associated with operational errors, investment banks must invest in robust operational frameworks along with advanced technology infrastructure, here Industry 4.0 plays a critical role. This involves implementing stringent controls, adopting advanced technology solutions, enhancing human oversight, conducting regular stress tests, and continuously refining operational processes. Industry 4.0 can substantially help in controlling settlement failures through its advanced technological capabilities and innovative approaches by integrating smart automation and real-time data analytics into settlement processes. Also learning from past incidents, firms must prioritize risk management, invest in training staff, and establish clear communication between trading, operations, and IT teams.

Conclusion

To sum up, by utilising intelligent operations and processes via Industry 4.0, and by embracing inventive approaches, the investment banking sector can bolster its capacity to meet changing challenges head-on. The assimilation of Industry 4.0 technologies, featuring AI, big data, and automation, presents the potential to revolutionise investment banking operations. This encompasses automated trading, data-driven decision-making, and refined investment strategies, all contributing to an improved customer experience and increased profitability.

Even with these opportunities at hand, it’s vital to enforce strong cybersecurity protocols and make necessary regulatory modifications to effectively tackle the challenges brought about by these innovations. Overall, the principles of Industry 4.0 hold a promising route for financial institutions to maintain their competitive edge, cater to the growing demands of investors, and navigate the intricacies of an ever-evolving landscape. It’s important to note that Industry 4.0 can play truly a significant role in streamlining operations, enhancing the overall efficiency of investment banks.

About the Authors :

Ms. Rashmi Rao
Senior Industry Consultant- Financial Markets.

Ms. Rashmi Rao is a trainer on Stock Market Investment and Trading.

Ms. Rashmi Rao is also a Eloquent Speaker on topics related to Financial Markets & has also authored many Articles.

Ms. Rashmi Rao can be contacted at :

LinkedIn | Blog

Mr. Raja Basu
Business Value Architect- Financial Markets.

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 FINANCE 4.0 – The Impact of INDUSTRY 4.0 on the Finance Industry. appeared first on Industry4o.com.

Investment decisions are always tough calls. The market is flooded with investment strategies that support fundamentals as well as behavioral finance. Both types of strategies have their successes and failures, in relation to the larger economic conditions. However, in the last two years, on the onset of the pandemic, despite significant adverse impact on economic activities, the stock markets rose to all-time highs after the initial fall. That forced us to rethink the influence of human behavior on the holistic investment decision-making process, as human decisions are subject to individual and societal bias.

The rise of technology, particularly Artificial Intelligence (AI) is contributing to this decision-making process with data driven insights. AI’s decisions are based on correlation and statistics from millions of data elements. Interpretation from data is without prejudice.

#artificial intelligence #cognitive bias #algorithimic bias #model bias #data bias

Will AI’s algorithms be an antidote to human biases in making more holistic decisions? or will it make the investment decision worse, as it may not factor in the nuances of human behavior? or will the machine learning models learn the human biases and replicate, producing consequences not thought of?

Researchers usually identify different investment attributes as strong or weak indicators that propel the overall decision-making capabilities, however this applies for conventional investors. By conventional we presume the investors who are part of a homogeneous system and have overall synergy in accessing the market information. Plenty of studies have already been put in place to figure out a constructive relationship among multiple self-limited and self-discriminated variables that affect the overall investor buying behavior. We were quite thrilled to dig deeper to understand whether this pandemic has made any significant changes in those numbers, more precisely from statistical inferences perspectives. One interesting point we could interpret from there is the predominance of cognitive biases and analogous heuristics, that means somehow investors are affected by some ancillary extraneous parameters those are introduced during the pandemic, those are impacting and driving the overall buying behaviors. This is indeed an interesting observation and provides us a lot of room to do further research to understand the umbrella effect.

We spent quite some time understanding the interrelations among the indirect variables with researcher lenses. We could realize the indelible presence of cognitive dissonance during analyzing ‘bandwagon effect’. It’s more popular as ‘herd behavior’ among investors in capital market. Investors tend to get pacified with a known yardstick even if the same is not relevant from the immediate transaction standpoint.

