Insurance — Actuarial Model Governance Workflow

Overview

The Actuarial Model Governance Workflow is designed to oversee the governance of actuarial models within the insurance industry, ensuring compliance with regulatory standards and performance benchmarks. The pipeline begins with the ingestion of performance data and audit results from multiple sources, including internal databases and external regulatory reports. Triggered by model updates and scheduled evaluations, the workflow processes this data through several key steps. Firstly, data is coll

The Actuarial Model Governance Workflow is designed to oversee the governance of actuarial models within the insurance industry, ensuring compliance with regulatory standards and performance benchmarks. The pipeline begins with the ingestion of performance data and audit results from multiple sources, including internal databases and external regulatory reports. Triggered by model updates and scheduled evaluations, the workflow processes this data through several key steps. Firstly, data is collected from sources such as actuarial performance logs and compliance audit reports. Next, the performance of the models is evaluated against established KPIs, which include compliance rates and update turnaround times. Following this evaluation, governance documentation is updated to reflect any changes or findings. Quality controls are integrated throughout the process, including compliance checks and traceability audits to ensure data integrity and adherence to regulatory standards. The outputs of this DAG include updated governance documents, compliance reports, and performance dashboards. Monitoring is critical, with KPIs tracked to assess the effectiveness of the governance process. In case of any failures or discrepancies, alerts are automatically generated and sent to governance stakeholders, enabling timely intervention. This DAG not only enhances compliance but also streamlines the governance process, providing significant business value by reducing risk and improving operational efficiency.

Part of the Scientific ML & Discovery solution for the Insurance industry.

Use cases

• Ensures compliance with regulatory standards in insurance
• Reduces risk through automated governance processes
• Improves operational efficiency with streamlined workflows
• Enhances data integrity with robust quality controls
• Facilitates timely decision-making with real-time alerts