India’s first credit information company, CIBIL, also becomes the first to provide market analytics to financial institutions ona SaaS model.
Summary:
India’s first credit information company, CIBIL, also becomes the first to provide market analytics to financial institutions on a SaaS model.
It was by the beginning of the new millennium and the India Shining story was just about to get started. Middle-class folks everywhere were hungry to spend and private banks were just too willing to feed their new found addiction to spend. It was only a matter of time before many loans went sour and the private banks resorted to other ways, reminiscent of 80’s Bollywood movies, to get their money back. It opened the gates for a credit information system that would help financial institutions better decide who to give loans to. That was when The Credit Information Bureau (India) or CIBIL, came into existence. Launched in 2001, it started providing banking and financial institutions with credit reports, which soon morphed into a CIBIL score, making it easier for banks to evaluate a loan applicant. And today, keeping with its tradition of bringing path-breaking solutions to financial institutions, CIBIL has become India’s first company to provide decision support system to banks and other NBFCs. Filling the Gaps Sudesh Puthran is a man used to playing with large numbers. The current CIO of CIBIL, he worked at OTC, India’s first Automated Stock Exchange, and CRISIL (Credit Rating and Information Services of India) before joining CIBIL in 2002. In the 7TB of data CIBIL had built over the years, he saw a business idea that could lend CIBIL competitive edge and create a new revenue channel. The idea was sparked by changes in the environment. Puthran noticed the strong emergence of NBFCs (non-banking financial companies) and MFIs (microfinance institutions). As of March 2011, for example, there were 12,409 registered NBFCs with the RBI. “NBFCs and MFI are still bullish about growth, and are gunning for rural penetration and expansion. To us, they were a whole new market we could partner with,” says Puthran. While NBFCs nurtured big dreams, they generally lacked the resources to support and fuel such growth. “The smaller institutions normally lack the skill sets of an analytical team and don’t have large risk management teams who can scrutinize reports,” he says. So if a small NBFC from the south wanted to expand its gold loan business to the north, it required in-house analysts to figure out the viability of such a move. Even if it managed to fund such a team, its analysts would only have a small amount of data—provided by the NBFC—to work with. Puthran learnt that every time these institutions got a report, they needed time to analyze it (it takes time even for an extremely experienced banker.) He figured that what they really needed was a solution, not a report. “With our existing database, we could help them figure out how borrowers from the north perform on gold loans, whether people tend to default on loans, and the age bracket of most defaulters,” Puthran says. He says that CIBIL could go as far as predict what the next loan of an existing client was likely to be.This was in addition to CIBIL’s previous offering: Delivering scores. With the decision support system, CIBIL could transform itself into a strategic partner, understanding the risk appetite of its partners and helping them make decisions. The service, launched in 2011, helps CIBIL extend a plethora of services to its member banks, including consulting services. CIBIL could advise its clients at every stage of their lifecycle—from acquiring customers to strengthening and expanding relationships with them, and from market-sizing, and competition benchmarking to ‘decisioning’ services. This decision support system which CIBIL now offers—through their technical partner TUSSPL—on a SaaS model to smaller organizations, helps them make faster decisions and save manpower cost.CIBIL could go as far as predict what the next loan of an existing client was likely to be.
CIBIL could go as far as predict what the next loan of an existing client was likely to be.