ARTIFICIAL INTELLIGENCE IN INVESTMENT DECISION-MAKING: BENEFITS, BIASES, AND MARKET IMPLICATIONS

Artificial intelligence (AI) has become more and more central to modern investment management, as it promises faster information processing, better pattern recognition, and more adaptive portfolio strategies. Asset managers, hedge funds, investment banks and robo-advisory platforms now use machine learning, natural language processing and algorithmic optimisation techniques to assist with security selection, portfolio construction, execution and risk monitoring. But the rise of AI in finance also raised serious concerns about data bias, model opacity, overfitting, herding and systemic fragility. This article critically discusses the use of AI in investment decision-making from three interrelated angles: its possible practical benefits, its methodological and institutional constraints, and its overall effect on market behaviour. It argues that AI can materially improve analytical efficiency and decision support, but its value is often oversold where predictive power is confused with sound investment judgement. Investment decisions are not just statistical optimisation problems. They involve uncertainty, institutional constraints, behavioural responses, and the interpretation of economic meanings. AI should therefore not be viewed as a substitute for human judgement but as a sociotechnical decision system whose effectiveness is based on governance, interpretability, and disciplined use.