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Article

Automatic Electronic Invoice Classification Using Machine Learning Models

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Department of Computational Mathematics and Decision Sciences, University of Pavia, 27100 Pavia, Italy
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Datev.it S.p.a., 20090 Assago, Italy
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Department of Political and Social Sciences, University of Pavia, 27100 Pavia, Italy
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Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2020, 2(4), 617-629; https://doi.org/10.3390/make2040033
Received: 1 October 2020 / Revised: 7 November 2020 / Accepted: 24 November 2020 / Published: 30 November 2020
Electronic invoicing has been mandatory for Italian companies since January 2019. All the invoices are structured in a predefined xml template which facilitates the extraction of the information. The main aim of this paper is to exploit the information contained in electronic invoices to build an intelligent system which can simplify accountants’ work. More precisely, this contribution shows how it is possible to automate part of the accounting process: all the invoices of a company are classified into specific codes which represent the economic nature of the financial transactions. To accomplish this classification task, a multiclass classification algorithm is proposed to predict two different target variables, the account and the VAT codes, which are part of the general ledger entry. To apply this model to real datasets, a multi-step procedure is proposed: first, a matching algorithm is used for the reconstruction of the training set, then input data are elaborated and prepared for the training phase, and finally a classification algorithm is trained. Different classification algorithms are compared in terms of prediction accuracy, including ensemble models and neural networks. The models under comparison show optimal results in the prediction of the target variables, meaning that machine learning classifiers succeed in translating the complex rules of the accounting process into an automated model. A final study suggests that best performances can be achieved considering the hierarchical structure of the account codes, splitting the classification task into smaller sub-problems. View Full-Text
Keywords: multiclass classification; text mining; accounting control system multiclass classification; text mining; accounting control system
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MDPI and ACS Style

Bardelli, C.; Rondinelli, A.; Vecchio, R.; Figini, S. Automatic Electronic Invoice Classification Using Machine Learning Models. Mach. Learn. Knowl. Extr. 2020, 2, 617-629. https://doi.org/10.3390/make2040033

AMA Style

Bardelli C, Rondinelli A, Vecchio R, Figini S. Automatic Electronic Invoice Classification Using Machine Learning Models. Machine Learning and Knowledge Extraction. 2020; 2(4):617-629. https://doi.org/10.3390/make2040033

Chicago/Turabian Style

Bardelli, Chiara, Alessandro Rondinelli, Ruggero Vecchio, and Silvia Figini. 2020. "Automatic Electronic Invoice Classification Using Machine Learning Models" Machine Learning and Knowledge Extraction 2, no. 4: 617-629. https://doi.org/10.3390/make2040033

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