AI-First Financial Institutions: The Next Competitive Frontier
The Indian financial services ecosystem is robust, delivering at a scale rivalling global standards, despite the complexities of diverse service models and products.
- Apr 10, 2026,
- Updated Apr 10, 2026 11:37 AM IST
Authors: Purushothaman KG, Partner and Head of Technology Transformation and AI, & Vishnu Pillai, Financial Services Technology Leader and Office Managing Partner – Kochi, KPMG in India
The future of financial services in India will be shaped by institutions that move beyond simply utilising AI to fully embracing an ‘AI-first’ mindset. According to the KPMG Global Tech Report, 92% of global financial services (FS) firms are already generating profits from AI investments. However, true value emerges from integrating AI across the enterprise, not just adding AI to isolated functions. In India, AI especially Generative AI (GenAI) is gaining momentum, promising faster ‘time-to-market,’ deeper insights, and relentless operational improvement. With substantial global investments in AI infrastructure, the transition to AI-led hyper-automation for business-as-usual (BAU) operations, both front and back office, is within reach.
AI is now a board-level priority for most FS institutions in India. Yet, to realise and sustain a competitive edge ‘bringing in the alpha,’ the speed of deployment and iterative cycles will be critical. It’s all about refining customer personas and engagement. Incremental changes won’t justify the considerable investments required; thus, the ‘AI-first’ approach is being proposed as a transformative model for banking and customer service.
India ranks among the top three globally in AI readiness. Competitive advantage, however, depends on workforce-wide AI fluency rather than just boardroom ambition. While 67% of Indian FS organisations have deployed AI at some scale compared to 42% globally, return on investment (RoI) metrics remains debatable. Indian enterprises are progressing from proof-of-concepts (PoCs) to scaled implementation, but governance and operating model redesign are emerging as key challenges.
The Indian financial services ecosystem is robust, delivering at a scale rivalling global standards, despite the complexities of diverse service models and products. Incumbent banks provide trust and scale; NBFCs extend reach and agility to underserved segments; FinTechs inject creative velocity often missing in larger organisations. All three segments are adapting their service orientation in response to new AI capabilities. This includes reorganising teams for additional skills, running agile PoCs, and optimising delivery models whether revitalising call center operations, improving customer profiling and conversion, integrating omnichannel experiences, or monitoring operational risk. Over the next twelve to eighteen months, a shift toward a more asset-light and integrated services model is expected.
Currently, most institutions operate in ‘smart overlay’ mode, deploying AI agents over existing systems to cut costs and accelerate workflows. This results in a faster, but familiar, process mix. AI-first banking, by contrast, requires reengineering processes, data foundations, and insights generation, elevating organisational intelligence. The envisioned progression is toward ‘Agents by Design’ modernising legacy platforms into Autonomous Networks, where multiple agents plan, reason, and act to achieve objectives. This transformation embodies principles such as asset-light operations, customer-centricity, service orientation, and data-driven foundations.
The gap between institutions using overlays and those becoming AI-native is widening rapidly, as large and small language models (LLMs and SLMs) expand their applications. Advanced deployment demands ongoing investment across people, processes, and technology, with improvements measured quarterly. Key performance indicators must evolve to keep up.
AI is fundamentally redefining customer engagement. AI-driven personalisation enables financial services to offer tailored products and services based on individual preferences and behaviours. Institutions can analyse vast datasets to generate insights that drive meaningful, timely interactions enhancing customer satisfaction and loyalty. This personalised engagement differentiates institutions in a crowded market and boosts operational efficiency by directing resources where they matter most.
AI also brings predictive analytics, allowing providers to anticipate customer needs and proactively resolve issues. Machine learning and natural language processing automate routine tasks, streamline workflows, and enable employees to focus on higher-value services. Integrating AI across customer touchpoints leads to faster resolution times, improved service quality, and greater cross-selling opportunities.
