Insurance — Actuarial Model Governance and Compliance Monitoring

Overview

The purpose of this DAG is to ensure the governance of actuarial models within the insurance industry, focusing on compliance with regulatory standards and performance metrics. The pipeline ingests data from various sources, including actuarial performance logs, regulatory compliance reports, and model output statistics. The ingestion process involves collecting these data streams into a centralized repository for analysis. Once the data is ingested, the DAG performs several key processing ste

The purpose of this DAG is to ensure the governance of actuarial models within the insurance industry, focusing on compliance with regulatory standards and performance metrics. The pipeline ingests data from various sources, including actuarial performance logs, regulatory compliance reports, and model output statistics. The ingestion process involves collecting these data streams into a centralized repository for analysis. Once the data is ingested, the DAG performs several key processing steps. First, it conducts a performance evaluation of the actuarial models by comparing current outputs against historical benchmarks. Next, drift testing is implemented to detect any significant deviations in model performance, which may indicate the need for recalibration or reevaluation. Following this, the DAG generates compliance reports that summarize the findings and highlight any areas of concern. These reports are then shared with governance teams for validation and further action. To ensure the quality of the outputs, the DAG includes monitoring mechanisms that track key performance indicators (KPIs) such as model accuracy, compliance rates, and drift metrics. This continuous monitoring allows for proactive adjustments to models, ensuring they remain compliant and effective over time. The business value of this DAG lies in its ability to minimize regulatory risks, enhance decision-making through reliable data, and improve overall customer personalization strategies by ensuring that actuarial models are both accurate and compliant.

Part of the Customer Personalization solution for the Insurance industry.

Use cases

• Reduces regulatory compliance risks for insurance models
• Enhances accuracy of actuarial predictions and insights
• Improves trust and transparency with stakeholders
• Facilitates timely decision-making based on reliable data
• Supports customer personalization through accurate model outputs