ELINAC: Autoencoder Approach for Electronic Invoices Data Clustering

Abstract

The most common method used to document monetary transactions in Brazil is by issuing electronic invoices (NF-e). The audit of electronic invoices is essential, and this can be improved by using data mining solutions, such as clustering and anomaly detection. However, applying these solutions is not a simple task because NF-e data contains millions of records with noisy fields and nonstandard documents, especially short text descriptions. In addition to these challenges, it is costly to extract information from short texts to identify traces of mismanagement, embezzlement, commercial fraud or tax evasion. Analyzing such data can be more effective when divided into well-defined groups. However, efficient solutions for clustering data with characteristics similar to NF-es have not yet been proposed in the literature. We developed ELINAC, a service for clustering short-text data in NF-es that uses an automatic encoder to cluster data. ELINAC aids in auditing transactions documented in NF-e, clustering similar data by short-text descriptions and making anomaly detection in numeric fields easier. For this, ELINAC explores how to model the automatic encoder without increasing the calculation costs to suppress a large number of short text data. In the worst case, the results show that ELINAC efficiently groups data while performing three times faster than solutions previously adopted in the literature.

Keywords

• autoencoder
• clustering
• neural networks
• electronic invoicing
• short-text