The ongoing coronavirus outbreak truly is a ‘black swan’ event in history of pandemics and has left businesses around the world wondering what recovery would look like.It has created a severe cash-flow challenge for businesses, and in particular micro, small and medium enterprises (MSMEs), are struggling to stay afloat with hindered access to credit.
Since most small businesses operate on razor-thin margins, the lack of capital has posed a threat to their existence. While accessing formal credit has always been a major challenge for MSME players, the situation has only worsen post pandemic. This is because MSME lending is considered a risky proposition, and Indian banks and NBFCs continue to rely on archaic methods of credit underwriting, which put small business owners at a disadvantage while seeking financing.
The pandemic has further exposed the shortcomings of traditional risk assessment models, as well as their inability to appraise the credit worthiness. Traditional credit assessment is focused mainly on asset pricing, which is generally based on some core data points such as the time in business, personal credit score, scope of the industry, and annual revenues. These data points are important, but they cannot be the sole indicator of the credit worthiness of a business, especially in the current scenario of economic uncertainty and changing business landscapes. Also when it comes to credit monitoring, many lenders lack resources and infrastructure to monitor and track MSME businesses regularly…
There is evidently an urgent need for robust tech-based solutions that leverage traditional as well as alternate data points to guide lenders in their credit decisions and post disbursal monitoring for MSMEs.
Automated credit underwriting
An automated credit underwriting system leverages advanced technologies such as machine learning to measure the amount of risk a potential borrower presents to the lender. It analyses a borrower’s application form as well as the alternative data such as the payment history of utility bills, filing of GST and EPFO, pending court cases etc.
After weighing in all of this information, the automated underwriting system determines if the applicant will be able to repay the loan within the agreed timeline. Unlike manual credit assessment, automated credit assessment is free of human bias and provides a more comprehensive evaluation. Banks and other financial institutes using automated credit underwriting models can make faster and more accurate lending decisions, and more importantly provide the same objective treatment of all borrowers, both established and small businesses.
Real-time Early Warning Systems for actionable signals
The uncertainty in the markets demands that the borrower portfolios are tracked almost daily and in fact even near-real time. Early Warning Systems can empower lending with real-time data on all their portfolios and bring the benefit of scale, effort optimization and tremendous cost savings (in the form of saved potential NPAs).
Based on machine learning-driven algorithms, these systems extract actionable insights from data signal libraries to detect fraudulent activities early on and differentiate between genuine and deceptive borrowers. The new Early Warning Systems are built for specific industries and markets to make the underwriting process quicker and less prone to errors. The Reserve Bank of India (RBI) has mandated the use of Early Warning Systems in banks – a move that has helped minimize the risk of red-flagged accounts (RFAs).
COVID Impact Score
Stress testing on credit portfolios is now imperative to gauge the impact of COVID-19 on businesses. Proprietary Data analytics platforms can help lenders assess COVID impact by evaluating and analyzing companies against multiple dimensions both at a sector-level (severity, longevity, operational and financial leverage and revenue growth) and at a company level (regulatory compliance, litigation check, media sentiment, financial performance, financial statement analysis, etc). Such detailed evaluation helps lenders not only understand if a business can weather the coronavirus crisis, but also avoid the trap of bad loans.
MSMEs are facing the brunt of COVID outbreak in terms of uncertainty around business continuity and lack of access to formal credit for scaling up their businesses. While the government has already recognized this problem by launching an economic stimulus package of Rs 3 lakh crores for the MSME sector, only Rs 35,000 crores have actually been disbursed so far. It is the need of the hour to deploy automated credit underwriting and monitoring mechanisms that can facilitate faster disbursal of credit faster for small businesses.
Replacing archaic credit score evaluation models with digital ones is no longer an option, but a necessity. Only when the entire lending lifecycle is digitized, lenders can truly take advantage of the data available and provide credit access to small businesses without compromising on the risk factor.
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All this is fine as long as the data is of immaculate quality. Most of the financial statements , ITR and GST does not show the real business or profits of the entity, more so in the micro and small segments, the segment where the cost of processing Loans through Credit resources is costly and difficult scale up. I have not got an answer whether fintech companies are getting similar loan losses are similar to others or better?