Management Accounting of Payment Risks of Online Trade during Military Operations
Management Accounting of Payment Risks of Online Trade during Military Operations

Summary/Abstract: The rapid development of technologies and intensifying military operations have caused several challenges for online trade, in particular, in terms of payment risks. In this context, management accounting as a key means of monitoring and controlling these risks is assigned a special role. The aim of the article is to identify the key peculiarities of the influence of management accounting on the payment risks of online trade in the area of fraudulent operations during active military operations. The study employed a number of economic and statistical method: an in-depth semi-structured interview using the author’s questionnaire, as well as an economic analysis of losses from fraud according to the methodical approaches of auditing KPMG and Crowe. The practice of identifying fast recurring transactions (78% of respondents) when working with payment risks was distinguished as a result of the research by interviewing respondents from a sample of management accounting specialists. The importance of estimating losses from fraudulent transactions (78% of respondents) in the management accounting system of payment risks is emphasized. Analysis of the results of interviewing respondents showed a positive effect of the systematic application of management accounting tools on reducing the risks of fraud in online trade. Thanks to adequate management accounting, the fraud losses is reduced by an average of 12% compared to the 2022 average industry loss rate. The obtained results revealed the key role of management accounting in overcoming the negative consequences of payment risks, namely in reducing losses from fraudulent transactions. These results can be applied to improve the financial and operational management of online trade in wartime and other crisis situations. Prospects for further research are in-depth analysis of artificial intelligence tools and machine learning in identifying payment risks of online trade.

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