Credit scores are a crucial data-point for lenders, revealing how financially stable (or how close to the edge) a small business is. But does a credit score capture a true picture of how a business is actually doing? What if a business owner has struggled financially in the past, but her social reviews show there’s always a line to get in at her restaurant? Or what if an entrepreneur has always paid his bills on time, but current revenues are flat and customer service is slipping?
Enhanced Data Points and Evaluating Risk
There are so many new ways to track the health of a business. For example, Google Analytics shows how much traffic a company’s website gets from month to month. If a business has claimed its results page on Google, that can be a sign they’re relatively well-established and web-savvy. Yelp reviews and social media activity can give you a sense of a company’s popularity, how loyal its customer base is, how effective their customer service is.
While the technology that tracks this kind of information is still new, the promise of these enhanced data points is huge. Informing the traditional lending model with additional data points can give lenders a more up-to-the-minute, accurate assessment of how a business is doing. They could become early indicators of trouble or the first signs of a big expansion. The first few negative reviews on Yelp could be a hint that customer service standards are slipping; increased traffic to a business’s website could indicate a promotional campaign is succeeding at drawing new customers.
Lenders can use these new data points to identify new creditworthy customers at limited risk. Under a traditional loan origination model, business owners who don’t meet certain cutoffs in terms of their credit scores and other financial factors simply can’t get a loan. For example, armed with new data points, community banks could allow something like strong social media reviews to counterbalance a credit score that appears edge-case based on narrow traditional requirements.
Of course, you wouldn’t make a loan based on a Yelp score alone. But over time, big data will make these newer data points more and more reliable. Over the course of several years, gathering data on thousands of businesses will develop an accurate system that combines traditional data points with newer data points to create an ever-more-complete picture of how any individual business is performing.
Using Big Data to Monitor Loan Quality
Enhanced data-points can improve small business lending beyond the origination phase, too. Online loan platforms like Mirador can harness big data to monitor loan quality on an ongoing basis. Early warning system based on relevant big data helps banks intervene in loans possibly going south. The result? Reduced loan defaults and less pressure on loan reserves.
Big data doesn’t just reduce bottom-line losses; it can also maximize top-line revenue. Risk-based pricing is another innovation enabled by adding enhanced data-points to your credit model. Current loan pricing is both binary and flat: a given loan is either accepted or not, and banks extend the exact-same pricing to all accepted loans. Alternative lenders charge much higher prices – some might say exorbitant – to underwrite loans that appear edge-case according to traditional lending models.
By enriching established credit models with enhanced data-points, traditional lenders can close this gap with loan pricing that more accurately reflects the borrower’s risk profile. These newer lending models ground these risk decisions in data, not merely a hunch. Risk-based pricing is more affordable for borrowers than digital marketplace loans, and it increases profits at limited risks for banks.