International Journal of Scientific Research and Engineering Development

Invoice management is a critical yet labor-intensive process in enterprise financial operations. According to industry reports, organizations process millions of invoices annually, with manual categorization accounting for a significant portion of accounts payable costs. This paper presents a comprehensive AI-based smart invoice categorization system that combines deep learning classification with multi-source data intelligence signals. The proposed system integrates four key analytical components: NLP-based classification using transformer models, OCR and document parsing for data extraction, vendor entity recognition for supplier intelligence, amount and date normalization for structured data processing, and a unified confidence scoring mechanism. By synthesizing these diverse data sources, the system achieves robust categorization capabilities that address the limitations of traditional rule-based approaches and manual classification methods. We evaluate the system’s architecture, feature engineering methodology, and performance characteristics, demonstrating how multisource intelligence fusion enables accurate, real-time invoice categorization with low misclassification rates across 18 standard enterprise expense categories.

Vinothkumar.C, S.Priyadharshini,"Smart Invoice Categorization: An AI-Driven Multi-Model Approach with Deep Learning and NLP Classification" International Journal of Scientific Research and Engineering Development, V9(2): Page(614-622) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.