Scientific Journal

Applied Aspects of Information Technology


The paper presents the demand for the spread of payment systems. This is caused by the development of technology. The open issue of application of payment systems - fraud - is singled out. It is established that there is no effective algorithm that would be the standard for all financial institutions in detecting and preventing fraud. This is due to the fact that approaches to fraud are dynamic and require constant revision of forecasts. Prospects for the development of scientific and practical approaches to prevent fraudulent transactions in payment systems have been identified. It has been researched that machine learning is appropriate in solving the problem of detecting fraud in payment systems. At the same time, the detection of fraud in payment systems is not only to build the algorithmic core, but also to build a reliable automated system, which in real time, under high load, is able to control data flows and effectively operate the algorithmic core of the system. The paper describes the architecture, principles and operation models, the infrastructure of the automated fraud detection mechanism in payment systems. The expediency of using a cloud web service has been determined. The deployment of the model in the form of automated technology based on the Amazon Web Services platform is substantiated. The basis of the automated online fraud detection system is Amazon Fraud Detector and setting up payment fraud detection workflows in payment systems using a customizable Amazon A2I task type to verify and confirm high-risk forecasts. The paper gives an example of creating an anomaly detection system on Amazon DynamoDB streams using Amazon SageMaker, AWS Glue and AWS Lambda. The automated system takes into account the dynamics of the data set, as the AWS Lambda function also works with many other AWS streaming services. There are three main tasks that the software product solves: prevention and detection of fraud in payment systems, rapid fraud detection (counts in minutes), integration of the software product into the business where payment systems and services are used (for example, payment integration services in financial institutions, online stores, logistics companies, insurance policies, trading platforms, etc.). It is determined that the implementation of an automated system should be considered as a project. The principles of project implementation are offered. It is established that for the rational implementation of the project it is necessary to develop a specific methodology for the implementation of the software product for fraud detection in payment systems of business institutions.

