Intelligent Receipt Analyzer for Classification and Fraud Detection

The rapid digitisation of financial transactions has resulted in an exponential increase in the number of receipts generated by individuals and organisations.Efficient management, verification, and fraud detection of these receipts are critical for financial transparency, auditing, and compliance.However, traditional receipt management systems rely heavily on manual data entry or template-based Optical Character Recognition (OCR), which lack adaptability, contextual understanding, and intelligent fraud detection capabilities.This paper presents a comprehensive AI-driven framework for automated receipt analysis and fraud detection.The proposed system integrates advanced image preprocessing, deep learning-based OCR, Natural Language Processing (NLP) for structured information extraction, contextual reasoning mechanisms, and anomaly-based fraud detection.Receipt images are transformed into structured, validated financial records through a multistage processing pipeline.Experimental evaluation demonstrates high accuracy in field extraction, robust classification performance, and effective identification of fraudulent or anomalous receipts.The framework provides a scalable, intelligent, and practical solution for modern financial automation and enterprise expense management.