Insurance AI Agents & Video

Insurance Video Use Cases

Claims Processing

• Damage assessment: Automatically evaluate vehicle/property damage severity
• Liability determination: Analyze accident footage to assess fault
• Documentation verification: Ensure claim documentation is complete

Fraud Detection

• Staged accidents: Detect patterns consistent with fraud
• Exaggerated damage: Compare damage to incident description
• Identity verification: Confirm claimant identity from video

Underwriting

• Property assessment: Evaluate property condition from video
• Risk identification: Spot hazards and risk factors

Claims Processing Agent

class ClaimsAgent:
    async def process_claim(self, claim_id: str, videos: List[str]) -> ClaimAnalysis:
        # 1. Enhance all claim videos
        enhanced_videos = await asyncio.gather(*[
            bettervideo.enhance(v) for v in videos
        ])

        # 2. Extract relevant frames
        frames = []
        for video in enhanced_videos:
            frames.extend(extract_damage_frames(video))

        # 3. Analyze damage
        damage_analysis = await self.analyze_damage(frames)

        # 4. Estimate severity and cost
        estimate = await self.estimate_repair_cost(damage_analysis)

        # 5. Check for fraud indicators
        fraud_score = await self.fraud_check(
            videos=enhanced_videos,
            damage=damage_analysis,
            claim_details=get_claim(claim_id)
        )

        return ClaimAnalysis(
            damage=damage_analysis,
            estimate=estimate,
            fraud_score=fraud_score,
            recommendation=self.get_recommendation(estimate, fraud_score)
        )

Fraud Detection Patterns

AI agents can detect fraud patterns humans might miss:

Video Analysis Signals

• Pre-existing damage: Damage visible before incident timestamp
• Inconsistent lighting: Suggests edited or composite video
• Metadata anomalies: GPS, timestamp, or device inconsistencies
• Staged behavior: Unusual positioning or movement patterns

Cross-Reference Signals

• Repeat claimants: Same faces in multiple claims
• Repeat vehicles: Same vehicle in different claims
• Location patterns: Claims clustered in fraud-prone areas

async def fraud_check(self, video: str, claim: Claim) -> FraudScore:
    # Extract faces and vehicles
    faces = await self.extract_faces(video)
    vehicles = await self.extract_vehicles(video)

    # Check against fraud database
    face_matches = await self.fraud_db.search_faces(faces)
    vehicle_matches = await self.fraud_db.search_vehicles(vehicles)

    # Analyze video authenticity
    authenticity = await self.verify_authenticity(video)

    # Check claim consistency
    consistency = await self.check_consistency(video, claim)

    return FraudScore(
        face_matches=face_matches,
        vehicle_matches=vehicle_matches,
        authenticity=authenticity,
        consistency=consistency
    )

Privacy Considerations

Claims video contains sensitive personal information:

• Claimant faces and identities
• Medical information (injury videos)
• Property interiors
• License plates and addresses

Use privacy-first processing:

• Zero-retention: Don't store video beyond processing
• No training: Never train on claimant data
• Access controls: Strict need-to-know access
• Audit trails: Log all video access

BetterVideo provides these guarantees by default.

Integration Architecture

┌─────────────────────────────────────────────────┐
│             Claims Management System             │
│                   (Guidewire, etc.)              │
├─────────────────────────────────────────────────┤
│                Claims API                        │
├─────────────────────────────────────────────────┤
│            ┌───────────────────┐                │
│            │   Claims Agent    │                │
│            └─────────┬─────────┘                │
│     ┌───────────────┴───────────────┐           │
│     ↓                               ↓           │
│ ┌─────────┐                   ┌─────────┐       │
│ │BetterVid│ → Enhanced → ┌───│ Vision  │       │
│ │   API   │    Video     │   │ Model   │       │
│ └─────────┘              │   └─────────┘       │
│                          ↓                      │
│                    ┌─────────┐                  │
│                    │  LLM    │                  │
│                    └────┬────┘                  │
│                         ↓                       │
│            ┌────────────────────┐               │
│            │ Damage Assessment  │               │
│            │ Fraud Score        │               │
│            │ Recommendation     │               │
│            └────────────────────┘               │
└─────────────────────────────────────────────────┘