REVIEW OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE ALGORITHMS FOR FINANCIAL DATA ANALYSIS AND PROCESSING

Abstract

The article analyses modern machine learning algorithms and their application for financial data processing, forecasting indicators and creating personalised recommendation systems. The effectiveness of various types of algorithms, in particular regression models, classification methods, neural networks and clustering, for solving specific financial tasks is investigated. Particular attention is paid to deep learning models, in particular recurrent neural networks (RNN), long short-term memory (LSTM) and generative adversarial networks (GAN), which demonstrate high efficiency in forecasting financial time series. The application of artificial intelligence in various sectors of the financial industry is considered, in particular in the banking sector for credit scoring, investment management, personal finance for budgeting, and corporate finance for forecasting performance. The potential of natural language processing models, in particular ChatGPT, for analysing financial texts and creating intelligent financial assistants capable of providing personalised recommendations in a natural dialogue mode is analysed. The key challenges of implementing artificial intelligence technologies in the financial sector are identified, including the ‘black box’ problem of algorithms, ensuring the quality of input data, compliance with regulatory requirements, and cybersecurity. Approaches to overcoming these challenges have been proposed and promising directions for the development of machine learning algorithms for the financial sector have been outlined, including the integration of federated learning, the development of explainable artificial intelligence models, and the creation of hybrid systems that combine symbolic and neural network approaches. The study demonstrates that the introduction of artificial intelligence technologies in the financial sector has significant potential for revolutionary changes in business processes, improving data analysis efficiency, and enhancing customer service quality.