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- W3154415392 abstract "Risk assessments have been critical missions for all kinds of operations management. Disruptive applications are developed to assess risks more precisely with the adoption of advanced technologies. Such a paradigm shift is ongoing in various industries, especially the auto insurance business. Contrasted with a common practice mainly based on static demographics, the mobility-based risk assessment predicts risks using dynamic driving data obtained via IoT-enabled telematics. This study proposes a novel solution to learn about drivers’ risks. We specifically consider two aspects of mobility, namely trait and trajectory, monitored by GPS, on-board diagnostics, and in-vehicle cameras in a real-time manner. Traits refer to the distinctive driving behaviors (styles) of drivers, while trajectories consist of the vehicle motion sequences along with the contextual factors on trips. We select the features extracted from the two to optimize a predictive model that assesses risks at both trip and driver levels. Using the fine-granular driving data and crash reports, we find that behavioral traits play a significant role in predicting crashes. We also notice that the risks of drivers are heterogeneous and could evolve over time. In a series of empirical experiments, the proposed solution outperforms the current practice and the alternative methods considered by prior literature. We show that the mobility-based models are consistently superior to demographic-based ones commonly adopted by practitioners. Quantitatively, our approach improves recall (precision) of logistic regression, support vector machine, and random forests by 43.45% (41.82%), 29.46% (27.87%), and 26.24% (25.27%), respectively. Last, the proposed solution is more robust and computationally efficient to unusual use cases (i.e., skewed and small training samples). The findings provide several managerial implications and a blueprint for the auto insurance industry to operationalize IoT-enabled risk assessments in the coming era of 5G communication." @default.
- W3154415392 created "2021-04-26" @default.
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- W3154415392 date "2020-01-01" @default.
- W3154415392 modified "2023-10-02" @default.
- W3154415392 title "Is It All About You or Your Driving? Designing IoT-Enabled Risk Assessments" @default.
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- W3154415392 doi "https://doi.org/10.2139/ssrn.3661062" @default.
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