Advances in Analytics and Intelligent System

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 37925

Special Issue Editor


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Guest Editor
College of Business (Nanyang Business School), Division of Information Technology and Operations Management, Nanyang Technological University, Singapore 639798, Singapore
Interests: business analytics; intelligent systems; machine learning; neural networks

Special Issue Information

Dear Colleagues,

We have witnessed the adoption of technology in all industries being escalated by the pandemic. Firms are increasingly being expected to be technology-ready in times of uncertainty. Among the different industries, finance has always been at the forefront of investing and deploying new technology. Financial technology (or FinTech in short) encompasses technology to support financial clients, customer support, in-house operations, to backend decision making.

A financial institution is expected to perform due diligence, and this has thus driven the research and development in areas such as know your clients (KYC), anti-money laundering (AML), know your transactions (KYT), know your business (KYB), AI for finance, and finance analytics. These require digitalized data for development and hence prompt researchers to establish digitalization initiatives to capture digital footprints at different touchpoints.

Insurance technology (or InsurTech in short), a subset of FinTech, is also a growing area. The adoption of InsurTech in insurance firms is estimated to be less than 50%, while more than half of insurance buyers look forward to seamless digital transactions. The insurance industry is hence looking to bridge this gap by transforming the industry to embed technology at a larger scale.

In this special issue, we look forward to articles that will advance the development of FinTech, InsurTech or intelligent system. We welcome discussions on related frameworks, applications, or challenges on topics listed below:

  • Know Your Clients (KYC);
  • Anti-Money Laundering (AML);
  • Know Your Transactions (KYT);
  • Know Your Business (KYB);
  • Artificial Intelligence;
  • Finance Analytics;
  • Business Analytics;
  • Intelligent Decision System;
  • Other Insurance Technology;
  • Other Finance Technology.

Prof. Dr. Yokyen Nguwi
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

  • FinTech
  • InsurTech
  • AI
  • machine learning
  • analytics

Published Papers (10 papers)

