Insurance — AI Model Governance for Insurance Claim Processing

Overview

The primary purpose of this DAG is to manage the governance of AI models that are instrumental in processing insurance claims. It begins with an audit of existing models, assessing their performance and compliance with industry standards. The data sources for this workflow include model logs and performance results, which provide insights into the effectiveness of each model. The ingestion pipeline collects these inputs, allowing for a comprehensive evaluation of model behavior. Key processing s

The primary purpose of this DAG is to manage the governance of AI models that are instrumental in processing insurance claims. It begins with an audit of existing models, assessing their performance and compliance with industry standards. The data sources for this workflow include model logs and performance results, which provide insights into the effectiveness of each model. The ingestion pipeline collects these inputs, allowing for a comprehensive evaluation of model behavior. Key processing steps involve documenting the models, updating standard operating procedures (SOPs), and implementing quality controls to ensure traceability and accountability. The DAG also includes monitoring mechanisms that track key performance indicators (KPIs) such as model drift and regulatory compliance. In the event of any failures or deviations from expected performance, alerts are dispatched to relevant teams for immediate action. The outputs of this workflow consist of detailed governance reports, updated SOPs, and compliance documentation that enhance the overall integrity of the insurance claim processing system. By maintaining rigorous governance of AI models, this DAG not only mitigates risks but also adds significant business value by ensuring that claims are processed efficiently and in accordance with regulatory requirements.

Part of the Data & Model Catalog solution for the Insurance industry.

Use cases

• Enhances compliance with regulatory standards
• Reduces risks associated with AI model failures
• Improves transparency in model governance
• Increases efficiency in claims processing
• Facilitates better decision-making through accurate data