An Explainable AI Multi-Agent Recommender System for Financial Document Access Control

Financial institutions require robust document access control mechanisms that balance security with transparency and explainability. Traditional classification systems often operate as black boxes, failing to provide justifications for access-control decisions. This work presents a novel explainable AI multi-agent recommender system for financial document sensitivity classification that addresses critical ethical concerns in AI-powered decision-making. We fine-tuned three state-of-the-art models—FinBERT, BERT-base-uncased, and GPT-4.1-mini—on a custom-labeled Financial PhraseBank dataset with four sensitivity levels: Public, Internal, Confidential, and Restricted. These fine-tuned models serve as specialized AI agents within a multi-agent architecture orchestrated by GPT-5.1, a large reasoning model operating in zero-shot mode. The orchestrator synthesizes agent predictions and generates natural language recommendations that justify classification decisions. Our agentic AI multi-agent recommender system achieves 83.71% overall accuracy, comparable to individual models (82.80%-84.93%), while providing interpretable explanations for each decision. Critically, agent agreement analysis reveals that unanimous decisions (3/3 agents agree, 78.8% of cases) achieve 92.28% accuracy—significantly outperforming any individual model—validating the collaborative decision-making approach. The system demonstrates that multi-agent architectures can provide both high-confidence predictions and natural language explainability, creating transparent, accountable AI systems for financial document access control. All code and methodologies are released as open-source on our GitHub (Applied-AI-Research-Lab 2025) to support reproducibility and further research in explainable AI for finance.