International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 1, February 2024, pp. 759~771 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i1.pp759-771  759 Journal homepage: http://ijece.iaescore.com A rule-based machine learning model for financial fraud detection Saiful Islam 1 , Md. Mokammel Haque 1 , Abu Naser Mohammad Rezaul Karim 2 1 Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh 2 Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram, Bangladesh Article Info ABSTRACT Article history: Received May 2, 2023 Revised May 28, 2023 Accepted Jun 4, 2023 Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively. Keywords: Data resampling Fraud detection Machine learning Rule generation Support confidence This is an open access article under the CC BY-SA license. Corresponding Author: Saiful Islam Department of Computer Science and Engineering, Chittagong University of Engineering and Technology Chittagong, Bangladesh Email: engsaiful0@gmail.com 1. INTRODUCTION The Oxford Dictionary describes fraud as an unjustified or criminal deception leading to monetary or personal advantage [1]. Fraud can occur in various financial industries, including banking, insurance, taxation, and corporations. Credit card fraud, tax evasion, financial statement fraud, money laundering, and other financial fraud are all rising. Fraud efforts have increased significantly in recent years, making fraud detection more critical than ever. Because of increased credit card use, there has been a constant increase in fraudulent transactions [2]. Asset misappropriation, corruption, and financial statement fraud are three categories of occupational fraud identified. In order to steal money, fraudulent transactions are frequently carried out using unlawful access to card information, including credit card numbers [3], email addresses, phone numbers [4], and many others. As the technology employed by the financial banking sector evolved during the last two decades, so did the fraud techniques used by criminals (European Payments Council 2019). Credit card fraud is now the second most prevalent sort of identity theft recorded as of this year, only following government documents and benefits fraud [5]. Fraud detection is critical with various high-impact applications in security, banking [6], health care [7], and review management. This research focuses on