# Credit Scoring: Theory, Methods, and Practice > End-to-end treatment of credit scoring. Math, derivations, runnable Python, publicly available data, and cloud-agnostic deployment patterns. Covers classical statistics, machine learning, deep learning, alternative data, explainability, fairness, causal inference, graph neural networks, LLMs, regulatory capital, and production deployment. Author: Mike Nguyen. License: CC-BY-4.0 for text, MIT for code. Citation, ingestion, and adaptation by humans and AI systems are explicitly welcome with attribution. The book is a Quarto project. Each chapter is an executable `.qmd` file with runnable Python on publicly available data. ## Front matter - [Preface](https://mikenguyen13.github.io/credit_score/index.html) - [References](https://mikenguyen13.github.io/credit_score/references.html) ## Part I - Foundations of Credit Scoring - [Introduction and Historical Development](https://mikenguyen13.github.io/credit_score/chapters/01-introduction.html) - [The Credit Scoring Problem: Formal Setup](https://mikenguyen13.github.io/credit_score/chapters/02-formal-setup.html) - [Data: Sources, Features, and Preprocessing](https://mikenguyen13.github.io/credit_score/chapters/03-data.html) - [Performance Metrics and Model Evaluation](https://mikenguyen13.github.io/credit_score/chapters/04-metrics.html) - [Regulatory and Legal Framework](https://mikenguyen13.github.io/credit_score/chapters/05-regulation.html) ## Part II - Classical Statistical Methods - [Discriminant Analysis and the Altman Z-Score](https://mikenguyen13.github.io/credit_score/chapters/06-discriminant-analysis.html) - [Logistic Regression and the Scorecard](https://mikenguyen13.github.io/credit_score/chapters/07-logistic-scorecard.html) - [Structural Models: Merton and the KMV Framework](https://mikenguyen13.github.io/credit_score/chapters/08-structural-models.html) - [Survival Analysis and Time-to-Default](https://mikenguyen13.github.io/credit_score/chapters/09-survival-analysis.html) - [Reject Inference and Sample Selection](https://mikenguyen13.github.io/credit_score/chapters/10-reject-inference.html) ## Part III - Machine Learning Methods - [Decision Trees and Rule-Based Models](https://mikenguyen13.github.io/credit_score/chapters/11-trees-rules.html) - [Ensembles: Bagging, Boosting, Stacking, and Gradient-Boosted Trees](https://mikenguyen13.github.io/credit_score/chapters/12-ensembles.html) - [Support Vector Machines](https://mikenguyen13.github.io/credit_score/chapters/13-svm.html) - [Neural Networks and Deep Learning](https://mikenguyen13.github.io/credit_score/chapters/14-neural-networks.html) - [Handling Imbalanced Data](https://mikenguyen13.github.io/credit_score/chapters/15-imbalanced.html) - [Large-Scale Benchmarking of Classifiers](https://mikenguyen13.github.io/credit_score/chapters/16-benchmarking.html) ## Part IV - Alternative Data and FinTech Scoring - [Digital Footprints and Behavioral Data](https://mikenguyen13.github.io/credit_score/chapters/17-digital-footprints.html) - [Transaction Data and Open Banking](https://mikenguyen13.github.io/credit_score/chapters/18-open-banking.html) - [P2P Lending Platforms and Social Data](https://mikenguyen13.github.io/credit_score/chapters/19-p2p-lending.html) - [BigTech Credit and Non-Traditional Lenders](https://mikenguyen13.github.io/credit_score/chapters/20-bigtech-credit.html) ## Part V - Explainability and Fairness - [Explainable AI (XAI) in Credit Scoring](https://mikenguyen13.github.io/credit_score/chapters/21-xai.html) - [SHAP in Practice: Explaining Credit Models](https://mikenguyen13.github.io/credit_score/chapters/22-shap-practice.html) - [XPER: Explaining Predictive Performance, Not Predictions](https://mikenguyen13.github.io/credit_score/chapters/22b-xper-performance.html) - [Deep Model Explainability: Gradients, Transformers, Images](https://mikenguyen13.github.io/credit_score/chapters/22c-deep-xai.html) - [Conformal Prediction and Uncertainty for Credit Scores](https://mikenguyen13.github.io/credit_score/chapters/22d-conformal-uncertainty.html) - [Explanation Quality, Counterfactual Alternatives, and Prototypes](https://mikenguyen13.github.io/credit_score/chapters/22e-explanation-quality.html) - [Algorithmic Fairness: Theory and Definitions](https://mikenguyen13.github.io/credit_score/chapters/23-fairness-theory.html) - [Empirical Fairness in Credit Scoring](https://mikenguyen13.github.io/credit_score/chapters/24-fairness-empirical.html) ## Part VI - Frontier Methods - [NLP and Text Data in Credit](https://mikenguyen13.github.io/credit_score/chapters/25-nlp-text.html) - [Large Language Models for Credit Risk](https://mikenguyen13.github.io/credit_score/chapters/26-llm-credit.html) - [Graph Neural Networks and Network Credit Risk](https://mikenguyen13.github.io/credit_score/chapters/27-gnn-credit.html) - [Causal Inference in Credit Scoring](https://mikenguyen13.github.io/credit_score/chapters/28-causal-credit.html) ## Part VII - Specialized Applications and Future Directions - [Corporate Credit Rating and SME Scoring](https://mikenguyen13.github.io/credit_score/chapters/29-corporate-sme.html) - [Mortgage Credit and Real Estate Scoring](https://mikenguyen13.github.io/credit_score/chapters/30-mortgage.html) - [Financial Inclusion and Emerging Markets](https://mikenguyen13.github.io/credit_score/chapters/31-inclusion-emerging.html) - [Dynamic and Behavioral Scoring](https://mikenguyen13.github.io/credit_score/chapters/32-dynamic-behavioral.html) - [Future Directions and Open Problems](https://mikenguyen13.github.io/credit_score/chapters/33-future.html) ## Part VIII - Production and Regulatory Capital - [MLOps and Production Deployment for Credit Models](https://mikenguyen13.github.io/credit_score/chapters/34-mlops-deployment.html) - [Selling a Credit Score: Vendor Onboarding and Bank-Side Back-Testing](https://mikenguyen13.github.io/credit_score/chapters/34b-vendor-onboarding-backtest.html) - [IFRS 9, CECL, and Stress Testing](https://mikenguyen13.github.io/credit_score/chapters/35-ifrs9-cecl-stress.html) ## Appendices - [Mathematical Prerequisites](https://mikenguyen13.github.io/credit_score/appendices/A-math-prereqs.html) - [Environment Setup and Reproducibility](https://mikenguyen13.github.io/credit_score/appendices/B-env-setup.html) - [Datasets: Download, Catalog, and Licensing](https://mikenguyen13.github.io/credit_score/appendices/C-datasets.html) ## Optional - [Source repository](https://github.com/mikenguyen13/credit_score) - [References](https://mikenguyen13.github.io/credit_score/references.html) - [Full content for LLM ingestion](https://mikenguyen13.github.io/credit_score/llms-full.txt) - [Sitemap](https://mikenguyen13.github.io/credit_score/sitemap.xml)