Psychologically it’s more distracting from a rational perception and akin to more conflicting beliefs. Now if we look back and try to understand the genesis of this behavior, inconsistency in ROI is a paramount trigger here, now analyst will say that’s a generic cause for years, which is fact. However, we wanted to extrapolate this to understand from the behavioral finance angle, as this has a huge potential to understand the causal relationships so that in future when we will design an ‘Artificial Intelligence’ led investment platform these parameters will help us to minimize the noise to a considerable extent. As per the self-standard model, we end up being normalized in both the ways. If initial assessment of stock price evaluation is compared to a known and universally accepted reference point and is up to our personal standard then it may transform into self-esteem moderation or idiographic dissonance arousal, however if the difference persists then it goes through normative standard and transmutes to no self-esteem moderation which is an example of nomothetic dissonance arousal. Knowing these relationships in detail will help in designing high accuracy decision support system capable of using predictive models.

Let’s move on to technology aided financial decision making. From Robo advisors helping in investment decisions to evaluating credit applications to underwriting insurance, AI algorithms have evolved over the years. Increased efficiency, faster timing, better experiences are common knowledge. Research shows, decisions are broader and fairer.

Quoting Andrew McAfee of MIT, “If you want the bias out, get the algorithms in.” AI can reduce the impact of human beings’ subjective interpretation of data, as ML algorithms learn from the available data sources in the process, enhancing their predictive accuracy making decisions fairer over time. These are the desirable outcomes of technology. However, going deeper, the underlying broad picture is not the same always.

AI runs on algorithms and is trained on data. Evidence shows bias in outcome – both algorithmic bias (model bias) and data bias.

Research by Joy Buolamwini and Timnit Gebru, “Gender Shades, 2017”, found errors in facial-recognition technologies, how the outcome differed by race and gender. The accuracy in detecting images of white faces was much higher, while the system almost failed in case of black images and particularly black women. Similarly, investigations by ProPublica on the use of AI in the US justice system. ProPublica found the system biased as it failed to demonstrate “balance for the false positives”, which can be compared to the way Facebook failed in detecting “misinformation” on subjective context in recent times.

In financial decision making, this may lead to approval of loans to a segment of applicants, who may be more vulnerable to default in case of any immediate adverse economic condition, affecting their credit standing creating a domino effect going forward. Similar scenarios may crop up in case of investment decisions as well, like favoring commonly traded stocks instead of value investing. Since algorithms work on statistics and correlation, it may tend to favor data overload overlooking survival deviations (when you look at things that survived, when the focus should have been on things that didn’t), leading to “algorithmic bias,” where the algorithms itself are most often the main source of the issue. Another case in point is the addictive algorithms deployed by social media platforms. After the onset of the pandemic, when the economic indicators are dependent on a global health crisis caused by a virus, the efficacy of the algorithm becomes critical. Because of their self-learning nature, while algorithms adopt and implement their learnings for their efficiency and efficacy, the underlying bias gets deployed at scale unnoticed and unchecked.

It’s not always the algorithm, which is the cause of the bias, underlying data might be. If we train an AI on inaccurate data, it will give us biased results. Diversity of the data pool is one of the vital cogs as the algorithm trains and learns from it. If we train the system on dirt, we can’t get beauty in outcome. Though there is no causal relationship between race and ability to make mortgage payments, studies have shown higher rejection of racial minorities. Other studies also show gender bias in financial underwriting. When self-learning AI systems work on these types of data inflicted with human bias, it runs the risk of amplification over time. With automated efficient processing, these AI systems have the potential to impact much larger customer segment in a short span, before being detected.

As some regulators pointed out:

“Algorithms shouldn’t have an exemption from our anti-discrimination laws” – MAS

“If we are increasingly going to use the assistance of, or delegate decisions to, AI systems, we need to make sure these systems are fair in their impact on people’s lives” – European Commission

“For consumers, the main risk from broad employment of AI technologies is discrimination” – BaFin

With AI quietly seeping into our life, from facial recognition in mobile phones to digital assistants like Google home, Siri or Alexa to virtual assistants almost everywhere, the necessity to reduce bias has become paramount. However, it calls for greater effort across stakeholders.

With this understanding of the behavioral science and AI technologies and the current business and economic scenario, it can go either way – reduce bias or amplify bias. At the same time, AI can’t replace humans across the financial decision-making process either. The human touch still plays a significant role at a moment of crisis. So, it is important to consider the nuances of human judgement and societal contexts while AI supports data driven insights – with humans and machines working together proving holistic financial decisions.

