A Survey of Trendy Financial Sector Applications of Machine and Deep Learning

Conference paper

Lestari, Nur Indah, Hussain, Walayat, Merigo, Jose and Bekhit, Mahmoud. (2023). A Survey of Trendy Financial Sector Applications of Machine and Deep Learning. Second EAI International Conference, BigIoT-EDU 2022. Virtual Event 29 - 31 Jul 2022 Switzerland: Springer. pp. 619-633 https://doi.org/10.1007/978-3-031-23944-1
AuthorsLestari, Nur Indah, Hussain, Walayat, Merigo, Jose and Bekhit, Mahmoud
TypeConference paper
Abstract

In the field of finance, machine learning encompasses a number of disciplines, including pattern recognition, financial econometrics, statistical computing, probabilistic programming, and dynamic programming. Machine learning (ML) is causing a revolution in the financial industry as more and more companies use ML-automated operations to make better decisions, increase productivity, and achieve other business goals. Machine learning algorithms are being utilised by financial institutions, including banks, fintech companies, insurance brokers, and other suppliers of financial services, in order to forecast financial risk, automate repetitive processes, and gain real-time investment advice. Because of the very nature of their operations, financial institutions generate enormous amounts of data that are often complex. This creates the ideal setting for machine learning, which requires vast datasets from which machines can learn. This research investigates the impact of machine learning, deep learning, and statistical approaches on key financial principles, including fraud detection, money laundering prevention, and credit risk assessment. To investigate the impact of these methodologies on financial indicators, secondary data sources such as books and peer-reviewed journals were analysed.

KeywordsMachine learning; Deep learning; Statistical models; Finance; Risk management; Fraud detection & money laundering prevention
Year01 Jan 2023
PublisherSpringer
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-031-23944-1
Web address (URL)https://link.springer.com/chapter/10.1007/978-3-031-23944-1_68
Open accessPublished as non-open access
Research or scholarlyResearch
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All rights reserved
File Access Level
Controlled
Page range619-633
ISBN978-3-031-23944-1
Web address (URL) of conference proceedingshttps://link.springer.com/book/10.1007/978-3-031-23944-1
Output statusPublished
Publication dates
Print11 Jan 2022
Publication process dates
Completed12 Jan 2022
Deposited01 Mar 2024
Additional information

© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Place of publicationSwitzerland
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