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 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.
