AI-Driven Automation of Financial Document Processing: Enhancing Accuracy, Efficiency, and Fraud Detection with OCR, NLP, and Deep Learning

Abstract

Automating financial document processing is a key priority in the financial sector to improve efficiency, reduce manual work, and increase accuracy. Instead, this paper proposes a whole process system that integrates OCR, NLP, and DL technologies for the efficient flow of documents. By integrating advanced preprocessing techniques, robust OCR models, and state-of-the-art NLP and DL architectures, the proposed system mitigates issues, including metadata variability due to multiple document formats, data noise resulting from irrelevant image details, and the automation of fraud detection. With preprocessing such as noise reduction and skew correction, an OCR accuracy of 96.1% is realized. The NLP modules, driven by BiLSTM-CRF and transformer models, provided high accuracy. Sourced critical entities, including payee names and transaction amounts, achieved an F1-score of 97.6%.

Furthermore, the DL module guarantees effective fraud detection with a classification accuracy of 98.7% through anomaly detection and signature verification using Siamese Networks and CNNs.It was tested in practice and provided respective efficiency (an average processing speed of 115 ms per document and stability in the face of growing workloads). Our work advances state-of-the-art methods in the field, as verified by comparative studies with conventional techniques, which show significant improvements in accuracy, speed, and scalability, making it an effective solution for automating financial workflows. This research demonstrates how AI-powered solutions can help overcome complex problems in financial document processing and presents a scalable architecture for practical implementations. In the future, the dataset will be expanded to cover more document kinds, fraud detection models will be optimized, and blockchain will be integrated for better transparency and security.