In global retail lending realm, alternative data is capturing the spotlight as it creates the opportunity to finance the unbanked and underbanked portion, as well as making the risk model more accurate with more data. Alternative data, or more even correctly “credit-adjacent” data includes lots of data that are not being used in conventional credit underwriting process, for instance, utility usage, locational movement, mobile data usage, employment track record, involvement in digital platform and the list continues.
From a global picture, fintechs started the use of these data in the process of credit evaluation to reach those customers who are out of banks’ periphery. Having discerned fintech’s growing portfolio and faster loan processing, legacy banks are coming out of their conventional shell, some collaborated with fintechs. In Bangladesh, usage of alternative data in lending is still not evident, except for taking performance of utility bill payment and rent payment in some cases. Availability of refined data is a challenge in our market. Banks have the largest customer data repository, but can hardly use them because they are scattered and unstructured. Another fact is, currently we do not have one single touch point where a customers’ all basic information, purchase history (online/offline), credit history, asset details are available (like Aadhar card, Permanent Account Number- PAN card in India). Ant Financial of China has created a financial services ecosystem by zeroing in on alternative data and bringing more consumers into the periphery. Having said that, our local startup ShopUp set a good example of lending with a credit scoring model using alternative data. It is high time that Financial Institutions should understand the importance of building their risk model using alternative data if they want to reach scalability and accelerate operational efficiency.More Print Editions Subscribe