Neural Networks and Learning Systems for Financial Risk Management

A special issue of FinTech (ISSN 2674-1032).

Deadline for manuscript submissions: closed (10 February 2023) | Viewed by 2369

Special Issue Editor

Department of Financial and Actuarial Mathematics, School of Science, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Interests: financial mathematics; artificial intelligence; neural networks for options; financial risk management; financial computing; financial data science; Markovian regime switching; high frequency trading; modeling of financial price; granular dynamics
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Special Issue Information

Dear Colleagues,

Financial risk management is a process of identifying, evaluating, and controlling the risk in an investment.  Financial risks can be broadly classified into three subclasses: credit risk, liquidity risk, and market risk. However, financial risk is such a complex and extensive concept that financial risk management practitioners often need to specialize only in a certain aspect of financial risk management. Notably, forecasting financial risk has become one of the main areas of probability and statistical modeling. In recent decades, artificial intelligence, including neural networks, deep learning, and machine learning, has seen significant progress and offered new opportunities for research in financial risk management. Many scholars have applied artificial natural networks and learning systems to construct financial risk prediction models with better forecast ability. The main goal of this Special Issue is to collect papers on the state of the art and the latest studies on neural networks and learning systems for financial risks and summarize different applications of artificial intelligence technologies in the relevant domains of financial risks and their management. Moreover, this issue is an opportunity to provide a forum where researchers will be able to share and exchange their ideas in the fields of financial risks. The area of interest is wide and includes several categories, such as neural networks and learning systems for financial derivatives, credit risk, liquidity risk, market risk, novel learning algorithms, the exploration of financial risk prediction, and so on.

Dr. David Liu
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. FinTech 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 1000 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

  • finance
  • credit risk
  • liquid risk
  • market risk
  • investment
  • neural network
  • learning system
  • risk management
  • risk model
  • financial derivatives
  • artificial intelligence
  • learning algorithms

Published Papers (1 paper)

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Review

15 pages, 585 KiB  
Review
The Use of Artificial Neural Networks in the Public Sector
by Ioannis Kosmas, Theofanis Papadopoulos, Georgia Dede and Christos Michalakelis
FinTech 2023, 2(1), 138-152; https://doi.org/10.3390/fintech2010010 - 10 Mar 2023
Cited by 2 | Viewed by 1937
Abstract
Artificial intelligence (AI) is an extensive scientific field, part of which is the concept of deep learning, belonging to broader family of machine learning (ML) methods, based on artificial neural networks (ANNs). ANNs are active since the 1940s and are applied in many [...] Read more.
Artificial intelligence (AI) is an extensive scientific field, part of which is the concept of deep learning, belonging to broader family of machine learning (ML) methods, based on artificial neural networks (ANNs). ANNs are active since the 1940s and are applied in many fields. There have been actions around the world for the digital transformation of the public sector and the use of new innovative technologies, but the trajectory and degree of adoption of artificial intelligence technologies in the public sector have been unsatisfactory. Similar issues must be handled, and these problems must be classified. In the present work, preparatory searches were made on Scopus and IEEE bibliographic databases in order to obtain information for the progress of the adoption of ANNs in the public sector starting from the year 2019. Then, a systematic review of published scientific articles was conducted using keywords. Among the 2412 results returned by the search and the application of the selection/rejection criteria, 10 articles were chosen for analysis. The conclusion that emerged after reading the articles was that while the scientific community has a lot of suggestions and ideas for the implementation of ANNs and their financial effects, in practice, there is no appropriate use of them in the public sector. Occasionally, there are cases of implementation funded by state or non-state bodies without a systematic application and utilization of these technologies. The ways and methods of practical application are not further specified, so there are no indications for the systematic application of specialized deep learning techniques and ANNs. The legal framework for the development of artificial intelligence applications, at least in the European Union (EU), is under design, like the necessary ISO standards from an international perspective, and the economic impact of the most recent AI-based technologies has not been fully assessed. Full article
(This article belongs to the Special Issue Neural Networks and Learning Systems for Financial Risk Management)
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