A model learns what normal looks like in your data — amounts, timing, counterparties, devices, sequences — and scores each transaction as it arrives. Anything above your threshold goes to a review queue with the reasons that triggered it and the comparable history a reviewer needs. Every decision a reviewer makes feeds back, so the model keeps learning from the calls your own team makes.
A threshold written when the business was smaller now flags half of the legitimate large orders and misses the pattern that actually costs money. Nobody dares change it because nobody knows what it protects.
The queue holds more than the team can look at, so the bottom half is approved unseen. Every genuine case sitting in that half is a loss nobody will ever attribute.
An over-tuned system declines good transactions, and each decline is a lost sale plus a support ticket plus a customer who tries a competitor next time.
Confirmed fraud, chargebacks, disputes, and cases that turned out to be legitimate. Fraud is rare, so how the positives are labelled matters more here than in almost any other model.
Deviation from the customer’s own pattern, velocity, device and location changes, counterparty history, time of day, sequence of actions. Absolute amount alone is the weakest signal in the set.
A model catches unfamiliar patterns; explicit rules catch known ones and anything a regulator requires. Rules stay editable by your team, and the two layers are reported on separately.
The trade-off between catching more and reviewing more is made explicitly, with the precision-recall curve in front of you. We set the operating point where your team can actually work the queue.
Reviewer decisions are recorded and become training data. Model performance and reviewer agreement are reported monthly, and retraining happens on a schedule rather than after an incident.
We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.
That is a dial, not a fact. We show you the precision-recall curve and set the threshold where your review capacity is, then report both numbers monthly so you can move it deliberately.
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