Move over models, successful lending starts with a strategy

Nearly two years in and COVID-19 continues to bring uncertainty to the global market. If the job demand for data scientists is any indication, businesses across all sectors are looking to quantitative analytics for clarity. But what about those financial institutions that don’t have the analytical bench strength or resources to build an analytics team that can deploy, monitor and retrain models at the speed of today’s market? Luckily, much has changed in the field of advanced analytics, and many lenders are finding a competitive edge by following a different “model” of starting with a new strategy.

Two decades ago quantitative analytics was still a relatively new concept in lending. But today we have access to robust data and attributes, and a much broader set of statistical techniques, including machine learning enabled in more mainstream applications. With all this progress, for better or worse, the analytical financial model was elevated to silver-bullet status.

However, a model is not always the end-all, be-all solution for reducing risk. That’s because it relies on historical data to work effectively. It doesn’t take a data scientist to tell you the last two years have been unlike any that came before them. With the pandemic continuing to change the economic picture for consumers by the minute, a model designed for today could in effect be outdated tomorrow.

 

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