About the authors:

Dr. Manas Panda Ph.D is a partner in a leading technology MNC advising banks and financial institutions implement their digital transformation strategies with focus on customer experience and operational efficiency. A Stanford LEAD alumnus, he talks about technology innovations in financial services. He is based in Toronto, Canada and can be reached at :

Dr. Manas Panda can be contacted at:

E-mail | LinkedIn

Mr. Raja Basu is a Senior Consulting professional in a leading technology MNC, in his current role he works as a business architect and helps clients to enable their digital transformation journey. He has special interest in Responsible use of AI. He is also pursuing his doctoral studies (Ph.D) from XLRI Jamshedpur. He is based out of Kolkata, India and reachable at

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 Can AI Aid in Financial Decision Making appeared first on Industry4o.com.

In this article we have tried to explore empirically how ChatGPT can enrich financial services industry. Artificial intelligence has become a household technology due to its high rate of adoption and tangible benefits.

Language model based Artificial Intelligence tools have seen spectacular rise in the last few years – Google’s transformer algorithm in 2017 to OpenAI’s GPT3 in 2020 & Dall-E in Sep 2022 to ChatGPT in Nov 2022. And in few days, it has kind of taken over the world, taking corporates to Govts on a frenzy ride. While kicking off an arms race among mega corporates, businesses exploring possible use cases, researchers talking about the explosion of parameters in large language models, ethics experts on the possible exploitation by propaganda machinesto regulators on creating guidance and framework of use…. the list goes on.

Artificial Intelligence and machine learning models trained to mimic human behavior, identify patterns from large amounts of data, respond from predefined information pool etc. has come a long way over years and reached an inflection point. From being predictive it has become generative, producing, and adjusting outcomes from prompts, generating codes, writing essays, to painting images with impeccable quality seemingly performed by human being. Creativity, the distinctive characteristics of human beings, is partially being acquired by machines… Artificial Intelligence closing in on human intelligence.

The use cases of ChatGPT and Generative AI are wide and spread across business functions and industries.

This transformative technology has the potential to augment functions across multiple industries. As defined by ChatGPT init’s own words, it can augment almost all critical and substantial business functions from customer service to product development to supply chain management to communication across industries – from financial services to auto to manufacturing to defense. Even an AI bot flew an F-16 fighter jet recently in Southern California skies, while agricultural usage combining AI with drone technology has gained pace.

Let’s try to have a closer look at BFS sector, where the capabilities can be leveraged with significant upward impact:

From the banking and financial services industry perspective, below are some the top business functions where we think ChatGPT and Generative AI, the technology behind ChatGPT can be leveraged with significant positive impact:

Customer service and personalization:

Data and AI can help better understand customer choices and preferences, automate customer inquiries and complaints, provide personalized recommendations, and offer 24/7 support. Combined with natural language processing capabilities, it can respond to global customer base at ease, agnostic of language barriers.

Chatbots:

Generative AI can assist in creating conversational chatbots that can help customers with their banking needs, providing a more personalized and engaging experience. Combined with natural language processing, it can bring back some of the missing human touch from pandemic era.

Fraud detection and prevention:

It can analyze patterns in transactional data, customer purchase habits, with related information and identify fraudulent behavior, reducing financial losses.Simultaneously, producing wide range of scenarios and supporting data sets helping improve the validation process and coverage.

Portfolio management:

Generative AI can help banks and financial institutions to optimize their investment portfolios, identifying opportunities for growth and mitigating risks. M/L models can be trained with domain specific knowledge like market behaviour, fundamental and technical analysis models to enable churn portfolios in sync with risk appetite.

Credit scoring:

One of the critical business functions, where AI based tools can analyze credit scores and risk profiles, improving accuracy and reducing the risk of default. Additional supporting parameters, like payment history, funds flow analysis, psychometric analytic models etc. can be incorporated and evaluated in sync providing a more detailed view of the customer aiding better decision making in credit approval process.

Wealth management and investment advisory:

This is one of the niche business segments for banks with high focus. AI based tools can assist relationship managers by analyzing customer data and recommending personalized savings &investment strategies with optimal capital allocation.

Predictive analytics:

Analytics has been on the rise, with large amounts of data flowing from all directions. AI aided with higher computational power can analyze large amounts of data and forecast future trends, such as market fluctuations or customer behaviorfor informed investment and business decisions.Though, it calls for the M/L models to be trained with specific clean data sets and algorithm, we think it will be worth the investment.

