Even before the pandemic, India had the worst bad debt ratio of the world’s top 10 economies. With Rs. 10 lakh crore in stressed assets, anticipated NPAs for the current fiscal year are expected to exceed a two-decade high. Of course, the pandemic exacerbated matters by limiting people’s ability to repay and banks’ ability to collect.
There are exposed flaws in India’s lending operations, where digitisation has been patchy at best. The desire to reduce consumer wait time influences efforts to expedite underwriting, identification verification, and decisioning. Artificial intelligence, big data analytics, and social listening capabilities are being used to shorten the time to disbursement. However, the scene after disbursement is largely archaic, with a decades-old approach to collections or loan restructuring.
Banks typically work with a collections agency and delegate responsibility to proprietors who act as collection agents. These are essentially unregulated organisations that frequently violate norms of conduct, resort to harassment, and endanger the lender’s reputation.
As a result, the Reserve Bank of India (RBI) issued a report that recommended legislative, technological, and consumer protection frameworks for loan recovery. These guidelines establish safeguards for banks and non-banking financial companies (NBFCs) that sponsor a digital lender, thereby making them guardians to prevent the burgeoning digital lending ecosystem from turning rogue.
Ronak Doshi, Head Sales, geoIQ, said that, “Indian consumers now want a high-speed experience. If any customer is placing an order on any eCommerce platform, they want the delivery on the same day, or they want their food to be delivered in the next thirty minutes otr their groceries to be delivered in ten minutes. To make this instant decision in less than two minutes, companies are leveraging machine learning algorithms, data science, and all the available data points, which they can use, consume, and profile their customers. And at geoIQ, we provide companies with such kind of data, but from a location perspective.”
