Insurance — Insurance Model Performance Monitoring Pipeline

Overview

The purpose of this DAG is to continuously monitor the performance of models utilized in the insurance sector, ensuring they meet quality standards and operate effectively. Data sources include model output results and user feedback, which are ingested into the system for analysis. The ingestion pipeline captures this data and prepares it for processing. The primary processing steps involve calculating performance metrics, analyzing for drift and bias, and generating alerts when issues are detec

The purpose of this DAG is to continuously monitor the performance of models utilized in the insurance sector, ensuring they meet quality standards and operate effectively. Data sources include model output results and user feedback, which are ingested into the system for analysis. The ingestion pipeline captures this data and prepares it for processing. The primary processing steps involve calculating performance metrics, analyzing for drift and bias, and generating alerts when issues are detected. Quality controls are integrated throughout the process to maintain high standards. The outputs of this DAG include performance reports, alert notifications, and retraining triggers. Monitoring key performance indicators (KPIs) such as accuracy, precision, and recall allows for ongoing assessment of model efficacy. The business value lies in the ability to proactively address model performance issues, thereby enhancing decision-making and customer satisfaction in the insurance domain.

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

Use cases

• Improves model accuracy and reliability in insurance decisions
• Enhances customer trust through consistent quality assurance
• Reduces operational risks associated with model failures
• Facilitates compliance with industry regulations and standards
• Enables data-driven decision-making for better outcomes