FRAUD DETECTION IN BANKING DATA BY MACHINE LEARNING TECHNIQUES
Keywords:
Bayesian optimization, data Mining, deep learning, ensemble learning, hyper parameter, unbalanced data, machine learning.Abstract
The study primarily centers on using machine learning methods to identify fraudulent activities in
banking data. This is a critical concern in the financial sector, where it's essential to detect and prevent fraudulent
transactions. To improve fraud detection, the study introduces class weight-tuning hyperparameters. These
parameters help the model differentiate between legitimate and fraudulent transactions more effectively, enhancing
the accuracy of the fraud detection system. The study strategically employs three popular machine learning
algorithms: CatBoost, LightGBM, and XGBoost. Each algorithm has unique strengths, and their combined use aims
to boost the overall performance of the fraud detection method. Deep learning techniques are integrated into
the study to fine-tune hyperparameters. This integration enhances the performance and adaptability of the fraud
detection system, making it more effective in identifying evolving fraud tactics. The project conducts thorough
evaluations using real-world data. These evaluations reveal that the combined use of LightGBM and XGBoost
outperforms existing methods when assessing various criteria. This indicates that the proposed approach is more
effective at detecting fraudulent activities compared to other methods. It includes, a Stacking Classifier has been
implemented, combining predictions from RandomForest and LightGBM classifiers with specific settings. This
ensemble algorithm, utilizing a GradientBoostingClassifier as the final estimator, enhances prediction accuracy by
leveraging the strengths of diverse models.
