MRM Report XGBoost for Interpretable Credit Models
Model documentation for Stakeholders and Validation
Created on June 8|Last edited on June 8
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Executive Summary and Model OverviewModel Stakeholders Model Development Purpose and Intended UseModel Performance and StabilityModel PerformanceExplored Model SpecificationsModel InterpretabilityKey Drivers and Dependence Plots Prediction ExplanationsShap as a Supervised Embedding
Executive Summary and Model Overview
Handcrafted credit scorecards are still common across many areas of Finance, partially due to their interpretability vs more complex credit score modeling methods. However there are ways to leverage more complex modeling techniques such as XGBoost in credit assessment that can increase the performance of the assessment, whilst retaining interpretability for internal Risk Management functions as well as external regulators
Model Stakeholders
Model Development Purpose and Intended Use
XGBoost Classifier will be used to classify whether submitted loan applications will default or not.
Typically, we would include a summary of the business need for this particular model. Concretely, how the model will be used to address this business problem. Furthermore, we should describe with great precision all model uses covered by this document. These descriptions will address this statement made in regulatory guidance, FRB SR-11-7, "Even a fundamentally sound model producing accurate outputs consistent with the design objective of the model may exhibit high model risk if it is misapplied or misused."
Model Performance and Stability
Model Performance
Explored Model Specifications
Sweeps to investigate hyperparameter space
Model Interpretability
Key Drivers and Dependence Plots
Prediction Explanations
Visualizing prediction explanations for 1000 random predictions as well as surfacing Prediction explanation visual for the top prediction and lowest predictions in the validation dataset.
Shap as a Supervised Embedding
Considering the Shap value + labels as a means to complete a supervised embedding, we'll use the tables functionality to vizualize this embedding in our report and some other details in our report.
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