Automobile Insurance Research Articles

Factors Influencing Vehicle Owners’ Intentions to Extend Special Perils Coverage in Motor Insurance

The rising frequency and severity of natural disasters have intensified the need for comprehensive motor insurance, particularly focusing on special perils coverage. This coverage plays a vital role in safeguarding vehicle owners against financial losses resulting from environmental hazards such as floods, landslides, and other catastrophic events. This study investigates the factors that influence vehicle owners' intentions to extend special perils coverage within their motor insurance policies in Klang Valley, Malaysia. A quantitative and descriptive research approach was adopted, employing convenience sampling techniques, with data analyzed using descriptive statistics through the Statistical Package for Social Sciences (SPSS) to extract valuable insights. A structured survey involving 100 vehicle owners examines the impact of knowledge, promotional efforts, and perceived benefits on their decisions to opt for extended special perils coverage. The findings highlight that these three factors - knowledge, promotion, and benefits - significantly influence the decision-making process. The study's insights are valuable for insurance companies, policymakers, and educational institutions in crafting strategies to encourage the wider adoption of special perils coverage. By increasing awareness, enhancing promotional efforts, and communicating the benefits of such coverage, stakeholders can better meet the needs of vehicle owners and boost the uptake of comprehensive motor insurance. This study underscores the importance of a multifaceted approach in promoting insurance products that offer essential protection against escalating environmental risks.

Features to Involve and Collect in Future Auto Insurance Pricing

The property insurance industry is a highly data-driven sector. In the traditional model, insurance pricing largely depends on the self-reported risk characteristics of clients, such as age, gender, vehicle type, etc. These characteristics help insurance companies categorize clients into different risk levels and set premiums accordingly. With the development of telematics, insurance companies can now collect and analyze more dynamic data that is more directly related to risk. This study presents a comprehensive analysis of three feature selection models for vehicle insurance pricing. Utilizing regression techniques, it evaluated a multitude of factors believed to influence claim frequency and severity. Through a rigorous comparative study, key factors that significantly impact the vehicle are assessed and fit to different regression models. The articles findings indicate that factors such as policy bonus, mileage base data, and car usage are paramount in determining insurance rates. Moreover, the study revealed that incorporating these factors into the pricing model enhances its accuracy and fairness.

Considerations on insurance recourse and the status of the consumer in motor third-party liability insurance – comments on the background of the preliminary inquiry submitted to the Court of Justice of the EU by the rajonen sad in Sofia, Bulgaria, on June 26, 2023 – ZD ‘BUL INS’ AD vs PV (Case C-387/23, BUL INS)

The article is an attempt to provide a doctrinal answer to the first question posed by the national court in the form of a preliminary question to the CJEU in the case ref. C-387/23. At the same time the text contains considerations on the essence of motor insurance, the construction of atypical recourse of the insurer, as well as, to the extent necessary, refers to the qualification of the consumer in civil and insurance law. Attention was also paid to the stature of the uniform law on the insurance contract using the example of PEICL. Reference is also made to the regulations of national law and final conclusions are made.

Interpersonal communication in the motor insurance sector during the claims stage

Orientation: The South African short-term motor insurance industry is highly competitive, offering clients many choices. Therefore, the long-term retention of clients is crucial for an insurance company’s sustainability.Research purpose: The study aims to report on the context-specific communication challenges during the claims stage and to propose recommendations to mitigate them from an interpersonal communication perspective.Motivation for the study: Studies on client retention have been conducted from a marketing or corporate communication perspective, but it is not known how interpersonal communication during the claims phase could impact long-term relationships.Research design, approach and method: We followed a qualitative approach, with a single case study design. Semi-structured interviews were conducted with senior management and claims administrators in the motor insurance department of a large South African insurer. The data were inductively analysed through qualitative content analysis from which the challenges were identified.Main findings: A lack of mutual understanding, ineffective conflict management, a lack of trust and one-way communication were identified as the main challenges experienced by claims administrators in their communication with clients during the claims stage.Practical/managerial implications: Practical principles such as sympathy and empathy, listening, feedback, respect, providing and clarifying information and providing access to resources, together with ethical and transparent communication, two-way symmetrical communication and dialogue should be included in claims administrators’ training to enable them to understand and adapt to the client’s needs.Contribution/value-add: A framework for training claims administrators operating in this context is presented, offering a theoretical and practical contribution.

