Traditional lending systems were built for borrowers with formal income records, strong credit histories, and structured financial documents. But many small businesses do not operate that way. Many MSMEs, even today, face challenges obtaining loans. Many of them have seasonal incomes, records may be informal, and cash flows may not always fit into standard lending models.
While businesses need capital to grow, lenders often struggle to assess their real repayment ability. And that is why data-driven risk strategies are important for facilitating access to credit.
Moving beyond traditional credit checks
MSME today no longer relies solely on traditional credit checks. It has moved toward a broader and more practical understanding of business health. Instead of focusing solely on bureau scores or collateral requirements, lenders can now assess a business’s performance in real time.
One of the strongest data sources in this process is bank statement data. The bank statement shows income and expenses in the form of coming in and money going out, reflects payment behaviour towards liabilities, and records account balances. A business that may not have a long credit history but shows regular inflows, healthy balances, and controlled expenses is stable.
When data from these alternative sources is combined with bureau data, lenders get a much stronger picture. Bureau data provides information on past credit behaviour. It shows repayment history, existing loans, delays, and credit discipline. Bank data shows what is happening now in terms of cash flows. Together, they create a more complete view of borrower risk. This combination is becoming the foundation of modern MSME underwriting.
How AI is transforming MSME underwriting
Artificial intelligence is making this process even stronger. AI can analyse large amounts of financial data quickly and identify patterns that manual underwriting may miss. It can study revenue consistency, spending behaviour, cash flow cycles, and even signs of financial stress.
For example, an MSME with fluctuating income may still be a strong borrower if those fluctuations follow a clear seasonal pattern. A human underwriter may see irregularity. AI-ML-led underwriting risk models can help identify predictable business cycles. This makes lending decisions smarter, faster, and more accurate. Today, and digital lenders are moving beyond single-scorecards. Instead of one credit score deciding everything, they are building multiple risk layers.
An application scorecard helps evaluate business and borrower details. A bureau scorecard looks at credit history. Alternative data scorecards that use non-traditional data sources support thin-file or new-to-credit customers. Behavioural are built from existing customers’ behaviour trends and repayment patterns. This funnel approach gives lenders flexibility to apply different underwriting strategies to different segments.
For borrowers with limited bureau history, additional data becomes critical. GST records can validate turnover. Digital payment data can show transaction consistency. Utility bill payments can indicate payment discipline. In rural and semi-urban markets, even alternative indicators such as mandi receipts, milk collections, or local business cash cycles can help. This is especially important because a large share of India’s small businesses remains underserved by traditional credit systems.
The rise of responsible and customised lending
Data-driven lending also supports responsible lending. Access to credit is important, but so is the ability to repay. By understanding actual cash flows, lenders can avoid over-lending. This protects both the borrower and the lender. Metrics such as FOIR, bounce ratios, average balances, and income stability can help lenders determine the right loan amount, not just the maximum possible.
Another major advantage is product customisation. Not every business needs the same repayment structure. A trader may need shorter cycles. A manufacturer may need longer working capital support. A seasonal business may need flexible repayment aligned with peak business periods. Data helps lenders build products that align with business realities.
However, many MSMEs still operate partly in cash. Digital adoption is growing, but not universally. Technology infrastructure also needs investment. Lenders need robust systems to efficiently collect, clean, and analyse financial data. Awareness is another challenge. Many business owners still do not realise that maintaining digital records, adhering to regular banking practices, and maintaining clean transaction trails can improve access to credit. That awareness gap must close.
The future of MSME lending will be built on intelligent systems that better understand businesses. MSMEs do not just need capital. They need credit pathways that recognise their potential, understand their cash flows, and support their growth journey. Data-driven credit is not just a technology upgrade. It is a smarter, fairer way to build financial inclusion. And for MSMEs, that could make all the difference.
Disclaimer: The information provided in this article is for informational purposes only and does not constitute financial, legal, or professional advice. While every effort has been made to ensure accuracy, readers should verify details independently and consult relevant professionals before making financial decisions. The views expressed are based on current industry trends and regulatory frameworks, which may change over time. Neither the author nor the publisher is responsible for any decisions based on this content.
Sachin Seth, Regional Managing Director, CRIF India & South Asia