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genpark-agentic-multimodal-product-return-fraud-detector-skill

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SUMMARY

AI Multimodal E-Commerce Return Fraud & Wardrobing Abuse Detector. Correlates return velocity, carrier scan discrepancies, product tag tampering indicators, and customer lifetime return ratios to compute a risk index and enforce restocking | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-agentic-multimodal-product-return-fraud-detector-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

AI Multimodal E-Commerce Return Fraud & Wardrobing Abuse Detector. Correlates return velocity, carrier scan discrepancies, product tag tampering indicators, and customer lifetime return ratios to compute a risk index and enforce restocking fees or dispute escalation.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import AgenticProductReturnFraudDetector

def main():
    detector = AgenticProductReturnFraudDetector()
    claim = {
        "order_id": "ORD-4481",
        "claimed_reason": "did not like color",
        "item_price_usd": 480.0,
        "carrier_weight_kg": 0.3,
        "expected_weight_kg": 1.5,
        "days_held": 29
    }
    history = {"total_orders": 3, "past_returns_count": 2, "past_fraud_disputes": 0}
    res = detector.evaluate_return_request(claim, history)
    print("--- Return Fraud Sentinel Evaluation ---")
    print(f"Order: {res['order_id']} | Risk Score: {res['composite_fraud_risk_score']}/100")
    print(f"Action: {res['enforced_action']}")
    print(f"Restocking Fee: {res['restocking_fee_percentage']}%")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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