ARTIFICIAL INTELLIGENCE IN INVESTMENT MANAGEMENT AND RISK ANALYSIS: TRANSFORMING FINANCIAL DECISION-MAKING IN THE DIGITAL ERA

Abstract

In the digital era, Artificial Intelligence (AI) has proven to be a groundbreaking technology that is revolutionizing the field of investment management and financial risk analysis. Complexity in financial markets, the surge of financial information, and the development of financial technologies have facilitated the use of AI-based tools for investment decisions, portfolio optimisation and risk analysis (Arner, Barberis & Buckley, 2016). By leveraging AI technologies such as machine learning, predictive analytics, big data analytics, and automated trading systems, investors and financial institutions can process vast amounts of structured and unstructured data, uncover market trends and make precise predictions (Huang & Rust, 2021). AI has thus emerged as an indispensable asset in the quest for better investment results and mitigating financial risks. This research explores how AI can be applied to investment management and risk analysis and how it affects the financial decision making process. In terms of the design of research, the used research design is descriptive design and analytical design with primary and secondary data sources. The structured questionnaire is used to collect primary data from the investors, portfolio managers, financial analysts, and banking professionals and secondary data is collected from scholarly journals, books, reports, financial publications. The results suggest that AI enhances portfolio efficiency, the optimization of returns, risk prediction, and investor satisfaction through data-driven investment strategies (Chowdhury et al., 2023). Furthermore, AI systems can boost credit risk management, market risk management, fraud prevention, and operational risk monitoring, which can foster financial robustness and organizational effectiveness (Nawaz, Gomes, & Khan, 2024). While these benefits are present, certain issues like data privacy concerns, algorithmic bias, uncertainty around regulations, model transparency problems, and the cost of implementation remain challenges to AI in finance (Jarrahi, 2018). In conclusion, the study highlights the strategic role of AI in the investment field and how it revolutionizes the way investments are made, paving the way for more sustainable, intelligent, and evidence-based finance in the digital world.