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, and structured customer interactions. The benefit of this design is a lower incidence of inaccurate outputs, a common concern with general-purpose large language models (LLMs).
This becomes particularly useful in areas like loan underwriting, risk scoring, fraud detection, and claims processing, where even minor errors can carry regulatory or financial consequences. An SLM tuned to banking and finance data is more likely to provide responses grounded in current compliance frameworks and terminology.
Digital Investment Trends Support Adoption
The digital push in banking, insurance, and financial services continues to gather pace. Precedence Research estimates the sector’s digital transformation market was worth $93.04 billion in 2024 and will reach $108.51 billion in 2025. By 2034, the figure is expected to exceed $419 billion, with an annual growth rate of over 16% projected for the next decade. This sustained investment indicates a long-term need for technology that not only automates processes but does so with domain alignment.
SLMs meet this requirement by offering an adaptive architecture that supports frequent updates based on regulatory changes, customer needs, and product revisions. They are particularly suited for financial environments that require consistency, traceability, and fast response times.
Customer Onboarding Benefits from Context-Aware Assistance
Onboarding remains a high-friction area for banks, NBFCs, and insurance providers. It includes multiple steps: document uploads, KYC verification, credit history checks, and product selection. Embedding an SLM in this process allows digital assistants or support systems to respond more accurately to customer inputs.
For example, if a customer uploads a PAN card and has a question about its acceptance format, the SLM can provide the correct response without having to consult generalised data. Likewise, if a user is filling out a loan application, the model can suggest applicable terms based on customer profile data and product-specific rules.
This helps reduce drop-off rates during digital onboarding and shortens turnaround time, both of which are measurable operational gains.
Security and Deployment Control for Compliance
Data privacy and compliance are non-negotiable in financial services. Many LLM-based tools rely on cloud infrastructure and external APIs, raising concerns about control over sensitive information. In contrast, SLMs are lighter in architecture and can be deployed on-premise or in secure private clouds.
This offers BFSI institutions better control over where and how their models run. It also aligns with regional data protection frameworks and industry compliance requirements such as RBI regulations, ISO/IEC 27001 standards, and sectoral data retention rules.
SLMs also offer better traceability of decisions. In areas like wealth advisory or loan eligibility scoring, the ability to audit how a conclusion was derived is essential. SLMs can be designed to store and replay decision logs for internal compliance reviews.
Cost Efficiency Without Performance Trade-Offs
One of the practical advantages of SLMs is their lower demand for computational resources. Large models often require high-end GPUs and dedicated engineering teams to maintain performance, which can be cost-prohibitive for mid-sized or specialised financial institutions.
SLMs provide a way to implement intelligent automation without a large infrastructure investment. They allow financial firms to test, deploy, and scale AI use cases without creating long-term dependency on external vendors or cloud services.
This makes them well-suited for regional banks, digital-first insurance firms, and newer NBFCs looking to modernise their operations gradually while managing cost exposure.
Fit for a Sector Focused on Accuracy and Consistency
Financial services rely heavily on consistent interpretation of rules, adherence to policy, and clarity in customer communication. The accuracy benefits of SLMs are measurable in day-to-day applications, from responding to EMI queries to flagging suspicious activity in transaction logs.
While general-purpose LLMs might offer broader capabilities, their outputs often require an additional layer of verification. SLMs, by design, reduce this overhead by delivering outputs that are closer to operational standards and terminology used in finance.
For institutions handling high-volume customer interactions, the reduced error rate translates into less rework, fewer escalations, and higher satisfaction scores. Internally, it allows risk and compliance teams to operate with clearer documentation and system accountability.
