Using Artificial Intelligence to Combat Fraud: Asian Experience, Russian Prospects

Anastasia Babanskaya, Daria Ermoleva, Nadezhda Efimenko, Ivan Kotlyarov and Denis Koleznev
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Anastasia Babanskaya: Financial University under the Government of the Russian Federation
Daria Ermoleva: Financial University under the Government of the Russian Federation
Nadezhda Efimenko: Financial University under the Government of the Russian Federation
Ivan Kotlyarov: Financial University under the Government of the Russian Federation
Denis Koleznev: Financial University under the Government of the Russian Federation

A chapter in Artificial Intelligence and Digital Transformation, 2025, pp 59-78 from Springer

Abstract: Abstract The use of artificial intelligence today is more widespread than ever. The areas and opportunities for using AI to combat fraud at all stages are expanding every year. Therefore, it is very important to use the best practices of other countries, especially those following the path of advanced development. The purpose of the article is to study the experience of the UAE and India to pinpoint best practices in using AI and combating corporate fraud. The object of the study is AI-based technological solutions to identify anomalies in financial data sets and prevent fraud. The work uses qualitative methods of analyzing scientific literature, state development programs and national projects, case situations, methods of comparative statistical analysis and analogies. Based on the case studies, it was established that the priority areas for developing AI solutions are the banking sector, government agencies, cybersecurity and transactions of organizations, and interaction with partners. The next stage involved identifying areas for combating corporate fraud for individual AI subtechnologies. As a result, the study comes up with proposals on using individual AI subtechnologies to counter fraud.

Keywords: Artificial intelligence; Fraud; Subtechnologies; Monitoring of financial transactions; Risks (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-00118-4_5

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