Insurance is experiencing a technology-driven transformation. Insurers are modifying traditional legacy systems, virtualizing their operations, and making use of the latest digital innovation to optimize efficiency, engender customer engagement, and stay up to date with all changing regulations and compliance standards. In the process of technology transforming insurance, QA is a key component in making and deploying trusted insurance software.
The Importance of QA in Insurance Software Development
The key focus of QA of insurance technology solutions is to prevent defects and issues in software before it is delivered to the customers. Testing and validation are important for insurers that are launching products that deal with sensitive customer data and money transactions. There are software flaws that bring downtime or loss of data that can have a huge impact on customer’s trust as well as an insurer’s reputation. When it comes to continuous release, to minimize disruption to the paying customer experience across channels, it’s essential to have rigorous QA practices at the same level for security, compliance, and resiliency.
It has been proven that fixing software defects after the production phase is 15 times more expensive than those that are detected in the requirements and design stage. In the early days, having an active QA budget helped insurers save hours upon hours and plenty of dollars on the road. Industry analysts say rigorous QA can save 30 percent of the cost of quality over the lifetime of a project. Quality assurance is not just about cost savings, and it is imperative for insurers to build quickly and iterate software that adheres to insurance regulations and customer expectations.
Testing Methodologies for Insurance Software
Insurance QA teams utilize a diverse set of testing techniques, tools, and frameworks to validate software quality. Some key testing methods include:
- Manual Testing. It is used for simple functionality testing, exploratory testing and usability testing. The software is executed manually on test cases to ensure correct behavior.
- Automated Testing. Pre-scripted test cases are executed using the software tools to ensure system functionality and performance. It is a way to run regression testing whenever the code is changed.
- Unit Testing. Individual units of source code (modules, functions, interfaces) are tested in isolation to verify they operate as intended.
- Integration Testing. Testing interactions between integrated system components to uncover issues in the interfaces between components.
- System Testing. Testing a completely integrated insurance system to evaluate the system’s compliance with specified requirements.
- Performance Testing. Testing insurance software under expected normal and peak production load conditions. Used to identify bottlenecks.
- Security Testing. Testing authentication, authorization, data security, encryption, and system vulnerabilities.
- Compatibility Testing. Validating insurance software compatibility across various hardware, operating systems, browsers, and devices it will support.
- Accessibility Testing. Testing compliance with accessibility standards and disabled user support requirements.
The specific combination and order of testing methods depend on the insurance software application, platforms, architecture and other internal and external requirements. Agile development approaches also influence testing procedures. QA teams work closely with insurance software developers, architects, business analysts, and UX designers to determine optimal testing strategies across the entire development lifecycle.
Automating Insurance Software Testing
Given the scale and complexity of insurance systems, manually executing test cases is no longer practical in most situations. Test automation is instrumental for performing repetitive tests, quick regression testing, and creating test data at scale. Automated testing also frees up QA staff to focus on higher-value testing that requires human judgment.
Research into global test automation suggests that organizations that focus on test automation are more prepared to improve product quality, as noted by 60 percent of the respondents, who indicate that quality improvement is one of the key drivers of test automation. In addition, 58 percent of organizations stated that their push for automation was to speed up deployment speed. These factors help to create more scalable automated testing and a higher standard of software quality than those with little or no automation. Test automation is now a common practice in the insurance industry for all such areas as frontend UI testing, API testing, unit testing, integration testing, and load/performance testing.
The Internet has a lot of open-source test automation frameworks, and insurance QA teams, including Selenium, Appium, JMeter, TestComplete, Ranorex, Robot Framework, Cucumber and TestNG etc., are using all these popular open-source frameworks. They are used to test web interfaces, mobile apps, APIs, databases, system performance, security, and so on. Cloud testing platforms also offer additional capabilities for insurance test automation at scale.
Insurers combine open-source frameworks with internal proprietary tools and scripts to automate end-to-end policyholder scenarios. Industry-wide, intelligent test automation adoption is still in the early stages. As capabilities grow, test automation will become faster, more resilient across test environments, and self-healing when application changes occur.
