THE USE OF ARTIFICIAL INTELLIGENCE IN DIGITAL FINANCE

Digital finance increasingly relies on digital systems for payments, banking, lending, investment, trading, and customer support. Artificial intelligence (AI), particularly machine learning, can analyse large datasets, detect unusual transaction patterns, support fraud prevention, improve customer service, forecast demand, and strengthen operational monitoring. Its effectiveness, however, depends on data quality, system integration, cybersecurity, model governance, transparency, and human oversight. This article reviews the major applications of AI in digital finance and proposes a conceptual framework for assessing operational, economic, and risk-related effects. The framework combines functional classification, implementation stages, evaluation metrics, and cost–benefit assessment, with particular relevance to emerging markets, including Uzbekistan. As verified organisational datasets were unavailable, the article presents a methodological basis for pilot implementation and future empirical research rather than claims of measured performance.

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