Gaining the trust of customers and providing them empathy are very critical
in the financial domain. Frequent occurrence of fraudulent activities affects
these two factors. Hence, financial organizations and banks must take utmost
care to mitigate them. Among them, ATM fraudulent transaction is a common
problem faced by banks. There following are the critical challenges involved in
fraud datasets: the dataset is highly imbalanced, the fraud pattern is
changing, etc. Owing to the rarity of fraudulent activities, Fraud detection
can be formulated as either a binary classification problem or One class
classification (OCC). In this study, we handled these techniques on an ATM
transactions dataset collected from India. In binary classification, we
investigated the effectiveness of various over-sampling techniques, such as the
Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative
Adversarial Networks (GAN), to achieve oversampling. Further, we employed
various machine learning techniques viz., Naive Bayes (NB), Logistic Regression
(LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF),
Gradient Boosting Tree (GBT), Multi-layer perceptron (MLP). GBT outperformed
the rest of the models by achieving 0.963 AUC, and DT stands second with 0.958
AUC. DT is the winner if the complexity and interpretability aspects are
considered. Among all the oversampling approaches, SMOTE and its variants were
observed to perform better. In OCC, IForest attained 0.959 CR, and OCSVM
secured second place with 0.947 CR. Further, we incorporated explainable
artificial intelligence (XAI) and causal inference (CI) in the fraud detection
framework and studied it through various analyses.

使用机器学习技术和过采样技术来识别银行 ATM 诈骗交易，并引入可解释的人工智能和因果推断来进行进一步研究。