Purpose

Design/methodology/approach

Findings

Originality/value

How risk management technologies are implemented in developing countries is largely underresearched.
Using a perspective on bio-politics, this paper dissects how an infusion of risk management
technologies permeates as a powerful managerial tool in governing subordinates. The notions of
power/knowledge relations, disciplinary power, and governmentality enabled the authors to rehearse
the Foucault’s biopolitics perspective in an analysis of risk-based rationalities and risk management
technologies. Qualitative case study research methods guided them to gather empirical evidence from a
privately owned, Egyptian insurance firm. They found that risk management technologies are conjoined
with institutional and discursive ramifications in a developing country where burgeoning neoliberal
economic remedies are being diffused and adopted. Further, risk management technologies go hand
in hand with this ensuing neoliberal agenda, making it inescapable for organisational managers in
such a developing country to adopt these technologies for their survival and sustainability.

The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.

Insurance is a policy that eliminates or decreases loss costs occurred by various risks. Various factors influence the cost of insurance. These considerations contribute to the insurance policy formulation. Machine learning (ML) for the insurance industry sector can make the wording of insurance policies more efficient. This study demonstrates how different models of regression can forecast insurance costs. And we will compare the results of models, for example, Multiple Linear Regression, Generalized Additive Model, Support Vector Machine, Random Forest Regressor, CART, XGBoost, k-Nearest Neighbors, Stochastic Gradient Boosting, and Deep Neural Network. This paper offers the best approach to the Stochastic Gradient Boosting model with an MAE value of 0.17448, RMSE value of 0.38018and R -squared value of 85.8295.

This paper focuses on the modeling and estimation of tail loss distribution parameters from Egyptian’s commercial fire loss severities. Using theoretical extreme value, we use the generalized distribution of Pareto (GPD) and compare it to standard parametric modeling based on exp, Weibull, gumbel, frechet, lognormal and gamma distributions. The goodness-of-fit tests included Kolmogorov-Smirnov, Anderson and Cramer-von Mises test is carried out, and the calculation of the value-at-risk and expected shortfall are performed. We use the bootstrap approach to create confidence intervals for the estimates.

Current insurance models, assuming that inter-arrival time of claims, are distributed randomly and thus well approximated by Poisson processes. Here we provide clear proof that the timing of inter-claims fits by non-Poisson patterns, marked by rapid events, separated by long periods of inactivity. The time of inter-arrival claims will be heavy tailed, most claims will be executed quickly, while a few will have very long waiting times. We will model and analysis of insurance based on claim inter-arrival time, the time interval between two successive claims and the ability to carry out such modeling was limited by a lack of ecologically relevant data collected on claims inter-arrival. We propose a structured process behavior model based on data from Egyptian fire insurance company. Our analysis shows that claim activities can be represented by non-Poisson processes and that the subsequent distribution of inter-arrival activity times follows the Pareto distribution. These results will help researchers understand daily behavioral trends and create more sophisticated predictive models of claims.

One of the most common types of fraudulent is insurance fraud. And in particular fraud in automobile insurance, the cost of automobile insurance fraud is substantial for property insurance companies and has a long-term impact on insurance firms' pricing strategies. And In order to minimize insurance rates, car insurance fraud detection has become necessary. Although predictive models for the detection of insurance fraud are in active use in practice, there are relatively few documented studies on the use of machine learning approaches to detect insurance fraud, likely due to the lack of available data. In this paper, by using real-life data, we evaluate 13 machine learning approaches. And Because of the imbalanced datasets in this area, predicting insurance fraud has become a significant challenge. Due to our data consist mostly of a "non-fraud claims " class with a small percentage of "fraud claims. " Thus that the prediction of fraud appears weakly with classification models; therefore, the present study seeks to suggest an approach that enhances machine learning algorithms' results by using resampling techniques, such as Random Over Sampler, Random under Sampler, and hybrid methods, to address the issue of unbalanced data. And we compare between them. This paper shows that after using resampling techniques, the efficiency of all ML classifiers is enhanced. Furthermore, the findings confirm that there is no one resampling method that overall outperforms. Besides, among all the other models, the Stochastic Gradient Boosting classifier obtained the best result when using the hybrid resampling technique.

Predicting the frequency of insurance claims has become a significant challenge due to the imbalanced datasets since the number of occurring claims is usually significantly lower than the number of non-occurring claims. As a result, classification models tend to have a limited ability to predict the occurrence of claims. So, in this paper, we'll use various data level approaches to try to solve the imbalanced data problem in the insurance industry. We developed 32 machine learning models for predicting insurance claims occurrence {(undersampling, over-sampling, the combination of over-and undersampling (hybrid), and SMOTE)× (three Decision tree models, three boosting models, and two bagging models) = 32}, and we compared the models' accuracies, sensitivities, and specificities to comprehend the prediction performance of the built models. The dataset contains 81628 claims, each of which is a car insurance claim. There were 5714 claims that occurred and 75914 claims that didn't occur. According to the findings, the AdaBoost classifier with oversampling and the hybrid method had the most accurate predictions, with a sensitivity of 92.94%, a specificity of 99.82%, and an accuracy of 99.4%. And with a sensitivity of 92.48%, a specificity of 99.63%, and an accuracy of 99.1%, respectively. This paper confirmed that when analyzing imbalanced data, the AdaBoost classifier, whether using oversampling or the hybrid process, could generate more accurate models than other boosting models, Decision tree models, and bagging models.

The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.

Purpose

Design/methodology/approach

Findings

Practical implications

Originality/value