Fraud & Anomaly Detection for E-commerce

Rules encode known fraud patterns precisely; models catch the novel ones. Every system worth running uses both, and we are explicit about which is doing what.

In e-commerce, every change must be justified by a controlled experiment against revenue. That single fact reshapes how fraud & anomaly detection has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is search relevance, usually integrated against payment gateways. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.

Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.