The Use of Artificial Intelligence in Business Valuation in Bulgaria, Romania and Greece

Pavlina Petrova ()
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Pavlina Petrova: Technical University of Liberec, Czech Republic

Stroitelno predpriemachestvo i nedvizhima sobstvenost = Construction Entrepreneurship and Real Property, 2025, issue 2, 19-29

Abstract: Nowadays, when digitalization and automation are key trends, technologies such as artificial intelligence are also used in the valuation process itself. Traditional valuation methods are increasingly supplemented with modern tools, including the use of artificial intelligence. In response to this development, it is possible to examine the extent to which these technologies are used in practice and how experts perceive them. This article is based on research and its aim is to map whether companies use automation and artificial intelligence, what specific applications they use and what benefits they perceive from combining modern technologies with traditional valuation procedures. To achieve the set goal, three research questions were formulated. A quantitative research method in the form of a questionnaire survey was used to obtain the necessary data. The data obtained were subsequently analyzed and evaluated using descriptive statistical methods and the set research questions were answered based on the results achieved. The research found that all respondents use modern approaches in the valuation process, such as automation, digitalization and artificial intelligence. Companies use applications such as Valutico, Chat GPT and BgGPT Chat in the business valuation process. Companies perceive the following benefits of integrating digitalization, automation and artificial intelligence into the business valuation process: big data analysis, time savings, accuracy, acceleration of routine calculations, elimination of human errors and reduction of human labor costs.

Keywords: Business valuation; digitalization; automation; artificial intelligence; company (search for similar items in EconPapers)
JEL-codes: C45 G12 M21 (search for similar items in EconPapers)
Date: 2025
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