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

Author

Listed:
  • Marina Pavlovna Khrestina
  • Dmitry Ivanovich Dorofeev
  • Polina Andreevna Kachurina
  • Timur Rinatovich Usubaliev
  • Aleksey Sergeevich Dobrotvorskiy

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.

Suggested Citation

  • Marina Pavlovna Khrestina & Dmitry Ivanovich Dorofeev & Polina Andreevna Kachurina & Timur Rinatovich Usubaliev & Aleksey Sergeevich Dobrotvorskiy, 2017. "Development of Algorithms for Searching, Analyzing and Detecting Fraudulent Activities in the Financial Sphere," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 484-498.
    • Handle: RePEc:ers:journl:v:xx:y:2017:i:3b:p:484-498
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    References listed on IDEAS

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    Cited by:

    1. Alexey Ruchay & Elena Feldman & Dmitriy Cherbadzhi & Alexander Sokolov, 2023. "The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning," Mathematics, MDPI, vol. 11(13), pages 1-15, June.
    2. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    3. repec:ers:journl:v:volumexxi:y:2018:i:issue4:p:524-532 is not listed on IDEAS
    4. Sharma, Gagan Deep & Tiwari, Aviral Kumar & Chopra, Ritika & Dev, Dhairya, 2024. "Past, present, and future of block-chain in finance," Journal of Business Research, Elsevier, vol. 177(C).
    5. Tolendi Ashimbayev & Sarkyt Tashenova, 2018. "Prospects for Using Cryptocurrency in the Economy of Kazakhstan and the Attitude of the National Bank," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 524-532.

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    More about this item

    Keywords

    Digital economy; antifraud; AML-systems; digital transformation; machine learning; Big Data; financial control; parallel processing; neural networks. JEL Classification: F50; F52; F59.;
    All these keywords.

    JEL classification:

    • F50 - International Economics - - International Relations, National Security, and International Political Economy - - - General
    • F52 - International Economics - - International Relations, National Security, and International Political Economy - - - National Security; Economic Nationalism

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