Citation :

Naveen Edapurath Vijayan, 2024. "Real-Time Fraud Detection in Financial Transactions Using Deep Learning Techniques" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 1: 175-181.

Abstract :

Fraudulent activities in financial transactions present significant challenges to financial institutions, resulting in substantial monetary losses and damage to reputation. With the exponential growth in the volume and velocity of financial data, traditional fraud detection methods often fail to deliver timely and accurate results. This paper presents an in-depth study on utilizing deep learning techniques for real-time fraud detection in financial transactions. The research explores models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), evaluating their performance on large-scale transactional datasets. The findings indicate that deep learning models significantly outperform traditional machine learning approaches in terms of accuracy and processing speed, making them suitable for real-time applications. The paper discusses the implementation challenges and proposes solutions to optimize the deployment of these models in real-world financial systems.

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Keywords :

Real-Time Fraud Detection, Financial Transactions, Deep Learning Techniques, Convolutional Neural Networks, Recurrent Neural Networks, Graph Neural Networks, Machine Learning, Anomaly Detection, Imbalanced Data Handling, Data Preprocessing, Feature Engineering, Model Optimization, Real-Time Processing, Latency Optimization, Scalability, Data Privacy, Ethical Considerations, Fraud Detection Models, Transactional Data Analysis.