Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data

Guillen, Montserrat; Pérez-Marín, Ana M.; Nielsen, Jens P.

doi:10.1016/j.heliyon.2024.e36501

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e36501

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Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data

Montserrat Guillen,

Ana M. Pérez-Marín,

Jens P. Nielsen

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2024

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Cited by 2 publications

References 70 publications

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Determining Driving Risk Factors from Near-Miss Events in Telematics Data Using Histogram-Based Gradient Boosting Regressors

Sun,

Guillen,

Pérez-Marín

et al. 2024

JTAER

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This study introduces a novel method for driving risk assessment based on the analysis of near-miss events captured in telematics data. Near-miss events, which are highly correlated with accidents, are employed as proxies for accident prediction. This research employs histogram-based gradient boosting regressors (HGBRs) for the analysis of telematics data, with comparisons made across datasets from China and Spain. The results presented in this paper demonstrate that HGBR outperforms conventional generalized linear models, such as Poisson regression and negative binomial regression, in predicting driving risks. Furthermore, the findings suggest that near-miss events could serve as a substitute for traditional claims in calculating insurance premiums. It can be seen that the machine learning algorithm offers the prospect of more accurate risk assessments and insurance pricing.

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Determining Driving Risk Factors from Near-Miss Events in Telematics Data Using Histogram-Based Gradient Boosting Regressors

Sun,

Guillen,

Pérez-Marín

et al. 2024

JTAER

View full text Add to dashboard Cite

show abstract

How AI from Automated Driving Systems Can Contribute to the Assessment of Human Driving Behavior

Driessen,

Siebinga,

de Boer

et al. 2024

Robotics

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This paper proposes a novel approach to measuring human driving performance by using the AI capabilities of automated driving systems, illustrated through three example scenarios. Traditionally, the assessment of human driving has followed a bottom-up methodology, where raw data are compared to fixed thresholds, yielding indicators such as the number of hard braking events. However, acceleration threshold exceedances are often heavily influenced by the driving context. We propose a top-down context-aware approach to driving assessments, in which recordings of human-driven vehicles are analyzed by an automated driving system. By comparing the human driver’s speed to the AI’s recommended speed, we derive a level of disagreement that can be used to distinguish between hard braking caused by aggressive driving and emergency braking in response to a critical event. The proposed method may serve as an alternative to the metrics currently used by some insurance companies and may serve as a template for future AI-based driver assessment.

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Pricing weekly motor insurance drivers’ with behavioral and contextual telematics data

Cited by 2 publications

References 70 publications

Determining Driving Risk Factors from Near-Miss Events in Telematics Data Using Histogram-Based Gradient Boosting Regressors

Determining Driving Risk Factors from Near-Miss Events in Telematics Data Using Histogram-Based Gradient Boosting Regressors

How AI from Automated Driving Systems Can Contribute to the Assessment of Human Driving Behavior

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