- Billions of dollars are spent annually on car insurance. While most insurance companies already segment clients based on their accidents history, drivers license score, age and other factors, none do it in real-time accounting for drivers psychotype and live behavior on the road.
- Our client is building a solution allowing insurance companies not only to update drivers credit score in real-time but also to produce driving-style recommendations to help prevent accidents.
- With the help of our team, the client built the platform using a simple smart phone and advanced Computer Vision technologies to track and adapt the insurance credit score of drivers in real-time and produce live recommendations.
- We consulted the client on real-time segmentation task which runs directly on mobile phone and achieves approximately 100 FPS.
- Our team created a customized batch streaming solution to reduce the latency and produce in-batch pre-aggregations on device.
- Based on the data streamed from the phone, the solution adjusts the credit score in real-time.
- By turning a mobile phone camera into a device that tacks the road activity and collects data from accelerometer, gyroscope and GPS, the solution is able to detect cars and track their behavior in real-time.
- Based on the received data, we built the scoring model that is used by insurance companies to adjust their clients privileges.
- Additionally, the model evaluates drivers’ behavior i.e. how rational they are, and identifies their psychotype. Having classified the behavior of each driver, the solution is capable of delivering timely reports on drivers' behavior, making gamified suggestions to improve their driving style.
- DRL has delivered a working scoring model which is used as a core component of a startup’s MVP.
- The model not only helps insurance companies to prevent value destruction, but also makes driving safer for all traffic participants.
- Currently, we are assisting the startup with raising funds and launching controlled beta pilot.
Updated Jul 24, 2019 — 00:00 UTC