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Methodological Tools for the Generation and Revenue Accounting in a Construction Company in Accordance with International Standards

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Big Data in Information Society and Digital Economy

Abstract

The order of formation and recognition of revenue in a modern company plays an important role. Revenue expresses the level of economic performance of the company. A radical change in the system of standards regulating the procedure for accounting for revenue forces the specialists of the accounting apparatus to change the current approaches to revenue generation. The main purpose of this article is to consider various situations of revenue recognition by construction companies under the current version of IFRS 15. Particular attention is paid in the article to the harmonization of approaches of domestic practice to the generation of revenue in accordance with the norms of IFRS. In the article, two methods are given for calculating the share of revenue as obligations are fulfilled—according to the resources spent and the results obtained. In the course of the research, the following methods were used: synthesis, abstraction, classification, analysis, modeling. Within the framework of IFRS 15, a settlement scheme is proposed between developers and shareholders-buyers of the dwelling through escrow accounts. The article suggests the use of several methods of generating revenue for a construction company. The results method or revenue approach, in which revenue is recognized based on estimates of the value of goods, scope of work or services delivered to the buyer or customer on the current date. A formula for calculating the degree of completion of work by the results method in a construction company is proposed.

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References

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Correspondence to Tatyana V. Bulycheva .

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Bulycheva, T.V., Volgina, I.V., Zavyalova, T.V. (2023). Methodological Tools for the Generation and Revenue Accounting in a Construction Company in Accordance with International Standards. In: Bogoviz, A.V. (eds) Big Data in Information Society and Digital Economy. Studies in Big Data, vol 124. Springer, Cham. https://doi.org/10.1007/978-3-031-29489-1_15

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