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Research

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19 pages, 394 KiB  
Article
A Crypto Yield Model for Staking Return
by Julien Riposo and Maneesh Gupta
FinTech 2024, 3(1), 116-134; https://doi.org/10.3390/fintech3010008 - 15 Feb 2024
Cited by 1 | Viewed by 910
Abstract
We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a [...] Read more.
We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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26 pages, 3580 KiB  
Article
Big Data-Driven Banking Operations: Opportunities, Challenges, and Data Security Perspectives
by Morshadul Hasan, Ariful Hoque and Thi Le
FinTech 2023, 2(3), 484-509; https://doi.org/10.3390/fintech2030028 - 19 Jul 2023
Viewed by 4239
Abstract
At present, with the rise of information technology revolution, such as mobile internet, cloud computing, big data, machine learning, artificial intelligence, and the Internet of Things, the banking industry is ushering in new opportunities and encountering severe challenges. This inspired us to develop [...] Read more.
At present, with the rise of information technology revolution, such as mobile internet, cloud computing, big data, machine learning, artificial intelligence, and the Internet of Things, the banking industry is ushering in new opportunities and encountering severe challenges. This inspired us to develop the following research concepts to study how data innovation impacts banking. We used qualitative research methods (systematic and bibliometric reviews) to examine research articles obtained from the Web of Science and SCOPUS databases to achieve our research goals. The findings show that data innovation creates opportunities for a well-developed banking supply chain, effective risk management and financial fraud detection, banking customer analytics, and bank decision-making. Also, data-driven banking faces some challenges, such as the availability of more data increasing the complexity of service management and creating fierce competition, the lack of professional data analysts, and data costs. This study also finds that banking security is one of the most important issues; thus, banks need to respond to external and internal cyberattacks and manage vulnerabilities. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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14 pages, 1021 KiB  
Article
Examining the Plausible Applications of Artificial Intelligence & Machine Learning in Accounts Payable Improvement
by Vijaya Krishna Kanaparthi
FinTech 2023, 2(3), 461-474; https://doi.org/10.3390/fintech2030026 - 13 Jul 2023
Cited by 1 | Viewed by 1948
Abstract
Accounts Payable (AP) is a time-consuming and labor-intensive process used by large corporations to compensate vendors on time for goods and services received. A comprehensive verification procedure is executed before disbursing funds to a supplier or vendor. After the successful conclusion of these [...] Read more.
Accounts Payable (AP) is a time-consuming and labor-intensive process used by large corporations to compensate vendors on time for goods and services received. A comprehensive verification procedure is executed before disbursing funds to a supplier or vendor. After the successful conclusion of these validations, the invoice undergoes further processing by traversing multiple stages, including vendor identification; line-item matching; accounting code identification; tax code identification, ensuring proper calculation and remittance of taxes, verifying payment terms, approval routing, and compliance with internal control policies and procedures, for a comprehensive approach to invoice processing. At the moment, each of these processes is almost entirely manual and laborious, which makes the process time-consuming and prone to mistakes in the ongoing education of agents. It is difficult to accomplish the task of automatically processing these invoices for payment without any human involvement. To provide a solution, we implemented an automated invoicing system with modules based on artificial intelligence. This system processes invoices from beginning to finish. It takes very little work to configure it to meet the specific needs of each unique customer. Currently, the system has been put into production use for two customers. It has handled roughly 80 thousand invoices, of which 76 percent were automatically processed with little or no human interaction. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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14 pages, 288 KiB  
Article
Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis
by Haris Alibašić
FinTech 2023, 2(3), 430-443; https://doi.org/10.3390/fintech2030024 - 03 Jul 2023
Cited by 1 | Viewed by 2993
Abstract
The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI [...] Read more.
The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
16 pages, 3277 KiB  
Article
Practical Application of Deep Reinforcement Learning to Optimal Trade Execution
by Woo Jae Byun, Bumkyu Choi, Seongmin Kim and Joohyun Jo
FinTech 2023, 2(3), 414-429; https://doi.org/10.3390/fintech2030023 - 29 Jun 2023
Viewed by 2521
Abstract
Although deep reinforcement learning (DRL) has recently emerged as a promising technique for optimal trade execution, two problems still remain unsolved: (1) the lack of a generalized model for a large collection of stocks and execution time horizons; and (2) the inability to [...] Read more.
