BFSI moves beyond AI pilots as firms chase measurable business value: Report

India’s banking, financial services and insurance (BFSI) sector is moving beyond experimenting with artificial intelligence (AI), with financial institutions deploying the technology across underwriting, customer service, fraud management, collections, compliance and product creation.

However, converting these operational gains into sustained and measurable financial impact remains a challenge, according to a new report by Beams Fintech Fund and Alvarez & Marsal (A&M) launched at the Global Fintech Fest 2026 on Wednesday.

The report, Beyond the AI Pilot: Scaling Value in BFSI, said banks, insurers and financial services companies are increasingly seeing improvements in productivity, throughput, turnaround time and service automation from AI deployments. But establishing a direct and repeatable impact on the profit and loss statement remains harder.


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Several companies cited in the report have already seen significant operational gains from AI. Tata Capital has reported around a 30% improvement in underwriting productivity, while Kissht has improved its first-time-right rates by 30%.

Niyo, meanwhile, increased the share of customer support handled by AI from 10% to 90%, while keeping its support headcount flat even as its customer base grew approximately four times. In insurance claims, InsuranceDekho and Artivatic reduced adjudication time from around six hours to seconds, while retaining human review for complex cases.

“The question for financial institutions is no longer whether AI can improve an individual task. It is whether they can redesign the workflow, operating model and governance around that capability to capture the benefit,” Sushil Zaregaonkar, managing director, Business Transformation Services at Alvarez & Marsal, said.

Institutions that simply add AI to existing processes may see productivity gains, Zaregaonkar said, but those that redesign how work gets done could capture a more structural advantage.

The report identified fragmented data, workflow dependencies, integration challenges, governance, security, talent and accountability as some of the key barriers preventing financial institutions from scaling AI beyond successful pilots.

It also highlighted workflow decomposability — the ability to break a process into individual components that can be automated or augmented — as an important factor in determining where AI can be effectively deployed.

For investors, this is also changing the question around where value will accrue as AI adoption deepens in financial services.

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“The AI opportunity in financial services is moving into a more consequential phase. The market is beginning to separate demonstrations of technical capability from evidence of durable business value,” Sagar Agarvwal, founder and managing partner at Beams Fintech Fund, said.

For fintech companies and technology providers, he said, the key questions are whether they are embedded deeply enough in a financial workflow to own an outcome, whether their data or distribution creates defensibility, and whether their economics strengthen rather than weaken as deployment scales.

The report also places the current AI wave in the context of the broader technology evolution of financial services — from paper-based processes to software, followed by internet, APIs and mobile.

AI represents the fourth generation of this evolution, according to Bhavik Hathi, managing director and co-head of the Transaction Advisory Group at A&M.

Unlike earlier technology shifts that primarily digitised or automated individual tasks, AI can change how processes are structured, decisions are made and human capabilities are deployed, Hathi said.

“As institutions move from pilots to scaled adoption, the opportunity will be to fundamentally rethink how work is performed, rather than simply adding AI to existing processes,” he said.

The build-versus-buy decision is also becoming less binary, the report said. Financial institutions are increasingly deciding which AI capabilities they should own based on factors such as strategic differentiation, proprietary data, workflow depth and scale economics, while sourcing specialist capabilities externally.

The report maps more than 100 AI vendors active in or adjacent to India’s BFSI sector. As the market matures, it said, defensibility is increasingly shifting away from technical capability alone towards differentiated workflows, proprietary data, distribution and demonstrable outcomes.

The report’s findings point to a broader shift in the sector: the question is no longer whether AI can work, but whether financial institutions can embed it into their operations and turn those gains into economic value that can be measured, repeated and captured at scale.