The Role of Artificial Intelligence in Behavioral Finance

The integration of Artificial Intelligence (AI) in behavioral finance represents a significant shift, aiming to improve financial decision-making by identifying and predicting behavioral biases. This thesis explores AI's potential to bridge the gap between theoretical finance models and practical applications, providing deeper insights into market behaviors and enhancing investment strategies.

Goals

The primary goals of this thesis are:

• Investigate AI Applications: Examine how AI technologies, particularly machine learning and natural language processing (NLP), are used to identify and predict behavioral biases in financial markets.
• Understand Investor Perceptions: Assess retail investors' perceptions and attitudes towards AI-based financial services, focusing on their trust and acceptance.
• Bridge Theory and Practice: Integrate psychological insights from behavioral finance with economic theories to explain the influence of cognitive biases on financial decisions.

Methodology

The study uses a mixed-method approach:

• Theoretical Framework: Split into two parts: an overview of behavioral finance concepts such as Prospect Theory, and an exploration of AI's application in finance.
• Literature Review: Analyzed academic papers, journals, and case studies using databases like Google Scholar and Swisscovery BFH to ensure high-quality and peer-reviewed sources.
• Survey Implementation: Conducted a survey with questions designed to capture retail investors' perceptions, acceptance, and trust in AI-based financial services. The survey included Likert scale, multiple-choice, and open-ended questions.

Results

Key findings from the thesis include:

• High Trust in AI Capabilities: A significant majority of respondents believe AI can effectively identify and predict behavioral biases, indicating strong theoretical trust.
• Generational Divide: Younger investors show higher engagement and trust in AI tools compared to older investors, highlighting the need for targeted education and training programs.
• Importance of Transparency: Emphasizes the necessity for AI tools to be transparent and explainable to enhance user trust and satisfaction.
• Challenges in AI Implementation: Identified challenges such as data management, model interpretability, and regulatory compliance, which need to be addressed for better AI tool adoption.

Implications

The research underscores the transformative potential of AI in behavioral finance. Financial institutions must focus on improving transparency, user experience, and continuous education to foster greater trust and satisfaction among users. By addressing these areas, AI can significantly enhance financial decision-making and investment strategies.