PACKAGED OFFER · FRAUD DETECTION ACCELERATOR
Fraud detection model development and evaluation
Fraud clears in seconds. Your review queue takes days. The Fraud Detection Accelerator stands up a real-time scoring model on your own transaction history, tuned to the tradeoff that matters to you: catch more genuine fraud without burying analysts in false alarms. In eight weeks you go from historical data to a deployed model scoring transactions live, with explainability on every flag and thresholds you control.
- A scoring model trained on your transaction history and known-fraud labels
- Real-time scoring with explainability on every alert, evaluated on precision and recall
- A threshold console so you tune caught fraud against false positives to your risk appetite
A working detection capability, not accuracy theater.
The accelerator is evaluated on precision and recall, the metrics that hold up on imbalanced data, rather than a headline accuracy number that hides missed fraud.
Six concrete deliverables land by week eight, each built to run against live traffic under your control.
Scoring model
Trained on your transaction history and known-fraud labels, tuned to the tradeoff you care about.
Real-time scoring
Flags transactions as they happen, not after settlement, so a decision is possible in the moment.
Explainability on every alert
Analysts see why a transaction scored high and can act on it, rather than trusting a black box.
Threshold and tradeoff console
Tune the balance of caught fraud against false positives to your own risk appetite.
Evaluation that fits the data
Reported on precision, recall, and AUC-PR per use case, the metrics that matter for imbalanced fraud.
Integration and monitoring plan
Wired into your review workflow, with a drift-monitoring plan as fraud patterns evolve.
Static rules miss evolving fraud and flag good customers. A tuned model catches more with fewer false alarms.
Risk, fraud, and payments teams losing ground to static rules.
For teams seeing losses slip through rules that fraudsters have learned to dodge, and analysts spending their days clearing false positives instead of chasing real threats.
Banks, payment providers, lenders, and marketplaces with transaction history and a labeled fraud signal have exactly what the accelerator needs to prove value fast. It is the entry point to our full fraud detection practice and the wider financial-services capability.
Five phases, from historical data to live scoring.
Frame
Define fraud types, labels, and the precision-recall target, producing an agreed detection objective.
Engineer
Feature engineering on transaction and behavioral data, producing the signals the model learns from.
Model
Train, evaluate, and tune against imbalanced-data metrics, producing a scored, defensible model.
Deploy
Real-time scoring, explainability, and the threshold console, producing a controllable live capability.
Prove
Live scoring, workflow integration, and a monitoring plan, producing a running detection capability.
What real-time scoring changes.
Share your labeled history. Eight weeks later, score fraud in real time.
A fraud model in production has to be retrained, monitored, and governed. When you are ready to keep detection ahead of the fraud it faces, we extend the accelerator through our MLOps practice, so nothing is relearned.