Building trust in AI-powered robo-advisors requires transparency, explainability, and ethical design to address concerns about bias and accountability.
Authors
Himanshi Rajora, Associate Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India
Huy Hung Ta, International School, Vietnam National University, Hanoi, Vietnam
Mananage Shanika Hansini Rathnasiri, Sabaragamuwa University of Sri Lanka, Belihuloya, Sri Lanka
Summary
The rise of AI-powered robo-advisors in financial services offers benefits like scalability and personalized investment recommendations. However, gaining consumer trust remains a challenge due to concerns about transparency, fairness, and accountability. Factors such as algorithmic bias, the “black box” nature of AI, and the lack of explainability contribute to a trust gap. Ethical design, regulatory compliance, and user-centric approaches are essential for building confidence in these systems. Explainable AI (XAI), transparency, and robust data privacy policies are pivotal to mitigating bias and fostering trust. Moreover, hybrid models integrating human expertise with AI can address consumer hesitations, while personalization and user education empower clients in decision-making. Future developments should emphasize ethical AI frameworks, real-time compliance monitoring, and global standards to ensure responsible financial advising. This paper explores strategies for cultivating trust and transparency to democratize financial services effectively.
Published in: Global Work Arrangements and Outsourcing in the Age of AI
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