[Editor’s Note: This is a guest post from Venkatesh Bala, PhD, the Chief Risk Officer for Biz2Credit, LLC, a leading online marketplace for small business credit based in New York. Biz2Credit has facilitated more than $1.7B in funding since 2007.]
As of February 2018, US bank lending of various kinds – auto loans, commercial credit, mortgages, credit cards or small business lending – constituted $11.7 Trillion, representing around 60% of US GDP and 70% of commercial banking assets. Maintaining the health and profitability of these assets over the business cycle is critical for the banking and financial sector and for the overall US economy.
Banks are grappling with how best to use digital technology in their organizations. Advances in digital technology, data science and machine learning/AI present new opportunities to manage lending risks and maintain a strong balance sheet that is resilient to changes in the credit cycle.
To achieve these objectives, it is possible to envision a new digitally-enabled playbook for risk management. Managing the risk with bank and other lending, instead of being a highly specialized silo, can instead become an enterprise-wide responsibility and capability.
The reason is that all business functions – from underwriting, servicing and collections to sales, marketing and treasury, impact the portfolio risk. Marketing, for instance, directly affects the riskiness of customers coming through the door, while sales interactions determine whether relationships are built with more or less risky customers. Equipping these functions with relevant risk information through digital means can empower them to reduce the totality of risk from lending.
Near-in, the biggest benefit lies in loan underwriting and monitoring. Digital data is now available from an ever-expanding set of sources, including bank statements, social media, credit bureaus, mobile phones and call centers. The key insight is that this data can be combined with lending outcomes and used for risk reduction.
Small business lending offers an illustrative example. Picture three satellite images of gas stations in different parts of the US, with various landmarks present in the images. The first is near a school and a church, the second is close to suburban housing and the third lies between a church and a large apartment complex. Which of these gas stations has the highest revenue, and which the lowest? Which of them is most at risk of default on a small business loan? [Read more…]