How Fintech players are leveraging AI/ML to bridge the MSME lending gap

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FinTech is a term that refers to a collection of companies that leverage advanced technology as a central ingredient of their business model to drive the rapid evolution and transformation of financial products and service delivery. Machine learning and AI, on the other hand, sit at the frontier of today’s technological evolution landscape. Therefore, FinTechs are, unsurprisingly, the most prolific users of ML and AI as a key ingredient in their strategy.

The fintech universe is made up of many different types of financial services solutions and platform providers to enable these services. Some of the larger ones include Lending Solutions, Payment Solutions, Insurance Solutions (often categorized under InsureTech), Wealth/Asset Management Solutions, and more.

FinTech companies use AI/ML solutions in most, if not all, of their critical processes and functions. For example, for many fintech lenders, advanced ML models for credit scoring have become an integral part of their underwriting process – this enables highly efficient and fast application processing and leads to improved portfolio quality, as powerful ML models predict the risk of failure quite accurately.

The advent of AI/ML has been hugely beneficial for fintech players operating in the MSME lending space (specifically, the unorganized micro/small enterprise lending space), especially strata located near the bottom of the pyramid, who have limited or no access to formal credit from banks and other lenders mainly active in organized sectors.

The MSME sector has a fairly wide range in terms of company sizes and degree of organization – at one end of the spectrum companies are large and formalized enough to be within the scope of the GST and have several years of detailed bank records, informational tax returns, etc. and at the other end, the businesses are much smaller and have little or no poor credit history and bank records and rarely tax or GST filings. But the conventional credit reporting and underwriting process followed by banks and other similar traditional lenders relies heavily on sufficient credit history, banking and accounting records, tax return information for several years , etc. Micro/small enterprises without this data, therefore, generally cannot access credit from these institutions.

Fintech players who intend to lend in this space are addressing the challenge of an inadequate credit history by devising their own innovative methods of assessing creditworthiness. Although some heuristic approaches are used for this purpose, proper AI/ML models are the most powerful tool in this regard. Custom ML models deviate from the traditional constraints of data requirements and allow lenders to fairly accurately predict the probability of delinquency/default using various alternative data types and thus assess risk credit and subscribe accordingly. For example, some fintech lenders that lend to these micro and small businesses use localized industry economic trends, corporate image data (e.g. merchandise stock, store space, store facade and location, etc.) , authorized mobile scratch data (e.g. SMS data transactions), as well as limited bank data available, informal accounting data from mobile apps, etc. to build such AI and machine learning models. In cases where business owners have some data from the credit bureau, even if they are small files with very little history, these are also incorporated into the calculation of their risk scores by models designed for this specific purpose and market segment. Thus, fintech lenders are leveraging AI/ML to extend financial inclusion to underserved sections of the MSME sector while maintaining strong portfolio performance through intelligent and quantitatively informed underwriting.

Those fintech companies that use AI/ML in their business processes are also applying the tool in functions other than underwriting. For example, they often use custom models to manage EMI bounces, optimize collection efforts, target product cross-sells and upsells, and more. .]are also some of the common practices in this regard.

In conclusion, AI/ML is proving to be a valuable tool to help Fintechs not only increase efficiency and profitability, but also fulfill a philanthropic duty of enabling financial inclusion for a sector that has suffered. decades of discrimination and exclusion from formal lending channels. in the countryside.



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