Automobile Insurance Research Articles

Generative hybrid models for fraud detection in auto insurance with a comparative analysis of VAE, GAN, and diffusion approaches

The process of evaluating vehicle damage and estimating repair costs is a critical component in the automotive and insurance industries. Traditional manual inspection methods are often time-consuming, inconsistent, and susceptible to human error. This study presents DeepClaim, an AI-driven framework designed to automate vehicle damage severity assessment and insurance cost estimation. The proposed system leverages Convolutional Neural Networks (CNNs) for image-based classification of vehicle damage into three categories—minor, moderate, and severe. Using computer vision and deep learning, the system processes vehicle images, predicts severity levels with high accuracy, and provides corresponding cost estimations. A Flask-based web interface enables users to upload damaged vehicle images and receive instant severity predictions along with repair cost insights and insurance recommendations. The framework significantly enhances the efficiency and transparency of insurance claim assessments by minimizing manual intervention and ensuring objective evaluation. Experimental results demonstrate the potential of the system to streamline claim processing, reduce operational costs, and improve customer satisfaction across the automotive and insurance sectors.

With the increase in the number of motor vehicles in China, the scale of the auto insurance market has expanded, and the problem of auto insurance fraud has become increasingly serious, causing huge losses to insurance companies. Data mining technology is widely used in the field of auto insurance fraud prevention, but there is a lack of a detection model with strong robustness. This paper briefly analyzes the common types of auto insurance fraud and their causes, selects the public dataset “Vehicle Insurance Fraud Detection” provided by Angoss Knowledge Seeker, uses the CTGAN method to address the data imbalance problem, and constructs integrated features by combining multiple algorithms such as random forest, XGBoost, and analysis of variance. Furthermore, a Stacking model is built. This model selects random forest, SVM, and neural network as base learners and logistic regression as the meta-learner. Research shows that compared with a single model, the Stacking model constructed after processing data with CTGAN is more efficient and accurate when dealing with imbalanced data such as fraud data. At the same time, the research also points out the current research dilemmas such as data quality issues, constraints of privacy protection laws and regulations, and application barriers.

This study explores the factors influencing online car insurance purchasing decisions among car owners in Mafikeng, in the context of rising digital adoption in the insurance sector. The extended Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) were applied to investigate determinants such as trust, service quality, consumer attitudes, and social media influence. The research used a quantitative approach with a cross-sectional design conducted among 200 car owners. Data were collected using a structured questionnaire and analyzed with Chi-square tests, with reliability and validity confirmed. The results show that trust, service quality, consumer attitudes, and social media promotion significantly influence online purchasing decisions, while social media usage and perceived tangibility have relatively weaker effects. The findings highlight the importance of enhancing trust, transparency, and service quality in online insurance platforms. Insurance providers should also leverage targeted and credible social media strategies to foster consumer engagement and adoption in Mafikeng’s digital insurance market. By integrating TAM and TPB, this study contributes to the limited literature on digital insurance adoption in emerging markets and provides actionable insights for insurers seeking to strengthen customer trust and participation in online platforms.

Abstract In the early 2000s, mainstream US wellness culture started to develop something of an obsession with the distant past. These “paleofantasies” (Zuk 2013), such as barefoot running and the Paleo diet, are not based in scientific evidence about prehistoric human behavior or accurate understandings of evolutionary theory. Why, then, do so many people (especially men) find them compelling? In this paper, I argue that the “stone age” chronotope is implicitly masculine and in fact tends to exclude women altogether. Women are largely absent from imaginings of prehistory, whether those imaginings are car insurance commercials, diet and exercise programs, or even anthropological texts. Looking at various popular discourses about the stone age chronotope, I consider how women are effectively rendered invisible, leaving behind what is perceived as a distilled masculine essence. I suggest that the proliferation of paleofantasy in the past two decades has been part of a broader cultural backlash against feminist progress.

Abstract Two modern trends in insurance are data-intensive underwriting and behaviour-based insurance. Data-intensive underwriting means that insurers analyse more data for estimating the claim cost of a consumer and for determining the premium based on that estimation. Insurers also offer behaviour-based insurance. For example, some car insurers use artificial intelligence (AI) to follow the driving behaviour of an individual consumer in real time and decide whether to offer that consumer a discount. In this paper, we report on a survey of the Dutch population ( N = 999) in which we asked people’s opinions about examples of data-intensive underwriting and behaviour-based insurance. The main results include: (i) If survey respondents find an insurance practice unfair, they also find the practice unacceptable. (ii) Respondents find almost all modern insurance practices that we described unfair. (iii) Respondents find practices for which they can influence the premium fairer. (iv) If respondents find a certain consumer characteristic illogical for basing the premium on, then respondents find using the characteristic unfair. (v) Respondents find it unfair if an insurer offers an insurance product only to a specific group. (vi) Respondents find it unfair if an insurance practice leads to the poor paying more. We also reflect on the policy implications of the findings.