Compliance and regulatory reporting:

Adapting to the fast-changing regulatory environment is always a challenge for financial services industry. Generative AI aided tools can assist in automating regulatory reporting and compliance by analyzing large amounts of data and identifying potential issues.

Explore the potential but tread with caution.

While the technological potential of AI and particularly Generative AI are immense, it is also subject to pitfalls and can be exploited by bad actors.

Capability increases so also bias:

Like any new technology, this has to be explored with caution. Large models trained over internet of data acquire social, cultural and all the myriad biases of internet as well.

As per a 2022 report by Stanford University: “A 280 billion parameter model developed in 2021 shows a 29% increase in elicited toxicity over a 117 million parameter model considered the state of the art as of 2018.”

Biases grow in proportion to increased capability.

More the data more the carbon footprint:

With the climate reporting regulations expected soon and with social commitments forming an integral part of corporate citizenship, these large data intensive models create a concern. As per the MIT Review report below, Google’s transformer model produced around 284 metric tons of CO₂ with 213 million parameters.

In comparison, GPT-3, the predecessor of ChatGPT was trained on 175 billion parameters. Also, as we feed more data to train the models, the carbon footprint increases.

Balancing the climate commitments with the choice of technology basket will be a tricky water to tread for the financial services institutions.

A propaganda machine in the hands of bad actors:

In the pervasive era of google search, the acceptance of system outputs has increased over in-person advice. With humanly response generated by generative AI based tools, it will be nearly impossible to segregate misinformation. Manipulated information has the potential to play havoc in financial and economic decision making.

All the eyes are on this new transformative technology. Its too early to assess its full potential, address ethical concerns while bringing in a framework for responsible use. Though BFS sector is always on the forefront of using new technologies, its advised to approach with caution with MVPs. Interesting days ahead to explore promising use cases. Stay tuned. Top of Form

Image Credit : https://aiheadshotgenerator.com/

About the authors :

Mr. Aroop Ray, is a consulting partner in a global technology MNC. A passionate problem solver, Aroop works closely with C-suite leaders shaping their future technology roadmap by leveraging new age digital technology.

Mr. Aroop Ray can be reached at :

E-mail | LinkedIn

Dr. Manas Panda Ph.D is a partner in a leading technology MNC helping banks and financial institutions implement their digital transformation strategies. A Stanford LEAD alumni, he focuses on technology innovations in financial services. Based in Toronto, Canada.

Dr. Manas Panda can be contacted at:

E-mail | LinkedIn

Mr. Raja Basu is a Senior Consulting professional in a leading technology MNC. In his current role he works as a business architect and helps clients to enable their digital transformation journey. He has special interest in responsible use of AI. He is also pursuing his doctoral studies (Ph.D) from XLRI Jamshedpur. He is based in Kolkata, India.

Mr. Raja Basu can be contacted at :

E-mail | LinkedIn

Also read Mr. Raja Basu’s earlier article :

The post Business Imperatives of ChatGPT & Generative AI appeared first on Industry4o.com.

Of late Artificial Intelligence has become the fulcrum of numerous transformative changes, because of this extensive usability, it is now inextricably connected with our typical daily life. Starting from healthcare to weather prediction and portfolio optimization, it is present everywhere. On the contrary the perception about its offerings is not so promising, to the extent it has raised some serious concerns about the fairness of its decision-making processes across numerous domains. We have limited choices on how the same will be accepted, as majority of our ecosystem is inequitable and exclusionary. This has become a challenge to implement AI with fairness and get a response from the end users the same way. Information service professionals need to be well versed with these ethical nuances as they work with AI applications extensively. Recent research has indicated that there are still some impediments, among the most severe concerns, explainability, data biases, data security and privacy and group specific discrimination in the evaluation process are a few so called noteworthy abnormalities. This has also been observed that the growing use of AI has a profound environmental impact. AI/ML models generate a large amount of carbon footprint and that adversely impact environmental ecosystem. Hence it is imperative that environmental ethicists and technology experts must make salient contributions.

