A Survey on Advanced Fraud Detection: Leveraging K-SMOTEENN and Stacking Ensemble to Tackle Data Imbalance and Extract Insights

DCCF is a major problem for financial security because the datasets are very uneven and fraudulent activities are becoming more and more sophisticated. To solve these problems, a new detection framework is made by combining k-means SMOTEENN with a stacking ensemble of ML algorithms. The dataset, which has a lot of class imbalance and contains anonymised transaction records, is preprocessed and balanced using k-means SMOTEENN. This method oversamples instances from the minority class while getting rid of noisy synthetic samples, which lowers the chance of overfitting. A stacking ensemble is built on this balanced dataset by combining different classifiers like XGBoost, decision Tree, and other base learners. A metalearner then combines their outputs to make a strong decision boundary. The experimental findings show that this model works far better than standalone classifiers, “with an F1-score of 0.92, a precision of 0.95, a recall of 0.88, an area under the precision-recall curve (AUPRC) of 0.96, and a perfect ROC-AUC of 1.00. To make dependability even better, Explainable AI (XAI) methods are used. for example, local Interpretable model-Agnostic explanations (LIME)” show how important features affect the model and explain the roles of base and meta-learners. This guarantees both high detection accuracy and ease of understanding, making it possible to find fraud safely and clearly in real-world financial settings.