In the United States, the Equal Credit Opportunity Act of 1974 (ECOA) represents one of the major laws employed to ensure access to credit and guard against discrimination. ECOA lists a series of protected classes that cannot be used in deciding whether to provide credit and at what interest rate it is provided. These include the usual—race, sex, national origin, age—as well as less common factors, like whether the individual receives public assistance.

The standards used to enforce the rules are disparate in treatment and impact. Some people within protected classes are treated differently than those of nonprotected classes. The Consumer Financial Protection Bureau defines disparate impact as occurring when:

“A creditor employs facially neutral policies or practices that have an adverse effect or impact on a member of a protected class unless it meets a legitimate business need that cannot reasonably be achieved by means that are less disparate in their impact.”

At DFX5 we design machine learning and automated systems that perform credit decisions using completely non-biased and legally compliant assessments. Individuals and classes do not impact the credit decision, instead a series of mathematical factors are generated to predict an accurate rate of payment and return for each credit decision. At DFX5, our algorithms are free of bias and the credit allocation is based on borrower risk, known simply as “risk-based pricing.”

Our credit decision tools help underwriters answer their most common questions:

• How to factor the credit history into the decision?
• What is the correct price for the risk?
• What are the historical patterns for users with same profile?
• Are there product enhancements available to reduce the risk?