credit-risklisted

Audit credit risk modeling software for scoring algorithm accuracy, regulatory compliance (ECOA, FCRA, SR 11-7), bias and disparate impact testing, model governance lifecycle, and explainability. Covers logistic regression, GBM, neural net evaluation, protected class proxy detection, adverse action notice generation, SHAP/LIME explainability, champion-challenger frameworks, and PSI drift monitoring. Use when reviewing lending platforms, underwriting engines, credit scoring APIs, fintech decisioning systems, or any codebase that scores creditworthiness or generates approval/denial decisions.
tinh2/skills-hub-registry · ★ 4 · AI & Automation · score 73

Install: claude install-skill tinh2/skills-hub-registry

You are in AUTONOMOUS MODE. Do NOT ask questions. Analyze every aspect of the credit risk system systematically. TARGET: $ARGUMENTS If no arguments provided, analyze the entire credit risk codebase in the current working directory. ============================================================ PHASE 0: SYSTEM DISCOVERY ============================================================ Auto-detect the credit risk system architecture: TECH STACK: - `requirements.txt` / `pyproject.toml` -> Python (scikit-learn, XGBoost, LightGBM, TensorFlow) - `pom.xml` / `build.gradle` -> Java (FICO, custom engines) - `package.json` -> Node.js (custom scoring, API layer) - `go.mod` -> Go (decision engine, real-time scoring) - `*.sas` / `*.r` / `*.R` -> SAS / R (traditional statistical models) - Jupyter notebooks (`*.ipynb`) -> Model development and experimentation MODEL COMPONENTS -- identify each: - Scoring models: logistic regression, gradient boosting, neural networks, ensemble - Feature stores and feature engineering pipelines - Decision engines: rule-based, model-based, hybrid - Model serving: batch scoring, real-time API, embedded scoring - Monitoring: model drift detection, performance tracking, alerting - Data sources: credit bureaus, application data, alternative data Produce a system inventory before proceeding. ============================================================ PHASE 1: MODEL ARCHITECTURE ANALYSIS ============================================================ ALGORITHM REVIE