  1. Dubina, M. V., Sadchikova, I. V. & Seredyuk, I. O. “Conceptual approaches to increasing the level of security of the banking payment environment of Ukraine”(in Ukrainian).Available from: – [Accessed: Jan, 2021].
  2. Lebichot, B. & Le Borgne, Y.-A. “Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection”. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds.) Recent Advances in Big Data and Deep Learning. Publ.Springer. New York: 2019. p. 78–88.
  3. Caelen, O. & Smirnov, E. N. “Improving Card Fraud Detection through Suspicious Pattern Discovery”. In: Benferhat, S., Tabia, K., Ali, M. (eds.) Advances in Artificial Intelligence: From Theory to Practice. Publ.Springer. New York: 2017. p. 181–190. 
  4. Pozzolo, A. D., Caelen, O., Bontempi, G. & Johnson, R. A. “Calibrating Probability with Undersampling for Unbalanced Classification”. Paper presented at the 2015 IEEE Symposium Series on Computational Intelligence. Cape Town: South Africa. 7-10 December 2015.
  5. Lebichot, B., Le Borgne, Y. A., He-Guelton, L., Oblé, F. & Bontempi, G. “Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection”. In: Oneto L., Navarin N., Sperduti A., Anguita D. (eds). Recent Advances in Big Data and Deep Learning. INNSBDDL 2019. Proceedings of the International Neural Networks Society.Publ. Springer. 2020; Vol. 1. Cham: DOI:
  6. Sorournejad, S. Z. Zojaji, R. E. & Atani Hassan Amir. “Monadjemi Fraud Detection Techniques”. Data and Technique Oriented Perspective. Cornel University Library. 2016. –  Available from: (date when it was last valid – 20.10.2020).
  7. Kuznietsova, N. V. “Analysis and forecasting the risks of credit card fraud”. Informatics and Mathematical Methods in Simulation (in Ukrainian). 2018; Vol. 8 No. 1: 16–25. 
  8. Kuznietsova, N. V. “Scoring Technology for Risk Assessment of Fraud in Banking”. Selected Papers of the XVI International Scientific and Practical Conference “Information Technologies and Security” (ITS 2016) (in Ukrainian). 2016. p.54–61. 
  9.  Delamaire, Linda, Abdou, Hussein & Pointon, John. “Credit card fraud and detection techniques: a review”. Banks and Bank Systems. 2009; Vol. 4 Issue 2. 
  10. Teslia, I., Yehorchenkov, O., Khlevna, I. & Khlevnyi, А. “Development concept and method of formation of specific project management methodologies”. Eastern-European Journal of Enterprise Technologies (in Ukrainian). 2018; No.5/3(95): 6–16. 
  11. Khlevna, І.,  Koval, B. “Fraud detection technology in payment systems” Information Technology and Interactions (Satellite): Conference Proceedings. December 04, 2020, Kyiv, Ukraine. Taras Shevchenko National University of Kyiv and [etc]; Vitaliy Snytyuk (Editor). Publ.Stylos (in Ukrainian). Kyiv: 2020. p.150–153.
  12. Sapozhnikova, M. U., Nikonov, A. V., Vulfin, A. M., Gayanova, M. M., Mironov K. V. & Kurennov, D. V. “Anti-fraud system on the basis of data mining technologies”. 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). Bilbao: Spain. 2017. p.243–248. DOI: 10.1109/ISSPIT.2017.8388649. 
  13. Lopez-Rojas, E. A. & Axelsson, S. “A review of computer simulation for fraud detection research in financial datasets”. Future Technologies Conference (FTC). 2016. p.932–935. 
  14. Fang, W., Li, X., Zhou, P., Yan, J., Jiang, D. & Zhou, T. “Deep Learning Anti-Fraud Model for Internet Loan: Where We Are Going”." In IEEE Access. 2021; Vol.9: 9777–9784. DOI: 10.1109/ACCESS.2021.3051079.
  15.  Wang Hongbin. “Research and Application of web Log Mining Technology Based on Distributed Computing Platform [D]”. (2015). Shandong University Jinan. 2020; Vol.3 No.3: 133–144.  DOI: 10.15276/aait.03.2020.2.
  16.  Sheremet, O. I., Korobov, O. Ye., Sadovoi, O. V., Sokhina, Yu. V. “Intelligent System Based on a Convolutional Neural Network for Identifying People without Breathing Masks”. Applied Aspects of Information Technology. Publ. Science i Technical. Odesa: Ukraine. 2020; Vol.3 No.3: 133–144. DOI: 10.15276/aait.03.2020.2. 
  17.  Halbouni, S. S.Obeid, N. & Garbou, A.  “Corporate governance and information technology in fraud prevention and detection: Evidence from the UAE”. Managerial Auditing Journal. 2016; Vol. 31 No. 6/7: 589–628. DOI:
  18.  “Cloud computing”. SoftServe. 2021. – Available from: – [Accessed: Jan, 2021].
  19.  “Overview of Amazon Web Services”. 2021. – Available from: whitepapers/aws-overview.pdf. – [Accessed: Jan, 2021].
  20.  “Cloud Computing Solutions Architect: A Hands-On Approach”. A Competency-based Textbook for Universities and a Guide for AWS Cloud Certification and Beyond by Arshdeep Bahga, Vijay Madisetti. VPT. 2019.  346 p.
  21.  “AWS Documentation”. 2021. – Available from: index.html?nc2=h_mo. – [Accessed: Jan, 2021].
  22.  “AWS Certified Cloud Practitioner Study Guide: CLF-C01 Exam”. 1st Edition by Ben Piper, David Clinton.
  23. “Architecting Cloud Computing Solutions: Build cloud strategies that align technology and economics while effectively managing risk”, by Kevin L. Jackson, Scott Goessling, May 30, 2018.
  24.  “Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems”, by Martin Kleppmann. April 18. 2017. 
  25.  Mezentseva, O. O. & Kolomiiets, A. S. “Optimization of Analysis and Minimization of Information Losses in Text Mining”. Herald of Advanced Information Technology. Publ. Science i Technical. Odesa:Ukraine. 2020; Vol.3 No.1: 373–382. DOI:10.15276/hait.01.2020.4. 
  26.  Bushuev, S.D. & Bushueva, N.S. “Development of technological maturity in project management”. Project management and production development. Collection of scientific works. Ed. В.А.Рач. (in Ukrainian). 2003; No. 2 (7): 5–12. 
  27. Teslia, I. M., Khlevna, I. L., Yehorchenkov, O. V. & Yehorchenkova, N. I. “Organizational bases of implementation of Specified project management”. Methodologies. Sciences of Europe. Technical sciences (in Ukrainian). 2018; Vol. 1 No. 34: 12–18.
Last download:
7 May 2021


[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2018.]