# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 2 Related Work  
It was not long ago that artificial intelligence and machine learning became the most promising field for solving complex issues by leveraging the computer's ability to learn from available data. Among the first studies to have employed fraud focused data analytics and machine learning include [12; 13].  
As noted by [14], while most studies addressing tax fraud detection employ supervised learning, the lack of historically labeled data limits such studies in most situations since manual auditing and labeling data is expensive and time-consuming. As a result, they proposed using unsupervised learning. This research was divided into three phases: clustering similarly valued tax declarations; adjusting probability distribution to each cluster tax base, and detecting suspicious activities by checking the quantiles of cluster adjusted distribution. Despite the fact that their model produced promising results, there was the limitation of the limited amount of data, and a low number of variables needed to deeply describe cases. The most worrying issue was that the model's accuracy would not be evaluated because the data was not labeled, which raises questions about its trustworthiness.  
In ref. [15] study, the multilayer perceptron neural network model was used to identify fraud in personal income tax forms. According to the authors, this model outperforms other predictive models in predictive ability and is the least expensive because it does not take into account some statistical criteria such as normality, incorrect processing of data, and others. Their findings demonstrated that the multilayer perceptron is effective for efficiently classifying fraudulent and non-fraudulent taxpayers, as well as determining each taxpayer's likelihood of cheating tax. According to their suggestion, the technique could be used to detect fraud behaviors in other types of taxes.  
The Hybrid Unsupervised Outlier Detection (HUNOD) model was developed by [16] to detect suspicious tax behaviors by incorporating user knowledge into a combination of clustering and representational learning to identify outliers from personal income tax data. Identified outliers were interpreted using an \"explainable-by-design surrogate model\" which was trained on internally validated outliers. This model was effective, as it was able to identify between 90% and 98% of outliers.  
As ref. [15] stated, Artificial Neural Networks (ANN) make it easy to handle bigger datasets and, despite their algorithms being complex, it gives easily interpretable results, which is why they are popular in the financial sector, marketing, forecasting, and more often in risk assessment and fraud detection. The current study applies Artificial Neural Networks to the real data set to detect factors related to income tax fraud. The approach will help to reduce the time, effort, and cost taken by auditors in the manual identification of cases to audit. It will also help to combat income tax fraud, as well as recover money lost to tax fraud. To the best of our knowledge, this is the first study to employ Artificial Neural Networks to detect income tax fraud in Rwanda using optimal parameters, and it considers both domestic and cross-border businesses. Different parameters such as layers, epochs, and others were compared and assessed, and the optimal ones were employed in our model to produce good results.  
For the study on financial prediction by [17] where they have compared Deep Convolutional Neural Networks(DCNN) and Multilayer Perceptron (MLP). They used MLP with 8 layers and DCNN with 13 layers to develop a credit scoring model. Their models were compared on German and Australian credit scoring data. For the two evaluation indices utilized to compare the performance of these models, such as overall accuracy and missed alarm rate, the experimental findings demonstrated that DCNN was significantly better than MLP. The DCNN accuracies for German and Australian data were 90.85% and 99.74%, respectively. The accuracies for MLP were 81.20% and 90.17%, respectively.