A Machine Learning and Deep Learning-Based Account Code Classification Model for Sustainable Accounting Practices

Durmuş Koç () and Feden Koç ()
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Durmuş Koç: Department of Computer Technologies, Uluborlu Vocational School of Selehattin Karasoy, Isparta University of Applied Sciences, Isparta 32650, Türkiye
Feden Koç: Department of Logistics, Karahallı Vocational School, Uşak University, Uşak 64000, Türkiye

Sustainability, 2024, vol. 16, issue 20, 1-23

Abstract: Accounting account codes are created within a specific logic framework to systematically and accurately record a company’s financial transactions. Currently, accounting reports are processed manually, which increases the likelihood of errors and slows down the process. This study aims to use image processing techniques to predict cash codes in accounting reports, automate accounting processes, improve accuracy, and save time. Deep learning embeddings from Inception V3, SqueezeNet, VGG-19, VGG-16, Painters, and DeepLoc networks were utilized in the feature extraction phase. A total of six learning algorithms, namely Logistic Regression, Gradient Boosting, Neural Network, kNN, Naive Bayes, and Stochastic Gradient Descent were employed to classify the images. The highest accuracy rate of 99.2% was achieved with the combination of the Inception V3 feature extractor and the Neural Network classifier. The results demonstrate that image processing methods significantly reduce error rates in accounting records, accelerate processes, and support sustainable accounting practices. This indicates that image processing techniques have substantial potential to contribute to digital transformation in accounting, helping businesses achieve their sustainability goals.

Keywords: accounting code classification; image processing; deep learning; accounting automation; sustainable accounting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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