Abstract
Safeguarding Personally Identifiable Information (PII) in financial documents is essential to prevent data breaches and maintain regulatory compliance. This research presents a scalable hybrid approach that integrates rule-based Natural Language Processing (NLP), Machine Learning (ML) approaches, and a custom Named Entity Recognition (NER) model for the accurate detection and anonymization of Personally Identifiable Information (PII). A varied and accurate synthetic dataset was created to replicate genuine financial document formats, enhancing model training and assessment. The model has attained a precision of 94.7%, a recall of 89.4%, an F1-score of 91.1%, and an overall accuracy of 89.4% on synthetic datasets. Additional validation on actual financial documents, such as audit reports and vendor bills, revealed a consistent performance with an accuracy of 93%. The study utilizes confusion matrices, ROC curves, and precision-recall curves to evaluate the model which further validates the model's capabilities and generalization ability. The suggested approach provides a robust and efficient solution for protecting sensitive information in operational financial contexts, markedly enhancing current methods for PII protection.