AI for fraud detection: From rules to real-time intelligence

AI-powered fraud detection represents a significant advancement over traditional rule-based systems by utilizing machine learning models to analyze transaction patterns and generate risk scores in real time. This approach reduces the need for manual rule creation, which often leads to "rule explosion" as new fraud patterns emerge. AI systems can quickly adapt to new tactics, as demonstrated by Danske Bank's reduction in false positives by half after implementing such technology. The systems analyze numerous signals, such as transaction amount, location, and device fingerprint, to detect anomalies and emerging threats more effectively than static rules. However, AI models require substantial training data and pose explainability challenges for regulatory compliance. Real-time fraud detection is crucial across industries like financial services, e-commerce, and healthcare, where the infrastructure must support high-throughput operations and low-latency decision-making. Unified platforms that manage feature storage, vector search, and model serving enhance performance by reducing network latency and operational complexity. While AI fraud detection offers proactive pattern learning and real-time decision-making, it necessitates robust infrastructure capable of delivering ultra-low latency to meet stringent service level agreements.