ARTIFICIAL INTELLIGENCE IN FINANCE: ANALYSIS OF POTENTIAL THREATS AND WAYS TO MINIMIZE THEM

Abstract

The article provides a comprehensive analysis of the potential threats associated with the use of artificial intelligence in the financial sector and substantiates practical ways to mitigate them. The relevance of the topic is determined by the rapid transition of banks, investment companies and fintech platforms from simple automation to predictive analytics, robo-advisory models and generative AI-based decision support. The purpose of the study is to identify the key technological, systemic, ethical and legal risks that arise when AI is integrated into financial consulting, lending, asset management and customer interaction, and to develop a set of measures that can reduce these risks without limiting useful innovation. The research methodology combines systemic and comparative analysis, classification of risks, synthesis of regulatory approaches, logical generalization and elements of scenario analysis. The study shows that AI improves the speed of data processing, reduces transaction costs, expands access to personalized financial services and increases the accuracy of operational decisions. At the same time, the article proves that the economic effect of AI cannot be considered separately from the quality of data, transparency of models, cybersecurity, accountability and consumer protection. The main threats include the black-box problem, algorithmic bias, adversarial manipulation of input data, excessive dependence on external technology providers, homogeneous trading strategies, blurred liability for automated decisions and the risk of erosion of customer trust. The practical value of the study lies in the proposed risk minimization framework based on Explainable AI, algorithmic audit, human-in-the-loop governance, continuous model monitoring, data quality control, cybersecurity-by-design, stress testing and regulatory sandboxes. The article argues that the most sustainable model for finance is not full replacement of human expertise but hybrid intelligence, in which AI performs analytical and operational tasks while humans retain strategic, ethical and legal responsibility for critical decisions.