Financial fraud detection and high-energy particle physics share a surprising amount of DNA. In both domains, you are looking for a "needle in a haystack", an incredibly rare, high-value event hidden in a background of overwhelming noise. In physics, it's the Higgs Boson. In banking, it's a compromised credit card.
Traditional banking fraud systems are rule-based. "If transaction > $10,000 AND location = foreign, then FLAG." This works for 1990s fraud, but it fails against modern, sophisticated attacks. It also generates massive amounts of false positives, which annoy customers and burn out analysts.
The Markov Chain Approach
We took a different approach. Instead of looking at individual transactions in isolation, we modeled customer behavior as a state machine. We borrowed Markov Chain algorithms used in particle detectors to define the "probability" of a sequence of events.
An innocent customer has a predictable trajectory through state space (Coffee Shop -> Office -> Gym). A compromised account has a much more erratic, high-entropy trajectory. By calculating the "likelihood" of the path rather than the value of the transaction, we built a much more robust detector.
Vectorizing the Graph
To run this in real-time (sub-50ms), we couldn't just query a SQL database. we had to vectorize the transaction graph. We used Graph Neural Networks (GNNs) to create embeddings for every node (customer/merchant). This allowed us to calculate "distance" in high-dimensional space instantly.
The result? A 40% reduction in false positives. The model learned that "Customer A buying a TV in Tokyo" might be weird, but "Customer A buying a TV in Tokyo 10 minutes after buying a coffee at Narita Airport" is actually perfectly normal. Context is everything.