In the current context we will try to evaluate the favorable outcome of human and machine collaboration in the view of industry 5.0 using Hunt-Vitell (H-V) theory. Though Hunt Vitell model intended for marketing domain, later it evolved more because of its distinct socio- economic relevance. More from a generic model perspective it enumerates ethical decision making from the context of behavioral marketing. However, Hunt–Vitell model establishes how ethical decisions are predicated not from the potential benefits only, essentially it states deontological evaluations are also critical that deduce norms and values, which are the backbones of trustworthy decision-making process. Ethical decisions in business interactions are a long-debated topic and plenty of researchers have worked on the same. The Hunt-Vitell (H-V) model of marketing ethics has been validated by many researchers. This model explains how people make ethical decisions. In the current context AI is trying to replace humans and applying algorithmic decision-making techniques. However, when we perceive this from Industry 5.0 perspectives, it focuses more on human and machine synergy. Machine should never replace human rather it must accommodate human centricity to augment the process holistically.

As it is depicted below, information is disseminated from the source and processed through computing platforms to produce final task output. For many end-users AI system appears to be a Blackbox. How the decision support system is working that needs a clear explainability. Many a times the training data fed to generate the models itself are the sources of unconscious
human biases. We will see how Hunt Vitell model can help to bolster the human machine synergy.

Deontology is an ethical theory that helps to distinguish ethical intent that substantiates right decision making is certainly a prerogative for the end users and it is a responsibility for the AI system designers. When both work in absolute synergy, information may be captured through any of them, most importantly enrichment should be collaborative. Purposive collaboration is the bridge between ethically responsive behavior that comes from human enables machines to create ethical context for the resolutions. Ethical aspiration is the driving force that will help combine the two and acts as a catalyst to enable debiasing techniques.

In other words, we can infer Human oversight is must for algorithmic decisions. Here human plays a role of ensuring quality and seamless delivery. It’s very important to have “human in the loop” for automated decision-making (Jones, 2017; Wagner, 2019). It’s not apparent how much oversight is required for human algorithms interactions. The researchers usually express reservations in limiting human oversight, specifically related to end user’s overdependence on algorithmic advice (Engstrom et al., 2020).

Above diagram represents how a comprehensive human machine collaboration can be implemented. We see at every stage of AI lifecycle human centricity is ensured. This is the best possible approach that can be thought of to protect unethical outcomes. To make it even simplified human supervisory lens is rechristened as a cohesive synergy. Ideally it should be an iterative process till the quality is met. We can safely refer this as trapped loop.

It resonates on the AI dependence on the human generated data derived from multiple and disparate data sources which are used and synthesized accordingly. Even after that it’s difficult to control as external ecosystems consume this data through different channels, that affects the information for future algorithms, therefore it represents a close loop that hinders controls on the decision support systems. Therefore, it’s an arduous task to design innovative operating model that will ensure better interactions between human and machines, that is the primary intention for industry 5.0.

Finally, AI is often verified and validated with multi layers of oversight, including external regulations and internal governance. A regulation is a government rule enforced by law. The periphery of regulations is changing dramatically, and it’s essential to understand primary pillars of regulation to design, build, and implement AI legally and ethically. Regulations and governance should be risk based, contextual, and can ensure assigning responsibilities to the parties those are highly exposed. To have a successful model industry 5.0 should focus to earmark these interstices.

About the Author:

Mr. Raja Basu

Mr. Raja Basu is a Senior consulting professional in the Technology
Consulting space specializing in financial services. He is a highly accomplished industry professional who has worked for numerous global clients. He has special interest in ethical use of AI and Sustainability.

Mr. Raja Basu is a Professional Mentor ( Industry ) in XLRI PGDM ( GM ).

Mr. Raja Basu is pursuing his doctoral program from XLRI Jamshedpur in the Information Systems area.

Mr. Raja Basu can be Contacted at :

Email : rajab05@email.iimcal.ac.in

LinkedIn : https://www.linkedin.com/in/rabasu/

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

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

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

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

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

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

Mr. Yogesh Khadilkar

Mr. Yogesh Khadilkar is a Senior Management professional with around 22
years of experience in Business Development. He has been in the Information Technology space since 2000 and has worked with leading IT MNC’s, managing critical customers and partners relationships.

Mr. Yogesh Khadilkar is currently pursuing his doctoral studies at XLRI, Jamshedpur in the Marketing area.

Mr. Yogesh Khadilkar can be Contacted at :

Email : yogeshjkhadilkar@gmail.com, yogesh3112@gmail.com

LinkedIn : https://www.linkedin.com/in/yogesh-khadilkar-a60b423/

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