Significance Of AI And Machine Learning In MSME Lending

Delving a little deeper into some of the major areas of application of AI and Machine Learning that the MSME lenders are focusing on first. Two of the major aspects that stand out and we’ll talk about here are credit underwriting and process automation.

Over the last decade or so AI and Machine Learning has become relevant almost to the degree of indispensability to forward thinking organizations across industries. As always Financial Services have been among the frontrunners in adoption and deployment across most if not all aspects of their business.

The MSME lenders, a critical and rapidly growing part of the Financial Services sector catering to the businesses with rather limited access to formal credit from banks, are making great use of these technologies as well. The leaders among them are using these quite successfully to expand their reach as well as to make smarter, sharper, data-driven decisions to enhance profitability.

Credit underwriting

While underwriting, a lender faces a challenge due to asymmetric information that results from the fact that they have incomplete information about the borrowers’ intent and ability to repay the loan. (asymmetry, because the borrower has complete information about it). This leads to difficulty in estimating credit risk of loan applicants. Availability of all relevant information and the lenders’ ability to access and analyse them when loan applications are processed, enable lenders to make an estimate of credit risk is and make decisions accordingly to minimized future credit losses to the extent possible. In the traditional underwriting practices,creditbureau data (credit history), banking transactions data, financial statements, and invoices for businesses, etc. are some of the most critical and major sources of such relevant information. For the underserved MSME borrowers, these kinds of data are scarcely available.

To cover the MSME segment, therefore, new-age financial companies and fintech lenders must look beyond the traditional models of underwriting and be able to do credit scoring using alternative data. Even though the borrowers from the sector lacks proper documentation, the recent developments like GST, Digital India Stack, etc., provide a host of alternate data points to reckon for MSME lending. Furthermore, some smart MSME lenders are using industry clusters-based information, geo-spatial data, utility payments data, psychometric data, social and demographic  data, etc. to compute credit score and infer the payback ability and intent of the MSMEs.

Smart use of AI and Machine Learning is making it possible to process this huge amount of structured and unstructured data from varied sources and extract actionable insights and delinquency predictions which enable the lenders to underwrite borrowers with little credit history. This is allowing for a larger number of businesses, who traditionally have not been able to access funds from organised sources, to be brought into the folds of formal lending. Also, with the advent of machine learning algorithms, MSME lenders can even largely automate credit scoring via alternate data sources and have at their disposal smart analytics that minimizes the effort and time in business decision making.

In addition, MSME lenders can further utilize data mining, AI, and machine learning to discover a number of potential borrowers left underserved due to traditional, obsolete processes. These borrowers can be targeted with focused digital and social media campaigns, and customized offline outreach programs. Product customization may also be carried out for those borrowers. Such an approach would reduce the cost of acquisition for FinTech lenders, helping them develop a focused and precise strategy instead.

Process Automation

The process of decision making in lending is a tedious, costly affair. The cost of processing a loan application can easily go upto few thousands. This is one of the key reasons why majority of banks and traditional FIs focus on loan applications with a big ticket sizes, in order to optimize the costs and profit from bigger margins. However, since loans required by the MSMEs can typically fit in a much smaller bracket, the segment isn’t as lucrative for the traditional institutions. AI-enhanced automation solutions resolve this issue to a great extent. Since automation reduces the need for time and human resources in processing smaller loan applications, AI makes it viable and profitable for lenders to make credit facilities available for the MSMEs. Apart from automating many internal processes, by integrating AI-driven chatbots and the legacy systems, MSME lenders are also automating a host of workflow loops requiring customer interactions to be run in auto-pilot mode. Powered by natural language processing and generation algorithms (a subdiscipline within the broad umbrella of AI), chatbots can understand and process text and speech based queries. With chatbots, MSME lenders are automating routine checks, address repetitive queries pertaining to processes along a list of checkpoints in the customer on-boarding journey. With AI automation bringing in operation excellence and cost optimization, digital lenders are able to best service the credit-starved MSME segment. This also imparts the critical element of non-linear scalability into their business.

In essence, innovation is most certainly going to separate leaders from the rest in the MSME lending sector as it has always done in all sectors. With Artificial Intelligence and Machine Learning at the core of today’s wave of innovation globally, adopting these technologies is no longer a matter of choice and rather, has become a necessity in the fast evolving business environment. Digital lenders should act fast to adopt these newer paradigms and embed them into their business core, in order to not only sustain their edge, but to also take their services to a staggering number of MSMEs requiring credit services, which is a huge and greatly untapped market.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house

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