Why a small language model makes practical choice for BFSI

By Ashutosh Prakash Singh, Co-Founder and CEO at Revrag.AI

Banks and financial institutions in India are dealing with a surge in digital activity. In May 2025, UPI transactions touched 18.68 billion, amounting to ₹25.14 trillion. That is a 33% rise in volume over the same month last year, according to the National Payments Corporation of India (NPCI). With this pace of growth, systems need to handle scale without losing accuracy or falling short on compliance requirements.

Standard automation systems often struggle with the nuances of domain-specific queries. A Small Language Model (SLM), trained on financial datasets, can process user interactions and backend operations more reliably by applying contextual relevance, reducing error rates, and system friction.

SLMs Address Specific Use Cases with Clarity

SLMs are designed to operate on curated, domain-specific datasets. In the case of BFSI, this includes financial documentation, regulatory rules, policy frameworks ...