Insurance — AI Model Governance for Fraud Detection

Overview

The purpose of this DAG is to manage the lifecycle of AI models deployed for fraud detection within the insurance industry. It begins with the ingestion of historical data and performance results from various sources, such as transaction logs and customer claims data. The pipeline processes this data through several steps, including data validation, model performance evaluation, and compliance checks against regulatory standards. Quality controls are implemented to ensure that the models adhere

The purpose of this DAG is to manage the lifecycle of AI models deployed for fraud detection within the insurance industry. It begins with the ingestion of historical data and performance results from various sources, such as transaction logs and customer claims data. The pipeline processes this data through several steps, including data validation, model performance evaluation, and compliance checks against regulatory standards. Quality controls are implemented to ensure that the models adhere to the required guidelines, and any deviations in performance trigger alerts for immediate attention. The outputs of this DAG include a governance dashboard that visualizes key performance indicators (KPIs) such as detection success rates and incident response times. Continuous monitoring of these KPIs allows stakeholders to assess the effectiveness of the fraud detection models and make informed decisions. The business value lies in enhanced regulatory compliance, improved fraud detection accuracy, and the ability to respond quickly to performance issues, ultimately leading to reduced financial losses and increased customer trust.

Part of the Knowledge Portal & Ontologies solution for the Insurance industry.

Use cases

• Ensures regulatory compliance for AI model usage
• Improves accuracy in detecting fraudulent activities
• Reduces financial losses from undetected fraud
• Enhances stakeholder visibility into model performance
• Facilitates quick response to performance issues