Usage-based Insurance Research Articles

A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping

The modern vehicle insurance industry is increasingly adopting Pay-As-You-Drive (PAYD) insurance models, aligning premium costs with driving behavior. Our study introduces a Bayesian approach to PAYD insurance, leveraging the strengths of Naive Bayes classifiers and Bayesian Networks to handle uncertainty and integrate prior knowledge in risk assessment. The Naive Bayes model achieved an 87.5% accuracy in predicting risk partitions. With the Bayesian Network providing insights into causal relationships through a Directed Acyclic Graph (DAG), we also address the challenges of traditional actuarial models — low interpretability of intra-factor relationships and thus hard to plan for risk management for both provider and policyholder. Our research contributes to optimizing insurance pricing strategies. Still, the causal mapping also dismisses the meaningfulness of using geographic grouping in insurance pricing (discriminatory or not). It reassures the theoretical advantage of the PAYD model over the traditional model, facilitating access to affordable coverage for policyholders.

Insurtech, sensor data, and changes in customers' coverage choices: Evidence from usage‐based automobile insurance

AbstractIn this paper, we examine the role of usage‐based auto insurance on customers' decisions to change their insurance coverage at the renewal. Using a sample of 135,540 customers, we study whether usage‐based insurance (UBI) can facilitate the upselling and cross‐selling efforts of the firm, possibly leading to higher coverage choices and additional insurance product purchases. Our results suggest that UBI customers are more likely to change their coverage choice than non‐UBI customers at first (but not second) renewal. Both price discounts and the information provided by UBI affect the customers' coverage changes. Among UBI customers, those who get higher UBI discounts are more likely to both increase their insurance coverage (upselling) and add the comprehensive coverage option (cross‐selling), which is not directly related to driving behavior at the time of first (annual) renewal. Moreover, customers who have received more negative feedback (daily hard brakes) are more likely to increase their insurance coverage.

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

Telematics boxes integrated into vehicles are instrumental in capturing driving data encompassing behavioral and contextual information, including speed, distance travelled by road type, and time of day. These data can be amalgamated with drivers' individual attributes and reported accident occurrences to their respective insurance providers. Our study analyzes a substantial sample size of 19,214 individual drivers over a span of 55 weeks, covering a cumulative distance of 181.4 million kilometers driven. Utilizing this dataset, we develop predictive models for weekly accident frequency. As anticipated based on prior research with yearly data, our findings affirm that behavioral traits, such as instances of excessive speed, and contextual data pertaining to road type and time of day significantly aid in ratemaking design. The predictive models enable the creation of driving scores and personalized warnings, presenting a potential to enhance traffic safety by alerting drivers to perilous conditions. Our discussion delves into the construction of multiplicative scores derived from Poisson regression, contrasting them with additive scores resulting from a linear probability model approach, which offer greater communicability. Furthermore, we demonstrate that the inclusion of lagged behavioral and contextual factors not only enhances prediction accuracy but also lays the foundation for a diverse range of usage-based insurance schemes for weekly payments.

