Behavioral AI in Finance: A Framework for Optimizing Human-AI Collaboration in Investment Decision-Making | ||
| مجلة الدراسات التجارية المعاصرة | ||
| Volume 11, Issue 22, October 2025, Pages 1256-1291 PDF (1.09 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/csj.2025.434601.1677 | ||
| Author | ||
| Eyas Gaffar A. Osman* | ||
| shaqra university | ||
| Abstract | ||
| This paper introduces the Behavioral AI Collaboration Framework (BACF), a novel theoretical and empirical approach to optimizing human-AI collaboration in financial decision-making. We address the critical limitation of traditional systems that attempt to eliminate human behavioral patterns, arguing instead for AI systems designed to complement these behaviors to achieve superior outcomes compared to conventional rational-agent approaches. Our framework identifies three critical dimensions of effective human-AI synergy: behavioral bias accommodation, trust calibration, and adaptive transparency. We tested the BACF through controlled AI simulation experiments with 847 participants and analysis of 2.3 million simulated trading decisions from a major robo-advisory platform. Results demonstrate that behaviorally-informed AI systems significantly enhance performance. Specifically, they reduced portfolio volatility by 23% while increasing risk-adjusted returns by 18% compared to standard robo-advisors. The framework successfully mitigated persistent behavioral biases, showing a 34% reduction in overconfidence and a 28% decrease in loss aversion when our accommodation protocols were employed. These findings have significant implications for fintech design, regulatory policy, and the broader integration of AI in financial services. | ||
| Keywords | ||
| Behavioral Finance; Artificial Intelligence; Human-AI Collaboration; Robo-Advisory; Decision Support Systems | ||
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