  When a financial institution declines an application for credit, an adverse
action (AA) is said to occur. The applicant is then entitled to an explanation
for the negative decision. This paper focuses on credit decisions based on a
predictive model for probability of default and proposes a methodology for AA
explanation. The problem involves identifying the important predictors
responsible for the negative decision and is straightforward when the
underlying model is additive. However, it becomes non-trivial even for linear
models with interactions. We consider models with low-order interactions and
develop a simple and intuitive approach based on first principles. We then show
how the methodology generalizes to the well-known Shapely decomposition and the
recently proposed concept of Baseline Shapley (B-Shap). Unlike other Shapley
techniques in the literature for local interpretability of machine learning
results, B-Shap is computationally tractable since it involves just function
evaluations. An illustrative case study is used to demonstrate the usefulness
of the method. The paper also discusses situations with highly correlated
predictors and desirable properties of fitted models in the credit-lending
context, such as monotonicity and continuity.
