Where Big Data and Lending Meet

Leveraging 'big data' in alternative lending: how fintech is challenging banks while simultaneously improving the lending process.

This article was written by Stephen Sheinbaum who is the founder of Bizfi, a connected online funding marketplace that provides small businesses with everything from short-term financing, equipment and invoice financing to long-term loans. Bizfi and its family of companies, which has developed sophisticated big data systems, is the origin of $1.3 billion to more than 25,000 small businesses since 2005.

 

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Want to make someone under the age of 30 laugh? Tell them that, once upon a time, whether you got a loan or not depended on the strength of your handshake. A firm grip of a banker’s hand was a litmus test for what kind of credit risk you’d be. 

Stephen Sheinbaum
Stephen Sheinbaum, Guest Blogger

Now, of course, data makes that determination. Funders have data on prospective borrowers’ credit scores, both personal and business. They have data on what individuals and businesses have borrowed previously, and how well they have done at repaying those loans. They have data on the transactions that a business does through credit cards and data on what’s being deposited in the bank. It’s all part of the giant cloud of information that has come to be known as ‘big data’.

For those who have embraced it, big data has made giant strides towards better lending. It is demolishing barriers based on age, gender and geography. It has made it possible to make a preliminary decision on an application in minutes, not months.

But big data is going to be getting even bigger and even better. A recent, fascinating story in The New York Times profiled several companies that are analyzing data beyond credit scores and business histories to create an even more nuanced portrait of a borrower. The data scientists behind these companies are finding, for example, that people who fill out an application in all capital letters are more of a risk than people who use upper- and lower-case letters correctly.

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Funders–both traditional lenders and upstart alternative finance companies–are watching these developments with interest and apprehension. In a report on the future of retail banking, PricewaterhouseCoopers found that fewer than 20 percent of bank executives feel well prepared for the future. Implementing new technology is the number three priority for banks in Europe, while those in the Asia-Pacific region and emerging markets have made Research and Development (R&D) and innovation their number two priority.

“We are in the middle of a multi-wave trend where digital is first focused on optimizing current products and services,” the report’s authors wrote. “The second wave, where enhanced data capture and analysis drives more targeted customer offerings and improved services is underway. Mobile banking will increasingly disrupt distribution models (e.g. instant videoconferences with product experts) and the payments industry (e.g. P2P mobile payments). Advances in security and verification will enable all aspects of sales, service and delivery to be conducted online. Technology is making it easier for customers to switch banks, making relationships much less sticky. This will drive the third wave, where banks and their partners develop sophisticated profiles on each of their customers.”

Of the banks surveyed by PwC, 54 percent said they would be enhancing customer data collection over the next five years. It’s easy to see why: big data has the potential to help funders get to know their clients better than ever before. It has the potential to drive down operating costs for funders and borrowing costs for their clients. Big data, properly captured and analyzed, has the potential to create new funding options that are tailored to their users like a custom-fitted suit. It also has the potential to make fintech companies bigger players in funding than those that shortchange technology investments.

That’s because independent fintech companies can potentially be better at gathering and analyzing a wide range of data points. They are not bound to one set of internal metrics. They can experiment with things like social media reputation scoring to see whether a customer complaint posted to Facebook or Twitter may portend other problems at a small business and raise its risk of default.

Putting all this data into service for a small business helps funders to create a bigger, sharper picture of its needs and its ability to repay the funding. This is something that would not have been possible with traditional lending even as recently as a few years ago, and top alternative funders are doing this at a speed that would not have been possible in their industry even a year ago.

The use of big data by independent fintech companies won’t just challenge big banking. Several observers of U.S. financial markets have already noted that these companies are already driving out high-cost non-bank lenders like payday loan operations. This has the potential to bring many more underbanked, and underfunded, businesses into the mainstream.

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