The use of context-sensitive insurance telematics data in auto insurance rate making

In this research we showcase how second-by-second GPS data can be integrated into existing or new auto insurance pricing structures. We use two types of data: real-time GPS trajectory data collected using a traffic app, as well as survey data. We incorporate vehicle trajectories and accident data to quantify the relationship between driving hazard and accidents with the goal of establishing the linkage between driving risks and accident costs. GPS data considered in this study include not only tradition UBI factors, but also the unique contextual-based risk measurements that compares the driving speed of the vehicle with other drivers on the same road segment. Although smartphone data is used in this study, the methodology developed can be applied to GPS trajectory data collected from other devices such as on-board diagnostics (OBD) or black box solutions. We find that hard brakes, hard starts, peak time travel, speeding as well as driving at a speed significantly different from traffic flow are highly correlated with accident rate. We illustrate the potential underwriting loss an insurer may incur resulting from adverse selection if omitting pertinent risk factors. The results of our study can help insurance companies that are interested in getting into the UBI area set up their auto insurance premium rates.