Design of a Generative AI-Driven Intelligent Investment Advisory System

Mengxin Wu

European Journal of AI, Computing & Informatics, 2025, vol. 1, issue 3, 120-129

Abstract: To address the need for dynamic strategy generation and semantic adaptation in intelligent investment advisory systems, this study proposes a generative architecture that integrates multi-source knowledge while supporting semantic reasoning, interpretability, and real-time user interaction. The system comprises modular components, including task scheduling, multi-source data fusion, a generation engine, semantic understanding, and strategy explanation, all enhanced by context-aware mechanisms and multi-dimensional security protection. The architecture leverages a multi-layered Transformer-based model and a tensor-level knowledge fusion framework, enabling real-time asset allocation and policy explanation. Empirical validation using heterogeneous financial datasets demonstrates the system's superiority in generative quality and robustness. Evaluation metrics indicate a BLEU-4 score of 44.89, a BERTScore of 91.31, semantic consistency of 0.89, strategy accuracy of 93.4%, and a recognition success rate exceeding 94.7% under adversarial perturbations. As shown in comparative experiments, the proposed system outperforms existing models such as GPT-2 and FinBERT in interpretability and interaction latency. The results confirm that the proposed system achieves high-quality generation, strong semantic alignment, and user trustworthiness in complex financial advisory scenarios.

Keywords: intelligent investment advisor; generation mechanism; knowledge fusion; semantic modeling; system security (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dba:ejacia:v:1:y:2025:i:3:p:120-129

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