AI Fraud Detection
Flags anomalous transactions and behavior patterns for investigation, without freezing legitimate activity.
What breaks without this
- Rule-based fraud detection generates high false-positive volumes that overwhelm investigation teams.
- Genuinely novel fraud patterns evade static rule sets until rules are manually updated.
- Investigators need a clear rationale for a flag, not just a risk score, to act efficiently.
Scores transactions, claims and account activity for anomaly risk in real time, prioritizing the results for investigator attention and explaining the contributing factors behind each flag — built to reduce investigation backlog without indiscriminately blocking legitimate activity.
What it does
Real-time anomaly scoring across transactions, claims or account activity.
Pattern detection that adapts to emerging fraud typologies, not only static rule sets.
Explainable flags showing the specific factors contributing to a risk score.
Investigator prioritization queues ranked by risk and potential exposure.
Feedback loop where investigator outcomes retrain and refine the scoring model.
From request to result
Score
Each transaction or activity event is scored for anomaly risk in real time.
Explain
High-risk flags are returned with the contributing factors, not a bare score.
Prioritize
Flags are queued for investigators ranked by risk and exposure.
Refine
Investigator dispositions feed back into the model to reduce future false positives.
Built to be audited, not just used
- The system flags for investigation; it does not autonomously block or reverse a transaction beyond explicitly configured thresholds.
- Explainability output supports fair-treatment and regulatory-inquiry requirements in scored decisions.
Fits existing infrastructure
Outcomes to expect
Qualitative, directional outcomes. We do not publish unverified performance figures — see the case studies section for engagement-specific, authorized results.
- Investigator attention concentrates on higher-risk, better-explained cases.
- Emerging fraud patterns are detected without waiting for a manual rule update.
- False-positive volume decreases as the model incorporates investigator feedback.
Works alongside
AI Analytics
Answers business questions in natural language against live operational and financial data.
Security & ComplianceAI Compliance Co-Pilot
Maps obligations to controls, tracks evidence, and flags gaps ahead of an audit or review.
Security & ComplianceCybersecurity Triage Agent
Triages security alerts, correlates signals across tools, and prepares incident context for analysts.
Evaluate AI Fraud Detection for your environment
Talk to an architect about integration into your existing stack, or request a scoped demo against a representative use case.