In the dynamic world of banking and finance, artificial intelligence (AI) is unlocking new possibilities: reshaping customer service, mitigating risks, and driving operational efficiency. A McKinsey report estimates that AI has the potential to generate up to $1 trillion annually for global banks, underscoring its role in future-proofing the financial sector. The question is no longer if financial institutions will integrate AI effectively, but how.
IBM is leading the charge on this rapidly evolving conversation through a series of in-depth and thought provoking discussions with industry thought leaders from a broad spectrum of industries. In this episode of AI Dialogues, presented by CNBC-TV18, in association with IBM, Chief Financial Officers explored the potential of AI to transform finance operations in BFSI.
CNBC-TV18’s Mridu Bhandari moderated a spirited discussion with Venkatraman Venkateswaran, CFO of Federal Bank; Ravindera Nahar, CFO of HDFC Securities; Gobind Jain, CFO of IndusInd Bank; and Viswanath Ramaswamy, Vice President, Technology, IBM India & South Asia; on AI-powered solutions for operational efficiency, the importance of governance and compliance, and AI’s future in reshaping customer service models.
AI-Driven Operational Efficiency
The panelists agreed that AI has become indispensable in improving operational efficiencies. Venkatraman Venkateswaran highlighted their Smile Pay initiative, which leverages facial recognition for seamless transactions. “We follow a very calibrated approach – we want to make sure that we do the proof of concept before we get into anything big. We are doing pilots in three different areas: the first one is on credit assessment and credit underwriting; the second is with policy documents which can run into hundreds of pages, so we’re using AI to summarise; and the third is with transaction monitoring and fraud prevention.”
Similarly, Gobind Jain elaborated on how AI-enabled operations are streamlining document processing, audit tools, and repetitive tasks. By reducing manual intervention, AI ensures faster and more accurate outcomes while enhancing customer experience. For Ravindera Nahar, the strength of the cloud comes into play in a different area. “We have to be ready with very flexible structures where we can increase our capacity as required. In our use case, the speed of transactions is very critical because any delays can have a negative impact on clients directly. This is where the flexibility that cloud technology gives us is crucial for stockbroking operations.”
Balancing Innovation with Regulation
Given that the BFSI sector operates in a highly regulated environment, compliance is a top priority. Venkatraman Venkateswaran stressed that innovation must align with regulatory requirements, as data privacy and cybersecurity are non-negotiable. He cited the integration of Bhashini, a multilingual chatbot from the Reserve Bank Innovation Hub, with Federal Bank’s AI-powered chatbot, Fedy, as an example of balancing innovation with compliance.
Viswanath Ramaswamy of IBM underscored the importance of governance in AI. With models evolving rapidly, the need for explainability, robustness, and fairness becomes paramount. “From a risk standpoint, you’re first looking at explainability of the AI – are you able to explain the AI model and the data pipeline both? The second is the robustness of the AI model itself, especially when we’re looking at a multi-model use case. The third, and possibly the most important, is fairness. We want to ensure that models aren’t biased. All financial parameters being equal, we want to ensure that if two people apply for a home loan, for instance, that the model doesn’t treat men and women differently, or people who are married or single differently, and so on. There should be fairness with zero bias. Another key consideration is data privacy, and of course, compliance and governance, agnostic of the model, agnostic of the data sources.”
IBM’s Watsonx.governance platform addresses these issues by ensuring transparency and managing bias, a crucial factor for responsible AI adoption.
AI as a Strategic Differentiator
AI creates unprecedented opportunities for effective personalisation. Gobind Jain talked about how AI allows wealth managers to tailor recommendations and predict customer needs based on behavioral patterns, enhancing both customer experience and operational efficiency. “I would say that the biggest advantage of AI is that it can process vast amounts of data in a short period of time. For instance, looking at wealth management customers, you have data on behaviourally where they’re investing, where they spend and invest money, and you can use that information to create customer experiences where you’re able to respond to them in a fraction of the time, and with very personalised insights. That makes everyone happy”
Ravindera Nahar, too, opined that AI’s true value lies in mining insights from large datasets to improve service delivery and reduce time-to-market through better and faster decision making. He further emphasized that AI empowers teams to monitor transaction patterns in real time, improving fraud detection and prevention.
Measuring ROI in AI Deployments
While the potential of AI is immense, the panelists admitted that measuring its return on investment (ROI) remains challenging. Venkatraman Venkateswaran stressed the importance of focusing on business outcomes over tool adoption. He explained that AI’s success lies not in the technology itself but in its ability to drive better business outcomes, be they improved customer service, faster product launches, deeper customer insights, or anything else the business is aiming at.
In a similar vein, Viswanath Ramaswamy outlined four key performance metrics for measuring AI-driven ROI, “Fundamentally, there are four vectors: either you’re improving the client experience, or improving the productivity of your people, or your revenue outcome, or your profitability outcome. There can be more variations and combinations of these vectors, but broadly, this is how we need to look at quantifying and measuring impact.”
Fraud Prevention and Cybersecurity: The AI Advantage
Fraud prevention remains a top concern for BFSI players, with emerging technologies increasing the complexity of threats. Gobind Jain noted that AI can be deployed to detect suspicious activities more effectively by analyzing transaction patterns and identifying anomalies proactively. AI-powered monitoring tools ensure real-time fraud detection, safeguarding both customer assets and institutional trust.
Adding to this, Viswanath Ramaswamy explained that AI must be embedded within security protocols to protect against increasingly sophisticated attacks. “Today the amount of digital exposure of a financial institution is large, and every perimeter is important. It’s not just that I manage my datacenter core security and I’m safe. The exposure is so large and the entry points are so many that we have to expand the perimeter of security. This is why we leverage a lot of our responsible AI framework in our own technologies. At this point it’s not just AI for security, but also security in AI. AI can help predict well in advance, seeing a pattern of movements at every layer, and probably heal that as well. If not that, at least it can give insights for the service providers’ security administrators to act upon.”
He also emphasised the importance of identity access management, threat detection, and digital trust frameworks to secure customer data in a highly interconnected environment.
Future Trends: AI in Banking and Beyond
The panelists predicted that AI technologies will flourish in conjunction with human intelligence rather than replace it. Venkatraman Venkateswaran emphasised the need for reskilling teams to adapt to AI-powered environments, highlighting that AI will act as a complement, not a replacement, for human workers.
Gobind Jain predicted a future where touchless banking becomes the norm, with automated branches offering seamless, self-service options. Meanwhile, Viswanath Ramaswamy encouraged banks to fast-forward their AI deployments to meet the expectations of tech-savvy younger generations. “We need to look at the generation who is going to be banking for the future; their experience matters most. Instead of looking at what are the types of technologies we need to use, we need to shift focus to the people who will be using these – their generation, and the psychographics of their generation. After all, this is a cohort that was born with technology.”
Content retrieved from: https://www.cnbctv18.com/business/finance/finance-in-the-age-of-ai-bfsi-cfos-explore-operational-excellence-enhanced-security-and-future-readiness-19518119.htm.
