Insurance — Governance Models for Pricing and Fraud Detection

Overview

The purpose of this DAG is to establish a robust governance framework for pricing optimization and fraud detection models within the insurance industry. By integrating various data sources, the pipeline begins with the ingestion of historical claims data, customer profiles, and market trends. The processing steps include model evaluation, performance analysis, and compliance checks, ensuring that the models are not only effective but also adhere to regulatory standards. Quality controls are impl

The purpose of this DAG is to establish a robust governance framework for pricing optimization and fraud detection models within the insurance industry. By integrating various data sources, the pipeline begins with the ingestion of historical claims data, customer profiles, and market trends. The processing steps include model evaluation, performance analysis, and compliance checks, ensuring that the models are not only effective but also adhere to regulatory standards. Quality controls are implemented at each stage to maintain the integrity and reliability of the models. The outputs of this DAG include updated pricing models, fraud detection alerts, and compliance reports, which are crucial for informed decision-making. Monitoring key performance indicators (KPIs) such as model compliance rates and evaluation turnaround times enables continuous improvement of the models. The business value derived from this DAG includes enhanced pricing accuracy, reduced fraud losses, and improved regulatory compliance, ultimately leading to increased profitability and customer trust.

Part of the Pricing Optimization solution for the Insurance industry.

Use cases

• Increased accuracy in pricing strategies and risk assessment
• Reduction in fraudulent claims and financial losses
• Enhanced regulatory compliance and reduced audit risks
• Improved customer trust through transparent practices
• Streamlined operational efficiency with automated processes