Saria Iqbal

With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significant threat. Detecting fraudulent transactions in financial sectors, such as banking, is a major issue because existing fraud detection methods are rule-based and unable to detect unknown patterns. The tactics and techniques used by fraudsters are far more advanced than they are, making machine learning (ML) a valuable approach to improve detection efficiency. While numerous studies have explored machine learning models for credit card fraud detection, most have prioritized accuracy metrics alone, offering little attention to how or why models make decisions. This lack of interpretability creates barriers for financial institutions, where regulatory compliance and user trust are critical. In particular, the systematic application of explainable AI (XAI) techniques such as SHAP and LIME to fraud detection remains scarce. This study addresses this gap by combining high-performing ensemble models (Random Forest and XGBoost) with advanced interpretability methods (SHAP and LIME), providing both strong predictive performance and transparent feature-level explanations. Such integration not only improves fraud detection but also strengthens the trustworthiness and deployability of AI systems in real-world financial contexts. A real-world credit card dataset is used to evaluate both models, and experimental results show that Random Forest achieved higher precision (89.09%) and F1 score (0.9159), while XGBoost yielded better recall (95.56%) and ROC AUC (0.9997). To address the crucial need for interpretability, SHAP and LIME analyses were applied, revealing the most influential features behind model predictions and enhancing transparency in decision-making. Overall, this study demonstrates the potential of integrating explainable artificial intelligence (XAI) into fraud detection systems, thereby enhancing trust and reliability in financial institutions.

With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significant threat. Detecting fraudulent transactions in financial sectors like banking is a major issue because the existing fraud detection methods are rule-based and unable to detect unknown patterns because the tactics and the techniques used by fraudsters are way more advanced then they are, making machine learning (ML) a valuable approach to improve detection efficiency. The present study implements and compares the performances of two widely used ML algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), to identify credit card fraud. To improve model transparency and interpretability, two explainable AI techniques, SHapley Additive exPlanations (SHAP) and Local Interpretable Model- Agnostic Explanations (LIME), are applied. A real-world credit card dataset is used to evaluate both models based on the standard key metrics such as accuracy, precision, recall, and interoperability. This study supports the integration of explainable artificial intelligence (XAI) into fraud detection systems to improve trust, reliability, and real-world applicability in the financial sector. These findings will help to choose the best model among them for the best scenario in the real world.