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Mathematics
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Mathematical Methods and Models of FinTech
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Mathematical Methods and Models of FinTech

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 8228

Special Issue Editors

Dr. Jim (Junmin) Shi

E-Mail Website
Guest Editor
Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: FinTech; PropTech; supply chain management; blockchain technology; healthcare operations
Dr. Haibing Lu

E-Mail Website
Guest Editor
Department of Operations Management and Information Systems, The Leavey School of Business, Santa Clara University, Santa Clara, CA 95053, USA
Interests: information privacy and security; data analytics
Dr. Aichih Chang

E-Mail Website
Guest Editor
Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: business data science; supply chain management; FinTech

Special Issue Information

Dear Colleagues,

Financial Technology (FinTech) has recently been considered as a hot topic that leverages technology to improve and innovate financial services and products. For example, blockchain, as a disruptive technology, has recently become synonymous with cryptocurrency (e.g., Bitcoin), but its potential has been dramatically expanding in the larger system of FinTech, such as smart contracts, P2P lending, decentralized finance (DeFi), and non-fungible tokens (NFTs).

The burgeoning FinTech requires theoretical models and innovative methodologies (e.g., forecasting, simulation, optimization, and algorithms) to address new business challenges. A FinTech business model is a plan for a financial technology business; this includes operating strategies, revenue sources, and intended customer bases. FinTech organizations generally adopt inclusive approaches to finance, enabling consumers to obtain suitable access to a wide range of financial services and products.

The current Special Issue solicits and calls for submissions concerning the research topic of “Mathematical Methods and Models of FinTech”. This includes novel research and studies on the context of FinTech for modeling, data analyses, and methodology development. Theoretical, analytical, empirical, or pedagogical articles and reviews on the application of FinTech are also invited for submission. The research topics include, but are not limited to, the following areas:

  • Data-driven financial models;
  • Innovative methodology for FinTech;
  • Cryptocurrency and investment;
  • Blockchain and smart contracts;
  • Decentralized finance (DeFi);
  • Non-fungible tokens (NFTs) and Metaverse investments;
  • Regulation, legislation, and policy making in the context of FinTech;
  • Pedagogical and educational studies on FinTech for the expanding STEM-skilled workforce.

Contributions should focus on the rigorous or robust models in the complex and challenging FinTech institution, at present, addressing the prevailing finance challenges and risks, enhancing financial performance and the ecosystem, improving the resiliency and sustainability of FinTech, and related innovative business applications.

Dr. Jim (Junmin) Shi
Dr. Haibing Lu
Dr. Aichih Chang
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • blockchain technology and DeFi
  • P2P finance
  • investment in Metaverse
  • smart contract
  • cryptocurrency
  • data-driven financial modeling
  • portfolio management
  • risk mitigation and management

Published Papers (2 papers)

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Research

14 pages, 2702 KiB  
Article
A Novel Fuzzy Unsupervised Quadratic Surface Support Vector Machine Based on DC Programming: An Application to Credit Risk Management
by Tao Yu, Wei Huang and Xin Tang
Mathematics 2023, 11(22), 4661; https://doi.org/10.3390/math11224661 - 16 Nov 2023
Viewed by 511
Abstract
Unsupervised classification is used in credit risk assessment to reduce human resource costs and make informed decisions in the shortest possible time. Although several studies show that support vector machine-based methods have better performance in unlabeled datasets, several factors still negatively affect these [...] Read more.
Unsupervised classification is used in credit risk assessment to reduce human resource costs and make informed decisions in the shortest possible time. Although several studies show that support vector machine-based methods have better performance in unlabeled datasets, several factors still negatively affect these models, such as unstable results due to random initialization, reduced effectiveness due to kernel dependencies, and noise points and outliers. This paper introduces an unsupervised classification method based on a fuzzy unsupervised quadratic surface support vector machine without a kernel to avoid selecting related kernel parameters for credit risk assessment. In addition, we propose an innovative fuzzy membership function for reducing noise points and outliers in line with the direction of sample density variation. Fuzzy Unsupervised QSSVM (FUS-QSSVM) outperforms well-known SVM-based methods based on numerical tests on public benchmark credit data. In some real-world applications, the proposed method has significant potential as well as being effective, efficient, and robust. The algorithm can therefore increase the number of potential customers of financial institutions as well as increase profitability. Full article
(This article belongs to the Special Issue Mathematical Methods and Models of FinTech)
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41 pages, 5210 KiB  
Article
Towards Future Internet: The Metaverse Perspective for Diverse Industrial Applications
by Pronaya Bhattacharya, Deepti Saraswat, Darshan Savaliya, Sakshi Sanghavi, Ashwin Verma, Vatsal Sakariya, Sudeep Tanwar, Ravi Sharma, Maria Simona Raboaca and Daniela Lucia Manea
Mathematics 2023, 11(4), 941; https://doi.org/10.3390/math11040941 - 13 Feb 2023
Cited by 27 | Viewed by 7037
Abstract
The Metaverse allows the integration of physical and digital versions of users, processes, and environments where entities communicate, transact, and socialize. With the shift towards Extended Reality (XR) technologies, the Metaverse is envisioned to support a wide range of applicative verticals. It will [...] Read more.
The Metaverse allows the integration of physical and digital versions of users, processes, and environments where entities communicate, transact, and socialize. With the shift towards Extended Reality (XR) technologies, the Metaverse is envisioned to support a wide range of applicative verticals. It will support a seamless mix of physical and virtual worlds (realities) and, thus, will be a game changer for the Future Internet, built on the Semantic Web framework. The Metaverse will be ably assisted by the convergence of emerging wireless communication networks (such as Fifth-Generation and Beyond networks) or Sixth-Generation (6G) networks, Blockchain (BC), Web 3.0, Artificial Intelligence (AI), and Non-Fungible Tokens (NFTs). It has the potential for convergence in diverse industrial applications such as digital twins, telehealth care, connected vehicles, virtual education, social networks, and financial applications. Recent studies on the Metaverse have focused on explaining its key components, but a systematic study of the Metaverse in terms of industrial applications has not yet been performed. Owing to this gap, this survey presents the salient features and assistive Metaverse technologies. We discuss a high-level and generic Metaverse framework for modern industrial cyberspace and discuss the potential challenges and future directions of the Metaverse’s realization. A case study on Metaverse-assisted Real Estate Management (REM) is presented, where the Metaverse governs a Buyer–Broker–Seller (BBS) architecture for land registrations. We discuss the performance evaluation of the current land registration ecosystem in terms of cost evaluation, trust probability, and mining cost on the BC network. The obtained results show the viability of the Metaverse in REM setups. Full article
(This article belongs to the Special Issue Mathematical Methods and Models of FinTech)
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