Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms
| العنوان: | Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms |
|---|---|
| المؤلفون: | Jiangshan Ai, Jiaxin Gao, S.V. Coggeshall, Zirui Zhou, Bingxin Xia |
| المصدر: | Journal of Intelligent Learning Systems and Applications. 11:33-63 |
| بيانات النشر: | Scientific Research Publishing, Inc., 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Computer science, business.industry, Credit card fraud, 020206 networking & telecommunications, Feature selection, Statistical model, 02 engineering and technology, Machine learning, computer.software_genre, Payment card, Support vector machine, Credit card, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Transaction data, computer, Algorithm, Decision tree model |
| الوصف: | Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity. |
| تدمد: | 2150-8410 2150-8402 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::74b39227786dd0b8476ff76a3e273b2d https://doi.org/10.4236/jilsa.2019.113003 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........74b39227786dd0b8476ff76a3e273b2d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21508410 21508402 |
|---|