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AI in Finance: Leveraging AI to Transform Financial Services
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AI in Finance: Leveraging AI to Transform Financial Services

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1022

Special Issue Editor

Dr. Xianrong (Shawn) Zheng

E-Mail Website
Guest Editor
Information Technology & Decision Sciences Department, Old Dominion University, Norfolk, VA 23529, USA
Interests: AI; cloud computing; FinTech
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on “AI in Finance”. AI is concerned with intelligent behavior in artifacts. Deep learning and generative AI are the recent innovations in artificial intelligence. AI has a wide range of applications, including healthcare, transportation, and finance. It will have transformative impacts on financial sectors, including banking, insurance, investments, securities, etc. Now, AI is used for lending, investing, risk analysis, fraud detection, customer service, etc.

This Special Issue calls for papers on AI in Finance. It welcomes research articles that present novel theory, algorithms, systems, and applications of AI in financial sectors and encourages submissions from multiple disciplines, including artificial intelligence, computer science, information systems, finance, etc. Topics of interest include, but are not limited to, AI in Finance, ML in Finance, algorithmic trading, robo-advisor, risk management, etc.

Dr. Xianrong (Shawn) Zheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI in finance
  • ML in finance
  • algo trading
  • robo-advisor

Published Papers (1 paper)

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Research

19 pages, 598 KiB  
Article
Generative Adversarial Networks for Synthetic Data Generation in Finance: Evaluating Statistical Similarities and Quality Assessment
by Faisal Ramzan, Claudio Sartori, Sergio Consoli and Diego Reforgiato Recupero
AI 2024, 5(2), 667-685; https://doi.org/10.3390/ai5020035 - 13 May 2024
Viewed by 454
Abstract
Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to [...] Read more.
Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations. Full article
(This article belongs to the Special Issue AI in Finance: Leveraging AI to Transform Financial Services)
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