ARTIFICIAL INTELLIGENCE AT THE HEART OF BEHAVIORAL FINANCE

This paper explores the innovative role of artificial intelligence (AI) in mitigating behavioral biases in financial decision-making. Based on a theoretical framework grounded in behavioral finance, it examines how AI-powered tools can enhance rationality in investment choices. Behavioral biases, such as confirmation bias, hindsight bias, fixation bias, and overconfidence, are widely documented major factors that distort investor judgments and lead to suboptimal financial outcomes (Kahneman & Tversky, 1979; Barberis & Thaler, 2003).

This study employs a conceptual and exploratory approach, developing a theoretical model that demonstrates how AI systems, through machine learning algorithms, predictive analytics, and data-driven decision support, can help identify, mitigate, or even correct cognitive distortions in financial behavior. The paper presents several hypothetical scenarios to illustrate how AI can improve portfolio allocation, risk assessment, and market timing decisions by reducing emotional and cognitive interference.

This study examines in detail the advantages and disadvantages of integrating artificial intelligence (AI) into financial decision-making processes. While AI improves accuracy, efficiency, and objectivity, it also raises concerns regarding the transparency of algorithms, ethical considerations, and the reliability of models. The findings suggest that AI represents a promising, albeit complex, tool for enhancing the quality of decisions in the financial sector, opening new avenues for academic research and practical financial innovation.