Authors : Khandakar Rabbi Ahmed, Arafat Rohan, Sohana Afrin Mitu, Shahanaj Akther, Mustafizur Rahaman, Urmi Chakraborty, Md Istiak Hasan Rial
Publication date : 2025/6/20
Conference : 2025 5th International Conference on Intelligent Technologies (CONIT)
Pages : 1-5
Publisher : IEEE
Description : The explosive expansion of digital financial transactions demand for robust solutions to address the increasing number of fraudulent activities, perpetrated against credit cards. Although traditional ML models, such as XGBoost, can produce a high level of accuracy, problems such as the class skew, incompatibility with computational resources, and difficulty in interpretability still exist. The proposed method is a new hybrid approach that integrates Temporal Convolutional Network (TCN) with CatBoost, and it is supported by Generative Adversarial Network (GAN)-based synthetic data generation and Bayesian hyperparameter optimization. Class imbalance in the context of SHAP may be mitigated using a hybrid SMOTE-Tomek resampling approach which enables a better interpretability of the selected features. Tested on the anonymized set of transactions, our model yields an accuracy of and an AUC-ROC …
Total citations : Cited by 23
Scholar articles :
KR Ahmed, A Rohan, SA Mitu, S Akther, M Rahaman… – 2025 5th International Conference on Intelligent …, 2025