2 research outputs found

    Automatic detection of relationships between banking operations using machine learning

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    In their daily business, bank branches should register their operations with several systems in order to share information with other branches and to have a central repository of records. In this way, information can be analysed and processed according to different requisites: fraud detection, accounting or legal requirements. Within this context, there is increasing use of big data and artificial intelligence techniques to improve customer experience. Our research focuses on detecting matches between bank operation records by means of applied intelligence techniques in a big data environment and business intelligence analytics. The business analytics function allows relationships to be established and comparisons to be made between variables from the bank's daily business. Finally, the results obtained show that the framework is able to detect relationships between banking operation records, starting from not homogeneous information and taking into account the large volume of data involved in the process. (C) 2019 Elsevier Inc. All rights reserved.This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)

    dynXcube – categorizing dynamic data analysis

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    Data analysis has gained strategic importance for virtually any organization. It covers areas like business analytics, big data, business intelligence, and data mining, among others. The past decades have also witnessed increasing efforts to capture, analyze, and interpret dynamic data instead of just static snapshot data. This is due to the fact that many real-life applications are characterized by changing data structures. Hence, analytic systems need to be able to adapt to changes. In recent years, many models for dynamic data analysis have been proposed and successfully applied in a diverse range of real-life projects. Since the number of respective algorithms has continuously risen, it has become increasingly demanding to keep track in this field. This is not only related to the algorithms that have been proposed so far and their relationships to each other. It also applies to the disclosure of gaps in research that need to be filled by appropriate new algorithms and, therefore, uncover new research opportunities. To contribute to the review of this field, we propose a holistic framework to categorize dynamic data analysis, the dynXcube-Framework. We show that dynXcube is very useful to present the state-of-the-art of dynamic data analysis in a consolidated way. Furthermore, it has the potential to disclose gaps in current research, thus providing a road map for future activities in the field of dynamic data analysis. Therefore, dynXcube is a significant step towards an improved accessibility of dynamic data analysis methods for academics and professionals alike and will help to stimulate future research in this important field
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