Credit scores have become an integral component of the credit landscape. As that landscape shifts, credit-score algorithms should adapt to changes in consumer behavior that are reflected in the information that creditors share with credit-reporting agencies. In addition to adjusting the algorithm’s mix of characteristics and associated score weights over time, model developers should also evolve the predictive characteristics⎯those building blocks of the score algorithm⎯in order to account both for changes in the ways consumers seek and use credit, and for the introduction of new financial products. Through such advances, scientists can develop increasingly predictive scores based on credit information, and they can develop more sophisticated logic that recognizes consumers who manage credit responsibly. This Article discusses three different research studies. The first study focuses on changes made while redeveloping an earlier generation of the FICO Score algorithm. FICO scientists introduced logic to improve the way the algorithm evaluated creditinquiry information, making it more appropriate to consumers who were rate shopping for the best loan. The second research study discusses how creditutilization calculations were modified to account for flexible spending accounts⎯a new type of credit card that possesses both a charge and a revolve feature. This enhancement was incorporated into the current suite of FICO Scores. The final research study examines whether, in future versions of the FICO Score algorithm, mortgage short sales should penalize scores less than foreclosures do. . .
