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.

有效地检测错误提交的纳税申报表是税务机构的主要任务之一。增值税（VAT）法规要求买卖双方交流超过一定金额的任何交易。两种陈述应一致，但有时卖方/买方及其对应方声明不同的金额。本文提出了一种方法来检测那些更容易在其增值税申报中误报的企业。利用2002年西班牙某地区的此类申报信息，我们生成了由买卖双方的纳税申报构成的交易网络。网络中的每个业务都分配了四种类型的错误，这是由有关公司与其对应方所声明的金额之间的不匹配定义的。我们应用了随机森林算法来检测哪些公司相关特征和哪些网络相关特征会影响每种错误类型。结果表明，企业之间的关系因素对于确定出现增值税申报错误的可能性非常重要。此信息可用于促进更有效的检查。