Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk
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Example‑dependent cost‑sensitive credit cards fraud detection using SMOTE and Bayes minimum risk Doaa Almhaithawi1 · Assef Jafar1 · Mohamad Aljnidi1 Received: 6 November 2019 / Accepted: 19 August 2020 © The Author(s) 2020 OPEN
Abstract This paper presents fraud detection problem as one of the most common problems in secure banking research field, due to its importance in reducing the losses of banks and e-transactions companies. Our work will include: applying the common classification algorithms such as logistic regression (LR), random forest (RF), alongside with modern classifiers with state-of-the-art results as XGBoost (XG) and CatBoost (CB), testing the effect of the unbalanced data through comparing their results with and without balancing, then focusing on the savings measure to test the effect of cost-sensitive wrapping of Bayes minimum risk (BMR), we will concentrate on using F1-score, AUC and Savings measures after using the traditional measures duo to their suitability to our problem. The results show that CB has the best savings (0.7158) alone, (0.971) when using SMOTE and (0.9762) with SMOTE and BMR, while XG has the best savings (0.757) when using BMR without SMOTE. Keywords Machine learning · Example-dependent cost-sensitive · Random forest (RF) · Extreme gradient boosting (XGBoost-XG) · CatBoost (CB) · Synthetic minority over-sampling technique (SMOTE) · Bayes minimum risk (BMR) · Fraud detection (FD) Abbreviations FD Fraud detection ML Machine learning LR Logistic regression RF Random forest GBM Gradient boosting machine XGBoost Extreme gradient boosting DT Decision tree ANN Artificial neural networks SVM Support Vector machine Acc Accuracy ROC Receiving operating characteristic AUC Area under the ROC curve Down Under-sampling Over Over-sampling SMOTE Synthetic minority over-sampling technique Cost Wrapped with cost-sensitive
TP True positive FP False positive TN True negative FN False negative
1 Introduction From the beginning of the monetary transactions, the fraudsters have tried to gain money in multiple illegal ways, so using protection methods was a necessity. The communications development and moving towards electronic monetary transactions make the fraud more common specially with the ease of exchanging experiences between the fraudsters and gaining access to the victim companies.
* Doaa Almhaithawi, [email protected]; Assef Jafar, [email protected]; Mohamad Aljnidi, [email protected]. sy | 1Higher Institute for Applied Sciences and Technology, HIAST, Damascus, Barzeh 31983, Syria. SN Applied Sciences
(2020) 2:1574
| https://doi.org/10.1007/s42452-020-03375-w
Vol.:(0123456789)
Research Article
SN Applied Sciences
(2020) 2:1574
The huge losses of banks and other financial institutions caused the increase of interest in research to prevent fraud and decrease its effects. However, methods and techniques could not be revealed to the public, because of the privacy imposed by the supporting companies of these researches, one reason
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