Key Metrics for Measuring Insurance Software Quality
QA teams rely on quantifiable quality metrics to make data-driven decisions around release readiness, prioritize testing efforts, and communicate progress to stakeholders. Metrics also help benchmark performance across software teams and projects. Some examples of key metrics tracked include:
- Defect Density. Defects detected per lines of code, use cases, test cases, etc. Trends show quality/stability.
- Test Coverage. Percentage of requirements, risks, code, etc., exercised by testing. Indicates test thoroughness.
- Test Pass Rate. Percentage of test cases passing from total test cases executed. Highlights scope of issues.
- Mean Time Between Failures. Average uptime between system/software failures in production. Signals reliability.
- Time to Restore Service. Speed of recovering from an incident causing an outage. Measures resilience.
- Escaped Defects. Defects reported by the customer in production and post-release. Quantifies real-world quality.
- Cost of Quality. Aggregate cost of performing QA activities plus fixing defects over software lifecycle. ROI of QA.
Insurance QA teams are always evaluating quality metrics to identify areas for improvement, as well as create a business case for better QA practices. More and more often, departments are sharing test metrics and service quality KPIs in order to incentivize collaboration and also to improve the end-user experience.
Challenges Facing Insurance Software QA
While software testing has always been integral for insurers, expanding technology footprint has made QA more challenging than ever:
- Insurance Regulation and Compliance Changes. Frequent updates to insurance compliance rules require continuous updating, testing, and recertifying of software.
- Omnichannel Customer Experience. Providing consistent UI, data, and workflows across web, mobile, call centers, etc, exponentially grows test scenarios.
- Increased Software Integration Complexity. With insurers relying on ever more 3rd party systems and data, testing interconnected systems is difficult.
- Shortened Release Cycles. Accelerated software delivery expectations provide narrower windows for testing activities before launch.
- Test Data and Environment Management. Generating, securing, and orchestrating test data across different environments remains a bottleneck.
- Lack of Sufficient Automation. Much insurance testing remains manual despite automation potential, slowing QA velocity.
- Shortage of Specialized QA Talent. Demand for QA professionals with both insurance and software testing expertise outweighs supply.
To overcome such obstacles, the insurers must strategically build out an end-to-end QA capability spanning the process, the talent and the enabling technology. During continuous development, software testing and compliance validation must also be performed in addition to the release.
The Future of Insurance Software QA
With insurers in a digital transformation race, QA will be instrumental in enabling the rapid release of high-quality, resilient software. QA is no longer a separate downstream step but has become an integrated part of the whole insurance software delivery pipeline.
- Continuous Testing. Automated unit, integration, and regression testing will continue to be done, but QA will occur continuously at every code commit.
- AI-Powered Testing. As part of machine learning expansion, test design, execution, reporting, and intelligence will be automated.
- Real-World Testing. Location-based crowd-testing solutions will better simulate real-life user, environment, network, and data conditions during testing.
- Compliance by Design. Regulations and compliance requirements will be programmed into automated testing tools to enable preventative governance.
- Infrastructure as Code Testing. Cloud infrastructure, security, and configurations will become codified to allow automated validation of infrastructure changes.
With continuous testing, AI, and cloud advances, insurance QA will shift left earlier into the software lifecycle to achieve both higher quality as well as higher delivery velocity. Testing will expand from being functionally focused also to confirming positive policyholder experiences across channels. Embedded compliance and infrastructure testing will reduce audit costs. Future insurance QA will ultimately realize the vision of delivering seamless, resilient end-to-end solutions at scale.
Conclusion
Software quality assurance is an indispensable capability in insurance operations, which are rapidly becoming digital. First, insurers have rigorous QA testing methodologies, test automation frameworks and quality metrics in place that allow them to create and update compliant software that delights their customers quickly. QA at the insurance scale has its challenges, but continuous testing, AI, cloud, and other technologies will be reshaping QA. Insurance software delivery pipelines will be tightly integrated with future QA practices, resulting in quality being no barrier to increased release velocity and no time released until it meets customer experience. Rather than being an afterthought, quality will become the lifeblood running through all aspects of insurance software.