Automobile Insurance Research Articles

New Quality Productivity Empowers Enterprises Risk-Driven Innovation -- Taking BYD's Entry into New Energy Vehicle Insurance Industry as an Example

Abstract: Driven by the new wave of revolution in the science and technology industry, emerging industries are emerging one after another, with huge potential and unpredictable risks coexisting. The concept of new quality productivity came into being,which represents the innovation of productivity and the future development trend of advanced productivity. This paper uses a conceptual analysis method to study and analyze the logical ideas of risk-driven innovation of enterprises empowered by new quality productivity, and takes BYD's entry into the new energy vehicle insurance industry as an example to conduct a case analysis. The study demonstrates the positive role that new productivity plays in empowering enterprise innovation at the technical level and building an innovation culture for enterprises when firms engage in risk-driven innovation. Based on this, the study suggests innovative behaviors for relevant enterprises to explore new industries in the future. Enterprises with innovation needs can realize risk-driven innovation by utilizing new quality productivity from multiple perspectives, such as enhancing core technology R&D capabilities, cultivating new production factors, forward-looking planning for new industries, and shaping the supply chain pattern of new industries.

Cream Skimming and Technological Innovations in Automobile Insurance Market

Abstract This study examines the effect of telematics devices on the automobile insurance market. Automobile insurance using these devices is marketed as pay-how-you-drive (PHYD) automobile insurance. Insurers can directly monitor the insured’s vehicle for risky driving behaviors such as speeding. Some of insureds receive discounts on their premiums for driving more cautiously. On the other hand, accumulating the insured’s driving skills, insurers may be able to only sign up insureds with low accident rates, widely known as cream skimming. By applying Hodgson (2014. “Adverse Selection in Health Insurance Markets: A Classroom Experiment.” The Journal of Economic Education 45 (2): 90–100), we tested whether PHYD programs makes it easier for insurers to contract with low-risk drivers. Repeated cream skimming suggests that only high-risk drivers remain in the market and a residual market cannot be established, which could lead to adverse selection and market failure in the absence of any regulations. However, the experiment revealed that low-risk but highly risk-averse subjects tend to dislike the fluctuation in premiums by enrolling in PHYD programs. The results could be effective in future regulatory construction.

Weekly dynamic motor insurance ratemaking with a telematics signals bonus-malus score

Abstract We present a dynamic pay-how-you-drive pricing scheme for motor insurance using telematics signals. More specifically, our approach allows the insurer to apply penalties to a baseline premium on the occurrence of events such as hard acceleration or braking. In addition, we incorporate a bonus-malus system (BMS) adapted for telematics data, providing a credibility component based on past telematics signals to the claim frequency predictions. We purposefully consider a weekly setting for our ratemaking approach to benefit from the signal’s high-frequency rate and to encourage safe driving via dynamic premium corrections. Moreover, we provide a detailed structure that allows our model to benefit from historical records and detailed telematics data collected weekly through an onboard device. We showcase our results numerically in a case study using data from an insurance company.

THE INSURANCE MARKET IN ROMANIA – MOTOR THRID PARTY LIABILITY INSURANCE: COMMUNICATION ON DEVELOPMENTS, RISKS, CHALLENGES AND PERSPECTIVES

The risks faced by the Romanian insurance market are significant and come from both the difficult global context and the internal dynamics. Paradoxically, however, the underdevelopment of the market, especially in the life insurance sector, and the dependence on the mandatory insurance type RCA, reduce the exposure of Romanian insurance companies to some risks, such as a fall in income or an increase in interest rates. In this context, the paper aims to make an estimate of the risks and challenges in the RCA insurance market for the coming years starting from a series of official data for these mandatory policies from 2019-2023. We find that compulsory car insurance stands out as a bright spot, comprising over 45% of total premiums. A small number of companies dominate the market, with the top 10 controlling roughly 60%. In the compulsory car insurance sector, there's a mix of growth and decline: voluntary premiums are rising, but the number of contracts is falling. Despite fewer claims, the average cost per claim has increased compared to the previous year. Overall, the results suggest that the dependence on mandatory insurance like RCA paradoxically shields Romanian insurance companies from certain risks.

