Insurance — AI Model Governance Pipeline for Insurance

Overview

The primary purpose of this DAG is to establish a robust governance framework for AI models utilized in the insurance industry. It begins by ingesting data from various sources, including model performance metrics, access logs, and change documentation. The architecture consists of multiple processing steps designed to ensure thorough monitoring and compliance. Initially, model performance data is collected and stored, followed by the implementation of Role-Based Access Control (RBAC) to manage

The primary purpose of this DAG is to establish a robust governance framework for AI models utilized in the insurance industry. It begins by ingesting data from various sources, including model performance metrics, access logs, and change documentation. The architecture consists of multiple processing steps designed to ensure thorough monitoring and compliance. Initially, model performance data is collected and stored, followed by the implementation of Role-Based Access Control (RBAC) to manage user access effectively. Documentation of changes is then processed to ensure all modifications are recorded and traceable. Quality controls are integrated throughout the pipeline to validate data integrity and compliance with regulatory requirements. The outputs of this DAG include comprehensive governance reports, compliance documentation, and a centralized model management system that tracks all changes and performance metrics. Monitoring Key Performance Indicators (KPIs) such as model accuracy, access violations, and documentation completeness is essential for ongoing governance. The business value of this DAG lies in its ability to enhance transparency, mitigate compliance risks, and improve the overall trustworthiness of AI models in insurance applications.

Part of the Governance & Compliance solution for the Insurance industry.

Use cases

• Ensures compliance with industry regulations and standards
• Reduces risks associated with model governance
• Enhances trust in AI-driven insurance decisions
• Improves operational efficiency through automation
• Facilitates better decision-making with accurate data insights