Pay-As-You-Drive Insurance: Modeling and Implications

Cheng, Jiang; Feng, Frank Yulin; Zeng, Xudong

doi:10.1080/10920277.2022.2077220

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…Insurers base their predictive modelling on records of distance driven during a certain period of time, usually one year, together with traditional rating factors and observed claim frequency. It has been proven (see Cheng et al 2022) that PAYD benefits the policyholder by reducing their premium payments and increasing the total utility derived from vehicle usage. Moreover, it also has positive effects for insurers, since it has been shown that early adopters experience a significant increase in their market share, as they are able to attract low-risk drivers from other insurers (see Che et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Boucher and Turcotte (2020) analysed panel telematics count data using Generalised Additive Models (GAMs) and found that the distance-claims frequency relationship seems to be linear, and the apparent non-linearity was caused by a residual heterogeneity captured by GAMs. Another issue that has recently been investigated in the literature is the existence of a cut-off value of distance driven, below which drivers who have a traditional policy should switch to a PAYD policy (Cheng et al 2022). The impact of non-linearities and interactions may be better captured with, for example, neural nets or the combined GLM/NN methods that have been proposed in the literature (see Blier-Wong et al 2020;Owens et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

Torra

Guillén

Pérez-Marín

et al. 2023

Risks

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Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well as vehicle and personal characteristics. This paper uses a monthly panel data structure and the Poisson model to predict the expected frequency of claims over time. Some meteorological information is included. Two types of claims are considered separately: only those related to at-fault third-party liability accidents, and all types of claims including assistance on the road. A sample of drivers in Spain in 2018–2019 is analysed with information on claiming frequency per month. Drivers were observed for seven months. Our analysis is novel because monthly summaries of telematics information are combined with weather data in a panel structure, revealing that external factors affect the expected claims frequencies. Reckless speeding behaviours and intense urban circulation increase the risk of an accident, which also increases with windy conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

Torra

Guillén

Pérez-Marín

et al. 2023

Risks

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“…25 The connections offer security and benefits from services such as pay-as-you-drive insurance and vehicle diagnostics. 26,27 The cellular network connection has many advantages. One of the most important aspects is to avoid the need for new infrastructure for communication.…”

Section: Introductionmentioning

confidence: 99%

“…The other mode involves the conventional mobile network downloading data through the base station 25 . The connections offer security and benefits from services such as pay‐as‐you‐drive insurance and vehicle diagnostics 26,27 . The cellular network connection has many advantages.…”

Section: Introductionmentioning

confidence: 99%

Optimized blockchain network model for 6G cellular vehicle‐to‐everything communication

Bilgehan,

Sabuncu

2023

Trans Emerging Tel Tech

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Road safety of electric cars has required large and fast data flow. The six‐generation (6G) networks that provide hyper‐speed, ultra‐reliable, and minimal delay meet the requirements. Data reliability can be supported with popular blockchain technology, which is still widely accepted as a decentralized data management system. This article used an optimum path with the highest signal‐to‐noise constraints for the proposed algorithms. The essential requirement of error‐free data transmission for high‐speed and large data has been successfully implemented. Three‐stage implementations are suggested. The edge computers at the partners are used as storage to resolve the first level. A C‐V2X network solution is used to resolve the second level of economic activity. A two‐stage optimization strategy is used to consider the last level of high network transaction levels. The results of the newly introduced optimization algorithms are simulated and contrasted with those of a traditional, time‐consuming step‐by‐step calculation method. The introduced algorithms tested with real‐time experimental signal‐to‐noise ratio (SNR) and bit error rate (BER) values, giving successful results.

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