Development of Trust-Weighted Reinforcement Learning SystemforPersonalized Financial Advisory Services

Abstract

The rapid growth of artificial intelligence (AI) in financial technology has transformed the landscape of personal finance management. Among emerging paradigms, reinforcement learning (RL) stands out for its ability to learn adaptive strategies through continuous feedback. However, one of the major challenges in deploying AI-driven financial advisory systems is maintaining user trust. This paper reviews the development of trust- weighted reinforcement learning (TWRL) frameworks designed for personalized financial advisory services. The integration of trust as a dynamic component within RL algorithms enables systems to adjustrecommendations based on the user’s confidence, risk perception, and behavioral patterns. This review explores the theoretical underpinnings, model architectures, data sources, and evaluation metrics used in the literature. It also highlights the potential applications, ethical implications, and future research directions for deploying trust-aware AI systems in finance.