Muutke küpsiste eelistusi

Fight Fraud with Machine Learning [Kõva köide]

  • Formaat: Hardback, 387 pages, kaal: 463 g
  • Ilmumisaeg: 04-Mar-2026
  • Kirjastus: Manning Publications
  • ISBN-10: 1633438228
  • ISBN-13: 9781633438224
  • Formaat: Hardback, 387 pages, kaal: 463 g
  • Ilmumisaeg: 04-Mar-2026
  • Kirjastus: Manning Publications
  • ISBN-10: 1633438228
  • ISBN-13: 9781633438224
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 book

Fight 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 reader

For fraud detection product managers, data scientists, and machine learning engineers confident with Python programming.

About the author

Ashish 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.

Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Arvustused

Overall, if youre serious about modern fraud detection, from tabular ML to deep-fake audio, youll dog-ear plenty of pages and keep this book within arms reach. 

Manav Kapoor, Senior Technical Product Manager, Amazon 





This book aligns very well with the growing interest in using machine learning and AI to detect and prevent fraud. It covers a wide range of practical use cases that reflect real-world challenges across industries, from identity fraud and document forgery to transaction monitoring and phishing detection. 

Hatim Kagalwala, Applied Scientist, Amazon 

1 WHAT IS FRAUD AND FRAUD DETECTION? 

PART 1: LEARNING THE BASICS 

2 RULE-BASED FRAUD DETECTION: A PHISHING EXAMPLE 

3 FRAUD DETECTION ON TABULAR DATA USING CLASSICAL ML 

4 DEEP LEARNING FOR FRAUD DETECTION 

PART 2: MULTIMODAL AI FOR SOPHISTICATED FRAUD 

5 DETECTING PHISHING WITH LLM 

6 DOCUMENT FORGERY DETECTION USING COMPUTER VISION 

7 KYC FRAUD DETECTION USING DEEP LEARNING 

8 DETECT VOICE FAKING USING TRANSFORMERS 

9 ANTI-MONEY LAUNDERING FOR BITCOIN TRANSACTIONS USING GRAPH ATTENTION
NETWORK 

APPENDIXES 

APPENDIX A: FUNDAMENTALS OF CLASSICAL ML FOR FRAUD DETECTION 

APPENDIX B: RUNDOWN OF VARIOUS CLASSICAL ML MODELS FOR PHISHING DETECTION 

APPENDIX C: DETECT FAKE INSURANCE CLAIMS USING DIFFERENT IMPLEMENTATIONS OF
GRADIENT- BOOSTED TREES 
Ashish Ranjan Jha is a veteran machine-learning engineer known for turning complex fraud problems into practical solutions. With ten years at Oracle, Sony, Revolut, and Tractable, he brings clarity and real-world rigor to every page. Jha distills enterprise-scale ML experience into actionable guidance that helps readers stop fraud fast.