Gaussian Mixture Regression Model with Sparsity for Clustering of Territory Risk in Auto Insurance

Abstract Insurance rating territory design and accurate estimation of territory risk relativities are fundamental aspects of auto insurance rate regulation. It is crucial to develop methodologies that can facilitate the effective design of rating territories and their risk relativities estimate, as they directly impact the rate filing and the decision support of the rate change review process. This article proposes a Gaussian Mixture Regression model clustering approach for territory design. The proposed method incorporates a linear regression model, taking spatial location as model covariates, which helps estimate the cluster mean more accurately. Also, to further enhance the estimation of territory risk relativities, we impose sparsity through sparse matrix decomposition of the membership coefficient matrix obtained from the Gaussian Mixture Regression model. By transitioning from the current hard clustering method to a soft approach, our methodology could improve the evaluation of territory risk for rate-making purposes. Moreover, using non-negative sparse matrix approximation ensures that the estimation of risk relativities for basic rating units remains smooth, effectively eliminating data noise from the territory risk relativity estimate. Overall, our novel methodology aims to significantly enhance the accuracy and reliability of risk analysis in auto insurance. Furthermore, the proposed method exhibits potential for extension to various other domains that involve spatial clustering of data, thereby broadening its applicability and expanding its usefulness beyond auto insurance rate regulation.

Motor Insurance Claim Frequency Prediction Using XGBoost

Insurance claim frequency modelling is an important task for non-life insurers, this together with other variables forms an important part of product pricing and risk management. The traditional frequency models such as Poisson, Negative Binomial and Zero Inflated models have several weakness such as scalibility issues, overdispersion and independence assumptions for large datasets and therefore not ideal to use when dealing with complex and unstructured data. Extreme gradient Boosting Algorithm (XGBoost) is an ensemble learning which has the capacity to effectively handle big complex and unstructured insurance data. XGBoost creates tree-based models by iteratively fitting decision trees to the residuals of the previous predictions, effectively reducing the error in each iteration. This research utilized and explored the XGBoost algorithm to process motor insurance claims large dataset in-order to predict the frequencies of insurance claims, that is 0,1,2,and 3. Using this algorithm we aim to enhance the accuracy of predictions that will yield better estimates for improved risk assessment and pricing of insurance products. Cross validation was performed to assess the true performance of our model. Cross validation results showed that XGBoost models for the claim frequency had a RMSE estimate of 0.949, MAE of 0.7741 and RSQ 0.781. This demonstrated a strong predictive performance, with an RMSE of 0.949 and an MAE of 0.7741, indicating a low average error in the predictions.The RSQ of 0.781 suggests further that the model explained a significant proportion of variability of the insurance data. Our model was evaluated with a confusion matrix. The results of the confusion matrix showed that for the frequency 0 99.59% of cases were correctly predicted, frequency 1 94.01% were correctly predicted and frequency 2 84.80% and finally frequency 3 only 40.96% of the observations were correctly predicted.These results highlights the potential of XGBoost as a robust modeling technique for handling big data and accurately predicting insurance claim frequency. The results corroborate with other studies that XGBoost is an invaluable tool for insurance companies.

Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation

This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables.

Tinjauan Atas Prosedur Pengajuan Hingga Pembayaran Klaim Asuransi Kendaraan Bermotor

The development of the number of vehicles is inseparable from the risk of uncertainty that lurks. Therefore, motor vehicle insurance is present as a place to transfer risk from unexpected things that happen to vehicles, or to parties included in the policy. The accounting information system exists because of the need to monitor business activities. In submitting an insurance claim, a procedure is needed regarding the claim submission flow to facilitate the company and monitor the company's performance. The purpose of this review is to determine the application of the system in the procedure for submitting to paying JP-Astor claims. This research was conducted by the author at PT. Asuransi Jasaraharja Putera Bogor Marketing Office located at Jl. Re. Martadinata Komp. Ruko Sentra Air Mancur No. 2h, Bogor Tengah, Bogor City. The results of the review indicate that PT Asuransi Jasaraharja Putera Bogor Marketing Office has been quite good at carrying out the procedure for submitting to paying JP-Astor claims. The claim submission and claim payment system at Jasaraharja Putera Bogor Marketing Office has helped the work of employees and insured parties, so that they know the extent of the claim process that has been submitted. Accounting Information System implemented through Care System helps companies in registering and remote authorization. Keywords: Submission, Payment, Claim, Motor Vehicle

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.

