Journal of Business Research 89 (2018) 448–454 Contents lists available at ScienceDirect Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres A. Pérez-Martín , A. Pérez-Torregrosa, M. Vaca Department of Economic and Financial Studies, Miguel Hernández University of Elche, Spain ARTICLE I NFO ABSTRACT Keywords: Nowadays, the volume of databases that ﬁnancial companies manage is so great that it has become necessary to Credit scoring address this problem, and the solution to this can be found in Big Data techniques applied to massive ﬁnancial Big Data datasets for segmenting risk groups. In this paper, the presence of large datasets is approached through the Monte Carlo development of some Monte Carlo experiments using known techniques and algorithms. In addition, a linear Data mining mixed model (LMM) has been implemented as a new incremental contribution to calculate the credit risk of ﬁnancial companies. These computational experiments are developed with several combinations of dataset sizes and forms to cover a wide variety of cases. Results reveal that large datasets need Big Data techniques and algorithms that yield faster and unbiased estimators. Big Data can help to extract the value of data and thus better decisions can be made without the runtime component. Through these
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