Please use this identifier to cite or link to this item:
Title: Identifying business misreporting in VAT using network analysis
Authors: González Martel, Cristian 
Hernández Guerra, Juan María 
Manrique De Lara Peñate, Casiano 
UNESCO Clasification: 5301 Política fiscal y hacienda pública nacionales
Keywords: Fraud Detection
Random Forest
Vat Declaration
Issue Date: 2021
Project: CN45/08240/57/100
Journal: Decision Support Systems 
Abstract: Efficient detection of incorrectly filed tax returns is one of the main tasks of tax agencies. Value added tax (VAT) legislation requires buyers and sellers to communicate any exchanges that exceed a certain amount. Both statements should coincide, but sometimes the seller/buyer and its counterpart declare different amounts. This paper presents a method to detect those businesses that are more prone to misreport in their VAT declaration. Using the information of such declarations for a region in Spain during year 2002, we generated a transaction network formed by the tax declarations of buyers and sellers. Four types of error were assigned to each business in the network, defined from the mismatch between the amount declared by the firm in question and its counterpart. We applied a random forest algorithm to detect which firm-related and which network-related characteristics influence each error type. The results show the importance of relational factors among businesses in determining the probability of presenting VAT declaration errors. This information can be used to promote more efficient inspections.
ISSN: 0167-9236
DOI: 10.1016/j.dss.2020.113464
Source: Decision Support Systems[ISSN 0167-9236],v. 141, (Febrero 2021)
Appears in Collections:Artículos
Show full item record


checked on Jan 29, 2023

Page view(s)

checked on Oct 22, 2022

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.