PRISMA_2020_checklist.docx

<b>Background:</b>Behavioral finance is a growing field that explores how cognitive biases and psychological factors—such as emotions, heuristics, and social influence—affect financial decision-making. This paper investigates how behavioral biases have evolved and influenced investment behavior in light of recent technological and market developments.<b>Methods:</b>A Systematic Literature Review (SLR) was conducted, focusing on peer-reviewed publications from 2020 to 2025. A total of 30 studies were analyzed, selected from databases such as Scopus, ScienceDirect, and Emerald using criteria including relevance, recency, and empirical focus. The selected literature was categorized based on psychological biases, methodological approaches, and emerging themes in investor behavior.<br><br><b>Results:</b>The findings highlight that traditional biases such as overconfidence, herding, anchoring, and loss aversion continue to dominate investment behavior. Moreover, the review identifies automation bias—a newer bias arising from investor over-reliance on AI and fintech platforms. Studies show that technological innovation, the COVID-19 pandemic, and rising retail investor activity have intensified behavioral distortions.<b>Conclusion:</b>Behavioral finance remains essential for understanding real-world investment behavior. Enhancing financial literacy, incorporating behavioral nudges, and updating regulatory frameworks are critical for mitigating the negative effects of these biases. The study offers a forward-looking perspective on integrating behavioral insights into investment strategies and calls for future research into AI-driven finance, cross-cultural effects, and the role of fintech in shaping investor psychology. This study conducts a Systematic Literature Review (SLR) of research published between 2020 and 2025.This review identifies key behavioral biases, such as overconfidence, herding, anchoring, and loss aversion, that continue to dominate investor behavior. It also highlights emerging biases, including automation bias, resulting from increased reliance on artificial intelligence (AI) and fintech platforms. This study analyzed 30 peer-reviewed articles using various methodologies, including surveys, experiments, and econometric models.