Fraudsters adapt daily; your defenses must evolve even faster. Stop revenue leaks before they cripple your business. Move beyond rules and guesswork toward data-driven certainty. Turn raw transaction streams into clear, actionable fraud signals. Master proven Python workflows used by top fintech security teams. Guard customers, profits, and reputation with confidence.
- Rule-based foundations: Build quick wins and create reliable baselines for later models.
- Classical algorithms: Use logistic regression and decision trees to flag card and transaction anomalies.
- Ensemble power: Apply random forests and gradient boosted trees for higher recall with fewer false positives.
- Deep learning: Deploy neural networks, vision transformers, and graph CNNs to catch modern, multi-channel attacks.
- Real datasets: Follow complete, annotated Python notebooks ready for adaptation to your production stack.
- Evaluation playbook: Measure accuracy, precision, recall, and cost impact to justify every security investment.
Fight Fraud with Machine Learning by Ashish Ranjan Jha is a guide that combines academic research with battle-tested industry practice. Jha draws on a decade at Oracle, Sony, Revolut, and Tractable to deliver clear, reproducible solutions.
The book progresses from simple rules to cutting-edge deep-learning approaches, each chapter adding complexity and capability. Step-by-step labs, code listings, and annotated diagrams let readers learn by doing. Case studies span credit cards, KYC, and social bots, illustrating breadth and depth.
Finish the final chapter ready to deploy robust models that slash fraud losses, impress auditors, and protect customer trust. Your new skill set will translate directly into safer products and stronger career prospects.
Ideal for data scientists, ML engineers, and fraud-prevention product managers comfortable with Python.
Fight Fraud with Machine Learning teaches you to build and deploy state-of-the-art fraud detection systems.
Financial and corporate fraud happen every day, and the fraudsters inevitably leave a digital trail. Machine learning techniques, including the latest generation of LLM-driven AI tools, help identify the telltale signals that a crime is taking place. Fight Fraud with Machine Learning teaches you how to apply cutting edge ML to identify fraud, find the fraudsters, and possibly even catch them in the act.
In Fight Fraud with Machine Learning you’ll learn how to:
- Detect phishing, card fraud, bots, and more
- Fraud data analysis using Python tools
- Build and evaluate machine learning models
- Vision transformers and graph CNNs
In this cutting-edge book you’ll develop scalable and tunable models that can spot and stop fraudulent activity in online transactions, data stores, even in digitized paper records. You’ll use Python to battle common scams like phishing and credit card fraud, along with new and emerging threats like voice spoofing and deepfakes.
About the bookFight Fraud with Machine Learning teaches you to build and deploy state-of-the-art fraud detection systems. You’ll start with the basics of rule-based systems, iterating chapter-by-chapter until you’re creating tools to stop the most sophisticated modern attacks. Almost every online fraud you might encounter is covered in detail.
Examples and exercises help you practice identifying credit card fraud with logistic regression, using decision trees and random forests to identify fraudulent online transactions, and detecting fake insurance claims through gradient boosted trees. You’ll deploy neural networks to tackle Know Your Customer fraud, spot social network bots, catch deepfakes, and more! Plus, you’ll even dive into the latest research papers to discover powerful deep learning techniques such as vision transformers.
About the readerFor fraud detection product managers, data scientists, and machine learning engineers confident with Python programming.
About the authorAshish Ranjan Jha has worked for large technology companies like Oracle and Sony, as well as tech unicorns such as Revolut and Tractable. He has a decade of working experience in the field of Machine Learning using Python.
