AI Rag Investment Recommender for Individuals: Compared with Deep Reinforcement Learning for Better Returns

In this study, an AI-driven investment recommendation system is designed using a Retrieval-Augmented Generation (RAG) approach for generating context aware, interpretable information. The personalized investment recommendations compared with a DRL-based approach designed in a previous study. The study proposes two models of the three. Group 1 comprises an existing DRL-based investment recommendation system that learns market decisions using reward-based market learning. Group 2 comprises the proposed RAG-based system which combines financial knowledge retrieval (market reports, news). And market history) with a transformer-based language model to generate personalized investment recommendations. The models are tested using Accuracy, Recall, Precision, F1-Score, Return Efficiency, Processing Time and Interpretability Score. The statistical tests are performed with SPSS 26. 0 and a one-sample t-test that considers the significance at 0. 05, with a 95% confidence interval. The obtained result reveals that the proposed RAG based investment recommendation system significantly outperforms the DRL-based system with attainment of 88. 63% accuracy, 87. 40% recall, 87. 40% return efficiency, processing time of 312. 50 ms and an interpretability score of 79. 85% statistically significant, p<0.01. The result suggests that the RAG-based system has higher accuracy, improved returns and improved interpretability than the DRL-based System and thus is a more effective and efficient solution for AI-based investment advisory systems.