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Lenders have to develop new risk models post-COVID

June 8, 2020 in Data Analytics



Lenders have to develop new risk models post-COVID

As the COVID-19 crisis presents a new normal for social and economic activities, lenders must also brace up for the future. Whilst lenders have tightened their grip on lending as an immediate response to the economic fallout of the pandemic, this situation can only go on for so long. Eventually, lenders will have to earn income on their liabilities to improve interest performance. Question then is; how do lenders lend to customers with a risk management framework that is relevant for this time?

Lenders most and generally rely on credit underwriting systems for lending decision making. These underwriting systems have a series of parameters that seek to predict the risk of credit default. In Nigeria, whilst regulated lenders are required by law to use the traditional credit bureaus, an unprecedented time like this may prove that those credit reports do not capture the full picture of a borrower’s financial profile.

Let us take the case of a retail borrower, who is employed in the gig economy. The period during and after the lockdown has been quite challenging for people in that segment of the economy. At this point, the chances of such a person servicing their loans will be quite low. The pressure of reduced incomes this period makes it quite impossible for people like this to repay their loan obligations. But this situation is not necessarily a structural change in their financial profile. It is currently more a cyclical pressure, which could change with a change in macroeconomic realities. Traditional credit reports, as important as it may be, maybe inadequate in doing this. 

This is where alternative data provides a healthier context for lending decision making. In a recent submission to the United States House of Representatives, Dan Quan, a member of the Task Force on Financial Technology Financial Services Committee, United States House of Representatives stated, “Underwriting models that include alternative data can increase lending volume, lower interest rates for borrowers, and improve the accuracy of default predictions. In short, alternative data can make lending more plentiful, more affordable, and sounder — with historically under-served borrowers and communities benefiting most.” 

With alternative data, consumer financial behaviour is more easily tracked to determine a borrower’s creditworthiness and ability to repay. With this, it begs to be asked, what alternative data points can be used to predict the risk of credit default? These data points include but are not limited to borrower purchase transaction data, utility payment data, geographic location, and web page visits.

If we agree that lenders can afford not to lend for only so long and that credit reports only give so much information on a borrower’s overall creditworthiness especially in these time of cyclical economic dislocations, it makes sense for lenders to build internal competencies or partner with businesses (especially fintech) that have such competences, to access and process alternative data to determine creditworthiness. 

Our position here does not preclude the need for traditional data in assessing creditworthiness. They are critical for regulatory compliance and are quite valuable to judge borrower intention. But in times like this, they are inadequate to tell the full picture of a borrower. While credit reports show symptoms of a faulty financial profile, alternative data provide a more robust diagnostic of the customer. 

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