Insurance — Data Normalization and Quality Assurance Pipeline

Overview

The purpose of this DAG is to ensure the normalization and validation of collected insurance data, thereby guaranteeing its quality and compliance with regulatory standards. The architecture comprises an ingestion pipeline that pulls data from various sources, including policyholder information, claims data, and underwriting assessments. The processing steps involve applying predefined quality tests and expectations tailored for each data source. These tests include checks for completeness, accu

The purpose of this DAG is to ensure the normalization and validation of collected insurance data, thereby guaranteeing its quality and compliance with regulatory standards. The architecture comprises an ingestion pipeline that pulls data from various sources, including policyholder information, claims data, and underwriting assessments. The processing steps involve applying predefined quality tests and expectations tailored for each data source. These tests include checks for completeness, accuracy, and consistency, ensuring that only high-quality data is cataloged. The provenance of the data is meticulously tracked to uphold governance standards, allowing for easy auditing and compliance verification. Once processed, the results are stored in a centralized data warehouse, facilitating easy access for analytics and reporting. In case of any non-compliance detected during the quality checks, alerts are generated to notify stakeholders promptly. Key performance indicators (KPIs) for this DAG include the compliance rate of the data and the processing time for each data batch. By ensuring high data quality and compliance, this DAG significantly enhances the pricing optimization efforts within the insurance industry, leading to more accurate pricing models and improved risk assessment.

Part of the Pricing Optimization solution for the Insurance industry.

Use cases

• Improved pricing accuracy through high-quality data
• Enhanced compliance with regulatory standards
• Faster data processing times for timely decision-making
• Increased trust in data-driven insights
• Streamlined auditing processes with clear data provenance