Whitepaper: Unlock Real-Time Fraud Detection with Supervised Machine Learning

Financial institutions face a relentless onslaught of fraud, with authorized push payment fraud (APPF) leading the charge.  Globally, APPF accounts for 75% of all digital banking fraud, and losses are projected to skyrocket in the coming years.  In this critical environment, accurate and timely fraud detection is paramount.

This white paper, developed by Lynx – a leader in AI-powered fraud prevention – explores how supervised machine learning (ML) provides the solution.  Authored by Carlos Santa Cruz, CTO of Lynx and Professor of Computer Science and Artificial Intelligence at the Universidad Autónoma de Madrid, the paper delves into the crucial aspects of building, training, testing, and deploying effective ML models for real-time fraud detection.

What you’ll learn:

• The challenges of traditional fraud detection methods: Discover why rule-based systems fall short in today’s complex digital landscape.
• The power of supervised machine learning: Understand how ML algorithms accurately classify transactions as genuine or fraudulent.
• The five crucial phases of ML model construction: From data preparation and feature extraction to model building and validation, learn the step-by-step process.
• The importance of frequent retraining: Explore why Lynx’s Daily Adaptive Models retrain daily to maintain accuracy and minimize false positives.
• The impact of model drift and how to overcome it: Discover how environmental changes and evolving fraud techniques necessitate frequent model retraining.
• Supervised vs. Unsupervised Learning: Learn the significant performance difference between these approaches in the complex fraud detection problem.

Download the white paper today and gain a comprehensive understanding of how Lynx’s innovative technology helps financial institutions effectively combat fraud and safeguard their assets.