CLARA Analytics Uncovers Early Insurance Fraud Using AI and Cohort Modeling

CLARA Analytics has released a breakthrough study demonstrating that advanced AI methods can identify potential fraud in property and casualty insurance claims just two weeks after filing — significantly faster than traditional detection methods. The research, completed in November 2024, analyzed 2,867 claims from 2020 to 2024 using unsupervised machine learning and revealed actionable insights that could save the industry billions annually.

1. A New Standard in Fraud Detection Timelines

• Early Identification: Fraud indicators surfaced as early as two weeks after a claim’s first notice of loss.
• Outpacing Traditional Methods: The AI model’s predictions closely matched real Special Investigation Unit (SIU) referrals — but weeks earlier.
• Broad Claim Analysis: 2,867 claims were analyzed over a four-year period using unsupervised learning techniques.

2. AI-Driven Insights and Cohort Modeling

• No Predefined Rules Needed: Unlike traditional models that rely on known fraud markers, CLARA’s system detects novel fraud patterns.
• Cohort Modeling: Grouping claims by development periods helped identify outliers in cost and treatment.
• Network Mapping: Identified hidden connections between attorneys and providers, often missed by standard methods.

3. Geographic and Statistical Findings

• Fraud-Prone States: Michigan and Arizona reported the highest percentages of fraud indicators.
• High-Risk Claims: 9% of open claims were flagged as high potential for SIU referral.
• Sentinel Effect in Action: Insurers with strong fraud detection reputations saw less targeting from fraudsters.

4. Human + Machine = Next-Gen Prevention

• AI-Augmented Reasoning: CLARA’s platform combines machine learning with agentic reasoning to interpret complex claim dynamics.
• Enhanced Decision-Making: AI-generated intelligence supports faster, more informed claims handling.
• Preventive Power: Effective fraud detection systems dissuade fraudulent behavior before it begins.

5. Industry Impact and Future Outlook

• $40 Billion Annual Problem: According to the FBI, insurance fraud (excluding medical) costs the industry $40 billion per year.
• Policyholder Relief: Better fraud detection could help curb rising premiums.
• Ongoing Development: CLARA is expanding its network analysis with medical and legal datasets to further enhance fraud detection precision.

CLARA Analytics is redefining how the insurance industry approaches fraud. By leveraging unsupervised machine learning and cohort modeling, the company has proven that insurers can detect fraud much earlier than previously possible. The result is a powerful blend of AI innovation and industry expertise — offering faster detection, reduced losses, and a new benchmark in claims intelligence. With continued advancements in AI and data integration, CLARA’s model could become a cornerstone in future-focused fraud prevention strategies across the insurance sector.