An explainable and privacy-enhanced LLM framework for sector-specific NLP in regulated financial and healthcare domains

This study illustrates a transparent and trustworthy procedure for the application of large language models (LLMs) in industries with strict regulations, like finance and healthcare. The framework put forward merges LLMs that are specific to the domain like FinBERT and BioBERT, with methods that explain things after the event, such as SHAP, and techniques for ensuring people’s privacy, like differential privacy (DP) and federated learning. There is a domain-adaptive training pipeline, a local explainability module, a differential privacy controller based on DP-SGD, and a secure API interface for working with models. The study used benchmark datasets like FiQA and MIMIC-III to evaluate their performance on key metrics like accuracy, F1-score, SHAP explainability score, and privacy budget ( \(\epsilon \) ). The outcomes indicate that the FinBERT-based model outperforms the BioBERT-based healthcare model in all respects. The FinBERT-based model achieves 89% accuracy, an F1 score of 0.875, and a SHAP score of 1.22. The BioBERT-based model has 86% accuracy and a SHAP score of 1.19. The financial model also exhibited better privacy-utility trade-offs, maintaining a stronger privacy ( \(\epsilon \) \(\approx \) 3.2) with only a minor decrease in performance. These outcomes affirm that the framework’s initial performance, interpretability, and data privacy compliance can be maintained. Hence, it is an attractive option for mission-critical NLP applications in those fields that are both sensitive and regulated.