Independent Researcher, USA.
World Journal of Advanced Engineering Technology and Sciences, 2025, 15(01), 677-690
Article DOI: 10.30574/wjaets.2025.15.1.0277
Received on 28 February 2025; revised on 07 April 2025; accepted on 09 April 2025
The financial services industry is experiencing a revolutionary transformation through deep learning technologies. As data volumes expand exponentially across market transactions, customer interactions, and regulatory filings, traditional analytical methods have reached their limitations. Deep learning, with its sophisticated neural network architectures, offers unprecedented capabilities to extract value from complex, multi-dimensional financial datasets. This article explores how various neural network architectures—including CNNs, RNNs, GANs, and Transformers—are being applied across critical financial domains. From enhancing credit risk assessment with alternative data to detecting fraud through real-time transaction monitoring, deep learning is fundamentally changing operational paradigms. The article examines technical foundations, training methodologies, current applications, and emerging trends. Despite challenges in interpretability, privacy, and model robustness, innovative solutions are emerging. With integration opportunities in blockchain, quantum computing, and AutoML, deep learning is positioned to become the defining technology shaping the future of financial services.
Neural Networks; Financial Risk Assessment; Fraud Detection; Algorithmic Trading; Explainable AI
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Aditya Arora. Unlocking value with deep learning: The future of financial services. World Journal of Advanced Engineering Technology and Sciences, 2025, 15(01), 677-690. Article DOI: https://doi.org/10.30574/wjaets.2025.15.1.0277.