Deep Learning Models for Fraud Detection in Financial Transactions: Reducing False Positives

Abstract

Fraud detection in financial transactions is a critical challenge faced by banks and financial institutions worldwide. Traditional rule-based systems often struggle with high false positive rates, which can lead to significant operational inefficiencies and customer dissatisfaction. Recent advancements in deep learning techniques offer promising solutions for enhancing the accuracy of fraud detection systems. This paper explores various deep learning models employed in financial transaction monitoring, emphasizing their ability to reduce false positives while maintaining high detection rates. It discusses the architecture of different neural network models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble methods. Furthermore, the paper highlights case studies demonstrating successful implementations of these models in real-world financial environments. By analyzing the strengths and limitations of various approaches, this research aims to provide insights into best practices for deploying deep learning techniques in fraud detection. Ultimately, improving the accuracy of fraud detection systems not only protects financial institutions but also enhances customer trust and satisfaction.