Insurance is one of the most data-intensive industries in the world, with one estimate predicting that the data created and gathered by insurers could reach 180 trillion gigabytes by the end of 2025.
With insurers using this data to fuel everything from underwriting and fraud detection to the customer experience and regulatory reporting, there is no margin for error. Faulty data leads to bugs, leading to compliance issues, unfairly denied claims, revenue loss, lost customer trust, and major headaches.
What’s the solution? Insurance providers need software testing strategies to ensure app functionality and data accuracy.
The unique complexities of insurance data
Every policy quote or claims decision is only as good as the data that powers it. Insurance providers juggle a wide range of first-party and third-party datasets, each with their own considerations. Datasets include:
Structured and regulated policy data that defines coverage limits, exclusions, premiums, and underwriting criteria
Claims data that may be manually entered by agents or customers, requiring validation across systems to ensure accuracy and prevent fraud
Unstructured actuarial data that leverages massive, complex datasets to define risk and establish rates
Third-party data from credit bureaus, DMVs, government sources, weather trackers, and property databases that may need to be integrated in real-time
Many of these datasets are outside the insurer’s control, were created by different sources for specific purposes, and were never designed to work together. As a result, providers often struggle to maintain data integrity, impacting underwriting decisions, claims payouts, and regulatory compliance.
With data flowing through increasingly complex, distributed architectures, traditional testing approaches often fail to catch these issues before they reach production. The sheer volume of sensitive data flowing through insurance platforms is both what makes it so valuable to the provider and so difficult for developers to test effectively.
Providers rely on highly regulated personally identifiable information (PII), customer financial details, and other sensitive data – which makes it challenging to use in test environments in a way that is compatible with GDPR and industry regulations. As regulations continue to evolve and new rules are written, insurers have to keep their test data management strategies flexible while maintaining the traceability required to prove compliance across the software development life cycle.
By leveraging testing strategies that are purpose-built for data-rich ecosystems, insurers can validate both software functionality and data accuracy.
4 best practices for improving data validation in insurance app testing
To ensure data remains functional and realistic, insurers can use the following best practices to simulate real-world conditions without creating real-world risk:
1. Define clear data quality requirements and standards
Before testing, create a shared definition of data quality across engineering, QA, and compliance teams. Establishing expectations around required fields, acceptable data formats, business logic, and regulatory constraints can help prevent non-compliance issues and unnecessary rework while ensuring all testing is aligned with business goals and industry standards.
2. Implement validation at the point of entry and throughout the pipeline
Data quality should be validated at ingestion, transformation, and output. This ensures you’ll catch errors early and be able to trace them to their source so that small issues don’t turn into major downstream issues. This is especially critical when data is migrated from one system to another, such as when modernizing legacy platforms, consolidating systems, or integrating third-party data.
3. Leverage synthetic data to reduce risk and increase flexibility
Insurers can use synthetic data to test high-volume or edge-case scenarios without using sensitive customer information. This is especially helpful when the testing team needs data that mirrors the complexity and patterns of production data, such as when simulating rare but critical workflows, validating business logic, or stress-testing systems ahead of peak periods.
4. Use data masking to preserve functionality
Data masking transforms sensitive data into fictitious but realistic values. Unlike synthetic data generated from scratch, masking maintains the referential integrity of the original data. This lets teams safely use production-like data for testing without breaking workflows, introducing errors, or violating compliance regulations.
Build confidence with smarter data testing
By leveraging smarter data testing strategies, insurers can reduce risk, improve engineering efficiency, and confidently deliver reliable digital experiences. Learn how Sauce Labs helps insurers streamline testing across the software development lifecycle.