Operational risk monitoring benefits significantly from AI. Advanced algorithms detect anomalies, flag potential fraud, and ensure regulatory compliance more efficiently than traditional methods. This reduces risk and frees up resources to focus on strategic priorities. AI-driven risk management tools help institutions identify emerging threats and adapt swiftly to changing market conditions.
Omnichannel integration powered by AI enables seamless customer experiences across digital and physical channels. Today’s customers expect consistent service no matter how they interact with their bank. AI-powered systems unify customer data and interactions, ensuring every engagement is informed and contextually relevant. This integration is essential for retaining clients and attracting new ones in a highly competitive environment.
AI-led hyper-automation is transforming back-office operations from loan processing to compliance checks and transaction monitoring. Automating repetitive tasks reduces costs, minimises errors, and speeds up turnaround times. This efficiency allows organisations to redirect resources toward innovation and growth, strengthening their competitive stance.
As Indian FS institutions continue their AI investments, the focus is moving from experimentation to full-scale implementation. Overcoming governance and operating model challenges will be crucial to unlocking AI’s full potential. Institutions that successfully navigate these hurdles will be positioned to lead in a rapidly evolving market. The shift towards an AI-native operating model is expected to drive major improvements in customer experience, operational efficiency, and risk management.
Looking forward, the next phase of AI adoption will involve deeper integration of advanced technologies, such as autonomous networks and intelligent agents. These innovations will enable organisations to operate with unprecedented efficiency and agility. The ultimate goal is to create a financial services ecosystem that not only responds to customer needs but also anticipates and shapes them.
In summary, the Indian financial services sector stands at a pivotal juncture. Institutions that adopt an AI-first approach and invest in workforce-wide AI fluency will secure a lasting competitive edge. The evolution from smart overlays to ‘Agents by Design’ signals a fundamental shift in how banking and customer service are delivered. By prioritising data as a foundation, focusing on customer centricity, and maintaining asset-light principles, organisations can unlock new value and drive sustained growth. The journey to AI-native operations will require commitment, collaboration, and a willingness to reimagine existing processes. Those who embrace the challenge will define the future of financial services in India.
Authors: Purushothaman KG, Partner and Head of Technology Transformation and AI, & Vishnu Pillai, Financial Services Technology Leader and Office Managing Partner – Kochi, KPMG in India
The future of financial services in India will be shaped by institutions that move beyond simply utilising AI to fully embracing an ‘AI-first’ mindset. According to the KPMG Global Tech Report, 92% of global financial services (FS) firms are already generating profits from AI investments. However, true value emerges from integrating AI across the enterprise, not just adding AI to isolated functions. In India, AI especially Generative AI (GenAI) is gaining momentum, promising faster ‘time-to-market,’ deeper insights, and relentless operational improvement. With substantial global investments in AI infrastructure, the transition to AI-led hyper-automation for business-as-usual (BAU) operations, both front and back office, is within reach.
AI is now a board-level priority for most FS institutions in India. Yet, to realise and sustain a competitive edge ‘bringing in the alpha,’ the speed of deployment and iterative cycles will be critical. It’s all about refining customer personas and engagement. Incremental changes won’t justify the considerable investments required; thus, the ‘AI-first’ approach is being proposed as a transformative model for banking and customer service.
India ranks among the top three globally in AI readiness. Competitive advantage, however, depends on workforce-wide AI fluency rather than just boardroom ambition. While 67% of Indian FS organisations have deployed AI at some scale compared to 42% globally, return on investment (RoI) metrics remains debatable. Indian enterprises are progressing from proof-of-concepts (PoCs) to scaled implementation, but governance and operating model redesign are emerging as key challenges.
