Applied Fraud Detection with Python: Analytics, Anomaly Detection, and AML Systems at Scale
Applied Fraud Detection with Python is a practical, systems-level guide to building modern fraud, anomaly detection, and AML infrastructure at scale.
Designed for analysts, data scientists, engineers, and financial professionals, this book goes beyond toy examples to focus on real operational constraints: noisy data, evolving fraud patterns, regulatory pressure, and the need for explainable, auditable models. You'll learn how Python is used in production environments to detect suspicious behavior across transactions, users, networks, and time.
The book covers the full fraud detection lifecycle, from data ingestion and feature engineering to statistical baselines, machine learning models, and real-time monitoring systems. Emphasis is placed on anomaly detection techniques, behavioral modeling, graph-based fraud analysis, and scalable pipelines suitable for banks, fintech platforms, payment processors, and compliance teams.
Rather than treating fraud detection as a single model problem, this book frames it as an adaptive system, one that must balance precision, recall, latency, and regulatory transparency. Python's ecosystem is used throughout to connect analytics, modeling, and deployment into cohesive AML and risk platforms.
What you'll learn:
Designing fraud and AML systems as end-to-end pipelines
Statistical and machine learning approaches to anomaly detection
Feature engineering for transactional and behavioral data
Detecting fraud using time-series and network analysis
Building scalable, auditable fraud detection architectures
Managing false positives, drift, and model decay in production
Integrating fraud analytics into compliance and risk workflows
Who this book is for:
Fraud and AML analysts
Data scientists and machine learning engineers
Financial engineers and risk professionals
Developers building transaction monitoring systems
Anyone designing large-scale trust, risk, or compliance platforms
This book is not about quick wins or black-box models. It is about building durable fraud detection systems that survive scale, scrutiny, and adversarial pressure, using Python as the connective tissue between analytics, automation, and real-world financial operations.
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