Artificial Intelligence Systems for Value Added Tax Collection via Self Organizing Map (SOM)

Abstract

Purpose: Corporates and private businesses, primarily uses Artificial Intelligence to influence business models, sales processes, customer segmentation, strategy formation and as well as to understand customer behaviours, thus increase revenue. There is substantial research on the influence of Artificial Intelligence on business strategies with the objectives of increasing revenue (Stallkamp & Schotter, 2021). However, there is limited research on the use of AI in information systems research to assist in the efforts of revenue collection and VAT fraud detection. Subsequently, the present study explores the use of Artificial Intelligence in VAT fraud detection. The main purpose of the study is to determine how corporate VAT fraud could be detected in real time (Alshantti and Rasheed, 2021). The research question poses to identify what type of AI technique or framework could be applied to determine VAT fraud? Problem Statement: VAT fraud is a major problem for tax administrations across the world. Tax fraud detection is likely to become even more important with recent developments in Artificial Intelligence. Auditing VAT declarations is a slow and costly process that is very prone to errors. Conducting VAT audits for example, involves costs to the tax administration as well as to the taxpayer. Furthermore, the field of fraud detection is characterised by unlabelled historical datasets. In this study the use of unsupervised machine learning algorithm is put forward. Unsupervised algorithms are well suited to unlabelled historical datasets, common in the fraud detection or classification arena. The authors conduct experiments using an unsupervised Neural Network algorithm to classify suspicious Value Added Tax declarations. Thus, assist in the efforts of tax audits by tax administrations. Consequently, it is envisaged that the chances of detecting fraudulent VAT declarations will be enhanced using AI techniques as proposed in this research. Methods: Neural Network Self-Organizing Map (SOM) methodology based on VAT fraud detection. Results: Using SOM methodology, the results shows that VAT fraud or suspicious behaviour can be detected or differentiated by observing VAT declarations from attributes such as VAT liability, exempt supplies, refund, and input VAT on capital goods purchased. Conclusion: In conclusion, the value added tax credit (VAT) and refund mechanism offers unique opportunities for abuse, and thus has recently become a crucial concern for revenue services (e.g., Internal Revenue Service, the South African Revenue Service (SARS) and HM Revenue and Customs). Consequently, this study presented artificial intelligenceframework that detects VAT fraud in real-time.