Validating Auto Insurance Quote Generators: A Persona-Driven Approach for Better Accuracy
The auto insurance industry in the USA is a dynamic market with millions of policies issued every year. Each quote generated by insurance companies is based on a complex set of rules created by actuaries, who consider numerous attributes related to both the individual driver and each vehicle covered under the policy. These rules, documented in detailed manuals, form the foundation of every insurance quote.
We Were Tasked with a Challenging Mission
At Satat Tech , we were tasked with ensuring the accuracy of auto insurance quotes by validating a rate engine. With so many parameters, inputs, and possible combinations to consider, it quickly became clear that traditional data-driven testing approaches weren’t cutting it. Testing every possible permutation and combination of input parameters to ensure quote accuracy is not only time-consuming but also lacks value, delivering diminishing returns.
For example, the rate engine formula involved around 820 parameters, each derived from over 100 tables, with each table containing hundreds of rows and over 10 columns. This resulted in millions of potential combinations—an overwhelming task for traditional methods and one that was inefficient and cumbersome to manage.
We found that relying solely on data-driven testing led to frustration, leading to FUD (Fear, Uncertaining, and Doubt) rather than actionable decision-making aligned with business goals. Additionally, we find it can strain resources and extend project timelines. This complexity often results in overlooked scenarios, incorrect pricing, and dissatisfied customers, ultimately impacting the bottom line.
The Complexity of Rule-Based Insurance Quote Generation
Role of Actuarial Rule Manuals
Actuaries are design rule manuals to guide insurance quotes. These manuals incorporate factors like driver history, age, vehicle type, location, driving behavior, association with organization, coverage choices and more.
Attributes and Variables
Each policy quote producer considers multiple attributes, including the driver’s credit score, vehicle usage, geographical risk factors, previous claims, and driving record, among others.
Data Overload
The enormous number of possible combinations makes it impractical for us to validate all scenarios using a traditional data- driven testing approach.
We Recognized the Limitations
Once we dove into the data, we quickly realized the limitations:
Impossible Combinations
With the exponential growth in possible input variations. It’s clear that we cannot cover all scenarios purely with data driven techniques.
Blind Spots in Testing
Critical scenarios have been overlooked, leading to inaccurate quotes that could either overcharge or undercharge customers.
Inefficient Testing Process
We find Testing all permutations requiresing immense time and resources, making the process slow and expensive.
The Challenge Ahead Was Clear – It Was Complex
Now the question arises: How can the auto insurance industry adopt a more effective approach to validating insurance quote generators that accounts for all possible variations without being overwhelmed by the data?
Faced with these complexity, our team brainstormed day and night, searching for more effective solutions. After countless discussions we discovered a breakthrough; adopting a behavior-driven development (BDD) and testing approach using insured personas.
We automated our test execution using Jenkins. Jenkins is a prominent Continuous Integration (CI) orchestration tool that automates the testing process by running test suites, summarizing results, and highlighting test failures. By leveraging Jenkins, we streamlined our testing workflows, enabling seamless execution of automated builds and tests.
Since we do not control the external environment we utilized Mountebank for API Emulation. Mountebank is a powerful tool for API emulation and service virtualization. By utilizing Mountebank, we enhanced our testing processes through parallel development and testing while reducing dependencies on external services. This approach not only helped us accelerate the testing cycle but also helped identify issues early in the development process, resulting in more robust and reliable application.
How We Solved It with BDD + CUCUMBER
Rather than focusing solely on data points, our BDD approach involves creating personas that represent different types of insured individuals, each with distinct behaviors, needs, and risk factors. Cucumber, a popular tool for BDD implementation, allows us to articulate business scenarios using the Given-When-Then format. This method enhances communication across team members and establishes a transparent framework for automated testing, ensuring a common understanding of the application’s behavior and its requirements.
Breaking Rate Tables into logical segments for customer categories helped us refine quotes even further. By categorizing drivers and their needs, we ensured that quotes were accurate, fair, and tailored to each customer.
How Insured Personas Work in Automated Testing
Defining Personas
Develop detailed personas that represent different driver profiles, such as a young driver with a sports car, a family with a minivan, or a senior citizen with a classic car.
Mapping Scenarios
Link these personas to specific scenarios that test the insurance quote generator’s response to different risk factors and policy requirements.
Iterative Testing
Continuously refine these scenarios based on observed behaviors, ensuring that each permutation of rules and attributes is accurately validated.
Throughout this journey, we faced numerous challenges. But through persistence and teamwork, we refined our methods and delivered a robust solution. Our team’s resilience ensured that no obstacle was too great, and we never lost sight of our goal: delivering accurate, reliable insurance quotes to our clients.
By shifting to a persona-based, behavior-driven development approach, we empowered insurance companies with a scalable solution. This approach not only improved system performance and speed to market but also enhanced customer satisfaction, driving long-term business success.
Satat stands for Continuous Delivery,
Persistent Improvement and
Relentless Innovation
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