Retail shops need credit to purchase inventory. Credit dependence is more prominent with small retailers (mom & pop stores).  In India, most of these stores don’t use digital transactions or worse, don’t even have bank accounts. Due to this, they can’t get loans from banks as no credit score is present. In absence of formal credit, retailers depend on distributors (businesses who supply retailers inventory) to extend them line of credit (informal credit). Most of this credit offering is based on historical relations between retailer and distributor with no scientific premise. This credit is based on personal relationships without taking into consideration the actual worth of the shop — therefore, there are high risks of loan default.

With data access of nearly 7.5MN retailers on real time basis we have enough retail transaction level data & hyper local data to train models that can predict credit worthiness of a store without need for any banking information . In this talk, I intend to explain how we use data along with the ML models to build Credit Score for Retailers. A major financial lender use this score to extend credit to retailer achieving lowest delinquencies in the industry