Automobile Insurance Fraud Detection Using Data Mining: A Systematic Literature Review

Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. This study surveys fifty recent publications on automobile insurance fraud detection, published between January 2019 and March 2023, and presents both the most commonly used data sets and methods for resampling and detection, as well as interesting, novel approaches. The study adopts the highly-cited Systematic Literature Review (SLR) methodology for software engineering research proposed by Kitchenham and Charters and collected studies from four online databases. The findings indicate limited public availability of automobile insurance fraud data sets. In terms of detection methods, the prevailing approach involves supervised machine learning methods that utilise structured, intrinsic features of claims or policies and that lack consideration of an example-dependent cost of misclassification. However, alternative techniques are also explored, including the use of graph-based methods, unstructured textual data, and cost-sensitive classifiers. The most common resampling approach was found to be oversampling. This SLR has identified commonly used methods in recent automobile insurance fraud detection research, and interesting directions for future research. It adds value over a related review by also including studies published from 2021 onward, and by detailing the used methodology. Limitations of this SLR include its restriction to a small number of considered publication years and limited validation of choices made during the process

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches

Gradient Boosting in Motor Insurance Claim Frequency Modelling

Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. This paper investigates the predictive performance of Gradient Boosting with Decision Trees as base learners to model the claim frequency in motor insurance using a private cross-country large insurance dataset. The Gradient Boosting algorithm combines many weak base learners to tackle conceptual uncertainty in empirical research. The findings show that the Gradient Boosting model is superior to the standard Generalised Linear Model in the sense that it provides closer predictions in the claim frequency model. The finding also shows that Gradient Boosting can capture the nonlinear relation between the claim counts and feature variables and their complex interactions being, thus, a valuable tool for feature engineering and the development of a data-driven approach to risk management

The use of Machine Learning in non-life insurance: Literature review

Insurance companies using risk modelling mainly focus on the mastery of Genelized linear models. Nevertheless, such models hinder constraints on the structure of risk and the interactions between the risk explanatory variables. Then, these limits can lead to a biased estimation of the insurance premium in certain populations of policyholders. The traditional insurers have to face these existential challenges. Indeed, they need a focus on data strategy and implementation of statistical learning to achieve better pricing. In the last decades, computer performance has been continuously increasing, which has allowed a widespread application of the so-called statistical learning theory (Machine Learning) in many field. Non-life insurance pricing occupies as paradoxical place in actuarial science, hence the need for the application of different algorithms to evaluate the risks that insurance companies must face. Indeed, actuaries put forward the classical methods, linear algorithms mainly generalized linear model (GLM). Unfortunately, restrictions linked to this type of model, which can bias the estimation of the insurance premium, have pushed actuaries to opt for efficient algorithms, referred to as statistical learning models.  To do this, it is essential to look at the principals of classical GLM method, to identify their limitations and then to discuss the contributions of certain statistical learning methods in non-life insurance.   Keywords: Pricing, Non-life insurance, Generalized Linear Models GLM, Statistical Learning, Classification and Regression Trees CART, Random Forest, XGBoost, Neural Networks Classification JEL: B23, C60 Paper Type: Theoretical researchInsurance companies using risk modelling mainly focus on the mastery of Genelized linear models. Nevertheless, such models hinder constraints on the structure of risk and the interactions between the risk explanatory variables. Then, these limits can lead to a biased estimation of the insurance premium in certain populations of policyholders. The traditional insurers have to face these existential challenges. Indeed, they need a focus on data strategy and implementation of statistical learning to achieve better pricing. In the last decades, computer performance has been continuously increasing, which has allowed a widespread application of the so-called statistical learning theory (Machine Learning) in many field. Non-life insurance pricing occupies as paradoxical place in actuarial science, hence the need for the application of different algorithms to evaluate the risks that insurance companies must face. Indeed, actuaries put forward the classical methods, linear algorithms mainly generalized linear model (GLM). Unfortunately, restrictions linked to this type of model, which can bias the estimation of the insurance premium, have pushed actuaries to opt for efficient algorithms, referred to as statistical learning models.  To do this, it is essential to look at the principals of classical GLM method, to identify their limitations and then to discuss the contributions of certain statistical learning methods in non-life insurance.   Keywords: Pricing, Non-life insurance, Generalized Linear Models GLM, Statistical Learning, Classification and Regression Trees CART, Random Forest, XGBoost, Neural Networks Classification JEL: B23, C60 Paper Type: Theoretical researc

Digital transformation in the insurance industry: applications of artificial intelligence in fraud detection

The insurance industry has always been a crucial part of the economy and society’s progress worldwide. However, it is currently facing an unprecedented scenario composed of high risks and opportunities. This study aims to explain and analyze the process of digitalization in this sector and what are the available applications of artificial intelligence for fraud detection in claim management.It also comprehends a discussion about Brazil, with recommendations that were validated with local professionals from major players in the industry. Hence, the methodological approach chosen for this study wasa combination of the qualitative method, essentially based on the review and analysis of academic literature and reports, with important interviews.Lastly, it was concluded that most insurance companies are still at the beginning of the digitalization process, seeking a better understanding of its landscape. Consequently, A.I.applications are slowly being implemented by some large insurance companies