Enhancing AI-Based Investment Decision Quality: The Roles of Perceived AI Transparency, Algorithmic Fairness, Trust, and Financial Literacy

Abstract

Artificial intelligence (AI) is becoming a game-changer in shaping investment decision-making processes, with automated financial advice services and intelligent recommendation systems leading the way. Nevertheless, transparency, fairness, and trust issues remain within the sphere of investor acceptance for AI-driven financial services. In this study, AI transparency and algorithmic fairness are shown to influence the quality of investment decisions made using AI, with the effects being mediated by the trust placed in AI-based financial services and moderated by financial literacy. The sample size consisted of 487 investors from Pakistan that were analyzed by the Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that trust through perceived transparency of AI and fairness of algorithms is a key factor in improving the quality of investment decisions based on AI. Additionally, financial literacy enhances the positive association between trust and investment decision quality. The study also builds on existing theories such as Trust Theory, Signaling Theory, and Information Processing Theory, and offers valuable implications for fintech companies, financial institutions, and policymakers aiming to foster trustworthy and efficient AI-powered financial services in developing nations.