You’ve probably never heard of Signet Bank. A small, Richmond-based regional lender formed in 1987 by the merger of two even smaller banks, Signet was typically associated with the types of plain vanilla loans that make the executives at the financial behemoths in New York fall asleep in their chairs.
But that would soon change. In the early 90s, Signet made a bold bet: the company staked the future of its consumer lending business on two scarcely-known pioneers in the nascent field of applied data science whose ideas had been largely ignored by Signet’s larger, global competitors in New York.
The two men, Richard Fairbank and Nigel Morris, theorized that they could use data related to the Signet’s best, most profitable credit card customers — even seemingly irrelevant information such as where they went to college, or how many kids they had — to help them predict or model the profitability of new applicants, which would in turn allow the bank to offer more favorable terms to entice these higher-value customers.