Private health insurance and health in a healthcare system with comprehensive public insurance

Private health insurance and health in a healthcare system with comprehensive public insurance

Modelling Motor Insurance Claims Using Arma Model And Appropriate Statistical Distribution Of Insurance Companies, Machakos County, Kenya

The prediction of future claim amounts based on past or present claims is a challenging task for insurance companies. According to research carried out by Michael Barth and David L. Eccles (2009); claim costs constitute large proportion of insurers expenses in evaluating the premium growth on the loss ratio of property causality insurers. Various statistical techniques have been employed to analyze claims data and assess its impact on productivity. Autoregressive (AR), Moving Average (MA), and Auto Regressive Moving Average (ARMA) processes are commonly used to model the temporal dependencies and trends in the data. These techniques enable insurance companies to understand the dynamics of claim incidence and their effects on productivity over time (Philip J, 2016). Advanced modeling techniques such as generalized additive models, negative binomial regression, and multinomial logit models have been used to evaluate claims processes, predict claim occurrence, and model claim severity (Renshaw A.E, 1994; Edward. W Frees et al., 2008; Daiane Aparecida Zanetti et al., 2006). These models consider various factors such as driver age, gender, vehicle type, and no claim discount to assess their impact on claim incidence and productivity. Previous studies have overlooked the comprehensive examination of the impact of insurance claims on productivity on insurance companies. Therefore, this research aims to address this gap by investigating the factors associated with insurance motor claims and their effects on the productivity of insurance companies by employing statistical techniques like appropriate probability distribution, incorporation of moving average technique, Autoregressive process and ARMA process, as identified by industry professionals and experts. The results obtained from the real-life data for distinctive 11 years indicated the potential strength of ARMA models to provide insurance company's claim amount prediction that could assist the companies to have better planning. The findings of this study, if adopted by various keynote players in the insurance and regulatory sectors, can be highly beneficial in formulating appropriate policies to enhance the management of motor insurance claims and improve the productivity of insurance companies.

Uninsured Motorists’ Risk Attitudes and Pedestrian Road Safety: Evidence from Metropolitan Lagos, Nigeria

With the increasing prevalence of uninsured vehicles in urban areas, motorists’ behaviours on pedestrian safety are crucial for developing effective road safety strategies. This study investigated the happenings around uninsured motorists’ risk attitudes and pedestrian road safety in metropolitan Lagos, Nigeria. Using the survey approach cum the two-way sampling techniques comprised of purposive and convenience, data were gathered to analyse the behavioural dispositions of uninsured motorists and their implications on pedestrian road safety. The study adopted a structured questionnaire as a research instrument for data collection from a sample population of 209. The data analytical employed was descriptive statistics comprised of simple frequency percentages presented in tabular form and bar chart description. Findings revealed, based on pedestrian opinions, that uninsured motorists display riskier driving behaviours, such speeding and reckless driving pattern; which was found prevalent among commercial uninsured motorists. In addition, insufficient implementation of insurance regulation and socio-economic factors contributed to the proliferation of uninsured vehicles. The study also showcased the descriptive analysis of uninsured motorists’ risk attitudes towards pedestrian road safety. Further finding presented a descriptive analysis of pedestrians’ road safety metrics on Lagos roads. Then, the study accentuates the urgent need for comprehensive policy intervention targeting uninsured motorists, including stricter application of insurance laws, public awareness campaigns, enhanced infrastructure to improve pedestrian safety in urban environment, like metropolitan, Lagos, Nigeria and in similar contexts in other African cities and beyond. Thus, insurance practitioners, especially motor insurance providers, should synergise their efforts to improve the accessibility and affordability of insurance for motorists, particularly, the low-income individuals; by creating flexible payment options, and subsidising insurance premiums. Lastly, local communities should engage with other stakeholders in the transport industry to ensure road safety culture, promoting responsible driving behaviour and encouraging compliance with insurance regulation

Modeling of Car Insurance Premiums Using the Bayes Method with Poisson and Exponential Distributions

Car insurance is included in the general insurance category. Determination of general insurance premiums can be done through various approaches, one of which is the Bayes method used in this study. The Poisson distribution is chosen to model the frequency of claims, while the Exponential distribution is used to model the amount of claims. Insurance premiums are calculated by multiplying the expected frequency of claims by the expected amount of claims. Based on the results of the analysis of car insurance data using the Bayes method, it was found that the highest premium rates were for Mercedes brand vehicles, while the lowest rates were for Saab brand vehicles. The results of this calculation can be used by insurance companies as a reference in managing car insurance reserve funds.

Digitalization and Corporate Social Responsibility: A Case Study of the Moroccan Auto Insurance Sector

The aim of this article is to explore the impact of digitalization on corporate social responsibility (CSR) in the automobile insurance sector in Morocco. This article first explores the theoretical and conceptual foundations of digital transformation and CSR. A mixed methods approach is then used, combining qualitative interviews with a wider quantitative survey, to investigate how digital innovations influence CSR practices. Interview analysis provides the basis for the development of a conceptual framework and eight hypotheses, which are then tested using quantitative techniques to analyze survey data. The results reveal several links between the benefits of digitalization and CSR. Claims management platforms, digital roadside assistance tools, and digital vehicle assessment and inspection all positively impact policyholders’ well-being in terms of compensation and asset preservation, thereby enhancing the CSR profile of automobile insurers. Similarly, augmented reality (AR) and virtual reality (VR) training and simulation, as well as repair assistance, have positive impacts on policyholders’ well-being and advance the CSR positioning of automobile insurers. This article has limitations as it is based on a narrow industrial sector in a single country, but it nonetheless highlights certain relevant interrelationships between digitalization and CSR, contributing to the development of theory and practice in these research areas.

