India’s bet on the new digital credit assessment model appears to be paying off.

According to the Finance ministry, public sector banks (PSBs) have approved more than 3.96 lakh loan applications worth INR 52,300 crore to Micro, Small and Medium Enterprises (MSMEs) under the said model between April 1 and December 31 2025, reports DDNews.

For the uninitiated, India introduced a new New Digital Credit Assessment Model for MSMEs in the Union Budget 2024-25. The model envisioned a setup wherein the PSBs would set up an in-house capability that will be responsible for assessing the MSMEs for lending. The objective is to reduce dependence on third-party service providers for the same. Moreover, the PSBs were tasked to develop a new model for credit assessment, which relied on digital footprints of MSMEs.

The Digital Boost

As mentioned above, the new model rides on digital footprints, which in this case refers to fetched and verifiable data, and subsequently, devises automated journeys for “MSME Loan appraisal via objective decisioning for all loan applications and model-based limit assessment for both Existing to Bank (ETB) as well as New to Bank (NTB) MSME borrowers.”

The process here is not very different typical KYC norms as the model includes authentication using National Securities Depository Limited (NSDL), Mobile and email verification using OTP, Application Programming Interface (API) fetch of GST data through service providers, Bank Statement Analysis using account aggregator, ITR upload and verification, API enabled commercial and consumer bureau fetch and due diligence using Credit Information Companies (CICs), fraud checks, through APIs, among others, according to the ministry.

“Under Traditional / Manual methods, banks rely on physical documents submitted by customers for manual underwriting. While under new credit assessment model, credit request and data submission as well as assessment is done entirely through digital process,” the ministry added.

Why does the new model work?

The ministry points out that the new system does not involve any fundamental changes in the basic eligibility criteria for MSME loans in terms of regulatory norms or policy guidelines of individual banks. Though it aims to make the process of sanctioning loans much easier, and strives to offer a more user-friendly and standardized approach by relying on digitally available data.

It’s worth noting that MSMEs quite often are short of capital as the working capital is predominantly stretched with debtors’ days rising in the risk averse environment. Hence, the model that is modifying the existing framework/ model eases the credit requirements and helps churn financial resources in a more prudent manner. The due diligence assists the MSMEs as it makes them more focused on how the venture/ business should be conducted and hence eases making decisions, experts say.

Furthermore, the model makes it a win-win proposition for lenders as well as credit-worthy businesses. For financial institutions, it proves to be an unbiased system that takes away relationship biases and simply scores worthiness on the basis of verified data points like GST, bank records, ITR, repayment history, etc. It also allows banks to significantly reduce manual paperwork and enable quick decision making, with an expected outcome of significantly lower risk of NPS. It also brings uniformity in how financial institutions perform their risk assessment.

“MSMEs in India often have thin credit files, informal income patterns, and seasonal cash flows, making traditional credit assessment slow and inefficient. As a result, many creditworthy businesses remain underserved. The digital credit assessment model bridges this gap by automating data collection and risk evaluation, it significantly reduces operating costs and turnaround time, while providing a more accurate view of repayment capacity. This enables faster disbursements, right-sized credit, and lower default risk, making formal lending to MSMEs both scalable and sustainable,” Rohit Jain, Chief Risk & Analytics Officer, Saarathi Finance told Entrepreneur India.

How does AI make things better?

Jeel Doshi, Director – Investor Relations at Vaimanika Aerospace, told Entrepreneur India that in the near future, they see strong credit underwriting tools to be developed amongst a plethora of startups and new age companies. Moreover, the same data shall be used and assessed by investors, Venture Capitalists, AIFs and Private Equity players to provide extended lines of credit amongst the available data.

“We see reduced human intervention going forward and machine learning shall be used on an end-to-end basis for the entire lending cycle right from loan origination/ application, detecting frauds, analysing financial data on portals and ultimately sanctioning loans which minimises the risk of the lenders and ultimately aiding in improved NIM and reduced NPAs” he added.

Having said that, what happens when AIs take over the credit model.

“AI has been a boon since the last 5 years. The progress has been in reducing human capital and is further introduced by the likes of Bajaj Capital and other leading banks for loan origination. Moreover, we see a lot more emphasis on awareness provided by AI tools and hence defaulting MSMEs are appropriately advised of adverse situations which reduces the cost of loan agents etc,” Doshi further said.

Shrijay Sheth, founder of Legalwiz, adds that given that the data is at the heart of the process, this opens up a massive opportunity to build as an AI-first approach. In future, the model can be extended to instantly include more assessment parameters, or run predictive analysis to understand seasonal demand and curate appropriate credit products. It could also open up opportunities to assess non-mainstream data points and bring micro-organizations that are currently not able to access credit because of lack of ITRs or strong financial data. AI can also play a vital role in risk assessment, fraud prevention, and drawing patterns that lead to NPS.

“In summary, it will bring transparency, pace, wider access and uniformity in assessment standards. Providing better opportunities to MSMEs that are worthy of credit, while flagging the ones that are not,” Sheth said.