Over the past five years, artificial intelligence (AI) has evolved from a niche innovation to a strategic imperative for India’s banking and non-banking financial companies (NBFCs).
What began with chatbots and fraud detection has rapidly expanded into enterprise-wide transformation encompassing credit underwriting, collections, compliance, customer servicing, treasury operations, and software development.
The next phase – powered by Generative AI (GenAI) and agentic AI – is poised to fundamentally redefine productivity across India’s financial services industry.
India’s financial sector has been uniquely positioned to capitalize on AI due to the convergence of three structural advantages: the digital public infrastructure built around India Stack, abundant transactional data generated through UPI and digital banking, and one of the world’s largest pools of technology talent. Consequently, AI adoption has accelerated significantly since 2021.
Recent industry estimates indicate that 74% of Indian financial institutions have initiated GenAI proof-of-concept projects, while 42% have already allocated dedicated AI investment budgets. Interestingly, NBFCs and insurance companies have emerged as faster adopters than traditional banks, largely due to their greater operational agility and fewer legacy technology constraints.
The transformation is particularly visible in customer operations. AI-powered virtual assistants today handle millions of customer interactions annually across leading private banks, reducing call-centre volumes by over 40% while delivering near-instant query resolution. Intelligent document processing has shortened retail loan processing times from days to hours, while machine learning models continuously monitor transactions for fraud, significantly improving detection accuracy with lower false-positive rates.
Credit underwriting has arguably witnessed the greatest disruption. Modern lending models now incorporate hundreds of alternative data variables – from GST filings and bank statement analytics to digital payment behaviour and cash-flow signals – to generate dynamic credit scores.
AI models are increasingly capable of analysing both structured financial information and unstructured documents, enabling faster and more accurate lending decisions across MSMEs and retail borrowers. Global banking surveys indicate that commercial credit remains one of the highest-value applications for Generative AI.
The productivity gains are substantial. According to EY, Generative AI could improve productivity across India’s financial services sector by 34–38% by 2030, with large NBFCs in India guided to shave of 30 – 40 BPS off the opex, with banking operations potentially witnessing productivity improvements of up to 46% through automation of repetitive workflows, customer servicing, compliance, and knowledge management.
Operational efficiency, however, represents only one dimension of value creation. AI is increasingly becoming a strategic differentiator in risk management. Advanced anomaly detection models are improving fraud prevention, while predictive collections engines enable lenders to identify delinquency risks weeks before traditional monitoring systems. AI-driven Anti-Money Laundering (AML) systems are simultaneously reducing manual investigations while strengthening regulatory compliance.
India’s NBFC sector has been particularly proactive in embedding AI across the lending lifecycle. Digital-first lenders now employ AI across customer acquisition, KYC verification, underwriting, pricing, collections, and portfolio monitoring. In the co-lending ecosystem, AI facilitates faster loan origination and better risk-sharing decisions between banks and NBFC partners, enabling scalable credit expansion into underserved customer segments.
Despite rapid progress, enterprise-wide AI transformation remains in its early stages. Industry studies suggest that while thousands of AI pilots have been launched globally, only a fraction have scaled into mission-critical production systems. Data quality, model governance, cybersecurity, regulatory compliance, and legacy core banking integration continue to be the principal execution challenges.
The next frontier lies in agentic AI – autonomous systems capable of executing end-to-end business workflows with limited human intervention. Rather than merely assisting employees, these AI agents can independently retrieve documents, analyse customer profiles, recommend credit decisions, generate regulatory reports, and resolve service requests across multiple systems. Early implementations are already emerging across credit origination, customer onboarding, collections, and customer servicing within leading financial institutions.
For India’s banking and NBFC landscape, the competitive landscape is therefore shifting from digital transformation to intelligent transformation. Institutions that successfully combine trusted data, responsible AI governance, and enterprise-scale automation will likely achieve structurally lower operating costs, faster decision-making, superior customer experience, and more resilient risk management.
Much as core banking systems defined the industry over the past three decades, artificial intelligence is now becoming the new operating system of financial services. The institutions that scale AI beyond experimentation will shape the next generation of Indian banking and NBFC leadership.
Headcount impact
This remains the most misunderstood area.
Indian banks have not announced large AI-driven layoffs. Instead, AI is changing hiring patterns.
Current trends include:
- Slower operations hiring
- Redeployment of employees into sales, analytics and customer advisory roles
- Lower dependence on outsourced processing teams
- Increased hiring in AI, data science and cybersecurity
Organisation-level productivity
McKinsey estimates that banks undertaking enterprise-wide simplification combined with AI can achieve:
- Up to 15% sustainable productivity improvement within two years
- 1.0–1.5 percentage-point improvement in ROE
- Selected AI-enabled workflows showing up to 30% productivity gains
- Customer experience improvements of around 20%
Indian banking & NBFC examples
Several leading private banking and NBFC institutions have publicly discussed AI deployment across underwriting, collections, customer service, and fraud management. Common reported outcomes include:
- 30–70% faster loan processing
- Higher straight-through processing rates
- Significant reductions in manual document verification
- Improved fraud detection accuracy
- Lower servicing costs per customer
- Higher employee productivity through AI copilots and workflow automation
Key takeaways
The evidence suggests that AI’s primary impact in Indian banking and NBFCs over the past 2–3 years has been productivity augmentation rather than workforce reduction. The strongest quantified outcomes are:
- 34–46% productivity gains across operations and customer service
- 58% of institutions reporting operating cost reductions
- 50–80% reductions in loan processing turnaround times for AI-enabled lending workflows
- 10–15% sustainable enterprise productivity improvement when AI is combined with operating model simplification
- Hiring moderation and workforce redeployment, rather than large-scale layoffs, as AI automates repetitive operational work
The next phase of BFSI transformation will not be defined by AI adoption alone, but by how effectively institutions scale it with trusted data, responsible governance and human expertise. The winners will be those that turn AI from a productivity tool into an enterprise-wide capability for smarter, faster and more resilient financial services.
Views expressed by: Ajit K Menon, Group Chief Operations Officer, Vivriti Capital
