MODELING OF AUTOMATIC DATA RECONCILIATION PROCESSES IN ELECTRONIC PAYMENT SYSTEMS BASED ON NATURAL LANGUAGE PROCESSING METHODS

Keywords: reconciliation, payment system, meta-model, natural language processing, integration

Abstract

The article is devoted to the actual problems of data reconciliation in electronic payment systems. The aim of the work is to develop a model for the process of data reconciliation in electronic payment systems based on natural language processing methods and ensure its integration into payment transaction processing systems. With the spread of electronic payment systems within the concept of Open Banking, the number of transactions, the variety of payment systems, and, accordingly, the complexity of reconciling payment data has increased dramatically. This is due to the fact that the data come from different sources and have a heterogeneous structure that is difficult to recover by formal methods. This increases the share of payments that have to be reconciled manually, which, in turn, leads to an increase in the costs of operating the payment system. Therefore, at present, research on the automation of reconciliation processes, based on the use of artificial intelligence methods, in particular, natural language processing, is becoming relevant. The existing approaches to a reconciliation of payment data are systematized and their genesis is analyzed. It turned out that in recent years the number of publications on reconciliation methods based on the analysis of big data and the use of semantic models has been growing, but a number of issues on building payment data reconciliation systems have not yet been disclosed. This concerns the integration of automatic reconciliation services into payment transaction processing systems. The main stages of payment data reconciliation, the differences between business and technical transactions, the main operations of the data reconciliation process, and reconciliation scenarios have been explored. A model of the reconciliation process in a company that is a Payment Service Provider (PSP) has been developed. A model for the operation of the service for automatic reconciliation of payment data and a scheme for its integration into the PSP information system has been developed. The problems of automatic data reconciliation are highlighted, namely: discrepancy between data exchange formats; a discrepancy in field names; data structure discrepancy; error in the content of the fields; different accuracy of numerical values; temporary gaps; splitting transaction amounts. The ways of solving these problems through the use of natural language recognition methods are outlined.

References

Національний банк України. Безготівкові розрахунки. URL: https://bank.gov.ua/ua/payments/nocash

Sidelov P. The World of Digital Payments. Kyiv: Publisher Ostap Khanko, 2017. 304 p.

Bahrami M., Bozkaya B., Balcisoy S. Using behavioral analytics to predict customer invoice payment. Big data. – 2020. – Т. 8. – №. 1. – С. 25–37.

Kolodiziev O., Mints A., Sidelov P., Pleskun I., Lozynska O. Automatic Machine Learning Algorithms for Fraud Detection in Digital Payment Systems. Eastern-European Journal of Enterprise Technologies. 2020. №5(9 (107)), pp. 14–26, 2020. DOI: https://doi.org/10.15587/1729-4061.2020.212830

Mathuva D. M. The Fading role of bank reconciliation in fraud prevention and detection. The accountant. 2016. №2. pp. 32–33.

Stott J. R. Mastering Principles of Accounts. Palgrave, London. 1982. 248 p.

Adala A. Reconciliation of Electronic Remittances: A Multi-Level Approach // Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance. 2018. P. 659–661.

Stickney G. F. The check payment and reconciliation program of the US Treasury: present status and future prospects. Fall joint computer conference. 1966. P. 479–499.

Furche A., Wrightson G. Computer Money: a systematic overview of electronic payment systems. Verlag fur digitale Technologie GmbH, 1996. 107 p.

Zhu X., Wang D. Application of blockchain in document certification, asset trading and payment reconciliation. Journal of Physics: Conference Series. – IOP Publishing. 2019. Vol. 1187. №. 5. P. 52–80.

Sunarya P. A., Nurhaeni T., Haris H. Bank Reconciliation Process Efficiency Using Online Web Based Accounting System 2.0 in Companies. APTISI Transactions on Management (ATM). 2017. Vol. 1. №. 2. P. 124–129.

Rahardja U. et al. Financial management system integrated by web-based payment cash link solution to invent smart reconciliation. International conference on industrial engineering and operations management. 2021. pp. 4733–4743.

Răscolean I., Rakos I. S., Calotă T. O. Analysis of the reconciliation of the accounting result with the fiscal result. case study: Annals of Constantin Brancusi'University of Targu-Jiu. Economy Series. 2015. pp. 297–304.

