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Practical Fraud Prevention: Fraud and AML Analytics for Fintech and eCommerce, using SQL and Python. Gilit Saporta, Shoshana Maraney

Practical-Fraud-Prevention.pdf
ISBN: 9781492093329 | 350 pages | 9 Mb

• Practical Fraud Prevention: Fraud and AML Analytics for Fintech and eCommerce, using SQL and Python
• Gilit Saporta, Shoshana Maraney
• Page: 350
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781492093329
• Publisher: O'Reilly Media, Incorporated

Download Practical Fraud Prevention: Fraud and AML Analytics for Fintech and eCommerce, using SQL and Python

Pdf free ebook download Practical Fraud Prevention: Fraud and AML Analytics for Fintech and eCommerce, using SQL and Python (English Edition) 9781492093329 by Gilit Saporta, Shoshana Maraney CHM ePub PDB

Overview

Over the past two decades, the booming ecommerce and fintech industries have become a breeding ground for fraud. Organizations that conduct business online are constantly engaged in a cat-and-mouse game with these invaders. In this practical book, Gilit Saporta and Shoshana Maraney draw on their fraud-fighting experience to provide best practices, methodologies, and tools to help you detect and prevent fraud and other malicious activities. Data scientists, data analysts, and fraud analysts will learn how to identify and quickly respond to attacks. You'll get a comprehensive view of typical incursions as well as recommended detection methods. Online fraud is constantly evolving. This book helps experienced researchers safely guide and protect their organizations in this ever-changing fraud landscape. With this book, you will: Examine current fraud attacks and learn how to mitigate them Find the right balance between preventing fraud and providing a smooth customer experience Share insights across multiple business areas, including ecommerce, banking, cryptocurrency, anti-money laundering, and ad tech Evaluate potential risks for a new vertical, market, or product Train and mentor teams by boosting collaboration and kickstarting brainstorming sessions Get a framework of fraud methods, fraud-fighting analytics, and data science methodologies

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