Financial Fraud Detection Using an Advanced Hybrid Model

Financial fraud detection remains a critical challenge in safeguarding transactions and maintaining trust in digital economies. This paper explores a novel approach integrating transfer learning with decision tree algorithms to enhance fraud detection accuracy and interpretability. It involves the use of deep learning models that have been pre-trained for feature extraction while explicitly adopting characteristics of decision trees in transparent decision processes. This hybrid model extensively improves key performance metrics. Our approach performs better and identifies fraudulent activities than the traditional methods with more robust generalization after rigorous evaluation and comparison. This work has made major contributions to the development of methodologies for efficient fraud detection in financial environments.