Alternative Models of Credit Scoring: New Options, New Risks
The process by which a lender evaluates an applicant’s creditworthiness can be a competitive differentiator. The first analytically derived credit scoring systems, introduced in the United States in the 1950s, enabled lenders to make accurate and less subjective risk decisions. The introduction of standardized credit bureau risk scores in the 1980s allowed lenders to increase operational efficiency, confidently evaluate applicants’ creditworthiness, and standardize decisioning without the need for manual credit review based on human judgment. Mathematical models executing these decisions have evolved since that time, with individual lenders applying resources to develop proprietary analytic assets for strategic advantage.
The recent emergence of financial technology (fintech) pushed the idea of credit scoring as a competitive differentiator. Many fintech providers, with an effusive investment community behind them, claim they have revolutionized consumer and small business credit risk management by applying “Silicon Valley smarts,” typically defined as a nebulous combination of algorithms and big data.
These claims caught the attention of government agencies and industry regulators such as the Consumer Financial Protection Bureau (CFPB), the Federal Trade Commission (FTC), and the U.S. Department of the Treasury, which appear cautiously optimistic about the potential for such innovations to improve financial inclusion. (“Cautiously” is the operative word.)
For more information, the Mercator Advisory Group research report, Credit Scoring as a Competitive Differentiator, examines the impact of fintech innovations, regulatory scrutiny, and, consumer preferences in the evolution of credit risk scoring and decisioning. We focus on three areas reshaping the competitive dynamics:
- Acquiring and retaining prime credit customers
- Reaching underserved customers
- Using credit scoring as a customer engagement tool