The Indian financial services ecosystem is robust, delivering at a scale rivalling global standards, despite the complexities of diverse service models and products. Incumbent banks provide trust and scale; NBFCs extend reach and agility to underserved segments; FinTechs inject creative velocity often missing in larger organisations. All three segments are adapting their service orientation in response to new AI capabilities. This includes reorganising teams for additional skills, running agile PoCs, and optimising delivery models whether revitalising call center operations, improving customer profiling and conversion, integrating omnichannel experiences, or monitoring operational risk. Over the next twelve to eighteen months, a shift toward a more asset-light and integrated services model is expected.
Currently, most institutions operate in ‘smart overlay’ mode, deploying AI agents over existing systems to cut costs and accelerate workflows. This results in a faster, but familiar, process mix. AI-first banking, by contrast, requires reengineering processes, data foundations, and insights generation, elevating organisational intelligence. The envisioned progression is toward ‘Agents by Design’ modernising legacy platforms into Autonomous Networks, where multiple agents plan, reason, and act to achieve objectives. This transformation embodies principles such as asset-light operations, customer-centricity, service orientation, and data-driven foundations.
The gap between institutions using overlays and those becoming AI-native is widening rapidly, as large and small language models (LLMs and SLMs) expand their applications. Advanced deployment demands ongoing investment across people, processes, and technology, with improvements measured quarterly. Key performance indicators must evolve to keep up.
AI is fundamentally redefining customer engagement. AI-driven personalisation enables financial services to offer tailored products and services based on individual preferences and behaviours. Institutions can analyse vast datasets to generate insights that drive meaningful, timely interactions enhancing customer satisfaction and loyalty. This personalised engagement differentiates institutions in a crowded market and boosts operational efficiency by directing resources where they matter most.
AI also brings predictive analytics, allowing providers to anticipate customer needs and proactively resolve issues. Machine learning and natural language processing automate routine tasks, streamline workflows, and enable employees to focus on higher-value services. Integrating AI across customer touchpoints leads to faster resolution times, improved service quality, and greater cross-selling opportunities.
Operational risk monitoring benefits significantly from AI. Advanced algorithms detect anomalies, flag potential fraud, and ensure regulatory compliance more efficiently than traditional methods. This reduces risk and frees up resources to focus on strategic priorities. AI-driven risk management tools help institutions identify emerging threats and adapt swiftly to changing market conditions.
Omnichannel integration powered by AI enables seamless customer experiences across digital and physical channels. Today’s customers expect consistent service no matter how they interact with their bank. AI-powered systems unify customer data and interactions, ensuring every engagement is informed and contextually relevant. This integration is essential for retaining clients and attracting new ones in a highly competitive environment.
AI-led hyper-automation is transforming back-office operations from loan processing to compliance checks and transaction monitoring. Automating repetitive tasks reduces costs, minimises errors, and speeds up turnaround times. This efficiency allows organisations to redirect resources toward innovation and growth, strengthening their competitive stance.
As Indian FS institutions continue their AI investments, the focus is moving from experimentation to full-scale implementation. Overcoming governance and operating model challenges will be crucial to unlocking AI’s full potential. Institutions that successfully navigate these hurdles will be positioned to lead in a rapidly evolving market. The shift towards an AI-native operating model is expected to drive major improvements in customer experience, operational efficiency, and risk management.
Looking forward, the next phase of AI adoption will involve deeper integration of advanced technologies, such as autonomous networks and intelligent agents. These innovations will enable organisations to operate with unprecedented efficiency and agility. The ultimate goal is to create a financial services ecosystem that not only responds to customer needs but also anticipates and shapes them.
In summary, the Indian financial services sector stands at a pivotal juncture. Institutions that adopt an AI-first approach and invest in workforce-wide AI fluency will secure a lasting competitive edge. The evolution from smart overlays to ‘Agents by Design’ signals a fundamental shift in how banking and customer service are delivered. By prioritising data as a foundation, focusing on customer centricity, and maintaining asset-light principles, organisations can unlock new value and drive sustained growth. The journey to AI-native operations will require commitment, collaboration, and a willingness to reimagine existing processes. Those who embrace the challenge will define the future of financial services in India.