Resistant AI Defense in Depth is a decisioning layer that sits on top of existing risk detection processes to aggregate and correlate signals from across an organization's entire risk tech stack. Rather than operating each fraud and financial crime prevention layer in isolation, the product combines outputs from document analysis, behavioral and device signals, transaction monitoring, and other data points to surface patterns that individual layers may miss. The product is designed to augment rules-based transaction monitoring systems by enriching them with additional signals, increasing detection recall while reducing false positives. It enables cross-comparison of submitted documentation, behavioral characteristics, and other customer attributes across the entire customer base, rather than evaluating each customer in isolation. Key signal types ingested include: - Documents: submitted at onboarding or as part of enhanced due diligence (EDD), cross-referenced against one another - Behaviors and devices: server timestamps, button clicks, keystrokes, IP addresses, device IDs, screen resolutions - Transactions: accounts, transactions, counterparties, money flows, and alerts - Other data points: passwords, security questions, phone numbers, company names, registration dates The product targets fraud detection and anti-money laundering (AML) teams in financial institutions, aiming to break down silos between those teams and enable earlier, more confident fraud and fincrime decisions. According to the vendor, it delivers 60% more detections than baseline Resistant AI products and a 20% reduction in false positives.