This chapter investigates behavioral biases influencing investment decisions, specifically loss aversion, herding, overconfidence, and recency bias. Integrating insights from behavioral finance and neuroscience, it reveals that these biases originate in brain regions linked to emotional processing, social cognition, and reward anticipation, resulting in systematic deviations from rational investment behavior. Historical case studies, including the Dotcom bubble, 2008 financial crisis, and Black Monday, illustrate how aggregated biases can trigger market-wide instability. The chapter further explores cognitive load as a critical factor exacerbating biases, demonstrating that decision fatigue significantly impairs investors’ rational decision-making capabilities. It evaluates bias-mitigation strategies, emphasizing the effectiveness of financial education, behavioral nudging, and adaptive AI-driven interventions. Finally, the chapter addresses ethical considerations associated with using AI to correct biases, stressing the importance of transparency, informed consent, privacy, and autonomy in deploying these technologies to safeguard investor welfare and maintain market integrity.