DataSigns Technologies

Monish Anand spent more than 20 years as a banker and technologist, much of it at large banks and technology companies. He describes growing frustrated with how those institutions operated. In his telling, people who deserved credit were not getting it because they could not make it through the banks' lending engine, and banks had relied on largely the same underwriting approach for decades. That frustration led him to leave the corporate world and build something aimed at the segment the banks were missing.

DataSigns set out to score credit differently. Anand argued that the traditional approach of checking income minus expenses or past credit behavior was not enough to predict repayment. The company instead collects data from many sources, treats each data point as one piece of a larger mosaic, and feeds them into an algorithm to produce a credit score. The main source is mobile data. India has over one billion phone subscribers, and Anand said 61% of phones sold are smartphones. Users leave digital footprints through messaging, video streaming and social media, and the company's mobile-first app asks for consent to access that data. The app is on the Google Play Store.

The company also helps users understand their own scores and build them over time before applying for loans through banks and NBFCs.

DataSigns did not rely mainly on digital advertising to acquire customers. Anand said the company partnered with large MSMEs, where it became part of the HR manual and induction process and ran financial literacy campaigns inside factories and offices. Through these partnerships the company reached about 75,000 employees across roughly 60 companies. About 10,000 of those people downloaded the app. The company said it had issued around 600 loans at an average of about 75,000 rupees, roughly $1,100, on a 12-month term, with rates of 22% to 26% IRR. Anand said that was about half of what local loan sharks typically charge, which he put near 56% IRR.

The business model combines a processing fee of 1.5% to 2% with a spread. DataSigns takes credit lines from banks and lends at higher rates, keeping a spread of about 7% to 10%. Loans are then sold on to banks, which Anand described as an asset-light model where the company helps banks build their loan books.

What worked was the distribution. By embedding in employers' onboarding processes, DataSigns could reach large numbers of people at low acquisition cost, which Anand said is critical in financial services. The company also said it was the only one in the country where multiple banks and NBFCs had given a mandate to acquire customers and accept its credit score.

The company was still early. It had only begun lending about three months before the interview, and only about 10,000 of the 75,000 exposed employees had downloaded the app. Anand also acknowledged that the borrowers were a riskier segment, since many lacked formal financial histories, which is why pricing sat above the 18% to 21% that traditional banks charge on unsecured personal loans.

At the time of the interview, DataSigns had a team of about 17 people, mostly data scientists based in Bangalore. It had raised about $400,000 in an angel equity round and said it was closing a $5 million round within roughly 45 days, with a lead already substantially committed. Anand said the money would fund product development, including gamification and AI bots in the app.