Authors - Venkata Saikumar Thalupuru, Shubham Kumar, Santhoshini Pranathi Singaraju, Vishal Gupta Abstract - As the use of online banking and digital payments grew faster, that has also left the institution at risk of becoming the victims of credit card fraud, which has become a major challenge for traditional banks and other financial institutions. This huge discrepancy in transaction datasets is one of the greatest challenges in fraud analytics wherein only the rare fraudulent activity takes up a tiny fraction of the total transaction. Traditional machine learning models are often quite accurate but not great at detecting occasional frauds. To overcome this limitation, this study proposes a cost-aware hybrid framework comprising Attention-based Long Short-Term Memory (Attention-LSTM) and ensemble-based machine learning. This method will take care to preprocess the data, maintain balance among classes using SMOTE, select features based on mutual information by leveraging a soft-voting ensemble of the Logistic Regression, Random Forest, and the XGBoost models. Cost-aware learning is coupled with decision threshold enhancement to minimize false negative predictions. Additionally, SHAP-based explainability is added on top for enhanced transparency and interpretability of the model. The experimental results show 99.3% accuracy, 0.905 precision, 0.892 recall, 0.898 F1-score, and 0.98 ROC-AUC, indicating that our new framework is effective in detecting genuine financial fraud.
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