Modelling of Claim and Pricing of Motor Insurance Based on Bonus-Malus System Considering the Frequency and Severity of Claims

This article proposes new distributions for claim frequency and severity, specifically tailored for a bonus-malus system in automobile insurance. The mixed Poisson with weighted quasi Lindley distribution is recommended for modeling claim frequency, while the mixed exponential with weighted quasi Lindley distribution is suggested for modeling claim severity. To estimate insurance premiums, the Bayesian method is employed, incorporating both frequency and severity distributions. The study validates the proposed models using real data from an Australian insurance company, which includes 67,856 policies. The assessment of model adequacy indicates that the Poisson-weighted quasi Lindley distribution is a suitable fit for modeling claim frequency, while the exponential-weighted quasi Lindley distribution is appropriate for modeling claim severity. Overall, the results suggest that the proposed models offer optimal premium estimations, considering both claim frequency and severity, which can lead to fairer pricing and increased customer appeal during claim occurrences compared to conventional models.

Trajectory representation learning with multilevel attention for driver identification

Massive trajectory data have originated from the development of positioning technology. Learning GPS trajectory representation to characterize a driver’s driving style is a challenging task with important applications in many areas, including autonomous driving, auto insurance, advanced driver assistance systems, urban computing, and the internet of things. Few studies have considered the interactions between different factors. In this study, we propose a novel trajectory representation method based on a multilevel attention mechanism (ATTraj2vec) and apply it to the task of driver identification. We use 1D CNN to summarize local features, including motion, spatial and temporal features. Then we utilize a multilevel attention mechanism to extract global features, aggregating the interactions of motion features with temporal and spatial features progressively. Additionally, we adopt multi-loss to optimize our model simultaneously, which consists of a softmax loss for driver classification and Siamese loss for making trajectories from the same driver more similar. Classification experimental results on a real-world automobile trajectory dataset demonstrated that our proposed model significantly outperforms existing baselines. Meanwhile, the proposed method provides significant gains in the trajectory clustering of unseen drivers. The code is available in the repository, https://github.com/limengyuan2021/ATTraj2vec,

Calculation of Motor Vehicle Insurance Premiums Through Evaluation of Claim Frequency and Amount Data

Insurance, as a risk control strategy by transferring the burden of risk from one party to another, consists of two main forms: life insurance, which covers financial losses from the risk of death of the policyholder, and general insurance, which involves the transfer of risk against property losses. Motor vehicle insurance has become a common product reflecting the high value and benefits of motor vehicles, which has resulted in an increase in vehicle ownership. Although the increase in the number of vehicles contributes to the increase in road accidents, many owners who suffer losses do not receive the compensation they deserve. In this context, the premium becomes a key factor, where the policyholder pays a certain amount of money to get protection. This research aims to apply risk premium calculation based on claim frequency and claim size data, as conducted by Ozgurel in 2005, especially for each vehicle category and region in XYZ insurance company. The main problem is to optimize the premium calculation to reflect the actual risk, providing a more accurate understanding of the influence of vehicle and regional characteristics in determining a fair and appropriate premium.

Rainfall Variability, Risk, and Resilience: A Comparative Study of Insured vs. Non-Insured Farmers in Maharashtra, India

This paper examines the risks involved in Maharashtra’s agriculture and investigates the mitigation strategies adopted by insured and non-insured farmers in Latur and Pune districts. By using a multi-stage sampling method, 400 sample households were selected from two districts. These households comprise 268 insured farmers and 134 non-insured farmers. The results reveal that both rainfall variability and reductions in crop prices significantly impact agricultural revenues. However, the specific risk that carries the most significant weight may vary depending on an individual’s insurance status. Insured farmers are most vulnerable to the unpredictable nature of rainfall, as insurance typically protects against yield losses but not necessarily against market fluctuations. Conversely, non-insured farmers face a constant threat from declining crop prices, even with a good harvest, as they lack the financial buffer provided by crop insurance. This study suggests that the government should integrate pricing risk with crop insurance to reduce risk in agricultural production. Additionally, the farmers should adopt an integrated approach to pest and disease management. The implementation of a comprehensive crop insurance program is needed to ensure equitable coverage for farmers, facilitating widespread benefits. Additionally, more efforts should focus on mitigation of identified risks such as input costs, market fluctuations, and production uncertainties.

