Treffer: Application of Machine Vision Technology in Automated Financial Auditing and Compliance Inspection.

Machine vision technology has revolutionized various fields by enabling automated analysis and decision-making through advanced visual data interpretation. In financial auditing and compliance inspection, the integration of machine vision with artificial intelligence (AI) offers significant potential for enhancing accuracy, efficiency, and regulatory adherence. Traditional auditing methods rely heavily on manual document verification, rule-based anomaly detection, and sample-based testing, which are time-consuming, error-prone, and often inadequate for detecting complex financial fraud. Existing machine vision approaches, while promising, face challenges related to scalability, adaptability to diverse document formats, and robustness against adversarial manipulations. To address these limitations, we propose a hybrid machine vision framework that combines deep learning, self-supervised learning, and knowledge graph-based regulatory compliance assessment. Our approach leverages optical character recognition (OCR) and convolutional neural networks (CNNs) for document digitization, while graph neural networks (GNNs) facilitate structured financial data analysis. Furthermore, attention-based anomaly detection mechanisms enhance fraud identification by capturing fine-grained inconsistencies in financial records. A domain adaptation module ensures robustness across diverse document formats and varying financial regulations. Experimental results demonstrate that our model achieves superior performance in fraud detection, document classification, and compliance verification compared to conventional techniques. This research contributes to the development of intelligent, automated financial auditing systems that improve transparency, security, risk mitigation, and regulatory enforcement in financial governance. [ABSTRACT FROM AUTHOR]

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