How do Lending Start-ups Catering to Millennials Ensure Credibility? Most start-ups have built in-house data metrics to validate the customer's background to ensure there are no discrepancies
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With changing times, the demands and wants of most people are also increasing. More and more millennials are dependent on the monthly salary which is often gone before the next one arrives. In such a scenario, they end up borrowing from friends and family.
But the end-of-the-month broke situation is a problem that start-ups have identified to bring about solutions. With the digitization of the economy, it has become easier to lend or borrow money. Start-ups are offering their users the option to borrow money from their platform or to pay through their apps for the things they buy with the option to pay back later.
However, the entrepreneurs behind these start-ups also realise that many of their customers could be unreliable. Considering the same, they have many in-built processes in place to keep a check on their consumers and ensuring that the money lent is returned. We spoke to entrepreneurs as they elaborated on how they implement the same.
Building a Trust Score
Most start-ups have built in-house data metrics to ensure that they not only keep a track on the number of transactions taking place per day but also validate the customer's background to ensure there are no discrepancies. The same is also one of the biggest risks that startups in this field undertake.
PayU India's LazyPay, which allows users to buy now but pay later, factors in delinquencies. They send reminders and notifications to the customers before the due date, on the due date, after the date. They also charge INR 10 per day for non-payment.
Pallav Jain, Head of Consumer Business, PayU India believes that the biggest risk for LazyPay is when a customer does not pay for his deferral payment facility. "Our product is engineered in a way where proactive analytics and machine learning algorithms are a part of the internal architecture," he said.
At PayU, they process three million transactions every day. Each transaction generates 15 direct variables, which helps them draw up a profile of the customer and come up with a "trust score'. "This trust score further helps to identify creditworthy customers, who can checkout using LazyPay without going through the payment process. We expect on an average 1-1.5% defaulting consumer as we have strong data points collected over the years. We use machine learning algorithms to predict customer defaults," he said.
While one would think the income of an individual is also taken into account, Akshat Saxena, co-founder ePayLater, income is just one of the inputs to risk decisions across credit products of all nature and therefore not a hard-prerequisite. "That said, there are data "surrogates" one can use, given the right technology and insights are put to use. For us, there are some sophisticated data-science algorithms we deploy that help us ascertain the inherent customer and transaction risk and thereby decide whether or not to approve," said Saxena.
Can You Trust Millennials?
Most youngsters these days have an unstable income or savings and the same also poses a risk for these start-ups. Slicepay, a start-up based out of Bengaluru, focuses mainly on lending to college start-ups. Rajan Bajaj, founder of Slicepay, believes that credit underwriting is about data and data is about the product. They ensure that before approval of a customer for credit, they analyze their profile based on all the relevant alternate data points they capture through our product like ability and behaviour and social circle related aspects which builds their credibility.
Dealing with users that are mostly college students, they have an inbuilt process to recover the money back. "We have campus managers working as freelancers for us. These are college students and they would follow up with the customers in their college on the reason of delays 90% recovery happens through such efforts over phone calls largely," said Bajaj.
They have also partnered with collections agencies and have in-house collections support and analytics team. "We work with external agencies to follow up physically with the consumers. This recovers back the balance money," he said.
Bajaj believes that the key here is analytics to make sure they are collecting for the right cases, not under a certain threshold so that it doesn't lead to losses and not with consumers where the chances of them not repaying back is more.