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 methods from particle physics 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.
The result was a detector that understood context. The model learned that "Customer A buying a TV in Tokyo" might be weird, but "Customer A buying a TV in Tokyo two hours after buying a coffee at Narita Airport" is actually perfectly normal. Context is everything.