8 Building AI fraud detectors: from supervised scoring to anomaly identification
This chapter covers
- Establishing core principles for AI-driven fraud detection
- Preparing specialized data for fraud analytics, addressing imbalance and privacy
- Implementing supervised learning for known fraud patterns
- Evaluating supervised models with business-relevant and strategic metrics
- Applying unsupervised learning to detect novel anomalies
The shadowy world of financial fraud is a high-stakes arena where innovation is a weapon wielded by both perpetrators and defenders. As we explored in Chapter 7, the adversaries are nimble, constantly adapting their tactics to find the faintest cracks in institutional defenses. To counter this ever-evolving threat, understanding fraud isn’t enough—we must build systems that learn, adapt, and respond swiftly. This chapter transitions from understanding fraud to building AI systems that fight it. We embark on a practical journey, constructing AI models to unmask illicit activities in vast financial data streams.
At its heart, fighting financial fraud with AI revolves around three fundamental quests. This chapter focuses on the first two: recognizing the known, which uses supervised learning to spot familiar fraud patterns, and finding the unknown, which uses unsupervised anomaly detection to identify novel threats. The third quest, unveiling hidden connections through graph analytics, will be covered in Chapter 9.