Artificial intelligence is now deeply embedded in insurers' operations, with 99% reporting that they have already invested in generative AI capabilities or are planning to do so. However, with insurers relying on AI to make decisions that can impact customer premiums, policy approvals, and the user experience, this new technology can create new risks.
In a highly regulated industry like insurance, any use of AI models has to be accurate, fair, explainable, and accountable, which puts software testing teams at the center of any AI conversation. Here's how insurers are incorporating AI into their core app functions and how AI-powered testing strategies can help QA teams release features faster while maintaining quality.
How Insurers Are Using AI in Core Applications
Automated data extraction: AI can ingest and analyze massive amounts of medical records, financial reports, or telematics data in seconds while eliminating human error.
Advanced risk profiling: Machine learning algorithms can surface patterns across datasets that human underwriters might miss, improving pricing accuracy and enabling real-time decisions.
Usage-based pricing: Rather than relying on static factors like age and zip code, insurers can use AI to analyze telematics and behavioral data to offer personalized policies.
Fraud Detection: AI can help insurers review applications and claims to identify suspicious patterns or inconsistencies that indicate fraud, Accelerated claims resolution AI can help automate tasks like image analysis for damage assessments, document verification, and claims payouts. Geico now uses AI to assess vehicle damage and compare it to millions of historical claims to create an accurate repair estimate in seconds.
First Notice of Loss Insurers are using AI-powered chatbots to help customers report claims, verify policy coverage, and collect key data more efficiently. For example, Swiss Re uses AI to give providers a 100% photo-based damage assessment that significantly decreases FNOL intake, improves assessor productivity, and reduces settlement variability.
How AI is Changing Insurance Software and Mobile App Testing
As insurers deploy AI-powered features across their applications, the pressure to release updates faster while maintaining quality has never been greater. Traditional testing approaches often create bottlenecks that slow down feature releases, but AI-enhanced testing capabilities are augmenting how insurance teams approach quality assurance:
Intelligent Test Automation: AI can assist with or automatically generate test cases based on user behavior patterns, application changes, and risk assessments. This means QA teams can achieve broader test coverage in less time, identifying edge cases that manual testing might miss. This comprehensive coverage is essential for maintaining regulatory compliance while accelerating release cycles for insurance applications handling sensitive customer data and complex business logic.
Predictive Test Selection: Machine learning algorithms can analyze code changes and historical test results to predict which tests will most likely catch defects. For insurance teams working with legacy systems and complex integrations, this targeted approach ensures critical functionality is thoroughly tested without unnecessary delays.
Automated Visual Testing: AI-powered visual testing tools can automatically detect UI changes, layout issues, and visual regressions across different devices and browsers. This is particularly valuable for insurance applications that must maintain consistent user experiences across web and mobile platforms while frequently updating customer-facing features like claims submission forms and policy management dashboards.
Dynamic Test Environment Management AI can optimize test environment provisioning and data management, automatically spinning up environments when needed and tearing them down when testing is complete. This reduces infrastructure costs and eliminates the wait times that often slow testing cycles, enabling insurance teams to test more frequently and catch issues earlier.
AI-Powered Failure Analysis: Optimizing Test Efficiency
Sauce Labs has developed AI and machine learning capabilities specifically designed to optimize test efficiency and efficacy for insurance applications. Our failure analysis tool leverages proprietary machine learning algorithms to review test pass/fail data and uncover patterns that impact the overall test suite performance.
This AI-powered system (distinct from generative AI) analyzes your test execution history to identify common failure patterns. It helps QA teams understand whether failures are due to genuine product issues, flaky tests, or environmental factors. By surfacing these insights, teams can focus on the most critical issues while reducing time spent investigating false positives. The result is a more efficient testing process that enables faster feature releases without compromising quality.
For insurance companies managing complex applications with stringent regulatory requirements, this intelligence helps prioritize testing efforts and ensures that real issues are addressed quickly while maintaining the high standards of reliability that customers expect from their insurance providers.
Learn how Sauce Labs helps insurers streamline and improve software testing across the development lifecycle with AI-powered insights and automation capabilities designed specifically for the unique challenges of insurance applications.
