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Dr. V. Sangeetha is an Associate Professor in the Department of Computer Science with Data Analytics at Dr. N.G.P. Arts and Science College, India. Her research areas include Data Analytics, Machine Learning, and Artificial Intelligence.
The swift expansion of digital financial services, cloud computing, Internet of Things (IoT), and e-commerce platforms has markedly heightened cybersecurity risks and financial offences. Conventional security measures frequently struggle to identify intricate fraud tactics because of the complexity, magnitude, and evolving characteristics of contemporary financial dealings. Deep Learning (DL), a branch of Artificial Intelligence (AI), has surfaced as a formidable method for financial security through its capabilities in automatic feature extraction, anomaly detection, behavioural analysis, and real-time threat forecasting. Deep learning architectures, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, Graph Neural Networks (GNN), and Transformer models, have exhibited exceptional proficiency in fraud detection, anti-money laundering, identity verification, and cyber threat intelligence. This chapter offers a comprehensive examination of deep learning methodologies, frameworks, applications, obstacles, and prospective advancements for financial security inside cloud-based IoT and e-commerce settings.