Optimize Insurance Claims Analysis with AI

Common Fraud Indicators in Claims Data

Claimant Red Flags

Multiple claims within short timeframes, especially across different insurers. Claim history showing pattern of losses just before policy cancellation. Lack of corroborating evidence or inconsistent statements. New policy with immediate claim. Uncooperative with investigation or overly familiar with claims process.

Provider and Vendor Red Flags

Unusually high volume of claims from specific providers, repair shops, or medical facilities. Charges significantly above market rates. Consistent patterns of specific diagnoses or procedures. Networks of related providers, attorneys, and claimants appearing together across multiple claims.

Claim Characteristic Red Flags

Losses occurring shortly after policy changes that increase coverage. Suspicious timing—claims filed just before weekends, holidays, or policy expiration. Damage inconsistent with reported cause of loss. Lack of police reports or witness statements when expected. Padded or inflated estimates.

Network Analysis

Fraud rings often involve repeated connections between claimants, providers, attorneys, and witnesses. Network analysis reveals these relationships, identifying clusters of potentially fraudulent activity. Track social connections, shared addresses, phone numbers, or bank accounts across claims.

Anomaly Detection

Statistical outliers deserve investigation. Claims with characteristics significantly different from typical patterns—unusually high amounts, uncommon loss types, atypical claimant demographics for the policy type, or geographical anomalies. Machine learning excels at identifying subtle anomalies human reviewers miss.

Essential Insurance Claims Metrics

Loss Ratio

Incurred losses divided by earned premiums, expressed as a percentage. The fundamental profitability metric for insurance operations. Track by line of business, geography, policy type, and time period. Target loss ratios vary by line but typically range from 60-75% for profitable operations.

Claim Frequency and Severity

Frequency measures claims per exposure unit (e.g., claims per 100 insured vehicles). Severity measures average cost per claim. Both drive overall losses, but respond to different interventions. Frequency reduction targets loss prevention; severity reduction focuses on claim management.

Cycle Time

Days from loss date or report date to claim closure. Fast cycle time improves customer satisfaction and reduces administrative costs. Track separately for different claim types as complexity varies. Monitor for process bottlenecks that delay settlement.

Closure Rate

Percentage of claims closed within specific timeframes (30, 60, 90 days). Indicates processing efficiency and adjuster productivity. Low closure rates suggest capacity issues, complex claims mix, or process problems requiring attention.

Reserve Adequacy

Compare initial case reserves to final settlement amounts. Consistent under-reserving indicates reserve setting models need recalibration. Over-reserving ties up excess capital. Track reserve development patterns to improve accuracy and reduce volatility.

Litigation Rate

Percentage of claims involving attorney representation or litigation. Litigated claims cost 2-5x more than similar non-litigated claims. Identify factors driving litigation and implement early intervention strategies to reduce litigation rates.