Feedback and Financial Incentives for Reducing Cell Phone Use While Driving

Handheld phone use while driving is a major factor in vehicle crashes. Scalable interventions are needed to encourage drivers not to use their phones. To test whether interventions involving social comparison feedback and/or financial incentives can reduce drivers' handheld phone use. In a randomized clinical trial, interventions were administered nationwide in the US via a mobile application in the context of a usage-based insurance program (Snapshot Mobile application). Customers were eligible to be invited to participate in the study if enrolled in the usage-based insurance program for 30 to 70 days. The study was conducted from May 13 to June 30, 2019. Analysis was completed December 22, 2023. Participants were randomly assigned to 1 of 6 trial arms for a 7-week intervention period: (1) control; (2) feedback, with weekly push notification about their handheld phone use compared with that of similar others; (3) standard incentive, with a maximum $50 award at the end of the intervention based on how their handheld phone use compared with similar others; (4) standard incentive plus feedback, combining interventions of arms 2 and 3; (5) reframed incentive plus feedback, with a maximum $7.15 award each week, framed as participant's to lose; and (6) doubled reframed incentive plus feedback, a maximum $14.29 weekly loss-framed award. Proportion of drive time engaged in handheld phone use in seconds per hour (s/h) of driving. Analyses were conducted with the intention-to-treat approach. Of 17 663 customers invited by email to participate, 2109 opted in and were randomized. A total of 2020 drivers finished the intervention period (68.0% female; median age, 30 [IQR, 25-39] years). Median baseline handheld phone use was 216 (IQR, 72-480) s/h. Relative to control, feedback and standard incentive participants did not reduce their handheld phone use. Standard incentive plus feedback participants reduced their use by -38 (95% CI, -69 to -8) s/h (P = .045); reframed incentive plus feedback participants reduced their use by -56 (95% CI, -87 to -26) s/h (P < .001); and doubled reframed incentive plus feedback participants reduced their use by -42 s/h (95% CI, -72 to -13 s/h; P = .007). The 5 active treatment arms did not differ significantly from each other. In this randomized clinical trial, providing social comparison feedback plus incentives reduced handheld phone use while individuals were driving. ClinicalTrials.gov Identifier: NCT03833219.

Machine learning based car accident risk prediction for usage-based insurance

Machine learning based car accident risk prediction for usage-based insurance

Integration of traditional and telematics data for efficient insurance claims prediction

AbstractWhile driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or favorable selection compared to the data points with traditional features.To handle this issue, we apply a data integration technique based on calibration weights for usage-based insurance with multiple sources of data. It is shown that the proposed framework can efficiently integrate traditional data and telematics data and can also deal with possible favorable selection issues related to telematics data availability. Our findings are supported by a simulation study and empirical analysis in a synthetic telematics dataset.

Relational Contracts in Usage-Based Insurance

Relational Contracts in Usage-Based Insurance

The Necessity and Feasibility of Usage-Based Insurance for New Energy Vehicles in China

Aims: With the continuous development and growth of China's new energy automobile market, new automobile owners desire exclusive auto insurance. However, most insurance companies use traditional automobile commercial auto insurance to cover them. Under this pricing model, there is little overall difference in premiums, regardless of whether the driver's driving behavior is good or not. This paper wants to implement usage-based insurance (UBI) in China because it can add more auxiliary pricing factors, adjust according to the driver's driving behavior changes, and make auto insurance pricing more scientific and fair.&#x0D; Study Design: This paper employs the literature review and market research methods to analyze the current product supply and consumer demand in the Chinese auto insurance market. Firstly, introduce the current situation of the development of the new energy automobile insurance market in China and its problems. Then, discuss the necessity and feasibility of developing UBI auto insurance for new energy vehicles in China. Finally, based on the reality of China's new energy vehicle UBI auto insurance gives some programs.&#x0D; Conclusion: This paper finds that from the perspective of insurance companies, consumers, and the social level, UBI auto insurance for new energy automobiles is necessary, and it is also the direction of future auto insurance reform and development. Subsequently, combined with China's current relevant policies and technologies, it is concluded that the development of UBI auto insurance for new energy vehicles in China is feasible.

Privacy Protection, At What Cost? Exploring the Regulatory Resistance to Data Technology in Auto Insurance

AbstractRegulatory and sociological resistance to new market-driven technologies, particularly to those that rely on collection and analysis of personal data, is prevalent even in cases where the technology creates large social value and saves lives. This article is a case study of such tragic technology resistance, focusing on tracking devices in cars which allow auto insurers to monitor how policyholders drive and adjust the premiums accordingly. Growing empirical work reveals that such “usage-based insurance” induces safer driving, reducing fatal accidents by almost one third, and resulting in more affordable and fair premiums. Yet, California prohibits this technology and other states limit its effectiveness, largely in the interest of privacy protection. The article evaluates the justifications fueling the restrictive regulation vis-à-vis the loss of lives resulting from this regulation. It concludes that the social benefits of the tracking technology dramatically outweigh the privacy and related costs.

