Home / Current Issue / Paper 1724375
Explainable AI for Imbalanced Credit Card Fraud Detection: A Comparative Study
Subject area: Science,Engineering and Technology · Area of research: Explainable AI
Abstract
Credit-card fraud detection is difficult because fraudulent transactions make up only a small fraction of observed payments. Under this class imbalance, a classifier can achieve high accuracy while failing to identify fraud, making accuracy alone unsuitable for model selection [1], [3]. This study compares Logistic Regression, Decision Tree, Random Forest, and XGBoost under three training conditions: no imbalance adjustment, Synthetic Minority Oversampling Technique (SMOTE), and class weighting. Experiments use the public ULB Credit Card Fraud Detection dataset, which contains 284,807 transactions, including 492 labelled as fraudulent (0.173%). After removing duplicate records, the data are divided using a stratified 80/20 split. Resampling and scaling are restricted to the training data to prevent test-set leakage. The twelve configurations are evaluated on an untouched test set using Precision, Recall, F1-score, ROC-AUC, and PR-AUC. A prespecified eligibility rule requires Recall of at least 0.60 and Precision of at least 0.30; eligible models are then ranked using a weighted composite of Recall, Precision, F1-score, and PR-AUC. XGBoost with class weighting is selected, with Recall 0.789, Precision 0.974, F1-score 0.872, and PR-AUC 0.823. Bootstrap comparisons indicate a clearer advantage over Logistic Regression and Decision Tree than over Random Forest. SHAP is used to explain global and transaction-level predictions, and a Flask prototype displays scores, risk bands, and feature contributions. Because most input variables are anonymized, these explanations identify influential components but do not establish real-world transaction causes. The findings are limited by a single random split, only 95 fraudulent test transactions, fixed hyperparameters, and a dataset covering two days of transactions.
Keywords
credit-card fraud; class imbalance; SMOTE; XGBoost; explainable artificial intelligence; SHAP; precision-recall analysis; model selection
References
[1] Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G. Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE Trans Neural Netw Learn Syst. 2018;29(8):3784–3797. https://doi.org/10.1109/TNNLS.2017.2736643
[2] Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357. https://doi.org/10.1613/jair.953
[3] Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015;10(3):e0118432. https://doi.org/10.1371/journal.pone.0118432
[4] Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining. 2016. p. 785–794. https://doi.org/10.1145/2939672.2939785
[5] Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems 30 (NeurIPS). 2017. p. 4765–4774.
How to cite this paper
@article{1724375,
author = {Vrushali Godse, Vani Ramkrishna Talkokul, Sanyukta Sasane, Khushi K. Singh},
title = {Explainable AI for Imbalanced Credit Card Fraud Detection: A Comparative Study},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {3776-3795},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724375.pdf},
abstract = {Credit-card fraud detection is difficult because fraudulent transactions make up only a small fraction of observed payments. Under this class imbalance, a classifier can achieve high accuracy while failing to identify fraud, making accuracy alone unsuitable for model selection [1], [3]. This study compares Logistic Regression, Decision Tree, Random Forest, and XGBoost under three training conditions: no imbalance adjustment, Synthetic Minority Oversampling Technique (SMOTE), and class weighting. Experiments use the public ULB Credit Card Fraud Detection dataset, which contains 284,807 transactions, including 492 labelled as fraudulent (0.173%). After removing duplicate records, the data are divided using a stratified 80/20 split. Resampling and scaling are restricted to the training data to prevent test-set leakage.
The twelve configurations are evaluated on an untouched test set using Precision, Recall, F1-score, ROC-AUC, and PR-AUC. A prespecified eligibility rule requires Recall of at least 0.60 and Precision of at least 0.30; eligible models are then ranked using a weighted composite of Recall, Precision, F1-score, and PR-AUC. XGBoost with class weighting is selected, with Recall 0.789, Precision 0.974, F1-score 0.872, and PR-AUC 0.823. Bootstrap comparisons indicate a clearer advantage over Logistic Regression and Decision Tree than over Random Forest. SHAP is used to explain global and transaction-level predictions, and a Flask prototype displays scores, risk bands, and feature contributions. Because most input variables are anonymized, these explanations identify influential components but do not establish real-world transaction causes. The findings are limited by a single random split, only 95 fraudulent test transactions, fixed hyperparameters, and a dataset covering two days of transactions.},
keywords = {credit-card fraud; class imbalance; SMOTE; XGBoost; explainable artificial intelligence; SHAP; precision-recall analysis; model selection},
month = {October},
}