Use Cases / Liability Claims

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 2-4 hours.

Investigation in progress

LIAB-2026-071289

Premises slip-and-fall · $34,200 exposure

Running

12m elapsed

Progress20%

Investigation phases

✓

Incident scene photo forensics5m

Claimant history cross-reference

Witness identity verification

Medical & wage claim validation

Geographic cluster & ring analysis

Evidence gathered2 items

PolicyGL coverage active since 2024-06-01

ScenePhoto metadata predates reported incident by 11 days⚠

Risk score

Low signal

28

/ 100

[01] Fraud patterns

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.

Est. loss impact

~$3B

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.

How Hesper detects it

Scene photo analysisPrior claim historyWitness verificationStaging indicators

Est. loss impact

~$1.8B

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.

How Hesper detects it

Cross-database matchingGeographic clusterAttorney networkID pattern analysis

Est. loss impact

~$2.2B

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.

How Hesper detects it

Medical bill forensicsWage claim validationTreatment timelineEmployment records

Est. loss impact

~$900M

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.

How Hesper detects it

Medical history analysisPre-incident detectionProvider billing patternsDiagnosis alignment

[02] Timeline compression

Manual workflow vs. Hesper

Every phase compresses. The cumulative effect is the difference between a multi-week cycle and a same-day decision.

Investigation phase

Manual workflow

Hesper AI

Scene forensics

Investigator + photographer, 2-3 days

Automated, 5 min

Claimant history review

ISO ClaimSearch manual, 1 day

Auto cross-ref, 7 min

Witness verification

Investigator interviews, 2-5 days

Public records match, 6 min

Medical / wage validation

Peer review + verification, 3-7 days

AI analysis, 12 min

Report assembly

Investigator write-up, 4-8 hrs

Auto-generated, 0 min

Total time

14-21 days

2-4 hours

[03] Investigation flow

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 min

Incident 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.

Pixel forensicsMetadata analysisWitness statement parsingDocument authenticity

Claimant history and identity check

~7 min

Claimant 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.

ISO ClaimSearchNICB lookupPrior claim historyIdentity graph

Witness and geographic analysis

~6 min

Named 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.

Witness verificationGeographic clusterVenue patternRing proximity

Medical and wage claim validation

~12 min

Medical 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.

Bill forensicsPre-existing detectionWage validationEmployment match

Investigation report and recommendation

~11 min

A 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.

Scene findingsIdentity graphMedical analysisLitigation-ready output

[04] By the numbers

$0B+

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

2-4h

Hesper AI investigation time per claim

Vs. 14+ days manually

Fraud signals analyzed per document

Pixel-level plus text-level