[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125728-en":3,"doc-seo-125728-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125728,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","A Rule-Based Machine Learning Model for Financial Fraud Detection","Financial fraud threatens banking, government services, and the public, and criminals continually adjust methods to bypass existing controls. Fraud transaction classification remains difficult because datasets are highly imbalanced, often requiring time-consuming resampling during training. This study develops a rule-based machine learning model that detects fraud without resampling and evaluates it with accuracy, specificity, precision, recall, confusion matrix, MCC, and ROC. Experiments on two benchmark datasets compare the approach against RF, DT, MLP, KNN, NB, and LR, achieving 0.99 accuracy and 0.99 precision.","A rule-based machine learning model for financial fraud  \ndetection  \nSaiful Islam1, Md. Mokammel Haque1, Abu Naser Mohammad Rezaul Karim2  \n1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh 2Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram, Bangladesh  \nArticle Info ABSTRACT  \n\n| Article history:\u003Cbr>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.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Data resampling Fraud detection Machine learning Rule generation Support confidence |  |\n\nCorresponding Author:  \nSaiful Islam  \nDepartment of Computer Science and Engineering, Chittagong University of Engineering and Technology Chittagong, Bangladesh  \nEmail: [engsaiful0@gmail.com](engsaiful0@gmail.com)  \n1. INTRODUCTION  \nThe 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  \nfinancial statement fraud. Traditional fraud detection methods, such as manual detection, are costly, inaccurate, time-consuming, and ineffective [8] . Financial fraud is a broad term with many different definitions. Still, it can be described as the deliberate employment of illegal procedures or activities to obtain financial benefi","cbCaibkoQ58q0pmU","https://ap.wps.com/l/cbCaibkoQ58q0pmU","pdf",709758,1,13,"English","en",105,"# Introduction\n## Fraud background and impact\n## Challenges of imbalanced datasets\n## Proposed approach: rule-based detection without resampling\n# Methodology and Evaluation\n## Rule generation and model interpretability\n## Metrics for assessment\n## Comparison with baseline machine learning models\n# Results\n## Performance on benchmark datasets","[{\"question\":\"Why is financial fraud detection challenging in machine learning models?\",\"answer\":\"Fraud datasets are typically highly imbalanced, with many more legitimate transactions than fraudulent ones. Without proper handling, models may fail to predict fraudulent transactions accurately.\"},{\"question\":\"What is the key idea of the proposed model?\",\"answer\":\"The research develops a rule-based machine learning model that classifies transactions as fraudulent or non-fraudulent using generated decision rules, avoiding resampling techniques.\"},{\"question\":\"How is the model evaluated and compared with other methods?\",\"answer\":\"Effectiveness is measured using metrics including accuracy, precision, recall, specificity, confusion matrix, MCC, and ROC values. The rule-based model is compared against RF, DT, MLP, KNN, NB, and logistic regression using two benchmark datasets.\"}]","A Rule-Based Machine Learning Model for Financial Fraud Detection | PDF",1785900875,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-rule-based-machine-learning-model-for-financial-fraud-detection","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-rule-based-machine-learning-model-for-financial-fraud-detection/125728/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is financial fraud detection challenging in machine learning models?","Question",{"text":75,"@type":76},"Fraud datasets are typically highly imbalanced, with many more legitimate transactions than fraudulent ones. Without proper handling, models may fail to predict fraudulent transactions accurately.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the key idea of the proposed model?",{"text":80,"@type":76},"The research develops a rule-based machine learning model that classifies transactions as fraudulent or non-fraudulent using generated decision rules, avoiding resampling techniques.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model evaluated and compared with other methods?",{"text":84,"@type":76},"Effectiveness is measured using metrics including accuracy, precision, recall, specificity, confusion matrix, MCC, and ROC values. The rule-based model is compared against RF, DT, MLP, KNN, NB, and logistic regression using two benchmark datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]