Proveniq ClaimsIQ

The Claims Problem

Insurance claims processing is broken:

• 10-15% leakage from overpayment, fraud, and inefficiency
• 30+ days average claim resolution time
• Adversarial relationships between insurers and policyholders
• Manual processes that don't scale

ClaimsIQ transforms this with data-driven adjudication.

How It Works

Traditional vs. ClaimsIQ

The ClaimsIQ Pipeline

┌─────────┐    ┌─────────┐    ┌─────────┐    ┌─────────┐
│  CLAIM  │───▶│ VERIFY  │───▶│  SCORE  │───▶│ RESOLVE │
│ INTAKE  │    │ HISTORY │    │  RISK   │    │  CLAIM  │
└─────────┘    └─────────┘    └─────────┘    └─────────┘
     │              │              │              │
     ▼              ▼              ▼              ▼
  Capture       Pull from      ML fraud      Auto-approve
  details       Ledger         detection     or escalate

Core Capabilities

1. Instant Verification

When a claim is filed:

• Pull complete provenance from Ledger
• Verify ownership at time of loss
• Confirm item specifications match policy

2. Fraud Scoring

Every claim receives a risk score based on:

• Claimant History — Prior claims, patterns, red flags
• Item Profile — Verification status, value trajectory
• Circumstance Analysis — Loss type, timing, location
• Network Effects — Connections to known fraud rings

3. Automated Adjudication

Low-risk claims are auto-approved:

• Score below threshold → Instant approval
• Score above threshold → Human review queue
• Suspicious patterns → Investigation referral

4. Valuation Engine

Fair market value determined by:

• Recent comparable sales
• Condition at last verification
• Depreciation models
• Market trend adjustments

Integration Points

Insurer Integration

ClaimsIQ connects to carrier systems via:

• API Integration — Direct system-to-system
• Portal Access — Web interface for adjusters
• Batch Processing — Bulk claim handling

Ecosystem Data

ClaimsIQ leverages:

• Ledger — Provenance and verification history
• Home — Policy details and asset registry
• Locker — Physical verification data
• SmartTag — Location and condition telemetry

Business Impact

For Insurers

For Policyholders

• Faster claim resolution
• Fair, transparent valuations
• Reduced documentation burden
• Better coverage accuracy

Fraud Prevention

Detection Methods

1. Anomaly Detection — Statistical outliers in claim patterns
2. Image Analysis — AI detection of manipulated photos
3. Velocity Checks — Unusual claim frequency
4. Network Analysis — Connections between claimants

Prevention Metrics

Compliance

ClaimsIQ maintains compliance with:

• State insurance regulations
• Fair claims practices acts
• Data privacy requirements (CCPA, GDPR)
• Anti-fraud reporting mandates