Fraud is a significant issue for insurance companies, generating much interest in machine learning solutions. Although … supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied … in this context, and there remains insufficient evidence to guide the choice between these branches of machine learning …

The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model … heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB …) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision …

One crucial task of actuaries is to structure data so that observed events are explained by their inherent risk factors …. Moreover, it does not take profit from the representation capabilities of recent machine learning algorithms. In this paper, we … performing and interpretable than some usual actuarial data mining baseline. …

essential for the intended tasks such as classification, clustering or regression analysis. In gene expression microarray data …, being able to select a few genes not only makes data analysis efficient but also helps their biological interpretation …. Microarray data has typically several thousands of genes (features) but only tens of samples. Problems which can occur due to the …

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient … across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we … learning regression. We find that both pre-selection and factor extraction significantly improve the accuracy of machine-learning …

The goals of this paper are twofold: we describe common features in data sets from motor vehicle insurance companies … basis to develop insurance tariffs. The strategy is applied to a data set from motor vehicle insurance companies. We use a …

Emerging technologies are in the core focus of supra-national innovation policies. These strongly rely on credible data … community, with a machine learning algorithm. The result is a novel possibility to allocate patents which (1) reduces expert …

many fields including healthcare and medicine. In this exemplary study, using digitized signal data, we developed … predictive models employing three machine learning methods to diagnose an asthma patient based solely on the sounds acquired from …

developed aiming to classify computational data and most of them are extended to classify textual data. We have used some of …

client data sets, many credit risk analysis methods are used. The assessment of the credit risk datasets leads to the choice … used for credit risk assessment. Data mining approach, as the most often used approach for credit risk analysis was …