A Bonus-Malus System (BMS) is a ratemaking mechanism used in insurance to adjust premiums based on a policyholder's claim history, with the goal of segmenting risk profiles more accurately. A BMS typically comprises three key components: the number of BMS levels, the transition rules dictating...

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...

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately...

Modeling, Analysis, and Optimization for Mathematical Finance, Economics, and Risks is a critical domain that integrates mathematical theory with practical applications to address the complexities of modern financial and economic systems. This special issue focuses on recent studies that are...

This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially...

A comprehensive auto insurance policy usually provides the broadest protection for the most common events for which the policyholder would file a claim. On the other hand, some insurers offer extended third-party car insurance to adapt to the personal needs of every policyholder. The extra...

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing...

In this paper, we develop a number of new composite models for modelling individual claims in general insurance. All our models contain a Weibull distribution for the smallest claims, a lognormal distribution for the medium-sized claims, and a long-tailed distribution for the largest claims....

Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency-severity models assume a linear relationship between a function...

Accurate forecasting of insurance claims is of the utmost importance for insurance activity as the evolution of claims determines cash outflows and the pricing, and thus the profitability, of the underlying insurance coverage. These are used as inputs when the insurance company drafts its...

Insurance is based on specific assumptions, including a "perfect performance" model. The principle of indemnification, mentioned in almost all insurance textbooks for non-life insurance, is nevertheless far more complicated than economists' perception. This complexity is a consequence of...

In actuarial practice, the modeling of total losses tied to a certain policy is a nontrivial task due to complex distributional features. In the recent literature, the application of the Dirichlet process mixture for insurance loss has been proposed to eliminate the risk of model...

Accuracy and interpretability of a (non-life) insurance pricing model are essential qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In recent years, the classification and regression trees (CARTs) and their ensembles have gained popularity in the...

While the topics of risk aversion and utility theory have been discussed extensively in the academic literature on risk and insurance, this literature does not include a pedagogical discussion that is widely accessible for classroom use. This paper provides a practical introduction to risk...

This study examines the effects of cream skimming using telematics devices on the automobile insurance market. Insurers can directly monitor insureds’ cars for risky driving behaviors, such as speeding, hard braking, and quick acceleration. These observations make the information symmetric...

The number of claims and the amount of claims (or the sum of claims, which is referred to as the amount of total claims/loss in the insurance literature) is among the most important information available to insurance companies, and its analysis can provide the necessary knowledge for targeted...

We study the vehicle owners’ Motor Own Damage insurance search frictions and choices. We use a consumer-level annual panel of policy and insurance offers’ data from 2010-2018 from the biggest insurance broker consolidating the Estonian car insurance market offers. We apply the two-stage...

Regulatory 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...

This paper uses novel sensor data to study drivers’ risky phone use behavior. The results challenge the conventional wisdom of moral hazard in insurance. We first identify handheld phone use behavior (“HPU”) and quantify its causal impact on accident likelihood (“riskiness”) using...

This research examines the effects of rate regulations and compulsory insurance laws on insurance purchases. Three stage least squares (3sls) regressions have been employed to control for the endogeneity of the main variables, which are premiums, regulations, and insurance purchases. The...

Using a proprietary data set of auto insurance claims from May 2014 to December 2016, this paper examines the influence of air pollution on the number and severity of traffic accidents in China. Combining an instrumental variable strategy with high-dimensional fixed effects, we find that air...

Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also...

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models....

Introduction Empirical Applications of the Median Voter Model -- Do Voter Initiatives Affect the Interest Group Orientation of Government Spending -- The Political Economy of Automobile Insurance Elected vs Appointed Regulators -- Conflicting Objectives of Cartel Members Analysis of Voting...

Insurance company executives issued statements condemning racism and urging change throughout society and in the insurance industry after the huge Black Lives Matter demonstrations in summer 2020. This time period presents a crucial opportunity both for examining insurance as it relates to race...

In recent years it has become possible to collect GPS data from drivers and to incorporate these data into automobile insurance pricing for the driver. These data are continuously collected and processed nightly into metadata consisting of mileage and time summaries of each discrete trip taken,...

What drives information asymmetry? What’s the relationship between information asymmetry and competitiveness? Based on a large dataset of China’s automobile insurance market, we find solid evidence of the existence of asymmetric information in China’s automobile insurance market according...

The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor...

This paper examines various explanations for the increase in the degree of regulation of the auto industry in the last ten years. Using cross section data for the State of California, the paper confirms earlier findings for the State of Massachusetts that the demand for auto insurance is highly...

The purpose of this paper is an analysis of the presence of self-selection mechanisms on the market that could bring the market closer to the separating equilibrium state, in line with the Rothschild-Stiglitz equilibrium model and its subsequent modifications. An example is the Polish market of...

Quantile regression provides a way to estimate a driver's risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total distance driven, age, gender,...

Implementation of the Affordable Care Act (ACA) has been linked to an increase in the population covered by health insurance through expansions of the Medicaid program and revisions to private health insurance markets. We add to the body of literature on unintended consequences of the ACA by...

How to consider the a priori risks in experience-rating models has been questioned in the actuarial community for a long time. Classic past-claim-rating models, such as the Buhlmann–Straub credibility model, normalize the past experience of each insured before applying claim penalties. On the...

This paper aims to better predict highly skewed auto insurance claims by combining candidate predictions. We analyze a version of the Kangaroo Auto Insurance company data and study the effects of combining different methods using five measures of prediction accuracy. The results show the...

This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These datasets include bodily injury (BI), property damage (PD) and collision (COLL) coverage, each documenting policy characteristics and claims across a...

Purpose - The insurance industry has not been able to effectively retain its customers and struggles to establish and maintain long-lasting relationships with them. The purpose of this paper is thus to identify the main factors that explain the cancellation of motor insurance policies by...

The aim of this paper is to propose a multivariate INAR(1) model for addressing all the challenges in high-dimensional non-life claim count data sets that exhibit time and cross dependence and a zero-inflation attribute. In particular, the innovation terms are modelled using a multivariate...

Non-life insurance companies most especially, are often faced with the challenge of predicting the number of claims and claim amount to be incurred at any given time. Overcoming such challenges requires examining claim patterns, which assists in premium determination and setting of reserves. The...