Evaluating Your Options in 2026 Every fraud prevention vendor pitches "AI-powered" solutions, but when you dig into the architecture, you find vastly different approaches. Some platforms still rely primarily on expert-defined rules—if transaction amount exceeds $X and merchant category is Y, flag it. Others lean heavily on machine learning models that discover patterns in historical data. Most production systems blend both. The question isn't which camp to join, but how to balance these approaches for your bank's specific risk appetite and operational constraints. Understanding the trade-offs between rule-based and ML-driven Fraud Prevention Automation is critical if you're responsible for transaction monitoring, AML compliance, or fraud operations at a retail bank. Let's break down each approach with the nuance these decisions demand.…