By Meghna Suryakumar, co-founder and CEO, Crediwatch
The ongoing coronavirus outbreak is truly a “black swan” event in the history of pandemics and has left companies around the world wondering what the recovery would be like. It has created a serious cash flow challenge for businesses, with micro, small and medium-sized enterprises (SMEs) in particular struggling to survive with hindered access to credit.
Since most small businesses operate on very thin margins, a lack of capital threatens their survival. While obtaining formal credit has always been a major challenge for SME workers, the situation only got worse after the pandemic. This is because SME lending is seen as a risky proposition and Indian banks and NBFCs continue to rely on archaic methods of underwriting credit, putting small business owners at a disadvantage when seeking finance.
The pandemic has further highlighted the shortcomings of traditional risk assessment models, as well as their inability to assess creditworthiness. Traditional credit rating focuses primarily on asset pricing, which is generally based on some fundamental data such as time spent in business, personal credit score, industry scope, and annual earnings. These data are important, but they cannot be the only indicator of a company’s creditworthiness, especially in the current scenario of economic uncertainty and changing business environment. Also, when it comes to credit monitoring, many lenders lack the resources and infrastructure to monitor and monitor MPMI companies on a regular basis …
There is clearly an urgent need for robust technology-based solutions that use both traditional and alternative data points to guide lenders in their credit decisions and postpayment monitoring for SMEs.
Automated credit acceptance
An automated credit acceptance system uses advanced technologies such as machine learning to measure the risk a potential borrower presents to the lender. Analyze a borrower’s application form and alternative data such as bill payment history, GST and EPFO documents, pending lawsuits, etc.
After considering all of this information, the automated underwriting system determines whether the applicant can repay the loan within the agreed time frame. Unlike manual credit assessment, automated credit assessment is free from human bias and provides a more comprehensive assessment. Banks and other financial institutions using automated credit underwriting models can make faster and more accurate credit decisions and, most importantly, treat all borrowers, both established and small businesses, in the same objective way.
Real-time early warning systems for actionable signals
Market uncertainty requires borrowers’ portfolios to be monitored on an almost daily basis and even near real time. Early warning systems can enable lending with real-time data across all their portfolios and offer the benefit of scaling, effort optimization, and huge cost savings (in the form of potential saved NPAs).
Based on machine learning-based algorithms, these systems extract useful information from data signal libraries to detect fraudulent activity at an early stage and distinguish between genuine and unauthorized borrowers. New early warning systems have been developed for specific sectors and markets to make the adoption process faster and less error-prone. The Reserve Bank of India (RBI) has enforced the use of early warning systems in banks, a measure that has helped to minimize the risk of red flag accounts (RFAs).
COVID impact score
The stress test on credit portfolios is now needed to measure the impact of COVID-19 on businesses. Proprietary data analytics platforms can help lenders assess the impact of COVID by evaluating and analyzing companies across multiple dimensions, both at the industry level (severity, longevity, operating and financial leverage and revenue growth) and at the company level (regulatory compliance, process control, media sentiment, financial performance, budget analysis, etc.). Such a detailed assessment not only helps lenders understand whether a company can withstand the coronavirus crisis, but also helps prevent bad debts from falling.
SMEs are the hardest hit by the COVID epidemic in terms of uncertainty about business continuity and lack of access to formal credit to expand their business. Although the government has already acknowledged this problem by launching a 3 lakh crore economic incentive package for the SME sector, only Rs 35,000 has actually been disbursed so far. It is the need of the moment to implement automated credit check and acceptance mechanisms that can facilitate faster credit disbursements for small businesses.
Replacing archaic credit score evaluation models with digital ones is no longer an option, but a necessity. Only when the entire credit cycle is digitized will credit institutions truly exploit the available data and grant access to credit to small businesses without compromising the risk factor.
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