Richardson J., Outlaw J. Implications of Budget Reconciliation for Commodity Programs. Choices. 2004. Vol. 19. №. 4. pp. 43–45.

Xing X. Financial Big Data Reconciliation Method. 2021 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE). IEEE. 2021. pp. 260–263.

Paneque M., Roldán-García M. M., García-Nieto J. A Semantic Model for Enhancing Data-Driven Open Banking Services. SSRN preprints. № 4151598. 11 p.

Минц А., Сиделев П. Анализ глобального уровня готовности банковской системы к имплементации концепции «Open Banking». Вісник Приазовського державного технічного університету. Серія: Економічні науки. 2019. № 37. С. 57–63.

National Bank of Ukraine. Cashless Payments. Available at: https://bank.gov.ua/ua/payments/nocash

Sidelov P. (2017) The World of Digital Payments. Kyiv. 334 p.

Bahrami, M., Bozkaya, B., & Balcisoy, S. (2020). Using behavioral analytics to predict customer invoice payment. Big data, 8(1), 25–37.

Kolodiziev O., Mints A., Sidelov P., Pleskun I., Lozynska O. (2020) Automatic Machine Learning Algorithms for Fraud Detection in Digital Payment Systems. Eastern-European Journal of Enterprise Technologies, 5(9 (107)), pp. 14–26. DOI: https://doi.org/10.15587/1729-4061.2020.212830

Mathuva, D. M. (2016). The Fading role of bank reconciliation in fraud prevention and detection. The accountant, no 2, pp. 32–33.

Stott, J. R. (1982). Mastering Principles of Accounts. Palgrave, London. 248 p.

Adala, A. (2018, April). Reconciliation of Electronic Remittances: A Multi-Level Approach. In Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance (pp. 659–661).

Stickney, G. F. (1966). The check payment and reconciliation program of the US Treasury: present status and future prospects. In Proceedings of the November 7-10, 1966, fall joint computer conference (pp. 479–499).

Furche, A., & Wrightson, G. (1996). Computer Money: a systematic overview of electronic payment systems. Verlag fur digitale Technologie GmbH. 107 p.

Zhu, X., & Wang, D. (2019, April). Application of blockchain in document certification, asset trading and payment reconciliation. Journal of Physics: Conference Series (Vol. 1187, No. 5, p. 52–80).

Sunarya, P. A., Nurhaeni, T., & Haris, H. (2017). Bank Reconciliation Process Efficiency Using Online Web Based Accounting System 2.0 in Companies. APTISI Transactions on Management (ATM), Vol.1(2), pp. 124–129.

Rahardja, U., Aini, Q., Santoso, N. P. L., Hardini, M., & Edliyanti, A. (2021). Financial management system integrated by web-based payment cash link solution to invent smart reconciliation. International conference on industrial engineering and operations management, pp. 4733–4743.

Răscolean, I., Rakos, I. S., & Calotă, T. O. (2015). Analysis of the reconciliation of the accounting result with the fiscal result. Case study. Annals of'Constantin Brancusi'University of Targu-Jiu. Economy Series. pp. 297–304.

Richardson, J., & Outlaw, J. (2004). Implications of Budget Reconciliation for Commodity Programs. Choices, 19(4), pp. 43–45.

Xing, X. (2021, December). Financial Big Data Reconciliation Method. 2021 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE) (pp. 260-263). IEEE.

Paneque, M., Roldán-García, M. D. M., & García-Nieto, J. A (2021) Semantic Model for Enhancing Data-Driven Open Banking Services. Available at SSRN 4151598. 11 p.

Mints, A., & Sidelov, P. (2019). Analiz globalʹnogo urovnya gotovnosti bankovskoy sistemy k implementatsii kontseptsii «Open Banking» [Analysis of the global level of readiness of the banking system for the implementation of the "Open Banking" concept]. Visnyk Pryazovsʹkoho Derzhavnoho Tekhnichnoho Universytetu. seriya: Ekonomichni nauky. Vol (37). pp. 57–63.

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Published
2021-12-28
How to Cite
Sidelov, P. (2021). MODELING OF AUTOMATIC DATA RECONCILIATION PROCESSES IN ELECTRONIC PAYMENT SYSTEMS BASED ON NATURAL LANGUAGE PROCESSING METHODS. Economy and Society, (34). https://doi.org/10.32782/2524-0072/2021-34-102
Section
ECONOMICS