Explainable AI for Fraud Detection is a hands-on, practical guide for building fraud detection systems that are not only accurate but also interpretable, ethical, and compliant. Tailored for developers, data scientists, and analysts, this book helps you move beyond black-box models by integrating explainability into every step of the machine learning pipeline.

The book begins by addressing the limitations of opaque models and the regulatory, ethical, and trust challenges they pose to customers. It introduces Explainable AI (XAI) concepts using SHAP—an industry-standard interpretability tool based on cooperative game theory—and scikit-learn to illustrate how you can expose the reasoning behind predictions. Readers then build a Random Forest model to detect credit card fraud, applying SHAP to unpack its inner workings in a way that’s human-readable and regulator-friendly. Later, it introduces an innovative approach to uncovering hidden fraud typologies—such as identity theft and high-amount fraud—using clustering algorithms like t-SNE and k-means on SHAP values. A final case study shows how this methodology adapts to emerging fraud patterns, helping ensure your models remain resilient and future-proof.

By the end, you will be equipped with the technical tools and strategic mindset to implement responsible AI in high-stakes environments. You'll be ready to deploy fraud detection systems that perform with precision—and explain themselves with clarity.

What you will learn:

• How to build, train, and evaluate a high-precision fraud detection model using Random Forest and Python.
• How to apply SHAP to a trained model to generate clear, reliable explanations for both individual predictions (local) and overall model behaviour (global).
• How to design and implement AI systems that meet regulatory demands for transparency and explainability, such as GDPR's "right to explanation".
• How to use this XAI framework to adaptively detect new and emerging fraud threats, as demonstrated through a practical case study.

Who this book is for:

This book is ideal for data scientists and machine learning engineers building and deploying fraud detection models, as well as fraud analysts and investigators who need to interpret and act on AI-generated insights.

Varun Awasthi is a visionary technology leader with over 18 years of experience driving innovation in software engineering, automation, and AI. He has authored impactful articles and published patents in payment technology and fraud detection. Passionate about mentorship, continuous learning, and real-world innovation, Varun is known for blending deep technical expertise with a human-centred approach to problem-solving. He is based in the US.