A Trustable and Secure Usage-Based Insurance Policy Auction Mechanism and Platform Using Blockchain and Smart Contract Technologies.

This study presents an architectural framework for the blockchain-based usage-based insurance (UBI) policy auction mechanism in the internet of vehicles (IoV) applications. The main objective of this study is to analyze and design the specific blockchain architecture and management considerations for the UBI environment. An auction mechanism is developed for the UBI blockchain platform to enhance consumer trust. The study identifies correlations between driving behaviors and associated risks to determine a driver's score. A decentralized bidding algorithm is proposed and implemented on a blockchain platform using elliptic curve cryptography and first-price sealed-bid auctions. Additionally, the model incorporates intelligent contract functionality to prevent unauthorized modifications and ensure that insurance prices align with the prevailing market value. An experimental study evaluates the system's efficacy by expanding the participant pool in the bidding process to identify the winning bidder and is investigated under scenarios where varying numbers of insurance companies submit bids. The experimental results demonstrate that as the number of insurance companies increases exponentially, the temporal overhead incurred by the system exhibits only marginal growth. Moreover, the allocation of bids is accomplished within a significantly abbreviated timeframe. These findings provide evidence that supports the efficiency of the proposed algorithm.

Mitigating moral hazard with usage‐based insurance

AbstractTechnological progress has improved insurers' ability to monitor policyholders and has led to usage‐based insurance (UBI) contracts that incorporate behavioral risk factors in pricing. Economic theory predicts that any informative monitoring signal is adopted in equilibrium. In practice, the demand for UBI is still low to date with market shares in the single digits. We modify the standard moral‐hazard model in insurance economics by trading off a simpler effort model for a richer strategy space, and by focusing on the use of monitoring for premium differentiation. In our model, an informative monitoring technology is in use if it is sufficiently accurate. Otherwise, the premium incentive from monitoring is not large enough to alleviate the incentive‐compatibility constraint to an extent that would make policyholders better off. Our results help explain the slow adoption of UBI contracts in practice and provide an avenue to increase their appeal to policyholders.

Analysis of profitability of Pay-As-You-Speed scheme

Objective Implementing usage-based insurance (UBI) schemes captured the attention and has found a better practical foundation, thanks to the presence of probes for recording the driving and traveling data. The UBI is believed to provide an incentive measure through premium discounts for correcting driving and traveling behavior. However, the success of UBI implementation is dependent on different factors including the presence of alternative insurance plans, the level of privacy concerns among society, and the amount of trust within the society. Therefore, designing proper discount schemes which affect UBI acceptance by drivers and its profitability for government and insurance companies differs across countries and different scenarios. We aim to investigate the profitability of UBI Pay-As-You-Speed in Iran with a focus on the government and insurance companies. This study will be beneficial for policymakers seeking to understand the potential impact of UBI Pay-As-You-Speed in Iran. Methods The research is conducted on a synthesized population with acceptance and accident frequency models based on a self-reported survey. We assumed six UBI schemes based on previous research. The acceptance model is a logit discrete choice model and the accident frequency is based on Poisson regression. Costs of crashes are estimated based on one-year data of the Central Insurance company in Iran. After models’ estimation, the simulated population is used to estimate the resultant profit of private insurance companies and government. Results It is found that the scheme with no premium discount and no rental prices for the required monitoring device, results in the highest revenue for the government. Moreover, with an increase in the probe penetration rate, the profitability of the government increases with a greater reduction in crashes. However, this trend is not observed for insurance companies because the cost of the monitoring device and premium discounts offset the profits gained from prevented crashes. Conclusion The presence of the government as a main actor in facilitating the implementation of UBI schemes is essential or private insurance companies would be reluctant to provide these schemes to customers.

Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data

Usage-based insurance (UBI) adjusts premiums based on an individual policyholder’s dynamic risk evaluation, incentivizing policyholders to maintain safe driving behavior in pursuit of a lower insurance premium. Although post-trip interventions can improve driving behavior significantly, most interventions do not provide risk mitigation strategies tailored to long-term driving behavior. This study proposes a novel approach for driving risk prevention in UBI services, providing risk analysis services and tailoring driving suggestions for policyholders. Advanced prediction models and model interpretation methods were adopted to assess and analyze personal driving risks, respectively. A multi-objective counterfactual model was developed to generate risk mitigation strategies for policyholders. The significance of driving behavior in risk mitigation and its actionability were assessed to provide realistic and actionable suggestions for policyholders. The case study, based on a real dataset from an insurance company in China, showed that the proposed approach can be directly embedded in the existing UBI service framework to provide personalized feedback and services to policyholders.

Road Rage and Aggressive Driving Behaviour Detection in Usage-Based Insurance Using Machine Learning

Driving behaviour is a critical issue in modern transportation systems due to the increasing concerns about the safety of drivers, passengers, and road users. Machine learning models are capable of learning driving patterns from sensor data and recognizing individuals by their driving behaviours. This paper presents a novel framework for aggressive driving detection and driver classification based on driving events identified from GPS data collected with smartphones and heart rate of the driver captured with a wearable device. The proposed system for road rage and aggressive driving detection (RAD) is realized with an integral framework with components for data acquisition, event detection, driver classification, and model interpretability. The system is implemented by generating a prediction model by training machine learning classifiers with a dataset collected in a cohort to classify drivers into good, unhealthy, road rage, and always bad. The proposed system is to improve road safety and to customize insurance premiums in the best interest of policy holders and insurance companies.

Using contextual data to predict risky driving events: A novel methodology from explainable artificial intelligence

Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by understanding when and how safe policyholders drive. However, telematics information can also be used to understand the driving contexts experienced by the driver within each trip (e.g., road types, weather, traffic). Since different combinations of these conditions affect exposure to accidents, this understanding introduces predictive opportunities in driving risk assessment. This paper investigates the relationships between driving context combinations and risk using a naturalistic driving dataset of 77,859 km. In particular, XGBoost and Random Forests are used to determine the predictive significance of driving contexts for near-misses, speeding and distraction events. Moreover, the most important contextual factors in predicting these risky events are identified and ranked through Shapley Additive Explanations. The results show that the driving context has significant power in predicting driving risk. Speed limit, weather temperature, wind speed, traffic conditions and road slope appear in the top ten most relevant features for most risky events. Analysing contextual feature variations and their influence on risky events showed that low-speed limits increase the predicted frequency of speeding and phone unlocking events, whereas high-speed limits decrease harsh accelerations. Low temperatures decrease the expected frequency of harsh manoeuvres, and precipitations increase harsh acceleration, harsh braking, and distraction events. Furthermore, road slope, intersections and pavement quality are the most critical factors among road layout attributes. The methodology presented in this study aims to support road safety stakeholders and insurers by providing insights to study the contextual risk factors that influence road accident frequency and driving risk.

Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective

Pricing using a Generalised Linear Model is the gold standard in the auto insurance industry and rate regulation. Generalised Additive Model applications in insurance pricing are receiving increasing attention from academic researchers and actuarial pricing professionals. The actuarial practice has constantly shown evidence of significantly different premium rates among the different rating territories. In this work, we build predictive models for claim frequency and severity using the synthetic Usage Based Insurance (UBI) dataset variables. First, we conduct territorial clustering based on each location’s claim counts and amounts by grouping those locations into a smaller set, defined as a cluster for rating purposes. After clustering, we incorporate these clusters into our predictive model to determine the risk relativity for each factor level. Through predictive modelling, we have successfully identified key factors that may be helpful for the rate regulation of UBI. Our work aims to fill the gap between individual-level pricing and rate regulation using the UBI database and provides insights on consistency in using traditional rating variables for UBI pricing. Our main contribution is to outline how GAM can address a more complicated functionality of risk factors and the interactions among them. We also contribute to demonstrating the territory clustering problem in UBI to construct the rating territories for pricing and rate regulation. We find that relativity for high annual mileage driven is almost three times that associated with low annual mileage level, which implies its importance in premium calculation. Overall, we provide insights into how UBI can be regulated through traditional pricing factors, additional factors from UBI datasets and rating territories derived from basic rating units and the driver’s location.