Abstract: Insurance fraud is a major problem that threatens both the stability and fairness of insurance systems. This study explores how machine learning techniques—such as Logistic Regression, Decision Trees, Random Forest, and XGBoost—can be applied to identify fraudulent auto insurance claims. The models obtain great accuracy, precision, recall, and F1-score, demonstrating their capacity to distinguish between false and legitimate claims. The performance of the models is further enhanced and improved prediction accuracy is ensured by the use of advanced approaches like feature selection and hyperparameter tuning. Overall, by offering a thorough review of machine learning algorithms and their use in identifying fraudulent claims, this project makes a contribution to the field of auto insurance fraud detection. Insurance businesses can use the created models and procedures to improve their fraud detection processes, reduce financial risks, and safeguard their operations from fraudulent activity Using a real-world dataset from Kaggle, we applied preprocessing techniques, feature selection via Recursive Feature Elimination, and data balancing through SMOTE. Out of all the models tested, XGBoost showed the highest performance, achieving an accuracy of 89% and an F1-score of 87%. The paper highlights the effectiveness of AI-driven detection systems in minimizing financial loss, improving risk management, and ensuring fairness in insurance systems.

Enhancing intraregional disaster preparedness and response capabilities is crucial for effectively managing noncatastrophic disasters in localized areas. This paper proposes a primary governmental strategy focused on signing disaster insurance contracts with capacity reservation, alongside two supplementary strategies: building predisaster stockpiles and spot market procurement. Among these, our focus is on developing a comprehensive disaster insurance model with capacity reservation functionality, which integrates both financial and operational elements to facilitate public-private collaboration. Using game-theoretical modeling, we analyze government-insurer interactions, with solutions derived through backward induction. The model is validated through a case study in China, focusing on the response of S Government and W Company to Typhoon Rumbia. The results offer a series of important insights. Zero-deductible contracts, though unconventional, emerge as an optimal mechanism in localized disasters by minimizing entry barriers and sustaining insurer profitability. For insurers, long-term cooperation is more attractive in low-volatility, short-duration events, as it enhances capacity amortization and operational efficiency. Meanwhile, policyholders exhibit highly context-sensitive behavior, with stockpiling decisions shaped by lead time, spot market prices, and disaster characteristics. The model uncovers distinct preparedness thresholds that support flexible, scenario-specific strategies, advancing the theory and practice of disaster readiness for regional governments.

Agricultural is basically vulnerable to a wide range of risk - climatic, biological, financial, technological and social-making it one of the most uncertain sectors. Before the introduction of Crop insurance, farmers drag the full burden of these risks, and a single adverse event could destroy an entire season's productivity threatening both livelihoods and food security. To mitigate such a vulnerabilities, crop insurance was introduced in transfer a portion of these risks to insures thereby providing financial protection and promoting investment in modern farming practices. The Pradhan Mantri Fasal Bima Yojana (PMFBY), launched in 2016 by the government of India, aims to provide comprehensive Crop Insurance coverage to farmers protecting them from financial losses arising from natural clameties, pets and disease. By offering substantial premium subsidies, the scheme particularly benefits small and marginal farmers, enhancing their economic resilience and fostering inclusive agricultural growth. The study evaluate the effectiveness of PMFBY by analysing data from both Kharif and Rabi seasons, directing on the key performance indicators such as net gain or lose, claim settlement ratios and premium rates. The analysis aims to assess the financial Sustainability of the scheme and its impact on farmers income stability. The findings contribute to concerned how crop insurance supports rural livelihoods and reinforces agricultural resilience in India.

Insurance interference in electrophysiology: Reclaiming control for timely cardiac care.

Maximum likelihood estimation (MLE) in infinite mixture distributions often lacks closed-form solutions, requiring numerical methods such as the Newton–Raphson algorithm. Selecting appropriate initial values is a critical challenge in these procedures. This study introduces a bootstrap-based approach to determine initial parameter values for MLE, employing both nonparametric and parametric bootstrap methods to generate the mixing distribution. Monte Carlo simulations across multiple cases demonstrate that the bootstrap-based approaches, especially the nonparametric bootstrap, provide reliable and efficient initialization and yield consistent maximum likelihood estimates even when raw moments are undefined. The practical applicability of the method is illustrated using three empirical datasets: third-party liability claims in Indonesia, automobile insurance claim frequency in Australia, and total car accident costs in Spain. The results indicate stable convergence, accurate parameter estimation, and improved reliability for actuarial applications, including premium calculation and risk assessment. The proposed approach offers a robust and versatile tool both for research and in practice in complex or nonstandard mixture distributions.