Navigating Natural Disaster Disruption in the Hospitality Sector: Insights From the September 8, 2023 Earthquake in Morocco

Purpose: This case study delves into the specific hurdles encountered by hotel managers in Marrakech during and after the September 8, 2023 earthquake, alongside the strategies deployed to tackle these challenges. Moreover, it aims to unravel the intricacies faced by risk claims professionals in processing claims and evaluating risks post-earthquake, aiming to formulate suggestions for enhancing disaster risk management within the hospitality domain. Design/Methodology/Approach: Semi-structured interviews were conducted with 27 hotel managers and five insurance professionals in Morocco, all intimately engaged in handling or alleviating the impacts of the earthquake on the hospitality sector. Thematic analysis was utilized to pinpoint key challenges, responses initiated by hotel managers, and insights provided by risk claims professionals. Findings: The study underscores that the earthquake caused significant operational disruptions and financial strains on hotels, resulting in cancellations, revenue loss, and infrastructure damage. Hotel managers responded with prompt evacuation, safety protocols, damage assessment, and customer communication strategies. Risk claims professionals underscored the significance of comprehensive insurance coverage, streamlined claims processes, and advanced risk modeling techniques to mitigate financial risks and bolster disaster resilience within the hospitality sector. Originality/Value: This study furnishes original insights into the challenges faced by hotel managers and risk claims professionals post-seismic event, illuminating the complexities of disaster risk management within the hospitality industry. The recommendations proposed contribute to the formulation of effective strategies and protocols for navigating similar unforeseen events in the future.

Key Determinants to Drive Motor Insurance Customer Acquisition in Western Province, Sri Lanka: A Focus on the Marketing Mix

Purpose: There is a significant need for a comprehensive insurance policy for all vehicle owners, especially considering the growing emphasis on health and safety, and public protection. The substantial preference for 3rd party insurance over comprehensive policy covers has created a hurdle for insurance companies, both in terms of acquiring new customers and retaining existing ones. This prevailing trend has presented a significant challenge to the growth potential of general insurance firms in the market. The main purpose of this study is to investigate how factors such as product, price, promotion, and distribution network influence the customer purchase intention towards motor insurance. Design/methodology/approach: This was a cross-sectional study using a deductive research approach. Data were gathered from 412 policy holders in Sri Lanka using a self-administered structured questionnaire. Multiple regression analysis was used to determine the most influencing factor among product, price, promotion and distribution network influencing customer purchase intention towards motor insurance. Findings: Based on the analysis, the insurance service [product], insurance premium [price], promotion and distribution network have a significant relationship with the customer purchase intention towards motor insurance policies in Sri Lanka. Among the factors tested, distribution network and insurance service have the most significant impact on customer purchase intention with reference to motor insurance segment. Originality: This study provides a unique contribution by highlighting the critical role of the distribution network, particularly the combination of Place and People elements, in driving motor insurance customer acquisition in Sri Lanka. Unlike prior studies that emphasize price and promotional strategies, this research focuses on the integrated influence of accessibility and personalized service. The findings demonstrate that a well-established distribution network outweighs traditional marketing factors in influencing purchase decisions, offering a new perspective on consumer behavior in the Sri Lankan motor insurance industry. Implications: The findings of this study will pave the way for a paradigm shift where, insurance firms could understand and develop relevant strategies to improve services provided to the existing and potential motor insurance customers. By analyzing what truly matters to Sri Lankan drivers, both insurance service providers as well as regulatory authorities can ally and develop a holistic insurance policy that comprehensively covers all aspects of vehicle ownership, filling the void in the current market. According to the findings, it can be advised that the insurance corporations should focus more on providing a product with more unique features and invest in creating a solid network of branches across the country which are fortified with skilled and service-oriented personnel who could persuade the customers in buying comprehensive insurance covers. Future researchers can also focus on income and educational level as moderators since both of which have an impact on consumer behavior as suggested in prior literature. Also, there is a possibility of widening their scopes by considering other types of insurance products and expand the geographical area as well.