Mitigating Moral Hazard With Usage-Based Insurance

Mitigating Moral Hazard With Usage-Based Insurance

The transformation of the insurance industry and road safety by driver safety behaviour telematics

In contemporary motorized societies, human factors remain overwhelmingly responsible for road crash occurrence. Urged by considerable technological developments, insurers have integrated telematics to their operations to develop an array of Usage-Based-Insurance (UBI) schemes. The aim of the present research is to expand on the aforementioned topics based on trends from recently published research and discuss the transformation of the insurance industry by driver safety behaviour telematics. Firstly, an overview of the relevant technological advancements is provided, followed by the integration practices of telematics adopted by the insurance industry. A more in-depth examination of the tangible benefits of telematics and driver feedback in road safety is subsequently provided. The paper concludes with a discussion on the future directions of the transformation of the insurance industry, along with the related challenges and limitations.

Evaluation and interpretation of driving risks: Automobile claim frequency modeling with telematics data

AbstractWith the development of vehicle telematics and data mining technology, usage‐based insurance (UBI) has aroused widespread interest from both academia and industry. The extensive driving behavior features make it possible to further understand the risks of insured vehicles, but pose challenges in the identification and interpretation of important ratemaking factors. This study, based on the telematics data of policyholders in China's mainland, analyzes insurance claim frequency of commercial trucks using both Poisson regression and several machine learning models, including regression tree, random forest, gradient boosting tree, XGBoost and neural network. After selecting the best model, we analyze feature importance, feature effects and the contribution of each feature to the prediction from an actuarial perspective. Our empirical study shows that XGBoost greatly outperforms the traditional models and detects some important risk factors, such as the average speed, the average mileage traveled per day, the fraction of night driving, the number of sudden brakes and the fraction of left/right turns at intersections. These features usually have a nonlinear effect on driving risk, and there are complex interactions between features. To further distinguish high−/low‐risk drivers, we run supervised clustering for risk segmentation according to drivers' driving habits. In summary, this study not only provide a more accurate prediction of driving risk, but also greatly satisfy the interpretability requirements of insurance regulators and risk management.

CCUBI: A cross‐chain based premium competition scheme with privacy preservation for usage‐based insurance

Usage-based insurance (UBI) provides reasonable vehicle insurance premiums based on vehicle usage and driving behavior. In general, there are three major issues in realizing intelligent UBI systems. First, UBI evaluation mechanisms are not auditable to drivers. Insurers may thus deliberately adjust the UBI premiums. Second, the process of collecting driving data by insurers may lead to serious privacy breaches. Third, forging safer driving data for reducing insurance premiums may cause economic losses for insurers. To address these challenges, in this study, we propose CCUBI, a cross-chain-based premium competition scheme with privacy preservation for intelligent UBI systems. We introduce tamper-resistant blockchain and smart contracts to construct credible insurance mechanisms. The cross-chain technology connects these blockchains in the entire network to form an open premium competition scheme. Vehicle owners can assess designated insurers by sharing historical data with them to get a suitable CCUBI plan. In addition, we propose a data aggregation method used for CCUBI analysis with privacy preservation. Vehicle owners only publish proofs of the driving data. Proofs can still maintain privacy and computability in cross-chain flows. Finally, we adopt roadside units to detect forged driving data. We conduct a detailed security analysis. Experimental results also demonstrate the efficiency of CCUBI.