Enhancing Subscription Fraud Detection Through Ensemble Learning: The Case of Ethio Telecom

Enhancing Subscription Fraud Detection Through Ensemble Learning: The Case of Ethio Telecom | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhancing Subscription Fraud Detection Through Ensemble Learning: The Case of Ethio Telecom Esubalew Asmare Desta, Kidus Workineh, Abenet Alazar Hailu, Fikadu Berie Adugna, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7297765/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Telecommunication companies globally face the critical challenge of subscription fraud, which threatens both financial stability and national security. This research addresses this issue by developing an advanced fraud detection model specifically for Ethio Telecom. The model utilizes Ensemble and Adaptive Learning techniques to enhance detection accuracy by combining multiple classifiers. The study used a dataset of 1,000,000 Call Detail Records (CDRs) collected over two months known for increased fraudulent activity3. After filtering out irrelevant data and aggregating multiple call records per subscriber, the dataset was refined to 349,164 records. Initially, 16 features were analyzed, with four excluded for lacking relevance. The remaining 11 features, excluding the target variable, underwent preprocessing including data cleaning, transformation, and balancing4. Feature selection, utilizing Correlation Matrix and Random Forest importance analysis, led to the removal of four additional features, resulting in a final set of 8 key features, including INT_DIALLED, RATIO_INT_TOTAL, and RATIO_UNIQUE_TOTAL4. Three individual models, namely Decision Tree (DT), Logistic Regression (LR), and Artificial Neural Network (ANN), were implemented alongside ensemble methods such as Bagging, Boosting, Stacking, and Voting, and adaptive models like Hoeffding Tree and Adaptive Random Forest45. The findings of this research recommend Stacking and Adaptive Random Forest (ARF) as robust tools for subscription fraud detection. Physical sciences/Engineering Physical sciences/Mathematics and computing Subscription Fraud Fraud Detection Ensemble Learning Adaptive Learning6 Decision Tree Adaptive Random Forest Stacking Call Detail Records (CDRs) Hyperparameter Tuning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 31 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 20 Sep, 2025 Reviewers agreed at journal 20 Sep, 2025 Reviewers agreed at journal 31 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor invited by journal 11 Aug, 2025 Editor assigned by journal 08 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 05 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7297765","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503283130,"identity":"e59a6164-15bc-4990-884d-52401bd86daf","order_by":0,"name":"Esubalew Asmare 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