Explaining Loan Application Denials

Requirements Clarification & Assessment

1. Objective: To explain to rejected loan applicants why their applications were denied without direct access to feature weights of the model.

2. Constraints:

   • No Access to Feature Weights: The model's internal weights or coefficients are not available.
   • Model as a Black Box: The model can be used to make predictions, but its internal workings are not transparent.

3. Data Availability:

   • Input Features: Information about the applicant's credit cards, current debt, and credit age is available.
   • Prediction Output: Whether an applicant is approved or rejected.

4. Interpretability Requirement: Need to provide understandable reasons to applicants for rejection, focusing on features that influenced the decision.

5. Scalability: The solution should be applicable to a large number of applicants efficiently.

6. Tools and Techniques: Consider using model-agnostic interpretability methods such as LIME, SHAP, and Partial Dependence Plots.

7. Outcome: A clear, actionable explanation for each rejected applicant, enhancing transparency and trust.