Yes Bank is exploring AI application in risk management: CIO Mahesh Ramamoorthy

Indian private sector bank Yes Bank has in recent years launched several AI-driven initiatives to enhance services, protect data, and improve customer experience. These initiatives include AI chatbots for customer support, predictive analytics for risk management, automated credit assessments, and advanced data analytics for personalised banking solutions, to name a few. The Mumbai-headquartered bank has also established a robust cybersecurity strategy to protect customer data and IT systems. In an interview with TechCircle, Mahesh Ramamoorthy, Chief Information Officer (CIO) at Yes Bank, sheds light on these initiatives and more. Edited excerpts:

What cybersecurity strategies have Yes Bank adopted to safeguard customer information and IT infrastructure against rising digital threats, and also how does it improve its IT governance?

Yes Bank ensures continuous improvement in IT governance through regular audits and updates. We have implemented a cybersecurity governance framework that promotes collaboration across units for comprehensive security coverage. Security is integrated into governance across policies, processes, technologies, and data. The bank continuously assesses its IT risk posture to align with regulatory expectations and industry best practices. Additionally, we have a sustainability initiative based on proactive governance practices that reinforce the principles of compliance, security by design, and a zero-trust approach. Systematic procedures guarantee that enhancements and upgrades comply with evolving security standards, particularly regarding data protection. Additionally, fostering a strong security culture among customers and employees through ongoing education is vital for us.

How is Yes Bank utilising Artificial Intelligence and Machine Learning in its operations?

Yes Bank collaborates with selected technology firms to drive innovation, particularly in AI and machine learning. While the names of these partners are confidential, the bank works with both established companies and emerging fintechs to create synergistic value. AI is embedded in customer interactions, transaction reconciliation, and data management processes. The bank is also exploring AI applications in risk management, which holds significant potential for operational and security management.

The Bank's AI-powered chatbot – Yes Robot – was introduced during the COVID-19 pandemic for customer support. Has there been any enhancements on this platform to improve customer interaction further?

Yes Robot serves as a vital channel for the Bank, facilitating our engagement with customers in terms of service and product cross-selling and upselling. We have observed a positive uptake, and there exists a significant opportunity to further integrate AI into our channel interaction framework to improve the customer experience.

How has the cloud improved the bank's operational efficiency and scalability?

Yes Bank has established a robust cloud assessment framework. The cloud strategy at Yes Bank is centred on three key pillars: scalable resources that align with growth, the agility to recover from setbacks, and the promotion of security standardisation throughout the infrastructure. We collaborate with various cloud service providers. Numerous efficiency improvements have been achieved, including sustainable cost reductions, enhanced elasticity of computing resources, on-demand scalability, and automated recovery and deployment of cloud-native development management tools.

How automation and AI-driven tools have specifically contributed to reducing the Bank's operational costs and improving service turnaround times?

At Yes Bank, the pursuit of efficiency is an ongoing endeavour, primarily facilitated by reducing friction to improve both customer and employee experiences. The Bank is transitioning from a reliance on robotic process automation (RPA) and workflow-based automation to a more innovative approach that involves reimagining processes, particularly through the application of machine learning models in targeted areas where significant enhancements in efficiency and experience can be achieved.