A sharp rise in lending products and services across e-commerce marketplaces, consumer internet products, traditional fintechs, mobile wallets, pay-later products and others are helping people access credit more easily in India. Several of these startups now claim to have sufficient data on potential users to perform alternative credit scoring that can speed up disbursal of credit directly into bank accounts in less than 24 hours.
Most consumer startups and fintechs do not have a non-banking financial company (NBFC) licence and depend on their banking or NBFC partners for repayment of loans. However, industry experts such as ZestMoney’s chief technology officer Ashish Ananthraman say that there is an opportunity to build technology features for repayment of loans as well. Edited excerpts of an interview:
How does an online lending platform build parallel tech on both credit scoring and collection?
If you are building tech or a platform to disburse money, whichever the mode of payment it is, it is equally important to build a stronger solid platform to collect the money back. Technology needs to be built end-to-end, else it is only a half-baked solution. It means that apart from the onboarding experience for the customers, loan collections should be equally smooth as well. At ZestMoney, we are focusing on collections as an important part of the user experience on our platform.
What kind of loan repayment tech are you building, and how are you helping your banking partner help build smarter collection and reduce fraudulent transactions?
We fundamentally believe that EMI (equated monthly instalments)-based purchase option should be available to everyone and not just to people who own credit cards. As soon as a customer logs in to our platform, the user goes through a process and a credit limit is granted to the customer based on several parameters and checks. We may ask them to set up the repayment during onboarding based on the user’s risk profile and some customers are given an option to setup repayments after their transaction as well.
There are certain patterns we pick up to help predict repayment behaviour of a customer. Our fraud engine is trained to recognise fraud patterns.
Experts have pointed out that rash spending is linked with high default rates. What is ZestMoney doing to prevent it?
If a newly registered customer makes multiple purchases in a short timeline of different amounts, we keep some restrictions on the spending limit. We also have restrictions for rash spenders by limiting their spending.
Do you think it makes sense to segregate self-employed applicants internally or treat all ZestMoney customers through the same credit check and repayment processes?
Not really. We look at all customers with the same lens and not build just for one particular segment, because we are not into SME lending.
If there is a founder of a company taking loans or pre-approved loans, then we are going to assess him as an individual and not as a company. However, we will take into account whether he or she recently started up without prior experience or whether he or she has been a long-time salaried employee before venturing out. So, we read all these signals, which are important and missed by other financial institutes.
What most of the fintech companies and banks do is just look at points like what has been the user’s salary for the last two months and points like whether there has been enough income for the month. If that doesn’t meet their criteria, they reject the applicant, which I believe is unfair. We need to be more predictive by looking at the history of the customer as well. If one of our loan applicants has just started his or her own business after years of working in the salaried segment, and if that person doesn’t show income into the bank account for the past two months, we do not reject them. It will not become a negative marker, though this parameter’s weighting may be low, but his ability to borrow will not be affected hugely.
The article was first reported on livemint.com