Credit Card Fraud Detection using Ensemble Learning with Boosting Technique
Mahmud Mustapha Gana
, Mustapha Ismail , Audu Musa Mabu
Credit Card Fraud, Ensemble Learning, Imbalanced Dataset, Boosting Technique & Machine learning
This research paper proposes a novel approach for credit card fraud detection in the banking sector. The study utilizes ensemble learning with boosting techniques, combining the Random Forest(RF), Support Vector Machine(SVM), and Extreme Gradient Boosting(XGBoost) algorithms to create a powerful ensemble classifier. The approach is evaluated using an extensive dataset of credit card transactions. The results demonstrate exceptional recall, accuracy, precision, and F-score values with result of 1.0 for each evaluation metrics. In this study ensemble model developed outperforms previous studies by incorporating multiple evaluation measures and effectively leveraging the strengths of each base classifier. The research highlights the importance of considering a range of evaluation metrics and suggests avenues for further research in improving fraud detection systems. By addressing the limitations of earlier studies and using resampling techniques to handle imbalanced data, the proposed ensemble model offers significant potential for enhancing fraud detection and security protocols in the financial sector. The findings are considered trustworthy and have important implications for the industry, as they improve the realism and generalizability of credit card fraud detection through the use of the Kaggle.com dataset and ensemble learning techniques.
"Credit Card Fraud Detection using Ensemble Learning with Boosting Technique", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 8, page no.1170 - 1179, August-2023, Available :https://ijsdr.org/papers/IJSDR2308172.pdf
Volume 8
Issue 8,
August-2023
Pages : 1170 - 1179
Paper Reg. ID: IJSDR_208390
Published Paper Id: IJSDR2308172
Downloads: 000347187
Research Area: Computer Science & Technology
Country: Nasarawa, Damaturu., Yobe, Nigeria
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
