article · AI
Deep learning has become a central driver of technological innovation across the financial sector, providing powerful mechanisms for processing vast, intricate datasets. Key architectures, including Convolutional Neural Networks, Long Short-Term Memory networks, Deep Belief Networks, Transformers, Generative Adversarial Networks, and Deep Reinforcement Learning, support sophisticated analytical tasks. These computational approaches show clear utility in core financial domains, particularly algorithmic trading, portfolio optimisation, risk management, credit scoring, and fraud detection. Each architecture demonstrates distinct operational strengths and weaknesses depending on the specific application context. Realising the full potential of these methods requires addressing persistent operational difficulties, notably compromised data quality, inadequate model interpretability, and heavy computational complexity. Continued development must focus on producing more efficient, dependable, and explainable models tailored to the changing requirements of financial systems.
Financial institutions increasingly rely on automated algorithms to evaluate risks, detect fraudulent activity, and allocate capital. Understanding the capabilities and pitfalls of diverse deep learning models ensures that automated financial systems remain stable and reliable. Furthermore, solving problems related to explainability and data quality is essential to building trustworthy financial tools that regulators, businesses, and everyday consumers can depend upon.
The identified techniques apply directly to financial services, benefiting asset managers, risk officers, and fraud detection teams. While models like Convolutional Neural Networks and Transformers are already applied in live settings such as market forecasting and algorithmic trading, broader operational deployment faces hurdles. Achieving widespread commercial adoption requires further development to overcome model opacity, high computational costs, and data-integrity challenges, pointing to applied and tested technologies that still demand refinement before safe full-scale deployment.
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Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex and large datasets. This paper provides a comprehensive overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. It also examines recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity. These insights can guide future research directions toward developing more efficient, robust, and explainable financial models that address the evolving needs of the financial sector.
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DOI: 10.3390/ai5040101
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