The Indian Banking, Financial Services and Insurance (BFSI) sector is experiencing a revolutionary transformation powered by Artificial Intelligence, with industry reports indicating that AI could reshape 35-50% of jobs in Indian banking. This technological shift is becoming essential for financial institutions to manage costs effectively and enhance productivity across operations.
The Fundamental AI Shift in Banking
According to experts Bharani Subramaniam, CTO for India and the Middle East at Thoughtworks, and Deepak Bhatia, who leads BFSI business for the region, the most significant change AI brings is genuine hyper-personalization. Unlike traditional software with predetermined user flows, AI provides banks unprecedented flexibility during runtime, enabling them to address individual customer needs uniquely rather than grouping them into broad categories.
This breakthrough is accelerating the transition of smart applications from experimental proof-of-concepts to full-scale production systems. Across operational fronts, AI is already making substantial impacts in critical business areas including customer experience, loan approvals, risk management, and fraud detection.
Deepak Bhatia highlighted the industry's accelerating maturity, noting that numerous pilot projects have already been successfully scaled into production, particularly in loan approval processes and customer experience enhancement.
Overcoming Legacy System Challenges
A significant obstacle facing the Indian BFSI sector involves the widespread presence of legacy systems that complicate the integration of advanced AI technologies. Subramaniam emphasized that the primary difficulty with legacy infrastructure lies in data management, specifically addressing questions about data trustworthiness and lineage that must be resolved for successful digital transformation.
Thoughtworks is tackling this challenge by leveraging Generative AI to accelerate modernization efforts. Through AI-powered analysis of legacy code, the transformation timeline can be dramatically reduced from years to mere weeks or months. For systems requiring gradual transition, the approach involves embedding AI agents within existing legacy workflows, creating secure environments for new AI applications to function effectively.
To scale AI adoption successfully, financial institutions must prioritize two foundational steps: enhancing AI literacy across the organization to manage technology hype and ensure proper problem-solving understanding, and providing adequate access to tools and infrastructure supporting new AI-driven processes.
Agentic AI and Future Strategic Direction
The future strategic direction of AI in BFSI points toward Agentic AI, where solutions operate with significant autonomy in their workflows. However, Subramaniam advised cautious implementation, suggesting that not all banking problems require agentic solutions. Internal applications provide ideal testing grounds for agentic systems, while external, client-facing applications often perform better with intelligent but clearly defined workflows.
Bhatia strongly recommended that BFSI leaders fully commit to AI integration across three critical dimensions: Business Model Redesign involving fundamental operational rethinking and reaching new customer segments through local language interfaces; improved Software Delivery utilizing AI throughout the technology lifecycle; and Cultural Adoption by making AI tools accessible to all employees for daily tasks, thereby cultivating a pervasive AI culture.
Financial Inclusion and Regulatory Considerations
In India's diverse landscape, AI holds tremendous potential for advancing financial inclusion by serving underbanked populations facing barriers such as language differences, social factors, and limited branch access. Local language voice interfaces powered by AI can significantly reduce these barriers while simultaneously lowering the cost of serving last-mile customers for banks.
When evaluating return on investment in India's low-margin market, Bhatia proposed a paradigm shift in perspective: The way we look at ROI is what do I lose if I don't do it. He cautioned that delaying AI adoption risks substantial market share loss in the future and advocated for Indianising solutions to align with local economic and market requirements.
Financial leaders must balance rapid innovation with strict regulatory compliance and trust maintenance in a heavily regulated environment. This requires establishing clear principles for AI usage within organizations and demanding transparency from both internal builds and external vendors, moving away from opaque black box systems.
Realizing AI's full potential demands a collaborative ecosystem. Bhatia concluded by urging BFSI companies, technology partners, startups, and regulators to work together in developing an AI stack for India that mirrors the success of India Stack, ensuring localized solutions and shared investments for the collective advancement of the financial sector.
