IBM is leading the charge in this rapidly evolving conversation through a series of in-depth and thought-provoking discussions with thought leaders from a wide range of industries. In this episode of AI Dialogues, presented by CNBC-TV18, in association with IBM, CFOs explored the potential of AI to transform finance operations at BFSI.
CNBC-TV18’s Mridu Bhandari hosted a lively 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 and South Asia; on AI-powered solutions for operational efficiency, the importance of governance and compliance, and the future of AI in reshaping customer service models.
AI-powered operational efficiency
The panelists agreed that AI has become indispensable for improving operational efficiency. Venkatraman Venkateswaran highlighted its Smile Pay initiative, which leverages facial recognition for seamless transactions. “We’re taking a very calibrated approach – we want to make sure we do proof of concept before we launch into something big. We’re doing pilots in three different areas: the first is credit scoring and underwriting credit; the second concerns policy documents that can be hundreds of pages long, so we use AI to summarize; and the third concerns transaction monitoring and fraud prevention.
Similarly, Gobind Jain explained how AI-enabled operations streamline document processing, audit tools and repetitive tasks. By reducing manual intervention, AI ensures faster and more accurate results while improving the customer experience. For Ravindera Nahar, the strength of the cloud comes into play in another area. “We need to be ready with very flexible structures where we can increase our capacity as needed. In our use case, transaction speed is very critical because any delay can have a direct negative impact on customers. Therein lies the flexibility that the cloud brings that technology brings us is crucial for stock trading.
Balancing innovation and regulation
Since the BFSI sector operates in a highly regulated environment, compliance is a top priority. Venkatraman Venkateswaran emphasized 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 and compliance.
IBM’s Viswanath Ramaswamy highlighted the importance of governance in AI. With the rapid evolution of models, the need for explainability, robustness and fairness becomes essential. “From a risk perspective, you first look at the explainability of the AI: are you able to explain both the AI model and the data pipeline? The second is the robustness of the AI model itself, especially when we look at a multi-model use case. The third, and perhaps most important, is fairness. We want to make sure that the models are not biased, all financial parameters being equal, we want to make sure that if two people apply for a mortgage, for example, that the model does not treat men and women differently, or people married or single differently, and so on. There should be fairness without prejudice. Another key consideration is data privacy, and of course, compliance and governance, regardless of the model, 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 personalization. Gobind Jain explained how AI enables wealth managers to tailor recommendations and predict customer needs based on behavioral patterns, thereby improving both customer experience and operational efficiency. “I would say the biggest advantage of AI is that it can process large amounts of data in a short time. For example, looking at wealth management customers, you have data on their behavior, where they invest, where they spend and invest money and you can use that information to create customer experiences that you are in. able to answer them in a fraction of the time and with very personalized information that makes everyone happy.
Ravindera Nahar also believes that the real value of AI lies in extracting insights from large data sets to improve service delivery and reduce time to market through insight better and faster decision. He further highlighted that AI allows teams to monitor transaction patterns in real-time, thereby improving fraud detection and prevention.
Measuring ROI in AI Deployments
Although the potential of AI is immense, panelists admitted that measuring its return on investment (ROI) remains a challenge. Venkatraman Venkateswaran emphasized the importance of focusing on business outcomes rather than tool adoption. He explained that the success of AI lies not in the technology itself, but in its ability to drive better business outcomes, from improved customer service to faster product launches. , a deeper understanding of customers or any other business objective.
In the same vein, Viswanath Ramaswamy outlined four key performance indicators for measuring AI-based ROI: “Basically, there are four vectors: either you improve the customer experience or you improve the productivity of your employees , either your income or your profitability result. There may be more variations and combinations of these vectors, but generally speaking, this is how we should think about quantifying and measuring impact.
Fraud prevention and cybersecurity: the advantage of AI
Fraud prevention remains a major 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 proactively identifying anomalies. AI-powered monitoring tools ensure real-time fraud detection, thereby preserving both client assets and institutional trust.
Additionally, Viswanath Ramaswamy explained that AI must be integrated into security protocols to protect against increasingly sophisticated attacks. “Today, the digital exposure of a financial institution is important and each perimeter is important. It’s not just about managing my data center security and being safe. The exposure is so great and the entry points are so numerous that we need to expand the security perimeter. This is why we leverage much of our responsible AI framework in our own technologies. At this point, it’s not just AI for security, but also security in AI that can help predict well in advance. a pattern of movements at each layer, and probably cure that as well. If not, it can at least provide information that service provider security administrators can act on.
He also highlighted 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
Panelists predicted that AI technologies would thrive in conjunction with human intelligence rather than replacing it. Venkatraman Venkateswaran highlighted the need to reskill teams to adapt to AI-enabled environments, emphasizing that AI will act as a complement, not a replacement, to human workers.
Gobind Jain predicted a future in which contactless banking would become the norm, with automated branches offering seamless self-service options. Meanwhile, Viswanath Ramaswamy encouraged banks to accelerate their AI deployments to meet the expectations of tech-savvy younger generations. “We need to look at the generation who will be banking in the future; it’s their experience that matters most. Instead of looking at the types of technologies we should use, we need to focus on the people who will use them – their generation and their generation’s psychographics. After all, this is a cohort born with technology.