ROBO-ADVISORS AND AI-DRIVEN FUNDS: CATALYSTS IN THE DYNAMIC EVOLUTION OF ASSET MANAGEMENT

Abstract

Integrating Artificial Intelligence (AI) into investment finance has been transformative; however, its dynamic evolution in asset management remains underexplored. This study aims to comprehend and investigate the presence of AI in Malaysian asset management, its evolution in relation to current traditional practices, and its performance. A mixed-methods approach was employed. Qualitative text analysis was conducted on 702 fund reports and official AMC documents to identify the presence of AI technologies and their role in transforming traditional practices. Quantitative methods were utilised to evaluate performance: user ratings of robo-advisor applications were analysed to measure adoption and satisfaction, while Welch's t-test compared the annual returns of AI-driven and human-managed equity funds to assess performance. This study identified two prominent AI in asset management: robo-advisors and AI-driven funds. Robo-advisors automate investor profiling and fund recommendations, whereas AI-driven funds use algorithms for autonomous trading decisions. AI adoption varies among asset management companies (AMCs), with differing levels of integration. This transformation has supplanted traditional unit trust consultants (UTCs) with robo-advisors and human fund managers with AI-driven funds. Preliminary findings indicate high user satisfaction with robo-advisors because of their efficacy and convenience. AI-driven funds yield annual returns comparable to those of human-managed funds in the equity category, demonstrating their proficiency in managing complex investment strategies. These findings illuminate AI's transformative potential in asset management, suggesting it could replace traditional roles and achieve competitive performance. This study lays the groundwork for future research on AI's long-term impact and scalability in the industry, enriching the understanding of AI's evolving role in finance.