# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 1 Introduction  
Income tax, along with other taxes, has long been a significant source of government revenues, which are then used to support public services, pay government debts, and contribute to a country's development goals. This is a tax imposed by the government on many types of earnings made by individuals and businesses, and it varies depending on the amount gained through wages, salaries, and the business's individual circumstances, to mention a few [1]. Despite the fact that many people understand the necessity of paying income taxes, taxpayers have been progressively developing new methods of income tax evasion that are naturally difficult to detect, necessitating the employment of more modern and strong methods of tax fraud detection, as [2] stated. Although income tax fraud is a hot topic in Rwanda, there have not been enough studies conducted to determine the extent of the problem and the amount of money lost as a result of it. According to [3], over Rwf 20 billion have been lost in the five years since 2016 due to tax fraud. The majority of these cases were discovered using traditional methods of random auditing and sometimes whistle-blowing and tip-off methods, which is also the case on a global scale, as revealed by the [4] global survey conducted by KPMG, which investigated 750 fraudsters between March 2013 and August 2015 and discovered that only 3% of these fraud cases have been identified using data analytics and machine learning, while 44% were discovered using intuition and aforementioned traditional methods. According to [5], the most commonly used fraud detection methods are rule-based systems, which are no longer effective enough given that auditors go through the income tax records of millions of individuals and businesses and rely on knowledge, experience, and intuition to determine whether or not there was tax fraud. As stated by [6], this method has two drawbacks: it relies largely on past experience, which means it mostly misses new fraud tactics; and it is knowledge-based, which makes it expensive to maintain and update. While fraudsters are continually developing new tactics to avoid paying taxes, it is no longer possible to track them just using human knowledge and intuition or hope that auditors will notice. Fortunately, machine learning has transformed the way we do things in a variety of fields, including fraud detection. Although they were not focused on income taxes, several studies [6; 7; 8] employed supervised and unsupervised machine learning methods, and while much of the research used unsupervised machine learning due to the scarcity of labeled data, supervised machine learning produced better and more accurate outcomes in detecting tax fraud. Numerous authors have suggested that Artificial Neural Networks (ANN) be employed for fraud detection in general. The study conducted by [9] compared Artificial Neural Networks (ANN), support vector machines (SVM), and K-Nearest Neighbours, and the results showed that ANN outperform other models. It was also recommended that ANN should be more employed in fraud detection because it is best suited for detecting credit card fraud. In a study conducted by [10] to detect fraud for Mellon Bank, ANN was proven to enhance accuracy and timeliness. ANN was also compared to decision trees and Naive Bayes on financial data in order to detect fraud, and as a result, multi-layer perceptron performed well in terms of accuracy (97.47%), and it has been recommended to be used for fraud detection [11]. With the availability of labeled data mainly from tax collection institutions, the efforts to apply supervised machine learning algorithms for fraud detection are considered significant. Nevertheless, not much has been achieved, especially on income tax fraud. Subsequently, the purpose of this research is to explore how different neural network algorithms might be used on income tax-related data from the Rwanda Revenue Authority (RRA), with an emphasis on identifying factors and therefore detecting income tax fraud in Rwanda.