About the service
I build machine learning systems that catch fraudulent transactions and anomalies as they happen, not in a report the next day that nobody opens. The work starts with your own history: I study the data, engineer features that truly separate honest behavior from fraud, and train models on patterns specific to your business. Speed checks, device signals and geolocation, amount distributions, time-of-day rhythms and graph connections between accounts become model inputs, so the final score accounts for context, not just one suspicious field.
Pure black-box fraud detection in payments is almost never enough, so I provide a hybrid: deterministic rules handle cases you must block by policy, and the ML layer tackles the fuzzy, constantly-changing fraud that rules don't anticipate. All this runs via a low-latency scoring API: you call it right at authorization and get a risk score and brief explanation of why the transaction is flagged. Alongside it I deploy a review dashboard where analysts process the case queue, confirm or reject them, and these decisions feed straight into the next retraining cycle.
What clients value most is tuning. A model that blocks everything is useless, so I work with you to balance precision and recall, set thresholds based on the real cost of missed fraud versus false declines, and eliminate false positives that quietly drive away good customers. After launch I watch for drift, retrain on schedule and hand over clear documentation so the system keeps working well long after start.
— Feature engineering and labeling on your transaction history
— Hybrid scoring 'rules + ML' matched to your risk appetite
— Scoring API and review dashboard for analysts
Pure black-box fraud detection in payments is almost never enough, so I provide a hybrid: deterministic rules handle cases you must block by policy, and the ML layer tackles the fuzzy, constantly-changing fraud that rules don't anticipate. All this runs via a low-latency scoring API: you call it right at authorization and get a risk score and brief explanation of why the transaction is flagged. Alongside it I deploy a review dashboard where analysts process the case queue, confirm or reject them, and these decisions feed straight into the next retraining cycle.
What clients value most is tuning. A model that blocks everything is useless, so I work with you to balance precision and recall, set thresholds based on the real cost of missed fraud versus false declines, and eliminate false positives that quietly drive away good customers. After launch I watch for drift, retrain on schedule and hand over clear documentation so the system keeps working well long after start.
— Feature engineering and labeling on your transaction history
— Hybrid scoring 'rules + ML' matched to your risk appetite
— Scoring API and review dashboard for analysts
