This article conducts a comparative analysis of the efficiency of various machine learning algorithms in addressing the task of classifying accounting transactions — a component ensuring the accuracy of financial reporting and enhancing operational efficiency. The aim of this study is to analyze different machine learning algorithms for the task of automated classification of accounting entries. The methodological basis of the research includes an extensive review of specialized literature, where the architectures of models such as logistic regression, support vector machine (SVM), random forest and gradient boosting are analyzed, as well as promising neural network solutions employing natural language processing (NLP) technologies. As a result of the experiment, a comparative analysis is presented according to key metrics (accuracy, recall, F1-score) and a hybrid architecture is proposed, combining an NLP module based on the BERT model and a gradient boosting classifier, which demonstrates the best results when processing transactions with complex textual descriptions. The scientific novelty of the work lies in the description of a conceptual model for selecting the optimal algorithm depending on the characteristics of the original data set and in substantiating the advantages of the proposed hybrid architecture, which integrates natural language processing methods for extracting semantic features and ensemble algorithms for final classification. In conclusion it is emphasized that the implementation of intelligent classification automation not only minimizes the influence of the human factor but also transforms the role of the accountant from a data entry operator into a strategic analyst. The obtained data are of interest to researchers in financial engineering and artificial intelligence, practicing accountants and auditors, as well as developers of software products for the automation of financial flow management.

machine learning, classification of transactions, accounting, artificial intelligence, natural language processing, automation, gradient boosting, deep learning, financial technologies, categorical data

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