From Rigid Robo-Advisors to Human-Like Interactions: Revolutionizing Financial Assistance with LLM-Powered Solutions

Résumé

Generative Artificial Intelligence (GenAI) is revolutionizing the business world by increasing availability, efficiency, cost reduction, and innovation. This paper explores the application of Large Language Models (LLMs) and GenAI to finance. It proposes a novel framework on how we can imagine robo-advisory systems, from a traditional rigid platform to a more humanized solution that further engages the investor in a hand-picking asset selection process and better understands their goals and profile using LLMs. We designed an end-to-end solution to overcome many limitations such as lack of flexibility in roboadvisors, lack of possible asset types (usually only equities) and the problem of real-time access to high quality data. The solution architecture includes dynamic client profiling, risk aversion estimation and portfolio optimization. Using robust data pipelines to curate the latest market information, the Asset Selector Agent has been customized. Through iterative development, we employed prompt engineering and multi-agent workflows to enhance user interactions and deliver meaningful insights. By developing an innovative chatbot platform, we demonstrate the potential of LLMs to transform customer service, increase engagement, and provide strategic financial advice.

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