Sensitel partnered with ERGO, a major global insurer, to prototype a “Pay-As-You-Drive” usage-based insurance (UBI) model as part of ERGO’s Artificial Intelligence & Internet of Things (AI/IoT) innovation initiative. The project leveraged smartphone sensors and cloud analytics to capture real-time driving behaviour at low cost. The resulting solution was designed to seamlessly integrate with ERGO’s existing insurance architecture on a secure AWS cloud, providing dynamic driver scoring and rich data for actuarial analysis. In a short time, the collaboration delivered a working prototype, proving the feasibility of this novel model and laying the groundwork for broader business adoption.

The Challenge

To grow its revenue, ERGO explored new business models. ERGO’s goal was to explore a usage-based insurance model that could offer personalized, behaviour-driven auto coverage. However, integrating this novel approach into a traditional insurance infrastructure posed significant challenges. Legacy systems were not designed to ingest or analyse continuous driving data from the field. Key hurdles included data integration, cost, and compliance. ERGO wanted to avoid expensive aftermarket devices and data loggers, so any telematics solution had to use low-cost hardware.

Note: Product Roadmap for SENSMYDRIVE

Moreover, collecting personal driving data raised privacy and security concerns, the solution would need to meet strict data protection standards and internal IT governance. Finally, the project had to demonstrate clear value quickly to gain buy-in from business stakeholders. Actuaries and product managers needed proof that real-time driving insights could translate into better risk assessment and innovative insurance products. In summary, ERGO faced the dual challenge of technology integration and organisational adoption in bringing a pay-as-you-drive concept to life.

The Solution

To address these challenges, ERGO partnered with Sensitel to design and implement an end-to-end Pay-As-You-Drive telematics solution. The joint ERGO-Sensitel team focused on leveraging devices that every customer already has – smartphones – to gather driving data, rather than requiring dedicated hardware. Leveraging smartphones as IoT sensors meant virtually zero hardware cost and easy deployment, while still capturing rich telemetry from everyday driving.

Smartphone Sensor Data: The prototype included a lightweight mobile app that uses the phone’s built-in sensors (accelerometer, gyroscope, GPS, etc.) to monitor driving behaviour passively. It measured key indicators of driving risk such as hard acceleration, sudden braking, and patterns of distracted driving (for example, phone handling or usage while on the move). These metrics provided a detailed view of how safely (or aggressively) a person drives, far beyond what traditional rating factors cover. Aside from gathering raw data, the platform also recorded contextual information, such as weather conditions and geolocation. Snow on the road can significantly impact someone’s driving, so filtering out weather conditions is essential for accurate driver scores. This IoT-driven approach added valuable color to the data, for instance, knowing if a harsh brake occurred on a rainy highway versus a clear day in the city. The platform could differentiate between hazardous behaviour and reasonable actions by combining driving events with location and weather data. These context-aware insights are crucial for actuaries to assess risk fairly. Moreover, Sensitel stored all the data securely on AWS. Thereby, all the data was streamed in real-time to a secure AWS cloud environment for processing and storage. Using AWS infrastructure allowed the prototype to scale quickly and remain isolated from core legacy systems during testing. Sensitel implemented analytics logic within the cloud platform to compute a driver scoring model. Each driver's behaviour data was analysed (using rules and AI algorithms) to generate a personalised driving score. Safe driving habits resulted in higher scores, while risky events would lower the score. This scoring approach could eventually be linked to insurance pricing – a cornerstone of the UBI concept. There was also the possibility of selling insurance by the mile, without considering driving. So, multiple new business models could emerge from this project. The solution effectively bridged the gap between IoT data and insurance analysis, demonstrating a clear path to integrating the UBI model into ERGO's workflow. Throughout the development, collaboration was key. ERGO's and Sensitel's teams worked in close sync, iterating quickly on features and ensuring that the prototype aligned with ERGO's business objectives. Sensitel brought deep expertise in IoT data handling and AI-driven analytics, while ERGO provided domain knowledge in insurance and oversight on compliance. This partnership model ensured the solution was technologically sound and fit for real-world insurance operations.

Business Impact

The “Pay-As-You-Drive” pilot successfully delivered a functional UBI system and valuable learnings for ERGO quickly. “This prototype proved not only that the technology works but that it can integrate with our insurance ecosystem,” says Sandra Behr, emphasising the breakthrough nature of the project within ERGO. Key outcomes and benefits included the rapid prototyping and integration with the joint team that developed and deployed the UBI prototype within a few weeks. Using existing smartphones for telemetry validated a highly cost-effective approach. ERGO can now scale up UBI without investing in thousands of proprietary devices, potentially saving hundreds of dollars per policyholder. The AWS cloud setup likewise means the solution can scale on demand to support more drivers or expanded analytics with minimal upfront investment in hardware. This project gave ERGO’s actuarial team a first-hand look at rich driving behaviour data. They gained new insights into risk factors, such as how frequently hard braking occurs in certain conditions or patterns of phone distraction among drivers. This data is a goldmine for refining risk models and could lead to more accurate, personalised premium pricing. It also opens the door to proactive risk reduction, for example, by providing feedback to drivers to encourage safer habits.