Use Cases / Pet Claims

Pet claims investigation, fully automated

Pet insurance is the fastest-growing P&C niche - and a soft target for vet-bill upcoding, pre-existing condition concealment, treatment-plan inflation, and pet-identity fraud. Hesper AI runs a full investigation on every flagged pet claim in hours.

Investigation in progress

PET-2026-118204

Canine TPLO surgery · $8,400 exposure

Running

12m elapsed

Progress20%

Investigation phases

✓

Vet invoice and procedure-code analysis5m

Pre-existing condition record check

Treatment necessity vs breed/age norms

Pet identity & enrollment verification

Provider pattern & ring scan

Evidence gathered2 items

PolicyCoverage active since 2024-02-15

InvoiceProcedure code upcoded: TPLO billed, partial repair documented⚠

Risk score

Low signal

28

/ 100

[01] Fraud patterns

Common pet insurance fraud schemes

Pet fraud spans individual exaggeration to organized clinic-side billing schemes. Hesper investigates every flagged claim across all four schemes simultaneously.

Est. loss impact

~$130M

Vet bill upcoding & duplicate billing

Procedures billed at higher complexity than performed, duplicate line items across claim submissions, and phantom services. Hesper AI maps procedure codes against documented treatment notes and detects altered invoice line items at the pixel level.

How Hesper detects it

Procedure-code vs notes matchLine-item alteration detectionDuplicate billing across claimsProvider benchmarking

Est. loss impact

~$110M

Pre-existing condition concealment

Owners enroll pets with known conditions and claim for them as if newly diagnosed. Hesper AI pulls and cross-references prior vet records, OSINT signals, and registry data to surface symptoms or treatments that predate policy effective dates.

How Hesper detects it

Multi-clinic record syncSymptom timeline reconstructionSocial-media OSINTEnrollment-vs-history gap

Est. loss impact

~$70M

Treatment-plan inflation

Treatment durations, medication courses, and rehab protocols inflated beyond accepted veterinary standards. Hesper AI benchmarks prescribed treatment against ACVS, ACVIM, and AAHA guidelines for the breed, age, and condition.

How Hesper detects it

ACVS/ACVIM benchmark checkBreed/age-normed protocolsMedication-dosage validationRehab-duration analysis

Est. loss impact

~$40M

Pet identity & breed misrepresentation

Wrong pet covered under the policy, or breed altered at enrollment to dodge premium loading on breed-specific conditions. Hesper AI verifies microchip IDs across enrollment, claim, and clinic records, and validates breed via DNA registry where available.

How Hesper detects it

Microchip cross-verificationPhoto identity matchBreed registry lookupEnrollment-doc forensics

[02] Timeline compression

Manual workflow vs. Hesper

Every phase compresses. The cumulative effect is the difference between a two-week medical-record chase and a same-day decision.

Investigation phase

Manual workflow

Hesper AI

Vet invoice forensics

Adjuster + SIU review, 2-4 days

Automated, 5 min

Pre-existing record pull

Multi-clinic records request, 5-7 days

API correlation, 8 min

Treatment validation

Adjuster + vet consult, 3-5 days

AI benchmark, 9 min

Pet identity check

Microchip and registry pulls, 1-2 days

Automated, 6 min

Report assembly

Adjuster write-up, 4-8 hrs

Auto-generated, 0 min

Total time

10-14 days

hours

[03] Investigation flow

How Hesper AI investigates a pet claim

Every pet claim runs through a structured investigation pipeline. Phases run in parallel where dependencies allow.

Vet invoice and medical record ingest

~5 min

Vet invoices, SOAP notes, lab reports, and imaging are analyzed across 200+ fraud signals. Every document is checked for line-item alteration, font rendering anomalies, and procedure-code mismatches against the documented treatment narrative.

Pixel-level invoice forensicsProcedure-code vs SOAP matchLab-report integrity checkEXIF analysis on imaging

Pre-existing condition forensic check

~8 min

Prior vet history is pulled across clinics and cross-referenced with enrollment disclosures. The system reconstructs the symptom timeline and flags any treatment, diagnosis, or owner-reported issue that predates the policy effective date.

Multi-clinic record pullEnrollment-disclosure compareSymptom timeline buildOSINT cross-check

Treatment necessity vs veterinary norms

~9 min

Prescribed treatment is benchmarked against breed, age, and condition-specific guidelines from ACVS, ACVIM, and AAHA. Medication courses, anesthesia protocols, and rehab durations are validated against documented standards.

ACVS/ACVIM benchmarksBreed/age normalizationMedication-dosage checkProcedure-necessity audit

Pet identity and enrollment verification

~6 min

Microchip IDs, photos, and identifying marks are cross-referenced across the enrollment document, claim submission, and clinic records. Breed claims are checked against DNA registry data where available and against the enrollment photo.

Microchip ID matchPhoto biometric matchBreed registry verificationEnrollment-form forensics

Provider pattern analysis and report

~9 min

The treating clinic and any referrals are scored against carrier flagged-provider lists and regional billing-pattern norms. A complete investigation report is generated with evidence packages, timeline, and payment recommendation.

Provider scoringRegional billing benchmarkRing graph analysisSIU-ready output

[04] By the numbers

$0M+

Annual US pet insurance fraud losses

~10% of paid claims, NAPHIA-aligned

Pet insurance market YoY growth

Fastest-growing P&C niche

Hours

Hesper AI investigation time per claim

Vs. 1-2 weeks manually

Fraud signals per document analyzed

Pixel-level plus text-level