Modeling of Motor Insurance Extreme Claims through Appropriate Statistical Distributions

In the area of insurance, probability modeling has a wide variety of applications. In life insurance, the compensation sum is calculated in advance and may often be estimated using actuarial techniques, while in motor insurance, the claim amount is generally not known in advance. In the insurance business, the improvement of actuarial risk control strategies is an essential technique for controlling insurance risk. Although an insurance company’s risk assessment about its solvency is a complex and detailed problem, its solution begins with statistical modeling of individual claims’ amounts. This article emphasizes the possible ways of obtaining a suitable probability distribution model that accurately explains insurance risks and how to use such a model for risk management purposes. For this reason, we have applied modern programming techniques and statistical software implemented the methods provided based on data on premium amounts of third-party motor insurance claims.

A practitioners approach to individual claims models for bodily injury claims in german non-life insurance

The aim of our paper is to discuss the difficulties non-life actuaries are currently facing from a practical point of view. Based on this, we show that individual claims models are the key to address these difficulties and discuss how such models give actuaries a new and very powerful tool to explore further fields of application. Moreover, we address a very essential question: What data is needed for developing individual claims models? For bodily injury claims in German motor liability insurance, we shall derive specific attributes based on a detailed discussion of the legal background. All our ideas are based on practical experience for a large German motor insurance portfolio.

JOINT MODELING OF CLAIM FREQUENCIES AND BEHAVIORAL SIGNALS IN MOTOR INSURANCE

Telematicsdevices installed in insured vehicles provide actuaries with new risk factors, such as the time of the day, average speeds, and other driving habits. This paper extends the multivariate mixed model describing the joint dynamics of telematics data and claim frequencies proposed by Denuit et al. (2019a) by allowing for signals with various formats, not necessarily integer-valued, and by replacing the estimation procedure with the Expected Conditional Maximization algorithm. A numerical study performed on a database related to Pay-How-You-Drive, or PHYD motor insurance illustrates the relevance of the proposed approach for practice.

Measuring the effectiveness of intermediary loyalty programmes in the motor insurance industry: loyal versus non-loyal customers

PurposeThe 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 individual customers, considering the influence of intermediaries on their decisions.Design/methodology/approachThe data used in this research is based on a sample of 3,500 insurance policies that lapsed during the period of analysis between January and July 2017, against another sample of 3,500 policies that did not lapse, from a major insurance company in Portugal. Binary logistic regression was used for data analysis, using IBM SPSS software.FindingsAggressive tactics by insurance companies for customer acquisition may induce the cancellation of insurance policies. More valuable customers, the policies with higher premiums and recent claims, as well as the ancillary intermediaries and agents, are determinants of insurance cancellation. Conversely, the payment of policies by direct debit and without instalments reduces the probability of cancellations.Research limitations/implicationsThe main limitation of this study is the restriction on data access. Insurance companies are significantly resistant to sharing their customer data – including with academic researchers – even in an anonymised form.Practical implicationsThe paper highlights internal and external practices of insurance companies that should be reformulated to significantly improve their performance regarding product cancellation, related to customer information management, mistrust behaviours related to stakeholders and new value propositions that deepen the relationships with intermediaries.Originality/valueThis research developed a framework with which to identify the factors that are mainly associated with motor insurance cancellation and to predict its likelihood.

Multichannel search patterns and webrooming behaviours in the service industries: the case of motor insurance

This article aims at analysing the empirical categories and the main determinants of channel choices in the contractualized individual services category, with particular reference to multichannel search patterns and webrooming behaviours, whereby customers search online but purchase offline (namely, at the service provider’s premises). Based on an international survey of motor insurance customers, a set of hypotheses on the determinants of customers’ shopping journeys, inclusive of search and purchase channel decisions, have been tested with a multinomial logistic regression. Our results show that channel choices—both relative to search and to purchase—are significantly influenced by the customer’s preference for personal interaction (which typically favours the personal, offline, channels); overall, the relevance of channel choice determinants differs in the different shopping phases: indeed, while the search patterns (mono vs. multiple; digital vs. personal) are mainly determined by the customer need for information and by her/his preference for shopping innovation and enjoyment, the purchase channel choices are mainly driven by the customer’s preference for service quality (personal purchase) and by his/her price consciousness (digital purchase). In particular, webrooming behaviours occur when a less price-conscious customer, after having actively explored multiple channels to satisfy the appetite for information, eventually prefers to purchase the service at the provider’s physical store (i.e. the insurance agent) to satisfy his/her preference for personal interaction and service quality. These results shed light on multichannel behaviours in service industries and may help providers better inform the retail strategies of contractualized individual services.

AN EFFECTIVE MACHINE LEARNING MODEL FOR CUSTOMER ATTRITION PREDICTIONIN MOTOR INSURANCE USING GWO-KELM ALGORITHM

The prediction of customers' churn is a challenging task in different industrial sectors, in which the motor insurance industry is one of the well-known industries. Due to the incessant upgradation done in the insurance policies, the retention process of customers plays a significant role for the concern. The main objective of this study is to predict the behaviors of the customers and to classify the churners and non-churners at an earlier stage. The Motor Insurance sector dataset consists of 20,000 records with 37 attributes collected from the machine learning industry. The missing values of the records are analyzed and explored via Expectation Maximization algorithm that categorizes the collected data based on the policy renewals. Then, the behavior of the customers are also investigated, so as to ease the construction process training classifiers. With the help of Naive bayes algorithm, the behaviors of the customers on the upgraded policies are examined. Depending on the dependency rate of each variable, a hybrid GWO-KELM algorithm is introduced to classify the churners and non-churners by exploring the optimal feature analysis. Experimental results have proved the efficiency of the hybrid algorithm in terms of 95% prediction accuracy; 97% precision; 91% recall & 94% F-score.