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Data
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Data Science in Fintech
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Data Science in Fintech

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: closed (10 October 2023) | Viewed by 18635

Special Issue Editors

Prof. Dr. Henry Han

E-Mail Website
Guest Editor
McCollum Family Endowed Chair, Computer Science and Informatics Department, Baylor University, Waco, TX, USA
Interests: data science; financial technology (Fintech)
Dr. Qiannong (Chan) Gu

E-Mail Website
Guest Editor
Information Systems and Operations Management, Miller College of Business, Ball State University, Muncie, IN 47306, USA
Interests: supply chain analytics; logistics and distribution; supply network modeling; global outsourcing; humanitarian supply chain
Dr. Diane Li

E-Mail Website
Guest Editor
Finance School of Business & Technology University of Maryland, Eastern Shore, Princess Anne, College Park, MD 21853, USA
Interests: financial economics; real estate
Prof. Dr. Tie Wei

E-Mail Website
Guest Editor
Professor of Management, College of Business, Guangxi University, Nanning, China
Interests: innovation management; intellectual property management
Dr. Jeffrey Yi-Lin Forrest

E-Mail Website
Guest Editor
School of Business, Pennsylvania State System of Higher Education (Slippery Rock campus), 1 Morrow Way, Slippery Rock, PA 16057, USA
Interests: decision analysis; mathematical and general systems theory and applications; statistics; regional economics; nonlinear analysis and applications; management science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the surge of all kinds of financial data, data science is playing a more and more essential role in
FinTech. Besides traditional model-driven methods, more and more data-driven AI and machine learning techniques are widely employed to analyze various Fintech data to unveil latent Fintech information and discover new knowledge. Different learning models and data analytics algorithms are being invented to handle the challenges of Fintech and relevant fields.

On the other hand, Fintech data from different areas urgently calls for more customized, explainable, and reproducible data science techniques in all kinds of problem-solving, which may range from online trading marker discovery in high-frequency trading to profitable trading machine construction in cryptocurrency tendency prediction. However, the speed and volume of Fintech generation far exceed those of existing machine learning, AI, and data science algorithms. As such, bridging data science and Fintech is an urgent demand in both Fintech and data science fields.

The goal of this special issue is to bridge novel data science theory and techniques with Fintech to spark new findings and interactions as well as advance Fintech and data science. The special issue welcomes the following topics but is not limited to：

  • Algorithmic Trading
  • Blockchain
  • Big Finance Data Analytics
  • Fintech Data Visualization
  • High-Frequency Trading
  • Cryptocurrency
  • Credit Risk Analytics
  • Deep Learning
  • Data Mining In Finance
  • Explainable AI In Fintech
  • Fintech Database
  • Fintech Theory
  • Supply Chain Finance
  • Quantum Machine Learning In Finance
  • Cyber-Analytics In Fintech
  • Quantitative Finance
  • Quantum Computing In Finance
  • Machine Learning In Finance
  • Option Pricing
  • Smart Contracts

Prof. Dr. Henry Han
Dr. Qiannong (Chan) Gu
Dr. Diane Li
Prof. Dr. Tie Wei
Dr. Jeffrey Yi-Lin Forrest
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. Data is an international peer-reviewed open access monthly 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.

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Published Papers (1 paper)

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Research

17 pages, 733 KiB  
Article
Machine Learning for Credit Risk Prediction: A Systematic Literature Review
by Jomark Pablo Noriega, Luis Antonio Rivera and José Alfredo Herrera
Data 2023, 8(11), 169; https://doi.org/10.3390/data8110169 - 7 Nov 2023
Cited by 5 | Viewed by 17795
Abstract
In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions [...] Read more.
In this systematic review of the literature on using Machine Learning (ML) for credit risk prediction, we raise the need for financial institutions to use Artificial Intelligence (AI) and ML to assess credit risk, analyzing large volumes of information. We posed research questions about algorithms, metrics, results, datasets, variables, and related limitations in predicting credit risk. In addition, we searched renowned databases responding to them and identified 52 relevant studies within the credit industry of microfinance. Challenges and approaches in credit risk prediction using ML models were identified; we had difficulties with the implemented models such as the black box model, the need for explanatory artificial intelligence, the importance of selecting relevant features, addressing multicollinearity, and the problem of the imbalance in the input data. By answering the inquiries, we identified that the Boosted Category is the most researched family of ML models; the most commonly used metrics for evaluation are Area Under Curve (AUC), Accuracy (ACC), Recall, precision measure F1 (F1), and Precision. Research mainly uses public datasets to compare models, and private ones to generate new knowledge when applied to the real world. The most significant limitation identified is the representativeness of reality, and the variables primarily used in the microcredit industry are data related to the Demographic, Operation, and Payment behavior. This study aims to guide developers of credit risk management tools and software towards the existing ability of ML methods, metrics, and techniques used to forecast it, thereby minimizing possible losses due to default and guiding risk appetite. Full article
(This article belongs to the Special Issue Data Science in Fintech)
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