When fair lending practitioners use the term “regressions,” they are typically referring to statistical techniques used to understand the relationship between different variables, such as borrower characteristics and loan approval or denial. For years, regression models helped fair lending practitioners identify which variables have a significant impact on outcomes for protected groups. But newer forms of explanatory math, like Shapley values, are gaining popularity as they offer a more comprehensive and nuanced understanding of the variables that drive disparities for protected groups.
Fair Lending Analysis
Identify and overcome tradeoffs between performance and disparity.