Перегляд за Автор "Kolodiziev, O."

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Automatic machine learning algorithms for fraud detection in digital payment systems
(PC TECHNOLOGY CENTER, 2020) Kolodiziev, O.; Mints, A. Yu.; Sidelov, P.; Pleskun, I.; Lozynska, O.; Мінц, О. Ю.
Data on global financial statistics demonstrate that total losses from fraudulent transactions around the world are constantly growing. The issue of pay-ment fraud will be exacerbated by the digitalization of economic relations, in particular the introduction by banks of the concept of "Bank-as-a-Service", which will increase the burden on payment services. The aim of this study is to synthesize effective models for detecting fraud in digital payment sys-tems using automated machine learning and Big Data analysis algorithms.Approaches to expanding the information base to detect fraudulent transactions have been proposed and systematized. The choice of performance metrics for building and comparing models has been substan-tiated.The use of automatic machine learning algorithms has been proposed to resolve the issue, which makes it possible in a short time to go through a large num-ber of variants of models, their ensembles, and input data sets. As a result, our experiments allowed us to obtain the quality of classification based on the AUC metric at the level of 0.977‒0.982. This exceeds the effectiveness of the classifiers developed by tradition-al methods, even as the time spent on the synthesis of the models is much less and measured in hours. The models' ensemble has made it possible to detect up to 85.7 % of fraudulent transactions in the sample. The accuracy of fraud detection is also high (79‒85 %).The results of our study confirm the effectiveness of using automatic machine learning algorithms to synthesize fraud detection models in digital payment systems. In this case, efficiency is manifested not only by the resulting classifiers' quality but also by the reduction in the cost of their development, as well as by the high potential of interpretability. Implementing the study results could enable financial institutions to reduce the financial and temporal costs of developing and updating active systems against payment fraud, as well as improve the effectiveness of monitoring financial transactions.
A cross-impact analysis of the bank payment card market parameters and non-financial sectors’ indicators in the Ukrainian economy
(LLC “Consulting Publishing Company “Business Perspectives”, 2022) Mints, A. Yu.; Kolodiziev, O.; Krupka, M.; Vyshyvana, B.; Yastrubetska, L.; Мінц, О. Ю.
In Ukraine, card payment systems develop at a rate similar to that of modern digital payment instruments in most European countries. The purpose of the paper is to establish interdependence and explain the nature of changing situations in the market of bank payment cards (BPC) taking into account the dynamics of economic development parameters in non-financial sectors of the Ukrainian economy. The methodology of the study includes graphic methods analyzing the dynamics of economic development indicators and a method for analyzing the cause-and-effect relationship between the studied parameters considered with different lags. Results showed that the most significant parameters for the development of the pay-ment card infrastructure were the level of provision with POS terminals and the share of non-cash transactions. Their correlation with the economic development indica-tors reached 0.97. Up to the stage when the volume of non-cash payments by cards reached 5% of GDP, the impact of the BPC market on the change in the level of eco-nomic development had been insignificant according to the general idea. The develop-ment of the economy up to that point stimulated the development of the BPC market. Subsequently, the BPC market that was already sufficiently developed became one of the drivers aimed at the development of non-financial sectors of the Ukrainian econo-my after overcoming the 5% GDP level.