Strengthening Resilience and Sustainability for Post-Disaster Recovery: A Comparative Law and Economics Analysis on Smart Mixes Between Mechanisms

This article advocates smart mixes of mechanisms of post-disaster recovery in terms of boosting resilience and sustainability through liability rules, government intervention, insurance, and their combinations. Liability rules can provide compensation to victims and, in theory, also reduce disaster risks, while they have their limits in practice. The government widely intervenes in disaster compensation in many countries, but it faces challenges in how it can intervene fairly and efficiently, whether through informal channels, such as ad hoc charity, or structured approaches, such as compensation funds. As governments may struggle to provide efficient and effective compensation ex post, insurance may offer proactive solutions with models of first-party catastrophe insurance and third-party liability insurance ex ante. Where market failure, liability failure, and government failure may all arise, a smart mix of mechanisms is often preferable from a law and economic perspective, rather than relying solely on one framework. However, upon examining empirical evidence regarding the effectiveness of these instruments in China, the UK, France, Germany, and Turkey, it becomes apparent that a mix of mechanisms is not always applied in an effective manner. Models of mandatory comprehensive disaster insurance, and Public–Private Partnership (PPP), which both receive government intervention as a last resort to the market and liability failures, can reach the goal of more effective compensation for disaster victims, risk prevention and resilience when faced with disaster recovery, and should therefore be substantially implemented beyond the current levels. To be clear, the proposed mix of solutions mainly focuses on legal and economic dimensions which are rather limited, ignoring, for example, building codes for structures and physical interventions.

Comparative Study of XGBoost, Random Forest, and Logistic Regression Models for Predicting Customer Interest in Vehicle Insurance

In today’s competitive insurance market, accurately predicting customer interest in additional products, such as vehicle insurance, is crucial for optimizing marketing strategies and maximizing sales. This study presents a comparative analysis of three machine learning models such as XGBoost, RandomForest, and Logistic Regression to predict customer interest in vehicle insurance based on a dataset that includes demographic, vehicle, and policy-related features. The dataset was analyzed using five-fold cross-validation, and the performance of the models was evaluated using AUC-ROC, precision, recall, and F1-score. XGBoost demonstrated the highest recall (0.9525) and AUC-ROC (0.7854), making it the most effective model for identifying customers interested in vehicle insurance, though at the expense of lower precision (0.2585). RandomForest showed a more balanced trade-off between precision (0.3064) and recall (0.5341) but performed lower overall. Logistic Regression, while the most interpretable model, exhibited high variability in performance across different folds, with a lower average precision (0.2372). The findings of this research highlight that XGBoost is ideal for maximizing recall in high-volume campaigns, while RandomForest may be better suited for applications requiring fewer false positives. These results offer valuable insights into model selection based on business objectives and resource allocation.

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

Enhancing guidelines for managing cognitively impaired drivers: Insights from Western evidence for Asian adaptation.

The global incidence of dementia is increasing, and cognitively impaired drivers are at a higher risk of crashes compared to healthy drivers. Doctors face challenges in assessing these at-risk drivers, with questionable adherence to existing guidelines. This study aimed to review and compare guidelines for managing cognitively impaired drivers from various countries. A scoping review was conducted to identify relevant guidelines, which were then descriptively compared with Singapore's guideline. Eleven guidelines from 8 countries: US (n=2), Canada (n=2), UK (n=2), Ireland, Belgium, Australia, New Zealand and Singapore were reviewed. All guidelines support driving assessments and conditional licensing in ordinary (i.e. non-professional) drivers with dementia. Canada stands out for not allowing co-piloting and geographical restrictions in conditional licensing practice. Few guidelines provide indemnity for doctors reporting to licensing authorities, and communication about the impact of dementia on car insurance is rarely addressed. Most Western guidelines include evidence-based approaches, provisions for drivers with mild cognitive impairment and early discussions on transitioning from driving. A clinic-based functional screening toolbox and 2 clinical algorithms (1 with and 1 without the Clinical Dementia Rating scale) were identified as having universal applicability. Singapore's guideline, by comparison, is outdated and lacks both developmental rigour and guidance on managing mild cognitive impairment and transitioning drivers out of driving. Comprehensive, evidence-based guidelines from Western countries provide valuable resources that can help Singapore design or update its guideline.