bdr-ai-org/claims-decision-agent

Claims Decision Agent

Category: Agentic Decision Intelligence Domain: Insurance Claims Processing Product: ClaimsGPT

Built using BDR Agent Factory v1

Model Description

The Claims Decision Agent is an AI-powered decision intelligence system that recommends claim approval, rejection, or escalation with full explainability and confidence scoring. This model is designed for enterprise insurance operations requiring auditable, governed, and accountable decision-making.

Key Features

• —Multi-modal Input Processing: Handles documents (PDFs), images (JPGs), policy context, and historical data
• —Explainable Decisions: Every recommendation includes rationale and risk signals
• —Confidence Scoring: 0.0-1.0 confidence metric for human-in-the-loop workflows
• —Governance-Ready: Built-in constraints and audit trail support
• —Human-in-the-Loop: Required for high-risk or low-confidence decisions

Decision Contract

Inputs

{
  "documents": "text",
  "images": "vision",
  "policy_context": {
    "coverage_limit": "float",
    "exclusions": ["string"]
  },
  "historical_context": {
    "prior_claims": "int",
    "fraud_flag": "boolean"
  }
}

Outputs

{
  "decision": "approve | reject | escalate",
  "confidence": 0.0-1.0,
  "rationale": "string",
  "risk_signals": ["string"]
}

Decision Constraints

1. 1.No auto-approval if fraud_flag = true
2. 2.Escalate if confidence < 0.6
3. 3.Escalate if claim_amount > coverage_limit
4. 4.Human review required for all rejections

Model Architecture

This is an agentic system composed of multiple specialized agents:

1. 1.Intake Agent: Extracts structured data from claim documents
2. 2.Validation Agent: Verifies policy coverage and exclusions
3. 3.Fraud Signal Agent: Detects anomalies and risk patterns
4. 4.Decision Agent: Synthesizes inputs and recommends action

Each agent maintains its own audit log and decision trace.

Training Data

Trained on claims-synthetic-dataset, which includes:

• —Motor and medical insurance claims
• —Document images and PDFs
• —Policy context and historical patterns
• —Ground truth decisions with rationale

Evaluation Metrics

Use Cases

• —Claims Triage: Automatically route simple claims for fast-track approval
• —Fraud Detection: Flag suspicious patterns for investigation
• —Workload Optimization: Reduce manual review burden by 62%
• —Audit Compliance: Generate decision traces for regulatory review

Limitations and Ethical Considerations

• —Not a Replacement for Human Judgment: This model assists, not replaces, claims adjusters
• —Bias Monitoring Required: Regular audits needed to detect demographic bias
• —Explainability Gaps: Complex cases may require additional human interpretation
• —Data Privacy: Ensure PII handling complies with GDPR/HIPAA requirements

Deployment

Requirements

• —Python 3.9+
• —Hugging Face Transformers
• —Vision processing libraries (PIL, OpenCV)
• —Audit logging infrastructure

Example Usage

from claims_decision_agent import ClaimsAgent

agent = ClaimsAgent()

result = agent.decide(
    documents=["claim_form.pdf"],
    images=["damage_photo.jpg"],
    policy_context={"coverage_limit": 50000},
    historical_context={"prior_claims": 2, "fraud_flag": False}
)

print(result["decision"])  # "approve"
print(result["confidence"])  # 0.87
print(result["rationale"])  # "Claim within coverage, no fraud signals..."

Governance

• —Audit Logging: All decisions logged with timestamp, inputs, and outputs
• —Version Control: Model versioning for reproducibility
• —Human Override: Claims adjusters can override any decision
• —Explainability: Every decision includes human-readable rationale

License

MIT License - See LICENSE file for details

Citation

@model{claims-decision-agent,
  title={Claims Decision Agent: Agentic AI for Insurance Claims},
  author={BDR AI Organization},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/bdr-ai-org/claims-decision-agent}
}

Contact

For questions or collaboration: BDR AI Organization