Credit card fraud is a growing problem nowadays and it has escalated during COVID-19 due to the authorities in many countries requiring people to use cashless transactions. Every year, billions of Euros are lost due to credit card fraud transactions, therefore, fraud detection systems are essential for financial institutions. As the classes’ distribution is not equally represented in the credit card dataset, the machine learning trains the model according to the majority class which leads to inaccurate fraud predictions. For that, in this research, we mainly focus on processing unbalanced data by using an under-sampling technique to get more accurate and better results with different machine learning algorithms. We propose a framework that is based on clustering the dataset using fuzzy C-means and selecting similar fraud and normal instances that have the same features, which guarantees the integrity between the data features.

Nowadays, <span>credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it has become more popular and essential in our lives. Fraudsters are developing new strategies and techniques over time, and it is not easy for humans to manually check out all transactions. The cost of fraudulent transactions is significant and without prevention mechanisms it is rising. Finding the best methodology to detect fraudulent transactions is a crucial asset to the industry to reduce the fraud financial loss. Artificial neural networks (ANN) technique is considered as one of the effective techniques that has proved its efficiency in detecting credit card fraud transactions with high precision and minimum cost. In this paper, we propose a multilayer perceptron (MLP) ANN-based model solution to improve the accuracy of the detection process. The performance of the methodology is measured based on the precision, sensitivity, specificity, accuracy, F-measure, area under curve (AUC) and root mean square error (RMSE). Moreover, we illustrate the performance results of these measures with a descriptive analysis. Experimental results have shown that the proposed ANN-based model is efficient and does improve the accuracy of the detection of fraudulent transactions.</span>

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Credit card fraud poses a significant challenge for both consumers and organizations worldwide, particularly with the increasing reliance on credit cards for financial transactions. Therefore, it is crucial to establish effective mechanisms to detect credit card fraud. However, the uneven distribution of instances between the two classes in the credit card dataset hinders traditional machine learning techniques, as they tend to prioritize the majority class, leading to inaccurate fraud pre- dictions. To address this issue, this paper focuses on the use of the Elbow Fuzzy Noise Filtering SMOTE (EFN-SMOTE) technique, an oversampling approach, to handle unbalanced data. EFN-SMOTE partitions the dataset into multiple clusters using the Elbow method, applies noise filtering to each cluster, and then employs SMOTE to synthesize new minority instances based on the nearest majority instance to each minority instance, thereby improving the model’s ability to perceive the decision boundary. EFN-SMOTE’s performance was evaluated using an Artificial Neural Network model with four hidden layers, resulting in significant improvements in classification performance, achieving an accuracy of 0.999, precision of 0.998, sensitivity of 0.999, specificity of 0.998, F-measure of 0.999, and G-Mean of 0.999.

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