In Ghana and many African countries, demand for non-life insurance remains strikingly low, even in South Africa, which dominates the continent’s insurance market. This is particularly perplexing given that many non-life insurance products, such as motor insurance, are legally mandated. This study aims to investigate the determinants of non-life insurance demand in Ghana. It utilizes a robust set of regularization methods—specifically Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic net—moving beyond traditional least squares and conventional dimension reduction techniques. The study uses data that span from 1995 to 2022. The findings indicate that the two most important determinants of non-life insurance demand in Ghana are income and economic freedom driven by government expenditure. Furthermore, the study reveals that the most parsimonious model produced by the LASSO algorithm is the most reliable. Based on these insights, we recommend that the government implement economic policies that promote job creation, wage growth, and entrepreneurship to enhance disposable income. Additionally, increasing expenditure on public goods and services—such as roads, utilities, healthcare, education, security, and social intervention programs—would alleviate financial burdens on individuals and businesses, making insurance more affordable and attractive.

29 Background: Non-metastatic breast cancer (BC) accounts for the majority of BC cases in Armenia and poses a growing economic challenge to the national health system. The country lacks a comprehensive public medical insurance system, and access to cancer treatment relies heavily on limited state support and out-of-pocket payments. The Armenian government provides partial financial assistance through a fixed annual subsidy per patient. This support can cover many standard chemotherapeutic agents, but access to novel and targeted therapies remains uneven. In this resource-constrained context, cost modeling is critical to inform national policy, optimize funding allocation, and support the development of a sustainable, equitable cancer care infrastructure. Methods: A cost estimation model was developed to quantify the annual direct medical expenses associated with the diagnosis and treatment of stage I–III BC in Armenia. The analysis was based on national cancer incidence data, institutional records from the Yeolyan Hematology and Oncology Center, and NCCN guidelines. The calculation included routine diagnostics, systemic anti-cancer therapies, and scheduled follow-up within the first year of treatment. Costs for each component of care were determined per patient and extrapolated to a reference population of 1,000 patients, representing the approximate annual number of newly diagnosed non-metastatic BC cases in Armenia. Costs included diagnostic procedures, systemic therapies, and selected supportive medications. The following were excluded: ICU care, antibiotics, antifungals, antihypertensives, medical supplies, BRCA1/2 testing, and certain medications used in the adjuvant setting. Results: Based on our calculations using national incidence data and standardized treatment protocols, the estimated annual cost of diagnosing and treating 1,000 patients with stage I–III BC in Armenia was AMD 2.75 billion (~USD 7.12 million). In 2024, the Armenian government allocated approximately AMD 169.9 billion to healthcare, which represents 1.6% of the national GDP and 5.3% of total public spending. Conclusions: Non-metastatic BC care imposes a substantial financial burden on Armenia’s health system, with treatment costs for early-stage BC accounting for approximately 1.6% of the national healthcare budget. These findings underscore the significant financial pressure a single, common cancer can place on the system—especially in a setting where a comprehensive insurance framework is not yet developed. This pattern is consistent with systemic barriers in low- and middle-income countries, where health system limitations delay access to essential cancer services. Cost modeling such as this is critical to guide policy, advocate for expanded coverage, and support the development of sustainable national insurance strategies in resource-limited settings.

Explaining switching behavior: Consumer attention and choice in car insurance market

Digital transformation is a critical driver of innovation and increased efficiency across various sectors, including insurance. This study examines the impact of digital transformation on the auto insurance market in Kazakhstan, focusing on the DTP.kz service. The primary objective of the research is to assess the effectiveness and efficiency of implementing DTP.kz–a digital platform designed to optimize the insurance claims settlement process. The study employs a mixed-method approach, combining quantitative and qualitative analysis, to comprehensively evaluate the platform’s impact. Data were collected from both primary sources, including detailed records from DTP.kz, and secondary sources, such as industry reports and regulatory publications. The findings reveal that the implementation of DTP. kz significantly reduced the average processing time for insurance claims from 90 to 5 days, leading to increased customer satisfaction and operational efficiency. Additionally, the platform’s integration with Kazakhstan’s GovTech system ensured seamless interaction with digital documents and fraud prevention, enhancing the service’s reliability. However, challenges such as limited local technological infrastructure and regulatory barriers were identified, highlighting the need for further investment and government support. The study concludes that the digitization exemplified by DTP.kz offers substantial benefits for Kazakhstan’s insurance industry, including cost reduction and improved customer engagement. These findings contribute to a deeper understanding of digital transformation processes in emerging markets and provide valuable recommendations for policymakers and market participants.

