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Enhancing subscription fraud detection through ensemble learning the case of Ethio telecom
Esubalew Asmare Desta1,2, Kidus Workineh Azale3,4, Abenet Alazar Hailu5,4
1Esubalew Asmare Desta: College of Informatics, University of Gondar, Gondar, Ethiopia. esu.asmare@gmail.com.
Scientific Reports
|February 8, 2026
Summary
This study introduces advanced fraud detection for telecommunication subscriptions using ensemble and adaptive learning. Stacking and Adaptive Random Forest models proved most effective for identifying subscription fraud.
Area of Science:
- Computer Science
- Data Science
- Telecommunications
Background:
- Subscription fraud poses significant financial and national security risks to telecommunication companies globally.
- Ethio Telecom faces critical challenges in detecting and mitigating subscription fraud within its network.
Purpose of the Study:
- To develop and evaluate an advanced fraud detection model tailored for telecommunication subscription fraud.
- To enhance fraud detection accuracy by employing Ensemble and Adaptive Learning techniques.
Main Methods:
- A dataset of 1,000,000 Call Detail Records (CDRs) was refined to 349,164 records after preprocessing and feature selection, retaining 8 key features.
- Individual models (Decision Tree, Logistic Regression, Artificial Neural Network) and ensemble methods (Bagging, Boosting, Stacking, Voting) were implemented.
- Adaptive models including Hoeffding Tree and Adaptive Random Forest were utilized for fraud detection.
Main Results:
- Feature selection identified 8 crucial features for effective fraud detection.
- Ensemble methods, particularly Stacking, demonstrated strong performance in identifying fraudulent activities.
- Adaptive Random Forest (ARF) also showed robust capabilities in subscription fraud detection.
Conclusions:
- Stacking and Adaptive Random Forest (ARF) are recommended as highly effective models for subscription fraud detection in telecommunications.
- The developed model offers a significant advancement in combating subscription fraud for companies like Ethio Telecom.
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