Development of Algorithms for Searching, Analyzing and Detecting Fraudulent Activities in the Financial Sphere

Marina Pavlovna Khrestina, Dmitry Ivanovich Dorofeev, Polina Andreevna Kachurina, Timur Rinatovich Usubaliev and Aleksey Sergeevich Dobrotvorskiy

European Research Studies Journal, 2017, vol. XX, issue 4B, 484-498

Abstract: According to Digital Evolution Index 2017, Russia is included to the category of so-called "Break Out" countries. The major problem to be encountered at transfer to the digital economy is adaptation of new technologies – such as Big Data, Blockchain, Internet of Things, Cryptocurrency, machine learning. No less important field is development of friendly informative environment facilitating international cooperation, cyber safety problems resolving, etc. This example provides the data of the report prototype of a system to detect suspicious transactions. This system shall read and analyze the transaction database and, in accordance with search algorithms, it detects suspicious transactions within the entire data base. The algorithm consists of several stages: development of a graph, selection of suspicious and trusted transactions, calculation of signs and machine learning. The methods of social connections analysis, parallel processing of graphs and mathematical apparatus of neural networks are used as the basis of this research.

Keywords: Digital economy; antifraud; AML-systems; digital transformation; machine learning; Big Data; financial control; parallel processing; neural networks. JEL Classification: F50; F52; F59. (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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