Enhancing Fraud Detection in Banking using Advanced Machine Learning Techniques

Abstract

This study demonstrates the effectiveness of advanced machine learning techniques in detecting fraudulent activities within the banking industry. We evaluated the performance of various models, including LightGBM, XGBoost, CatBoost, vote classifiers, and neural networks, on a comprehensive dataset of banking transactions. The CatBoost model exhibited the highest accuracy in identifying fraudulent instances, showcasing its superior performance. The application of diverse sampling and scaling techniques significantly improved fraud detection accuracy, emphasizing their crucial role in the process. Furthermore, the incorporation of the CatBoost ensemble method substantially enhanced the efficiency of fraud identification. Our findings underscore the potential of these advanced machine-learning approaches in mitigating financial losses and ensuring secure transactions, ultimately bolstering trust and security in the banking sector. Future research directions include refining the CatBoost model’s hyper parameters, adapting to evolving fraud patterns, and integrating real-time data for enhanced responsiveness. Additionally, efforts will be made to improve the interpretability of the model’s decision-making process, providing valuable insights into its trust-building capabilities and enhancing the transparency of fraud detection methodologies.