POSSIBILITIES OF APPLYING ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR DEVELOPING A FINANCIAL ASSISTANT

Abstract

Purpose of the work. To explore the possibilities of applying artificial intelligence technologies (machine learning, natural language processing, deep learning, recommendation systems, and computer vision) to create an effective financial assistant capable of analysing user data, forecasting financial indicators, detecting anomalies in transactions, providing personalised recommendations for budget optimisation, and automating financial planning processes. Methodology. The article systematically analyses the current state of development of financial platforms and artificial intelligence technologies. A comparative study of the capabilities of various AI technologies for integration into financial systems was conducted. Financial platforms were classified according to their functional purpose. An analysis of practical examples of the application of artificial intelligence in the financial sector was performed based on scientific publications and industry research. Information synthesis methods identify key directions for developing intelligent financial assistants. Scientific novelty. For the first time, the possibilities of integrating five key artificial intelligence technologies (machine learning, natural language processing, deep learning, recommendation systems, and computer vision) for creating a multifunctional financial assistant have been comprehensively investigated. The specific functional tasks of a financial assistant have developed a classification of applications for each technology. The main problems and challenges of integrating AI into financial platforms have been identified, particularly the need to ensure high accuracy in processing large volumes of data, detecting new types of fraud, personalising services and complying with regulatory requirements. Five key areas for further research have been identified, determining the prospects for developing intelligent financial systems. Conclusions. Integrating artificial intelligence technologies into financial platforms provides process automation, improved data analysis accuracy, personalised recommendations and increased security of financial transactions. Machine learning enables the prediction of financial results, the analysis of payment behaviour, and the detection of anomalies in realtime. Natural language processing automates the creation of personalised reports, the development of intelligent chatbots and the analysis of market sentiment. Deep learning optimises the construction of accurate predictive models and investment portfolios. Recommendation systems provide personalised advice on financial management. Computer vision automates data entry from financial documents. Implementing these technologies contributes to effective financial management, risk reduction, and achieving users’ long-term financial goals.