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

L’intégration de la démarche Lean Green au Supply Chain Management et la performance globale de l’entreprise marocaine

Finding a balance between reducing costs, growing profit and taking into account the environment is a requirement of today's business environment. Becoming Lean and Green is a trend that companies operating in the industrial sector are beginning to recognize as a sine qua non for overcoming this challenge and achieving economic, social and environmental prosperity. The lean approach of supply chain management allows continuous improvement of all activities related to the management of physical and information flows through the hunt for waste and through the management of human resources. The green supply chain, for its part, seeks to minimize the impact of the footprint of logistics activities on the environment through, in particular, an efficient environmentally friendly transport distribution system. The integration of these two approaches in the management of the supply chain seems to constitute a consubstantial lever in the achievement of the overall performance of companies, which is reflected in the satisfaction of the expectations of all the stakeholders of the company. This theoretical article aims to study the relevance of the Lean-Green combination in supply chain management and its impact on the overall performance of the Moroccan industrial company. First, it allowed us to find that several studies have shown the positive impact of lean management on the operational performance of companies by showing that lean practices and principles are positively correlated with the operational performance of companies. Second, the positive impact of environmental management on environmental performance has been verified through several case studies. Finally, it showed us the existence of a gap in previous research work, particularly in terms of analyzing the impact of Lean Green Supply chain management (LGSCM) management system on the overall performance of Moroccan industrial company.     JEL Classification: M11, D24, D21, l23, Q53 Paper type: Theoretical Research  La recherche d’un équilibre entre la réduction des coûts, la croissance du profit et la prise en compte de l’environnement est une exigence de l’environnement actuel des entreprises. Devenir Lean et Green est une tendance que les entreprises industrielles commencent à reconnaitre comme une condition sine qua non pour surmonter ce défi et parvenir à la prospérité économique, sociale et environnementale. La démarche lean de la supply chain management permet une amélioration continue de l’ensemble des activités liées à la gestion des flux physiques et informationnels à travers la chasse aux gaspillages et à travers le management des ressources humaines. La green supply chain de son côté cherche à minimiser l’impact de l’empreinte des activités logistiques sur l’environnement à travers, notamment, un système de distribution de transport efficient ami de l’environnement. L’intégration de ces deux démarches dans le management de la chaine logistique semble constituer un levier consubstantiel à la réalisation de la performance globale des entreprises se concrétisant dans la satisfaction des attentes de l’ensemble des parties prenantes de l’entreprise.  Cet article d’ordre théorique a pour objectif d’étudier la pertinence de la combinaison Lean-Green dans le management de la supply chain et son impact sur la performance globale de l’entreprise industrielle marocaine.  Premièrement, il nous a permis de trouver que plusieurs études ont montré l’impact positif de Lean management sur la performance opérationnelle des entreprises en mettant en évidence que les pratiques et les principes de Lean sont corrélés positivement avec la performance opérationnelle des entreprises. Deuxièmement, l’impact positif de management environnemental sur la performance environnementale s’est vérifié à travers plusieurs études de cas. Finalement, il nous a montré l’existence d’un gap dans les travaux de recherche précédents notamment en matière de l’analyse de l’impact d’un système de management Lean Green Supply chain management (LGSCM) sur la performance globale de l’entreprise industrielle marocaine.     Classification JEL :  M11, D24, D21, l23, Q53 Type de l’article : Article théoriqu

Analytic hierarchy process applied on the stock exchange market: The Moroccan case

The multitude of the stocks in a financial market could make the investors puzzled when it comes to taking a decision. Indeed, finding the best choice of stocks that would make a good investment could be a very difficult process. In order to solve this difficulty, many researchers and financial analysts have developed during the last decades several methods of stock selection in order to guide capital holders to the best investment strategy. This work based on the Analytic Hierarchy Process (AHP), which is a multicriteria decision making method, aims to classify publicly traded stocks on the Moroccan stock exchange from most to least interesting. First, a rigorous financial analysis will be performed for clearly identifying and carefully choosing ratios in order to use the maximum of information available on the shares. Then an empirical study will be established to provide a ranking of Moroccan companies by using AHP method, which would help investors to choose the most advantageous stocks

Strong Approximation of Empirical Copula Processes by Gaussian Processes

We provide the strong approximation of empirical copula processes by a Gaussian process. In addition we establish a strong approximation of the smoothed empirical copula processes and a law of iterated logarithm

