Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, have been extensively used in consumer finance to assess credit risk, develop automated models for loan attribution, and forecast households’ insurance claims and spending. However, their application in analyzing retail investors’ behaviors and designing tools to shape financial advice has emerged more recently.
This paper revisits the key applications of AI for Behavioral Finance, emphasizing its ability to analyze investor profiles and predict investor behavior. These capacities pave way for the development of personalized recommendation systems and behavioral coaching tools designed to help retail investors avoid common mistakes and mitigate cognitive biases. More recently, large language models (LLMs) have shown promise in delivering conversational financial advice by understanding natural language queries and generating tailored responses. Their ability to process vast amounts of financial information and interact in a human-like manner opens new opportunities for scalable, personalized investor support.
You can now read the full whitepaper at the link below
