Methods: Neural Network Self-Organizing Map (SOM) methodology based on VAT fraud detection. Consequently, it is envisaged that the chances of detecting fraudulent VAT declarations will be enhanced using AI techniques as proposed in this research. Thus, assist in the efforts of tax audits by tax administrations.

The authors conduct experiments using an unsupervised Neural Network algorithm to classify suspicious Value Added Tax declarations. Unsupervised algorithms are well suited to unlabelled historical datasets, common in the fraud detection or classification arena. In this study the use of unsupervised machine learning algorithm is put forward. Furthermore, the field of fraud detection is characterised by unlabelled historical datasets. Conducting VAT audits for example, involves costs to the tax administration as well as to the taxpayer. Auditing VAT declarations is a slow and costly process that is very prone to errors. Tax fraud detection is likely to become even more important with recent developments in Artificial Intelligence. Problem Statement: VAT fraud is a major problem for tax administrations across the world. The research question poses to identify what type of AI technique or framework could be applied to determine VAT fraud? The main purpose of the study is to determine how corporate VAT fraud could be detected in real time (Alshantti and Rasheed, 2021). Subsequently, the present study explores the use of Artificial Intelligence in VAT fraud detection. 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. There is substantial research on the influence of Artificial Intelligence on business strategies with the objectives of increasing revenue (Stallkamp & Schotter, 2021). 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.