New age companies build their own credit risk models. This works either in traditional credit scores like CIBIL or in some cases as a better alternative. There was no chance Rohith is going to get a loan approval from a loan officer. The entire conversation played out like a disaster in slow motion. Loan officer tried her best to explain why Rohit was not eligible for a loan from her bank. “You’re young. You just started your first company. It’s been a year,” she said, trying to sound sympathetic.
Rohit could think about how his government employed parents to put him through a good college, and how he worked hard to get through it to finally land a plum job at his dream company. But now, his mother is admitted to the hospital. She got diagnosed with a life-threatening disease with an expensive treatment. All he wanted was to be there for her and bear some of the expenses. He felt crushed.
This story and variants played out every day in India. Traditional players like banks are struggling with the changing landscape of their clientele. The loan officer could do anything to approve Rohit’s loan request. She felt just as crushed, shackled by the limitations of the existing tools in her hands.
Many new techno-financial companies are facing this problem. Their hypothesis is the new era of Big Data. For large volumes of data, we have two technological advances — 1. High-Speed Utility Computing and 2. Artificial Intelligence (AI). These advances have the tools to process large volumes of alternative data generated from our data-rich lives. They build sophisticated AI models. They also predict lending behaviours like propensity to pay on time or delay repayment, the risk of default behavior, product-customer fitment, product yield analysis etc.
These new age companies are building their own credit risk models. This works with traditional credit scores like CIBIL or salaried millennials. CIBIL has no predictive capability. As a lending entity, they have a simple value proposition by gathering alternative data.
“The more we know you, the more we can model our risk and the more you can access credit on the strength of your actual creditworthiness. The less we know you, the less credit is available to you.” It’s that simple. These companies have a responsibility to protect and prevent misuse of the data. They collect to determine the borrower’s creditworthiness. But that’s the grand bargain in a nutshell. This results in lending to the right person. That means a lower default rate and a lower Non-Performing Asset (NPA) rate. An overall improvement in the health of the credit market.
Artificial Intelligence applied to alternative Big Data sources allows the credit market by identifying the “true” creditworthiness of borrowers across the market. Thus helping to improve and build the country’s credit profile. And for people like Rohit, these new age companies are a godsend, as they have taken on the mantle to truly understand that he is not a credit risk and thereby extend him the loan he so dearly needs to meet his personal needs.