Unless fraudsters are stopped in their tracks, carriers eat the cost of fraudulent claims, and premiums rise for insurance customers. (In the US, that’s a median annual increase of $400 to $700 per household, according to the FBI.) It’s also become clear that conventional fraud detection methods like if-this-then-that scoring won’t be enough to keep insurers ahead of scam artists.
Several factors complicate fraud prevention and detection: First, there’s the sheer volume of claims and their associated data. The delicate balance of maintaining up-to-date security protocols while staying compliant with data privacy laws also contributes to the difficulty of stopping fraud.
Above all, though, it’s a matter of technology: The classic rules-based fraud detection systems many insurers still use can’t keep up with new fraud tactics. Also, carriers still using modern legacy core systems can’t easily integrate with the AI and machine learning anti-fraud solutions that could help them.
The only way to address these concerns is with truly modern fraud detection tools powered by AI and machine learning (ML). These solutions aren’t limited to the strict parameters of outdated fraud detection tools: ML models can detect patterns indicative of fraud in real time, regardless of an insurer’s claim volume, and as they ingest more data from multiple sources, their analytical capabilities improve.
ClaimSmart™ by EIS includes the powerful ClaimGuard™ decision engine to detect and stop fraud — analyzing claims based on data from hundreds of evolving scenarios.
ClaimGuard analyzes and risk-scores all claims, routing suspicious claims to fraud investigators, and sending low-risk claims to standard, straight-through processing.
Real-time automated monitoring continues for every claim when new data affects the original risk score.
By accelerating the examination of routine claims and keeping fraud investigators focused on high-risk filings, ClaimGuard helps improve operational efficiency while fighting fraud — saving you money.
ClaimSmart correctly identifies high-risk cases with only a minuscule rate of false negatives: For one insurer, 87% of high-risk cases were confirmed as fraudulent, while only 0.03% of low-risk cases were incorrectly scored.
ClaimSmart can be deployed alongside EIS OneSuite™ and its core products — or seamlessly work alongside existing claims management and policy administration systems. It also integrates with internal or external third-party data sources. This way, insurers can leverage AI and ML to find signs of fraud, even in the most far-flung places.
A: EIS uses governed AI and analytics to flag suspicious patterns early and continuously so investigators focus on the right claims and activities.
A: The differentiator is fraud detection that is core-connected and workflow-driven, so insights translate into controlled actions instead of orphaned alerts.
A: Yes—EIS is designed to evolve fraud detection through machine learning and pattern detection.
A: Fraud detection can analyze structured and behavioral signals across the insurance lifecycle to identify anomalies and suspicious patterns.
A: Fraud detection can be implemented quickly because EIS supports modular rollout with reusable components and integration-ready architecture.
A: Yes—fraud detection improves efficiency by reducing manual review, cutting false positives, and accelerating correct routing and resolution.
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