An open-source educational framework for financial fraud analysts in the BNPL and digital payments ecosystem. Build expertise in data-driven fraud detection through comprehensive reference materials and real-world insights.
Financial fraud has evolved into a sophisticated, multi-dimensional challenge requiring analysts to correlate signals across behavioral, technical, financial, and geographical data sources. Modern fraudsters exploit vulnerabilities not through single anomalies, but through coordinated patterns that span multiple data dimensions simultaneously.
Fraud detection is fundamentally a pattern recognition discipline. Individual transactions rarely expose fraud in isolation—it's the relationships, repetitions, and rhythms across data that reveal malicious intent. Understanding these patterns enables predictive detection that stops fraud before it causes financial damage.
Transaction velocity, account timing, synchronized activities
Device clustering, IP patterns, bot detection
Payment instruments, BIN concentration, money flow
Impossible travel, address clustering, location mismatches
Your mission is prevention. Our mission is education. This platform serves as a living reference framework—not a training course, but a comprehensive knowledge base you can return to when investigating suspicious patterns, qualifying new fraud types, or building detection logic.