This article provides a comprehensive analysis of current applications of machine learning and artificial intelligence technologies in the financial sector. Based on a systematic review of scientific research, key areas for implementing ML algorithms are identified: algorithmic trading, risk management, and financial time series forecasting. Particular attention is paid to a comparative analysis of the effectiveness of various neural network architectures, including models with attention mechanisms and deep learning. The paper presents the results of an empirical study of AI technology implementation in Russian financial institutions based on data from the Bank of Russia and the Fintech Association. It is found that the most common application areas are predictive analytics (95% of respondents), while intelligent process automation covers only 53% of financial institutions. Promising areas for technological development in finance are identified, including the development of methods resilient to extreme market conditions and increased model interpretability to enhance regulatory confidence.
machine learning, artificial intelligence, financial markets, algorithmic trading, risk management, neural networks, Bank of Russia, Fintech
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