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Machine Learning for Credit Risk with Python: A Practical Guide to Default Prediction, Credit Scoring, Model Explainability, and Portfolio Risk Analysis

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Description

Reactive Publishing Machine learning is changing the way credit risk professionals analyze borrowers, estimate default probability, and monitor portfolio exposure. Machine Learning for Credit Risk with Python provides a practical, structured introduction to applying modern machine learning techniques to credit risk analysis using Python. This book guides readers through the core components of credit risk modeling, including borrower data preparation, default prediction, credit scoring, feature engineering, model evaluation, and explainability. It also introduces practical approaches to stress testing and portfolio-level risk analysis, helping readers understand how machine learning models can support more transparent and data-driven credit decisions. Inside, readers will explore how to: Prepare credit risk data for machine learning workflows Build default prediction and credit scoring models in Python Evaluate model performance using appropriate risk metrics Interpret model outputs with explainability techniques Apply stress testing concepts to credit portfolios Connect individual borrower models to broader portfolio risk analysis Written for analysts, finance professionals, data scientists, and students, this book bridges technical machine learning concepts with real-world credit risk applications. It is designed for readers who want a clear, practical framework for building and interpreting credit risk models without relying on unrealistic promises or black-box assumptions. Machine Learning for Credit Risk with Python is a practical resource for anyone looking to understand how Python-based machine learning can be applied to modern credit risk modeling, scoring, and portfolio analysis.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
12 June 2026
Listed Since
12 June 2026

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