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Risks
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Computational Technologies for Financial Security and Risk Management
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Computational Technologies for Financial Security and Risk Management

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 7039

Special Issue Editors

Prof. Dr. Simon Grima

E-Mail Website1 Website2
Guest Editor
1. Faculty of Economics, Management and Accountancy, Insurance and Risk Management Department, University of Malta, MSD 2080 Msida, Malta
2. Faculty of Business, Management and Economics, University of Latvia, LV-1050 Riga, Latvia
Interests: financial technologies; financial management and asset management; risk management; compliance and regulations; corporate finance; corporate governance; audit management; financial services; behavioral economics
Special Issues, Collections and Topics in MDPI journals
Dr. Balamurugan Balusamy

E-Mail Website
Guest Editor
Associate Dean Student Engagement, Shiv Nadar University Delhi-NCR, Noida, Uttar Pradesh 201314, India
Interests: intelligent transport system; internet-of-things; data analytics

Special Issue Information

Dear Colleagues,

Financial technology (FinTech) essentially involves financial innovations that help in deploying business strategy frameworks, processes and products. FinTech focuses on technological advancements and innovations using disruptive technologies, such as business intelligence, big data analytics, artificial intelligence, augmented reality and blockchain, with ever growing opportunities in the global market space. These disruptive technologies are supportive in nature, providing a way for more inclusive financial services at large. Financial institutions are facing many risks and challenges in their financial transactions, such as biased credit scores, the underestimation of the credit worth, the incompliance of market risk in consumer protection, fraud detection in money lending, illegal crypto markets, money laundering and cyber-attacks, all which need attention. Disruptive technologies offer smooth offshore operations across the globe. However, apart from the advantages of using disruptive technology, there is also a loop hole created within the system. Fintech monitors the performance of such risks using intelligent tools. Here, computational intelligence is capable of bridging the gap between the technical and regulatory experts protecting the interest of consumers and investors. Such implementation is possible by means of novel access control techniques, strong encryption and decryption techniques, and digital signature paradigms for securing financial data. “Computational Technologies for Financial Security and Risk Management” provide automated solutions, and thus, an approach to monitoring the performance of the regulated financial institutions.

This Special Issue aims to include research papers, review papers, frameworks, methodologies, performance analyses and in-depth surveys.

Topics may include, but are not limited to:

  • Computational technologies and business models in FinTech;
  • Frameworks, policies and regulations in digital asset evaluation using expert systems;
  • Risk management models in FinTech and InsuTech;
  • Artificial intelligence in autonomous finance instruments;
  • Big data analytics in credit risk assessments;
  • Systematic risk assessment for responsible finance using AI ethics;
  • Blockchain technology in crypto asset management;
  • AR/ VR-based innovations in augmented finance;
  • Smart contracts and blockchain for digital asset management;
  • Metaverse and Web3 in financial services;
  • Quantum computing for secured FinTech;
  • Robotic process automation in financial institutions;
  • Non-fungible tokens in insurance and financial institutions;
  • Zero-trust security algorithms for FinTech;
  • Insights into global standards in Fintech and the banking industry.

Prof. Dr. Simon Grima
Dr. Balamurugan Balusamy
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. Risks 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 1800 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

  • financial security
  • computational technologies
  • risk management
  • big data
  • FinTech
  • InsuTech
  • autonomous finance instruments
  • artificial intelligence
  • smart contracts
  • digital ledger technology
  • augmented finance

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

17 pages, 1291 KiB  
Article
AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods
by Lu Xiong, Vajira Manathunga, Jiyao Luo, Nicholas Dennison, Ruicheng Zhang and Zhenhai Xiang
Risks 2023, 11(7), 131; https://doi.org/10.3390/risks11070131 - 14 Jul 2023
Viewed by 2435
Abstract
In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry [...] Read more.
In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing risks and determining insurance premiums for personal vehicles. However, the app is not limited exclusively to actuaries. Other individuals or entities, such as insurance companies, researchers, or analysts, who have access to the necessary data and require insights or analysis related to personal auto insurance, can also benefit from using the app. It is the first web-based application of its kind that is free to use and deployable from the personal computer or mobile device. AutoReserve is a software solution that caters to the needs of insurance professionals where only a few existing web-based applications are available. The application is divided into three parts: a summary of the loss data, a classical loss reserving tool, and a machine learning loss reserving tool. Each component of the application functions differently and allows for inputs from the user to analyze the provided loss data. The user, in other words, individuals or entities who utilize the Auto Reserve application, can then use the outputs for these three sections to improve his or her risk management or loss reserving process. AutoReserve is unique compared to other loss reserving tools because of its ability to employ both traditional, spreadsheet-based and modern, machine-learning-based loss reserving tools. AutoReserve is accessible on the web. The app is currently usable and is still undergoing frequent updates with new features and bug fixes. Full article
(This article belongs to the Special Issue Computational Technologies for Financial Security and Risk Management)
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24 pages, 489 KiB  
Article
A Comparison of Competing Asset Pricing Models: Empirical Evidence from Pakistan
by Eleftherios Thalassinos, Naveed Khan, Shakeel Ahmed, Hassan Zada and Anjum Ihsan
Risks 2023, 11(4), 65; https://doi.org/10.3390/risks11040065 - 24 Mar 2023
Cited by 1 | Viewed by 4029
Abstract
In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the [...] Read more.
In recent years, the rapid and significant development of emerging markets has globally led to insight from potential investors and academicians seeking to assess these markets in terms of risk inheritance. Therefore, this study aims to explore the validity and applicability of the capital asset pricing model (henceforth CAPM) and multi-factor models, namely Fama–French models, in Pakistan’s stock market for the period of June 2010–June 2020. This study collects data on 173 non-financial firms listed on the Pakistan stock exchange, namely the KSE-100 index, and follows Fama-MacBeth’s regression methodology for empirical estimation. The empirical findings of this study conclude that small portfolios (small-size companies) earn considerably higher returns than big portfolios (large-size companies). Ultimately, the risk associated with portfolio returns is reported to be higher for small portfolios (small-size companies) than for big portfolios (large-size companies). According to the regression output, the CAPM was found to be valid for explaining the market risk premium above the risk-free rate. Similarly, the FF three-factor model was found to be valid for explaining time-series variation in excess portfolio returns. Later, we added human capital into FF three- and five-factor models. This study found that the human capital base six-factor model outperformed the other competing asset pricing models. The findings of this study indicate that small portfolios (small-size companies) earn more returns than big portfolios (large-size companies) to reward the investor for taking extra risks. Investors may benefit by timing their investments to maximize stock returns. Company investment in human capital adds reliable information, replicates the value of the company and, in the long term, helps investors make rational decisions. Full article
(This article belongs to the Special Issue Computational Technologies for Financial Security and Risk Management)
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