Exploring the Role of Next-Generation Investment Management Robotic Automation Architecture for Portfolio Management and Risk Mitigation through AI and ML - Trivedi, Mansi (Digital Repository of Theses, 2025)
This thesis provides an exhaustive examination of the transformative potential of next-generation investment management robotic automation architecture, leveraging “artificial intelligence (AI) and machine learning (ML) to revolutionize investment analysis, portfolio management, and risk mitigation”(Mahalakshmi et al., 2022), thereby enhancing the efficiency, productivity, and decision-making(Zakaria, Z., & Razak, 2023a) capabilities of investment managers and organizations. By integrating data analytics, predictive modelling, and decision support systems, this innovative architecture facilitates the automation of complex investment processes, enabling more accurate and timely investment decisions. The research undertakes “a comprehensive review of existing literature on AI, ML, and robotic automation(Chakraborti et al., 2020a) in investment management, identifying gaps and opportunities for improvement.” A mixed-methods approach is employed, combining theoretical modelling, empirical analysis, and experimental design. Historical market data and simulated investment scenarios are utilized to evaluate the architecture's performance against traditional investment management methods. The study investigates “the impact of AI and ML on investment analysis(Chen, Y., & Wang, 2019a), portfolio management, and risk mitigation, exploring applications such as predictive modelling, natural language processing, and deep learning”(Teng, C., Liao, Y., & Tseng, 2023a). Key research questions addressed include the potential “robotic automation architecture to enhance investment analysis and portfolio management, the impact of AI and ML on risk mitigation”(Duarte, F., & Girardi, 2022a), and the potential for next-generation robotic automation architecture to improve investment decision-making. The thesis contributes to advancements in investment management technology, providing insights into AI and ML applications and empirical evidence on the effectiveness of next-generation investment management solutions. Expected findings include enhanced investment analysis accuracy, improved portfolio management efficiency, reduced risk exposure, and increased operational efficiency. This research aims to provide “a comprehensive understanding of the potential benefits and challenges associated with integrating AI and ML” (Chavarnak, J., Lee, M., Patel, S., & Tran, 2018; Khan, A., & Bhatti, 2023a)into investment management robotic automation architecture, discussing implications for investment management practice, policy, and future research. The study highlights the potential for next-generation robotic automation architecture to transform the investment management industry, enabling more informed investment decisions, improved risk management, and increased operational efficiency. Furthermore, this research explores “the potential applications of AI and ML in investment management(Frank J. Fabozzi (Editor), 2011), including predictive modelling, natural language processing, and deep learning, and examines the role of data quality, governance, and security in ensuring the integrity and reliability of AI-driven investment decisions”. The thesis also investigates the human-AI collaboration paradigm, examining how investment managers and AI systems can work together to achieve better outcomes. By ‘investigating the intersection of AI, ML, and investment management, this research contributes to the development of more sophisticated and effective investment management systems, shedding light on the opportunities and challenges associated with this emerging technology”(Madakam, Holmukhe and Revulagadda, 2022a). Ultimately, this thesis provides a critical examination of the transformative potential of “next-generation investment management robotic automation architecture, offering actionable insights and recommendations for investment managers, organizations, and policymakers seeking to harness the power of AI and ML in investment management”(Zakaria et al., 2023a). The research underscores “the importance of ongoing innovation and investment in AI and ML research and development, ensuring that investment management organizations remain competitive and resilient in an increasingly complex and dynamic market environment”(Gill et al., 2022a). By advancing our understanding of AI and ML applications in investment management, this thesis informs the development of more effective investment management strategies, policies, and practices.