Explain a Credit-Risk Model with SHAP for a Fintech
Overview
What this challenge is about.
You receive a trained XGBoost credit-risk model (binary default prediction), the training feature schema (38 features), and a held-out 10,000-sample test set with labels. Compute global SHAP feature-importance plots, beeswarm + dependence plots, and per-decision local explanations for the test set. Run a fairness slice on default-rate predictions across 6 SME industry categories (services, retail, manufacturing, logistics, hospitality, agri). Produce a one-page explanation card template a credit officer can paste into the review file. Write a 2-page compliance memo with caveats on SHAP's limitations.
The Brief
What you'll do, and what you'll demonstrate.
Produce a SHAP-based explainability + fairness toolkit for a credit-risk model that satisfies the compliance team's model-risk requirements.
Earning criteria — what you'll demonstrate
- Apply SHAP for global and local model explanation on tabular data
- Run a fairness slice analysis across protected-ish subpopulations
- Translate explanation outputs into operator-friendly artifacts
- Communicate XAI limitations to a compliance audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Data Scientist
Producing a SHAP-based explainability + fairness package that a compliance team signs off on is exactly the day-one work of a data scientist on any regulated-credit team.
This challenge sharpens
- shap
- fairness-analysis
- interpretability
AI Safety Researcher
Honestly characterizing SHAP's failure modes in a compliance memo bridges directly to AI safety work that emphasizes calibration and limitation reporting.
This challenge sharpens
- interpretability
- fairness-analysis
- model-validation
Machine Learning Engineer
Building a per-decision explanation generator that integrates with operator workflows is the MLE craft of shipping ML into regulated environments.
This challenge sharpens
- shap
- xgboost
- python