Will cashflow based digital lending be the antidote to the credit crunch in India’s MSME sector?


A couple of months back, India pipped the United Kingdom to become the fifth largest economy in the world. Significant praise for this has to be given to India’s MSMEs (micro, small & medium enterprises). MSMEs across the country accounted for nearly 30 percent of India’s GDP in FY21. Over 63 million MSMEs employ more than 110 million people across business sectors. However, observers rightly believe that it is just a fraction of the true potential. Limited access to credit is preventing them from unlocking their capabilities.

It is no secret that MSMEs have traditionally been a credit-starved business segment. However, the digital revolution in finance has now opened up more avenues to meet their demand for funds. An optimal equation of digitisation & digitalisation has paved the way for innovative lending models. Cashflow based lending is one of the models that promises to help the MSMEs obtain much-needed credit.

Traditional lending

Two basic factors which all lenders like to evaluate are PD (Probability of default) & LGD (Loss given a default). PD is a function of repayment intention & capability of borrower. LGD is dependent on the security/collateral provided by borrower & ease with which it can be possessed & sold to recover monies in the event of default. Ability to enforce contractual obligations through courts also plays an important role in evaluating LGD.

India’s lenders have traditionally relied on collateral-based lending or the financial statements evaluation model. In the former, lending institutions disburse loans up to a certain proportion of the value of an asset pledged with it. For instance, a lathe machine operator would mortgage his equipment to take a loan for working capital. This method is popular because it ensures that losses in the case of default can be compensated by selling the collateral. MSMEs typically don’t have a lot of collateral to offer as security & consequently are unable to access credit from large traditional lenders. It results in a scenario where the small enterprises, needing credit, may be capable & willing to repay prudently but may not have the resources to prove the same as per the prevailing benchmarks.

Traditional lenders also evaluate the prospective borrower’s financial statements, including tax returns, balance sheets, etc., to assess their repayment capacity. The credit bureau records, read CIBIL reports, allow them to further evaluate the intention to pay. Basis the earning capacity & repayment track record, lenders decide the amount of financial exposure. This approach can have pitfalls.

For starters, the financial information could be dated as it pertains to the last financial year. Again, it’s an open secret that MSMEs are prone to underreport revenue or inflate expenses, due to a combination of high cash dealings & lack of accounting knowledge. Also, there may be instances of cash outflows without corresponding inflows due to seasonality patterns, a new enterprise, etc. Moreover, small businesses may not have ready collateral to avail credit.

These traditional lending parameters are limited in their outlook & do not serve the purpose of a dynamic MSME space where funds are required in a timely manner.

Enter cashflow based lending backed by data science

With the advent of GST (Goods & Services Tax), smaller entities are finding it advantageous to report the top line resulting in robust compliance. The GST records can then be combined with other financial parameters such as bank statements, etc., to judge an entity’s repayment capabilities. Lenders can ascertain the enterprise’s profitability by examining its business dynamics. It would enable them to ascertain the business’ cashflows & as a natural extension, its profitability. Again, when blended with CIBIL (Bureau) reports, it gives a data-backed objective picture of the business. Lenders can then deploy machine learning algorithms which can help compile information from all three sources (GST, banking & bureau) to allow them to evaluate the creditworthiness in minutes, leading to better, faster, & more accurate credit decision-making.

An MSME’s cashflows are volatile in nature & hence traditional methods were found to be deficient in assessing them accurately. With GST & banking-based models evolving & powerful machine learning tools now available, the future of cash flow-based lending is bright.

Digital ecosystem is evolving exponentially

The Central Government’s planned interventions will further strengthen the cashflow model. Open credit enablement network or OCEN (pronounced O-ken), promises to unlock the next level in digital lending to MSMEs. OCEN has created a standard protocol for an interface between registered buyers, sellers & financers. With Open APIs available to all, this initiative will truly democratise availability of transaction data. The OCEN’s first pilot is GeM Sahay – an e-marketplace for PSU (public sector undertaking) buyers & MSME sellers. U GRO Capital Ltd. is the first lender to get empanelled on the platform & has disbursed loans to MSMEs basis automated business rule engine on GeM Sahay.

The Account Aggregators (AA) initiative, another Central Government brainchild, will enable aggregation of all financial information, including account statements, mutual fund history, insurance policies, GST sales data, etc. Once the customer gives consent, lenders can access the information conveniently through a common interface. Members of the network will act as both financial information providers & users. Currently, most large banks & financial institutions are part of the network. More are scheduled to follow suit shortly depending on their technological readiness.

With more & more financial institutions getting access to this data & accepting the standard protocols, Cashflow based lending models to MSMEs will now find more takers. It augurs well for India’s MSME segment, which will get access to easy & convenient credit. It will enable them to drive India’s wheels of progress.



Views expressed above are the author’s own.


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