Enhancing Bank Fraud Detection: A Comparative Analysis of using Machine Learning Techniques

This study focuses based on machine learning approaches to detect fraudulent activities in banking data — a major concern in the financial sector where preventing fraud is critical. To improve detection accuracy, the research introduces class weight tuning, a strategy that strengthens machine learning models distinguish between genuine and fraudulent transactions. The study uses three powerful machine learning algorithms — CatBoost, LightGBM, and XGBoost each known for its strengths in handling complex datasets. By combining thesemodels, the system aims to improve overall performance in identifying fraud. Alongside, integration of deep learning techniques is carried out to fine-tune the model’s hyperparameters, increasing its adaptability and effectiveness in recognizing evolving fraud patterns. The models are evaluated using real- world banking data, and the results show that combining LightGBM and XGBoost outperforms traditional approaches across various performance metrics. To further boost accuracy, a stacking ensemble model is implemented. It combines predictions from RandomForest and LightGBM classifiers and uses a GradientBoostingClassifier as the final estimator. This ensemble approach leverages the synergy of multiple models to make more accurate predictions.