Use Cases / Liability Claims
Liability claims investigation, fully automated
General liability fraud costs $15 billion annually. Staged incidents, professional claimants, and exaggerated damages are difficult to investigate at scale. Hesper AI runs a full investigation on every liability claim in under 60 minutes.
[01] Fraud Patterns
Common liability claims fraud schemes
Liability fraud ranges from opportunistic exaggeration of real incidents to professionally organized schemes involving serial claimants and coordinated witness networks.
Slip-and-fall staging
Manufactured incidents in retail locations, restaurants, and commercial properties. Hesper AI analyzes incident scene photos for staging indicators, cross-references the claimant's history for prior similar claims, and verifies witness identities against known fraud ring databases.
Professional claimants
Serial fraudsters who file repeated liability claims across different businesses and locations. Hesper AI maps claimant identities across ISO ClaimSearch and NICB databases, detects patterns of similar claims filed within geographic clusters, and identifies shared attorneys across cases.
Exaggerated damages
Legitimate incidents with inflated medical bills, fabricated lost wages, or overstated property damage. Hesper AI detects altered amounts in medical billing documents, validates treatment timelines against injury severity, and cross-references wage claims with employment records.
Pre-existing condition exploitation
Attributing pre-existing injuries or conditions to a covered incident. Hesper AI analyzes medical record timelines to detect treatment patterns that predate the incident, identifies inconsistencies between current and historical medical documentation, and flags providers with unusual billing patterns.
[02] Investigation Flow
How Hesper AI investigates liability claims
Every liability claim runs through a structured investigation pipeline - from document forensics and claimant history analysis through network detection to resolution.
Document ingestion and forensic analysis
Incident reports, surveillance footage stills, medical records, billing statements, witness statements, and property damage assessments are ingested and analyzed across 200+ fraud signals. Pixel-level forensics detect altered dates, amounts, and fabricated documentation.
Claimant history and identity verification
Claimant identities are verified and cross-referenced against NICB, ISO ClaimSearch, and litigation databases. Prior claims history, lawsuit records, and connections to known fraud schemes are surfaced automatically.
Medical and financial validation
Medical records are analyzed for pre-existing conditions, treatment patterns that predate the incident, and billing anomalies. Wage loss claims are cross-referenced with employment databases and tax records where available.
Network and ring detection
Claims are mapped against claimant networks to identify shared attorneys, medical providers, and witnesses across multiple claims. Hesper AI detects organized rings where the same group files coordinated claims across different locations and businesses.
Investigation report and resolution
A complete investigation report is generated with claimant history, medical timeline analysis, network diagrams, and evidence citations. Denial justifications, subrogation recommendations, and law enforcement referral packages are prepared automatically.
[03] By the Numbers
Annual general liability fraud in the US
Of liability claims involve exaggeration
Hesper AI investigation time per claim
More claims investigated vs manual SIU
See a liability claim investigation in action
Book a 30-minute demo and watch Hesper AI investigate a real liability claim from intake to audit-ready report.
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