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various frequentist methods, including Maximum Likelihood Estimation (MLE), Cramer–von Mises (CVM), Anderson–Darling (ADE), Right Tail Anderson–Darling (RTADE), and Left Tail Anderson–Darling (LEADE), with a comparative simulation study assessing their performance. Risk analysis is conducted using actuarial key risk indicators (KRIs) such as Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), Tail Variance (TV), Tail Mean Variance (TMV), and excess function (EL), demonstrating the model’s applicability in financial and insurance contexts. The practical utility of the GLEP family is illustrated through applications to real and simulated datasets, including house price dynamics and insurance claim sizes. Peaks Over Random Threshold Value-at-Risk (PORT-VaR) analysis is applied to U.K. motor insurance claims and U.S. house prices datasets. Some recommendations are provided. Finally, a comparative study is presented to prove the superiority of the new family.

The article outlines the main risks that arise in connection with the implementation of entrepreneurial activities by family members, and discusses possible legal means to minimize them. The necessity of specifying information about the business address in the Unified State Register of Individual Entrepreneurs is substantiated, a number of recommendations aimed at streamlining the legal status of minor entrepreneurs and the self-employed are formulated, the use of state-subsidized comprehensive insurance programs for family enterprises is proposed as a unique measure of their support, and arguments in favor of legally consolidating the category of “family enterprise” are presented. The results of the research can be used in lawmaking activities and in providing state support to family business entities.

The article addresses the issue of civil liability of mediators in the context of the lack of mandatory civil liability insurance. The analysis takes into account the basic premises of tort liability, such as illegality, guilt, damage and adequate causal relationship, referring them to the specificity of mediation activity. It indicates the consequences of the lack of institutional protection in the form of mandatory third party liability insurance, both for the parties to mediation proceedings and the mediators themselves, who may bear personal financial liability. It also emphasizes the growing popularity of mediation among legal practitioners and parties to the conflict, which increases the need to regulate professional liability standards in this area. In conclusion, the postulate of introducing mandatory third-party liability insurance for mediators is formulated as a tool to increase the professionalization of the profession and to provide real legal protection to mediation participants.

In developing countries, chronic patients face dual challenges: high healthcare expenditures coupled with inadequate utilization of outpatient services. Leveraging an administrative claim dataset and applying a two-way fixed effects approach, this study makes one of the first attempts to examine the impacts of chronic disease coverage, which extends additional insurance benefits for outpatient care, on healthcare utilization and expenditures among enrollees diagnosed with hypertension or diabetes in China. The empirical results reveal a dual effect of chronic disease coverage: (1) enrollees with hypertension or diabetes experienced a substantial reduction in outpatient cost-sharing rates, leading to a significant increase in both outpatient service utilization and associated expenditures; (2) concurrently, we observed decreases in general outpatient visits without this special coverage, inpatient utilization, and corresponding expenditures. Notably, the magnitude of expenditure reduction in these non-targeted services was outweighed by the increased spending on covered outpatient services, resulting in a net increase in total healthcare expenditures. Heterogeneity analysis further demonstrates that the impacts were more pronounced among older adults, those with more comprehensive insurance benefits and residents in areas with better-endowed medical facilities. This study offers empirically validated insights for enhancing chronic disease management within medical security systems and establishing age-friendly medical insurance schemes in China as well as other developing countries.

Motor vehicle usage in Indonesia ranks among the highest globally, reaching approximately 141,992,573 units. The growing variety and number of automobiles contribute significantly to traffic congestion and heightened risks to public safety. Given the inherent dangers associated with motorized transportation, including auto theft and accidents, efforts to shift these risks to insurance companies have become crucial. The fundamental idea of insurance is to establish a pool in which policyholders can manage their risk, with premiums determined by the amount of risk that each participant adds to the group. Actuaries in the field of motor vehicle insurance must generate a reasonable premium rate utilizing a variety of methodologies, including the Bonus-Malus approach. The latter, a widely utilized approach, classifies policyholders based on their claims history, incentivizing safe driving. Examining the internal dynamics of the Bonus-Malus system necessitates studying mathematics, particularly algebra, and the use of linear algebra in transition matrices is critical in anticipating changes in bonus-malus rates over time. This research is a quantitative descriptive analysis that explores the implementation of the Bonus-Malus system using a transition matrix framework. It aims to investigate the collaboration of algebra and actuarial science in a real-world application of the Bonus-Malus scheme for motor vehicle insurance, focusing on the use of the transition matrix in premium computation, utilizing secondary data from PT. Jasa Raharja Kota Semarang for the years 2021–2022. The transition matrix analysis shows that Model 2 allows for smoother class transition, lowers the possibility of high-risk class recurrence, and provides more consistent premium adjustments. This demonstrates the model's ability to create a balanced incentive structure while interpreting claim trends. Furthermore, Model 2 has a greater expected value of Loimaranta efficiency than Model 1, supporting findings that added status improves Bonus-Malus system efficiency.