Impact of Ecological Cognitive Bias on Pesticide Reduction by Natural Rubber Farmers in China: Insight from Price Insurance Satisfaction

Some natural rubber farmers mistakenly equate the ecological functions of rubber plantations with those of primary forests. This cognitive bias can hinder pesticide reduction efforts. Meanwhile, natural rubber farmers gain security through price insurance, which helps them adopt a long-term perspective on environmental protection, mitigating the negative impact of cognitive biases on pesticide use decisions. However, existing research often overlooks the influence of ecological cognitive bias on pesticide reduction and the moderating role of price insurance satisfaction. This study utilizes field survey data from Hainan and employs logit models and double machine learning models to empirically analyze the impact of ecological cognitive bias on pesticide reduction among natural rubber farmers. It further tests the underlying mechanisms using moderation models. The results indicate that (1) ecological cognitive bias negatively affects pesticide reduction among natural rubber farmers, and (2) price insurance satisfaction mitigates the impact of ecological cognitive bias on pesticide reduction. Based on these findings, the government should enhance education and training to raise ecological awareness among natural rubber farmers and reduce ecological cognitive biases. Disseminating knowledge about price insurance and establishing a comprehensive insurance system can secure farmers’ income and promote the rational use of pesticides.

Factors Affecting Consumers' Comprehensive Vehicle Insurance Policy Purchasing Behavior

Research Originality: With increasing vehicle values in Türkiye and high insurance costs, consumers' vehicle insurance purchasing preferences emerge as a dilemma. The study examines consumers' insurance purchasing behavior in this dilemma's context of risk and overconfidence tendencies.Research Objectives: This study aims to reveal the effects of individual characteristics, attitude toward risk, and overconfidence tendencies on comprehensive vehicle insurance policy purchasing behavior.Research Methods: A questionnaire was administered to 428 selected vehicle owners in Turkey, and the questionnaire results were analyzed using the binary logit model.Empirical Results: The study found that gender, education level, income level, market value of the vehicle, attitude towards risk, and self-confidence level have statistically significant effects on comprehensive vehicle insurance policy purchase decisions.Implications: The relationship between risk-taking behavior, overconfidence, and insurance purchasing behavior requires further discussion. Government policies that increase insurance awareness are recommended to weaken this relationship.JEL Classification: D12, G22, C25How to Cite:Kutlu, M., Sumer Adin, S., Mola, H., & Şahin Kutlu, S. (2024). Analysis of Factors Affecting Consumers' Comprehensive Vehicle Insurance Policy Purchasing Behavior. Etikonomi, 3(2), 539 – 554. https://doi.org/10.15408/etk.v23i2.37807.

Multivariate Zero-Inflated INAR(1) Model with an Application in Automobile Insurance

The objective of this article is to propose a comprehensive solution for analyzing multidimensional non-life claim count data that exhibits time and cross-dependence, as well as zero inflation. To achieve this, we introduce a multivariate INAR(1) model, with the innovation term characterized by either a multivariate zero-inflated Poisson distribution or a multivariate zero-inflated hurdle Poisson distribution. Additionally, our modeling framework accounts for the impact of individual and coverage-specific covariates on the mean parameters of each model, thereby facilitating the computation of customized insurance premiums based on varying risk profiles. To estimate the model parameters, we employ a novel expectation-maximization (EM) algorithm. Our model demonstrates satisfactory performance in the analysis of European motor third-party liability claim count data.

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.

Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behavior

With the advancements in technology, telematics data that capture vehicle movement information are becoming available to more insurers. Because these data capture the actual driving behavior, they are expected to improve our understanding of driving risk and facilitate more accurate auto insurance ratemaking. In this article, we analyze an auto insurance dataset with telematics data collected from a major European insurer. Through a detailed discussion of the telematics data structure and related data quality issues, we elaborate on practical challenges in processing and incorporating telematics information in loss modeling and ratemaking. Then, with an exploratory data analysis, we demonstrate the existence of heterogeneity in individual driving behavior, even within the groups of policyholders with and without claims, which supports the study of telematics data. Our regression analysis reiterates the importance of telematics data in claims modeling; in particular, we propose a speed transition matrix that describes discretely recorded speed time series and produces statistically significant predictors for claim counts. We conclude that large speed transitions, together with higher maximum speed attained, nighttime driving, and increased harsh braking, are associated with increased claim counts. Moreover, we empirically illustrate the learning effects in driving behavior: we show that both severe harsh events detected at a high threshold and expected claim counts are not directly proportional with driving time or distance but they increase at a decreasing rate.