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LiabilityIQ MVP - Implementation Summary

Overview

This MVP implements a comprehensive motor claim liability determination system that automates document collation, analysis, and decision-readiness assessment. The system processes multiple claim documents, extracts key information, validates against baseline expectations, identifies risks and gaps, and generates actionable recommendations.

Architecture

The system is built as a modular Python application following the 7 Functional Requirements (FR-1 through FR-7):

Core Modules

  1. Data Models (src/models.py)

    • Comprehensive Pydantic models for all entities
    • Document, Entity, TimelineEvent, Gap, Conflict, Risk, Recommendation
    • ConsolidatedFacts, ReadinessIndex, ScoringResult, ClaimDossier

  2. Synthetic Data Generators (src/data_generators/)

    • synthetic_claim_generator.py: Generates realistic synthetic motor claims
    • Creates FNOL forms, statements, police reports, repair quotes, correspondence, call transcripts, photos
    • Uses Faker library for realistic data generation

  3. Document Processing (src/document_processing/)

    • document_retriever.py (FR-1): Retrieves documents for a claim
    • normalizer.py (FR-2): Normalizes, classifies, and deduplicates documents
    • summarizer.py (FR-3): Summarizes documents and extracts structured facts

  4. Analysis (src/analysis/)

    • bed_checker.py (FR-4): Cross-checks facts against Baseline Expectations Document
    • risk_detector.py (FR-5): Detects gaps, conflicts, and risks
    • recommendations.py (FR-6): Generates actionable next steps

  5. Output (src/output/)

    • dossier_generator.py (FR-7): Generates comprehensive claim dossiers in Markdown format

  6. Orchestration (src/liability_processor.py)

    • Main processor that orchestrates all modules
    • Processes claims through the full pipeline

Key Features Implemented

Document Types Supported

  • First Notice of Loss (FNOL)
  • Claimant Statements
  • Insured Statements
  • Police Reports
  • Repair Quotes/Invoices
  • Photo Metadata
  • Email/SMS Correspondence
  • Call Transcripts
  • Broker Submissions
  • Third Party Statements
  • Medical Reports (structure ready)
  • Telematics (structure ready)

Analysis Capabilities

  1. Baseline Expectations Cross-Check

    • Existence scoring (required documents/fields present?)
    • Completeness scoring (all required fields filled?)
    • Quality scoring (meets quality criteria?)
    • Overall Readiness Index (0-100)

  2. Risk Detection

    • Late FNOL submission
    • Missing police reports
    • Template-like statements
    • High repair costs
    • Stale documents

  3. Gap Detection

    • Missing required documents
    • Missing signatures
    • Missing third-party details
    • Stale documentation

  4. Conflict Detection

    • Date inconsistencies
    • Location inconsistencies
    • Registration inconsistencies
    • Damage description inconsistencies

  5. Recommendations

    • Prioritized action items
    • Templated outreach messages
    • Links to related gaps/conflicts/risks

Output Format

Each claim generates a comprehensive Markdown dossier containing:

  • Executive Summary with Readiness Index
  • Scoring Dashboard (requirement-by-requirement breakdown)
  • Master Timeline of events
  • Consolidated Facts (entities, dates, locations, etc.)
  • Gaps Identified (with severity and evidence)
  • Conflicts Detected (with conflicting values)
  • Risks Flagged (with indicators)
  • Recommended Next Steps (with templated outreach)
  • Document Summaries
  • Appendix with metadata

Configuration

Baseline Expectations Document (BED)

Located at data/bed/motor_claims_bed.yaml:

  • Defines 10 requirements for motor claims
  • Specifies expected fields, quality criteria, thresholds
  • Configurable scoring weights and readiness thresholds
  • Risk indicator definitions

System Configuration

Located at config/config.yaml:

  • Document processing settings
  • BED path and scoring weights
  • Risk detection thresholds
  • Output format preferences

Synthetic Data

The system includes comprehensive synthetic data generators that create:

  • Realistic claim scenarios with multiple document types
  • Varied incident dates, locations, and circumstances
  • Realistic names, addresses, vehicle details (via Faker)
  • Document metadata (dates, authors, sources)
  • Optional documents (correspondence, photos, transcripts)

Usage

Basic Usage

# Install dependencies
pip install -r requirements.txt

# Run demo
python demo.py

Visualization

# Generate summary charts
python visualize_results.py

File Structure

LiabilityIQ/
├── src/
│   ├── models.py                    # Data models
│   ├── liability_processor.py      # Main orchestrator
│   ├── data_generators/
│   │   └── synthetic_claim_generator.py
│   ├── document_processing/
│   │   ├── document_retriever.py   # FR-1
│   │   ├── normalizer.py           # FR-2
│   │   └── summarizer.py           # FR-3
│   ├── analysis/
│   │   ├── bed_checker.py          # FR-4
│   │   ├── risk_detector.py       # FR-5
│   │   └── recommendations.py     # FR-6
│   └── output/
│       └── dossier_generator.py    # FR-7
├── data/
│   ├── bed/
│   │   └── motor_claims_bed.yaml   # Baseline Expectations
│   └── synthetic/                  # Generated claims
├── config/
│   └── config.yaml                 # System configuration
├── output/                         # Generated dossiers
├── demo.py                         # Demo script
├── visualize_results.py            # Visualization helper
├── requirements.txt
└── README.md

Extensibility

The system is designed for easy extension:

  1. New Document Types: Add to DocumentType enum and update classifiers
  2. New Requirements: Add to BED YAML file
  3. New Risk Indicators: Add to risk detector with custom logic
  4. New Output Formats: Extend dossier generator (PDF, HTML, etc.)
  5. Integration: Replace document retriever with ClaimCenter API calls

Future Enhancements

Potential improvements for production:

  • Real OCR/NLP for document processing
  • Machine learning for entity extraction
  • Integration with ClaimCenter database
  • PDF/HTML output formats
  • Web UI for interactive review
  • Real-time processing capabilities
  • Advanced conflict resolution algorithms
  • Historical claim pattern analysis

Testing

The MVP includes:

  • Synthetic data generation for testing
  • End-to-end pipeline demonstration
  • Visualization tools for result analysis

For production, add:

  • Unit tests for each module
  • Integration tests for full pipeline
  • Performance benchmarks
  • Accuracy validation against real claims

Dependencies

  • pydantic: Data validation and models
  • faker: Synthetic data generation
  • pyyaml: Configuration file parsing
  • python-dateutil: Date parsing
  • matplotlib: Visualization (optional)
  • reportlab: PDF generation (optional, for future)

Notes

  • This is an MVP focused on demonstrating the concept and architecture
  • Document processing uses simplified text extraction (production would use OCR/NLP)
  • Entity extraction uses pattern matching (production would use NER models)
  • All analysis is rule-based (production could add ML models)
  • Output is Markdown (easily extensible to PDF/HTML)

Success Criteria Met

✅ Document collation into single consolidated record
✅ Document understanding and analytical assessment
✅ Confidence scoring of available evidence
✅ Gap and completeness analysis
✅ Recommended next steps to support claim progression
✅ Synthetic data generation for demonstration
✅ Visualization of system working
✅ Actual output generation (Markdown dossiers)