Deep Learning for Real-time Fraud Detection in Financial Transactions

Abstract

The increasing sophistication of financial fraud necessitates the development of advanced detection systems that can operate in real-time. This study aims to explore the application of deep learning techniques in detecting fraudulent activities in financial transactions, focusing on real-time implementation. Using a qualitative research approach, the study gathers insights from industry experts, financial analysts, and data scientists through interviews and focus groups. Thematic analysis is employed to identify key challenges, opportunities, and the effectiveness of various deep learning models in fraud detection. The findings reveal that deep learning models, particularly those utilizing recurrent neural networks (RNN) and convolutional neural networks (CNN), offer significant advantages in identifying fraudulent patterns that traditional methods might overlook. However, challenges such as data privacy concerns, computational costs, and the need for extensive labeled datasets are identified as barriers to widespread adoption. The study concludes that while deep learning presents a promising solution for real-time fraud detection, further research and development are necessary to address these challenges and improve the scalability and efficiency of these models in practical financial environments.