Version 1
: Received: 18 August 2024 / Approved: 19 August 2024 / Online: 20 August 2024 (03:42:17 CEST)
How to cite:
Mienye, E.; Jere, N.; Obaido, G.; Mienye, I. D.; Aruleba, K. Deep Learning in Finance: A Survey of Applications and Techniques. Preprints2024, 2024081365. https://doi.org/10.20944/preprints202408.1365.v1
Mienye, E.; Jere, N.; Obaido, G.; Mienye, I. D.; Aruleba, K. Deep Learning in Finance: A Survey of Applications and Techniques. Preprints 2024, 2024081365. https://doi.org/10.20944/preprints202408.1365.v1
Mienye, E.; Jere, N.; Obaido, G.; Mienye, I. D.; Aruleba, K. Deep Learning in Finance: A Survey of Applications and Techniques. Preprints2024, 2024081365. https://doi.org/10.20944/preprints202408.1365.v1
APA Style
Mienye, E., Jere, N., Obaido, G., Mienye, I. D., & Aruleba, K. (2024). Deep Learning in Finance: A Survey of Applications and Techniques. Preprints. https://doi.org/10.20944/preprints202408.1365.v1
Chicago/Turabian Style
Mienye, E., Ibomoiye Domor Mienye and Kehinde Aruleba. 2024 "Deep Learning in Finance: A Survey of Applications and Techniques" Preprints. https://doi.org/10.20944/preprints202408.1365.v1
Abstract
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 at processing and analyzing complex and large datasets. This paper provides a concise 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). The study examines their learning processes, mathematical foundations, and practical applications in finance. It also explores recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity, offering insights into future research directions that can guide the development of more robust and explainable financial models.
Keywords
CNN; Deep Learning; Finance; GRU; LSTM; Machine learning; RNN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
