Agentic AI and Multimodal AI to Quantitative Finance using Algorithmic Trading for Wealth & Portfolio Management - Satish Babu Banka, Tagur Ranga (Digital Repository of Theses - SSBM Geneva, 2025)
This DBA research covers the progress in quantitative finance for WAM (wealth & asset management) and pinpoints the notable difference that Agentic AI and Multimodal AI integration may make. The transition from traditional statistics to AI agents that can plan, search for solutions, learn from various types of inputs and exchange information with humans has an important impact in how to examine and deal with financial markets. The framework has been devised using various sources, sees an AI system working like a partner in algorithmic trading and controlling personal wealth and portfolios for WAM professionals and firms. LLMs are used to coordinate functions, access data from a range of channels and adjust learning as part of reinforcement learning. It is important to focus on RLHF for value alignment—systems with these methods to promise better outcomes, adjust to different financial markets and can address diverse personalized financial goals. The advantages can range from better risk-adjusted results, to discovering original investment ideas, better running the operation and offering more investment strategies to a wider audience. The financial organizations should focus on being ethical and reliable as they seek to succeed in today’s market. Proper regulations should be put in place to support new developments that benefit the market and investors. Essentially, merging Agentic AI and Multimodal AI with quantitative finance transforms the way Wealth management firms work together in finance and machine capability in one of WAM’s most critical areas. This study helps continue the discussion by looking at the current situation and designing an outline of future systems helps by setting out the course of actions.