Liability claims investigation, fully automated
General liability fraud relies on manufactured incidents, professional claimants, and pre-existing condition exploitation. Mapping networks and validating scenes is slow manually. Hesper AI runs a full investigation on every flagged claim in hours.
Common liability fraud schemes
Liability fraud spans individual exaggeration to organized professional claimants operating across multiple venues. Hesper investigates every flagged claim across all four schemes simultaneously.
Slip-and-fall staging
Manufactured incidents in retail locations, restaurants, and commercial properties. Hesper AI analyzes 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 filing repeated liability claims across different businesses and locations. Hesper AI maps claimant identities across ISO ClaimSearch and NICB, 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.
Manual workflow vs. Hesper
Every phase compresses. The cumulative effect is the difference between a multi-week cycle and a same-day decision.
How Hesper AI investigates a liability claim
Every liability claim runs through a structured investigation pipeline. Phases run in parallel where dependencies allow.
Incident scene and document ingest
~5 minIncident photos, accident reports, witness statements, medical records, and wage claim documentation are ingested simultaneously. Hesper analyzes each file across 200+ fraud signals at the pixel and text level.
Claimant history and identity check
~7 minClaimant identities are verified against ISO ClaimSearch, NICB, and public records. Prior claims history, professional-claimant patterns, and connections across multiple incidents are flagged with citations.
Witness and geographic analysis
~6 minNamed witnesses are verified against public records and prior claim databases. Incident locations are mapped against known fraud clusters - repeat venues, shared addresses, and geographic ring indicators.
Medical and wage claim validation
~12 minMedical bills are analyzed for altered amounts, pre-existing conditions, and treatment patterns inconsistent with the reported incident. Wage claims are cross-referenced against employment records and published salary data.
Investigation report and recommendation
~11 minA complete investigation report is generated with scene findings, identity graph, medical analysis, and a denial or settlement recommendation. Output is SIU-ready and defensible in litigation.
Annual general liability fraud in the US
Across slip-and-fall, premises, and bodily injury
Higher professional claimant rate in dense urban clusters
Vs. general claim population
Hesper AI investigation time per claim
Vs. 14+ days manually
Fraud signals analyzed per document
Pixel-level plus text-level
Go deeper on liability claims fraud
Research, technical deep-dives, and playbooks from the Hesper AI team.
See Hesper investigate your liability claims
We'll run a sample investigation on your real flagged claims and show you the evidence package and report it produces.