Contribution à l'étude du processus empirique de copule

In this thesis, we are concerned with strong approximations of the empirical copula process, possibly smoothed, by a suitable sequence of Gaussian processes. When the margins are known, we study the behavior of bivariate empirical copula process on pavements of [0,1]2. In the case of unknown margins and for any dimension d, we derive, via two different constructions, the rates of the strong approximation of empirical copula processes by sequence of Brownian bridges (d-parameters) or by sequence of Kiefer process ( (d+1)- parameters). Some applications on statistic linear rank, empirical process of density of copula, L.I.L and the smoothed empirical process of copula are studied. We develop a test of equality between two dependence structures estimated through empirical copulas. The null hypothesis consists in the identity of two copulas associated to the two samples under the hypothesis of independence of the margins. We state the asymptotic distributions of some functional as Cramer-von-Mises type or Kolmogorov-Smirnov statistics.Cette thèse traite des propriétés statistiques fines des processus empiriques de copules, éventuellement lissées, dans une optique d'approximations fortes. Lorsque les marges sont connues, nous avons établi une approximation forte du processus empirique bivarié de copules sur des pavés de [0,1]^2. Nous considérons ensuite un cadre plus général où la dimension d de la variable est supérieure à 2 et les marginales sont continues mais inconnues. Nous fournissons, par deux techniques différentes, des approximations fortes du processus empirique de copule par une suite de ponts Browniens attachés à paramètres, ou par une suite de processus de Kiefer attachés à (d+1)-paramètres. Ceci nous permettra d'obtenir des résultats asymptotiques pour le processus empirique de densité de copule, pour les statistiques de rang multivariées et pour le processus empirique de copule lissée ainsi que l'ordre de grandeur du module d'oscillation et la L.L.I du processus empirique de copule. Nous abordons le problème du test à deux échantillons; l'hypothèse nulle consiste en l'identité des deux copules sous-jacentes aux deux échantillons, simultanément avec l'hypothèse d'indépendance des marges. Deux hypothèses alternatives sont considérées, selon qu'on rejette la propriété d'indépendance. Nous proposons plusieurs statistiques de tests basées, essentiellement, sur les normes infinie ou L^2 de la différence entre les deux processus de copules empiriques sous-jacents (statistiques de type Kolmogorov-Smirnov et Cramer Von Mises). Sous l'hypothèse nulle, des bornes et vitesses de convergence presque sûres vers des processus gaussiens sont obtenues

Contribution à l'étude du processus empirique de copule

Cette thèse traite des propriétés statistiques fines des processus empiriques de copules, éventuellement lissées, dans une optique d'approximations fortes. Lorsque les marges sont connues, nous avons établi une approximation forte du processus empirique bivarié de copules sur des pavés de [0,1]^2. Nous considérons ensuite un cadre plus général où la dimension d de la variable est supérieure à 2 et les marginales sont continues mais inconnues. Nous fournissons, par deux techniques différentes, des approximations fortes du processus empirique de copule par une suite de ponts Browniens attachés à paramètres, ou par une suite de processus de Kiefer attachés à (d+1)-paramètres. Ceci nous permettra d'obtenir des résultats asymptotiques pour le processus empirique de densité de copule, pour les statistiques de rang multivariées et pour le processus empirique de copule lissée ainsi que l'ordre de grandeur du module d'oscillation et la L.L.I du processus empirique de copule. Nous abordons le problème du test à deux échantillons; l'hypothèse nulle consiste en l'identité des deux copules sous-jacentes aux deux échantillons, simultanément avec l'hypothèse d'indépendance des marges. Deux hypothèses alternatives sont considérées, selon qu'on rejette la propriété d'indépendance. Nous proposons plusieurs statistiques de tests basées, essentiellement, sur les normes infinie ou L2 de la différence entre les deux processus de copules empiriques sous-jacents (statistiques de type Kolmogorov-Smirnov et Cramer Von Mises). Sous l'hypothèse nulle, des bornes et vitesses de convergence presque sûres vers des processus gaussiens sont obtenues.PARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

Uniform Consistency for Functional Conditional U-Statistics Using Delta-Sequences

U-statistics are a fundamental class of statistics derived from modeling quantities of interest characterized by responses from multiple subjects. U-statistics make generalizations the empirical mean of a random variable X to the sum of all k-tuples of X observations. This paper examines a setting for nonparametric statistical curve estimation based on an infinite-dimensional covariate, including Stute’s estimator as a special case. In this functional context, the class of “delta sequence estimators” is defined and discussed. The orthogonal series method and the histogram method are both included in this class. We achieve almost complete uniform convergence with the rates of these estimators under certain broad conditions. Moreover, in the same context, we show the uniform almost-complete convergence for the nonparametric inverse probability of censoring weighted (I.P.C.W.) estimators of the regression function under random censorship, which is of its own interest. Among the potential applications are discrimination problems, metric learning and the time series prediction from the continuous set of past values