Solutions
AI Fraud & Anomaly Detection
Rule-based checks miss new fraud patterns and bury analysts in false positives.
In one paragraph
AI fraud and anomaly detection combines supervised models trained on confirmed cases with unsupervised methods that flag unusual behaviour in transactions, accounts, logins, claims or sensor data. Each alert carries a score and the reasons behind it so analysts can prioritise. AIYatch builds real-time or batch pipelines, integrates with case-management tools and monitors precision and drift after launch.
What you get
- New and evolving fraud patterns caught earlier
- Fewer false positives and less analyst fatigue
- Explainable alerts that support investigations and audits
- Continuous monitoring and retraining as behaviour shifts
Services involved
Frequently asked
Labels help, but they are not required to begin. Unsupervised anomaly detection surfaces unusual activity from day one, and analyst feedback on those alerts builds a labelled dataset that improves the supervised model over time. Existing rules are kept and combined with model scores rather than discarded.
Yes. We deploy models behind low-latency APIs or streaming pipelines so scores are available within the transaction flow. Batch scoring is used for reviews and reporting. Thresholds and actions are configured with your risk team, and every score is logged with its inputs and reasons for later audit.
Related work
Outcomes, not demos.
Ready to chart your AI course?
A free 30-minute consultation: we listen, ask hard questions and tell you honestly where AI will and won't help.