How This Fintech Startup is Using Machine Learning to Mitigate Lending Risk
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NPA is one of the largest issues in the lending sector in India, which is why the traditional lenders including the banks and NBFCs are more or less apprehensive to serve the salaried class.
But all thanks to the new-age lenders which have emerged in the fintech revolution – the problem seems to have a solution
With over a decade of work experience in investment banking and private equity, Tushar Aggarwal moved back to India from the US to join as the Everstone Capital. Along with spearheading the firm's retail and financial services portfolio, he was also on the board of two NBFCs where Aggarwal realised the gap in how these companies where underwriting risks as against lenders should approach it.
This prompted him to start StashFin. The company has RBI’s NBFC license and it focuses on extending personal loans to salaried individuals. The fintech lends roughly about 10-12 crore per month and has NPA was less than a per cent.
In a talk with Entrepreneur India, Aggarwal shares how technology has helped the startup to mitigate lending risk.
The Low Number
StashFin extends the line of credit of up to INR 3 lac to borrowers either directly on to their bank account or through VISA card, which can be used like a debit across all the ATMs in the country.
Aggarwal claims that the startup approves loans within a few seconds. “The key advantage to our borrower is that they can get a yes and no decision within fifteen seconds. And once they have an approval, if the customer chooses to process with us, then they get the money in their accounts within four hours, which makes us truly instant,” he added.
Discussing how the company mitigates risks, the founder shares the StashFin uses a host machine learning and analytics techniques to mitigate risk.
“This also how our borrowers get to a quick decision from us as compared to the companies out there, he says while adding that, “Also, our loans are based on a risk-based pricing wherein different segment of populations get different ranking - it's not a one size fits all. They don't have to lock in with us. As their credit needs to go up and down, their risks go up and down.”
So, take an example of an IT company. If the machines learn the employees of this company are great borrowers, then people working in other firms within the sector and at the same level are likely to get a better response from StashFin extends the line of credit of up to INR 3 lac to borrowers either directly on to their bank account or through VISA card, which can be used like a debit across all the ATMs in the country.. This is because fintech company's machine learning systems think they are good borrowers based on its learning over a period of time.
Moolah in the Business
Presently, StashFin extends the line of credit of up to INR 3 lac to borrowers either directly on to their bank account or through VISA card, which can be used like a debit across all the ATMs in the country.has raised close to USD 9.3m, of which USD 5m was raised last year and USD 4.3million recently. The firm's investor line-up includes Kirloskar Group’s venture capital arm, Snow Leopard Ventures, as well as Singapore-based Alto Partners, GrowX Ventures and DMI Finance.
As of date, Aggarwal says the company has enough capital to run the business for next 12-18 months. However, the company is open to a fundraising activity.