MSMEs 2.0: How AI Can Help Standardizing Small Business Lending

New Delhi is set to host the annual AI summit this month. And the summit comes at a time when AI has become a big topic of discussion in India and across the world. Its impact is already visible in different industries. And one of the unique things about India’s approach to AI is that it aims to be holistic – creating a democratised access for everyone. This also includes a focus on the Indian Micro, Small, and Medium Enterprise (MSMEs).

Just to recall, Niti Aayog in its recent report had proposed an AI-powered centralized digital platform integrating MSME schemes, compliance, finance, and market intelligence. Drawing on global best practices, the portal would feature information, process, compliance, and market research modules, supported by AI chatbots, dashboards, and mobile access for real-time support to MSMEs.

Identifying the Bottlenecks

So far, some of the bottlenecks have included things like document handling and the back-and-forth between underwriters, partners, and applicants. Teams spend significant time manually reviewing files, checking whether requirements are met, and asking for missing information. This slows down decision timelines even when the applicant is eligible.

In simpler terms, conventional ways included a lengthy process of document collection, verification, and a judgement with possible assessment manager bias – technology transforms this into a fast, comprehensive and rather merit-based lending.

The Shift to AI-Driven Credit

If implemented, Indian MSMEs are likely to see more efficiency. The AI can also help another big problem for the MSMEs – credit. For starters, the AI has already begun reducing the turnaround time for credit facilitation. And the integration of agentic AI is ensuring better risk management and curb frauds.

Now AI ingests real-time bank/UPI transaction streams, invoices and POS/PSP rails to build cash-flow-based risk scores—enabling credit for collateral-light SMEs with stable revenue patterns. Consented digital rails plus the Unified Lending Interface and New Digital Credit Assessment Model for MSMEs have cut appraisal latency significantly. Also, it helps the worthy MSMEs to avail credit facility without managing “banking relationships” or going through rounds of documentations and justifications. Micro-finance and RBF players often combine alternative signals (GST, UPI, e-commerce receipts) with ML to profile risk and offer revenue-based advances where term loans fail.

AI enabled tech also builds a more dependable and scalable model for lenders. It provides an opportunity to make credit decisions beyond financial statements or CBIL. Now banks can access a variety of datasets, both quantitative and qualitative, to better profile the customer and provide a fitting financing solution. In future, there is so much scope to further tailor the solutions that may help them to identify and retain worthy clients, while significantly cutting down on NPAs.

The Role of Private Players

This however is not possible without the participation from private players.

For instance, Gurgaon-based FinBox simplifies credit origination, decisioning and risk management with its plug-and-play products, essentially an operating system-sort for digital lending. Speaking to Entrepreneur India, FinBox cofounder Srijan Nagar explained his company enables lenders to use real-time financial data to assess individuals, SMEs and midmarket organizations beyond traditional collateral. Our infrastructure helps financial institutions analyse inputs such as transaction behaviour, cash flow patterns, repayment history, and operational consistency so they can build a more current and holistic view of creditworthiness.

“This gives lenders a more current view of creditworthiness, especially for small businesses that may be asset-light but show strong financial discipline in their everyday operations. We have also built an AI Credit Co-pilot that strengthens this process during underwriting. It reads and structures financial documents, extracts relevant information, and feeds those insights into decisioning workflows. By combining real-time data with structured document intelligence, lenders can evaluate SMEs faster while still maintaining risk controls,” he added.

Ensuring Fairness and Transparency

legalwiz.in founder Shrijay Sheth, however, cautions that technology is only as good as who commands it. How to keep the AI-driven systems unbiased is the key. It depends on how the models are built and trained.

“Simply put, how do you make AI systems learn over time? To keep denials fair: deploying explainable-ML (feature attributions/counterfactuals), maintaining provenance logs, running periodic bias audits, and human review is important. Also, with recent government efforts in this space, we may see further standardization in this space. The New Digital Credit Assessment Model for MSMEs is a great example in that context,” he added.

Having said that, transparency is critical in any AI-driven credit system, and our approach has always been to keep enders in control of decisioning. Our models are designed to provide clear reasoning signals, solenders can understand which data points or policy rules influenced an outcome.

Nagar of FinBox further says that the AI Credit Co-Pilot supports this by creating a structured trail of how documents were interpreted, what information was extracted, and how it aligned with policy requirements.

“When decisions are made through AI-driven policy engines, lenders can review the triggers and thresholds that led to the outcome rather than seeing a black-box result. We also run ongoing monitoring and governance processes to evaluate model behaviour and reduce the risk of bias. By combining automation with strong oversight and explainability, the goal is to help lenders make faster decisions while maintaining fairness and accountability,” he said.