Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Automatic electronic invoice classification using machine learning models

Version 1 : Received: 1 October 2020 / Approved: 5 October 2020 / Online: 5 October 2020 (09:05:53 CEST)

How to cite: Bardelli, C.; Rondinelli, A.; Vecchio, R.; Figini, S. Automatic electronic invoice classification using machine learning models. Preprints 2020, 2020100057 (doi: 10.20944/preprints202010.0057.v1). Bardelli, C.; Rondinelli, A.; Vecchio, R.; Figini, S. Automatic electronic invoice classification using machine learning models. Preprints 2020, 2020100057 (doi: 10.20944/preprints202010.0057.v1).

Abstract

Electronic invoicing has become mandatory for Italian companies since January 2019. Invoices are structured in a predefined xml template where the information reported can be easily extracted and analyzed. The main aim of this paper is to exploit the information structured in electronic invoices to build an intelligent system which can facilitate accountants work. More precisely, this contribution shows how it is possible to automate part of the accounting process: all sent or received invoices of a company are classified into specific codes which represent the economic nature of the the financial transactions. In order to classify data contained in the invoices a machine learning multiclass classification problem is proposed using as input variables the information of the invoices to predict two different target variables, account codes and the VAT codes, which composes a general ledger entry. Different approaches are compared in terms of prediction accuracy. The best performance is achieved considering the hierarchical structure of the account codes.

Subject Areas

multiclass classification; text mining; accounting control system

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