A Practical Approach to Quality Assurance in Data Enrichment for Insurance

Why Data Enrichment Quality Matters

In today's fast-paced insurance market, real-time pricing empowers insurers to adapt swiftly to changing market conditions and customer demands.

At the heart of this capability is data enrichment, the process of combining internal and external data to build a complete, up-to-date view of risk. Accurate premium calculation, fair underwriting decisions, and effective fraud detection all rely on this enriched data being correct and consistent.

However, with data arriving from multiple sources, validation can quickly become complex. Without robust quality assurance, even small inconsistencies can cascade into pricing errors, compliance issues, and reputational damage.

Navigating Data Complexity in Real-Time Pricing

In many insurance organisations, validating the correctness and consistency of enriched data has long been a bottleneck. Insurers manage hundreds of data fields for every quote, drawn from a mix of internal systems and third-party providers.

This validation process has traditionally been:

• Manual and arduous to validate hundreds of fields per quote, demanding extensive human effort.
• Prone to error where the volume and complexity made human mistakes inevitable.
• Lacking early feedback meaning errors in enrichment propagate undetected into pricing and underwriting decisions.

These challenges highlighted a need for a robust, automated solution to ensure data accuracy and completeness throughout the entire quote enrichment journey.

Automating the Data Enrichment Journey

To address these challenges, we implemented a Python-based test automation framework that makes data enrichment validation repeatable, scalable, and transparent.The framework leverages Behave BDD, enabling us to define tests in a human-readable format, fostering collaboration between technical and business teams.

The system's core capabilities include:

• Quote lifecycle management to receive existing test quotes or generate new ones, each assigned a unique ID. This ID becomes the central key for tracking the quote through its entire journey.
• Multi-system verification, including cloud-based storage and analytical data warehouses. 
• Enrichment functionality validation starting with data retrieved from external enrichment providers and verifying how this enriched data is mapped to internal formats used by pricing models.
• Data processing confirmations for downstream processes, such as waiting for Glue jobs to complete, before performing validation checks, ensuring all transformations have occurred.
• Data shredding validation for analytical use in Athena, ensuring data accuracy and completeness from quote source.
• Actionable insights summarised in an interactive report providing clear, immediate feedback on success or failure, accessible to both technical and non-technical stakeholders.

This approach ensures high confidence in data enrichment validity by covering the full data journey and critical mappings.

Precision Validation in Action

Our framework performs a series of automated checks for each quote, tailored to the details of insurance data enrichment. Here's a glimpse into how these principles translate into practice:

1. The core of the framework is built on Python 3.9, utilizing Boto3 for interaction with AWS services like S3, Athena, and Glue.
2. The system locates the correct Quote ID and associated source enrichment message. The message content is parsed, and attributes are loaded into a Python dictionary, verifying that all attributes are within expected ranges, this allows for validation of thousands of fields.
3. Pricing mapping consistency compared to the external data, ensuring that all relevant values are correctly transferred to the pricing model, and that missing fields are appropriately handled.
4. Quote data is "shredded" into AWS Athena tables for advanced analytics. The framework reads the mappings for this shredding process, retrieving all relevant data for the quote from Athena tables, and then verifies that the data attributes and values from both the enrichment and pricing model messages are correctly represented in the shredded data.

Example Scenario

The framework generates an interactive report, showing the overall status for each quote ID with detailed field-level validation results. This step-by-step verification ensures insight into data quality.

Integrating Data Enrichment QA into CI/CD

This solution integrates into a Continuous Integration and Continuous Delivery (CI/CD) pipeline, running within AWS CodeBuild. With every code change or new deployment, the entire suite of QA tests can be automatically triggered providing:

• Fast feedback for engineers on the impact of their changes before they reach production.
• Elimination of the drudgery of manual testing of data mappings.
• Automated, comprehensive checks providing confidence in the accuracy and completeness of enriched data.
• Scalability to handle thousands of quotes simultaneously, making it suitable for high-volume environments.
• Evidence for auditors and stakeholders of data quality and compliance, crucial for audits and stakeholder reporting.

This closes the loop between development, QA, and operations, ensuring enriched data remains accurate and reliable at scale.

Summary

In the insurance sector, data enrichment is vital for accurate pricing, fair underwriting, and proactive risk management, but only if the enriched data is correct, complete, and consistent. Automating its validation removes bottlenecks, reduces errors, and provides confidence that real-time decisions are based on trustworthy information.

A robust QA framework, integrated into the development lifecycle, transforms data enrichment from a potential risk into a strategic advantage.

If you would like to explore how automated data enrichment validation can strengthen your insurance operations, get in touch with our team to start the conversation.