Although deep reinforcement learning (DRL) has recently emerged as a promising technique for optimal trade execution, two problems still remain unsolved: (1) the lack of a generalized model for a large collection of stocks and execution time horizons; and (2) the inability to accurately train algorithms due to the discrepancy between the simulation environment and real market. In this article, we address the two issues by utilizing a widely used reinforcement learning (RL) algorithm called proximal policy optimization (PPO) with a long short-term memory (LSTM) network and by building our proprietary order execution simulation environment based on historical level 3 market data of the Korea Stock Exchange (KRX). This paper, to the best of our knowledge, is the first to achieve generalization across 50 stocks and across an execution time horizon ranging from 165 to 380 min along with dynamic target volume. The experimental results demonstrate that the proposed algorithm outperforms the popular benchmark, the volume-weighted average price (VWAP), highlighting the potential use of DRL for optimal trade execution in real-world financial markets. Furthermore, our algorithm is the first commercialized DRL-based optimal trade execution algorithm in the South Korea stock market. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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16 pages, 634 KiB  
Article
Modeling the Brand Equity and Usage Intention of QR-Code E-Wallets
by Faten Aisyah Ahmad Ramli, Muhammad Iskandar Hamzah, Siti Norida Wahab and Rishabh Shekhar
FinTech 2023, 2(2), 205-220; https://doi.org/10.3390/fintech2020013 - 23 Mar 2023
Cited by 3 | Viewed by 2344
Abstract
The proliferation of digital payments has paved the way for the greater use of E-wallets or mobile payments in over-the-counter (OTC) retail transactions. Nevertheless, given its economic and accessibility benefits over NFC forms of mobile payment, relatively little is known about QR-code E-wallet [...] Read more.
The proliferation of digital payments has paved the way for the greater use of E-wallets or mobile payments in over-the-counter (OTC) retail transactions. Nevertheless, given its economic and accessibility benefits over NFC forms of mobile payment, relatively little is known about QR-code E-wallet (QREW) adoption from the consumer–brand relationship perspective. The study aims to address this knowledge void by augmenting brand equity elements (perceived value, brand image, and brand awareness) to comprehensively analyze consumers’ QREW usage intention in the OTC retail environment. A structural equation modeling analysis was performed on 305 consumers in the greater Klang Valley, Malaysia. The empirical findings suggest that brand awareness positively affects QREW usage intention and mediates the effects of both perceived quality and brand image on the outcome. Moreover, the results reveal a serial mediation effect involving all of the examined factors. Theoretically, this study supplements the literature on mobile payments from the consumer–brand relationship view, in which the predictive nature of brand equity factors is examined separately. In practical terms, considering that the Malaysian market QREW is in a relatively early growth stage, the findings should offer QREW providers insights into how to capitalize on brand equity mechanisms for attracting consumers to utilize their offerings. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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17 pages, 5920 KiB  
Article
An Intelligent System for Trading Signal of Cryptocurrency Based on Market Tweets Sentiments
by Man-Fai Leung, Lewis Chan, Wai-Chak Hung, Siu-Fung Tsoi, Chun-Hin Lam and Yiu-Hang Cheng
FinTech 2023, 2(1), 153-169; https://doi.org/10.3390/fintech2010011 - 16 Mar 2023
Cited by 2 | Viewed by 3442
Abstract
The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker [...] Read more.
The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visualization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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20 pages, 2283 KiB  
Article
Modelling the Impact of the COVID-19 Pandemic on Some Nigerian Sectorial Stocks: Evidence from GARCH Models with Structural Breaks
by Monday Osagie Adenomon and Richard Adekola Idowu
FinTech 2023, 2(1), 1-20; https://doi.org/10.3390/fintech2010001 - 21 Dec 2022
Cited by 1 | Viewed by 1368
Abstract
This study provides evidence of the impact of COVID-19 on five (5) Nigerian Stock Exchange (NSE) sectorial stocks (NSE Insurance, NSE Banking, NSE Oil and Gas, NSE Food and Beverages, and NSE Consumer Goods). To achieve the goal of this paper, daily stock [...] Read more.
This study provides evidence of the impact of COVID-19 on five (5) Nigerian Stock Exchange (NSE) sectorial stocks (NSE Insurance, NSE Banking, NSE Oil and Gas, NSE Food and Beverages, and NSE Consumer Goods). To achieve the goal of this paper, daily stock prices were obtained from a secondary source ranging from 2 January 2020 to 25 March 2021. Because of the importance of incorporating structural breaks in modelling stock returns, the Zivot–Andrews unit root test revealed 20 January 2021, 26 March 2020, 27 July 2020, 23 March 2020 and 23 March 2020 as potential break points for NSE Insurance, NSE Food, Beverages and Tobacco, NSE Oil and Gas, NSE Banking, and NSE Consumer Goods, respectively. This study investigates the volatility in daily stock returns for the five (5) Nigerian Stock Exchange (NSE) sectorial stocks using nine versions of GARCH models (sGARCH, girGARCH, eGARCH, iGARCH, aPARCH, TGARCH, NGARCH, NAGARCH, and AVGARCH); in addition, the half-life and persistence values were obtained. The study used the Student t- and skewed Student t-distributions. The results from the GARCH models revealed a negative impact of COVID-19 on the NSE Insurance, NSE Food, Beverages and Tobacco, NSE Banking, and NSE Consumer Goods stock returns; however, the NSE Oil and Gas returns showed a positive correlation with the COVID-19 pandemic. This study recommends that the shareholders, investors, and policy players in the Nigerian Stock Exchange markets should be adequately prepared in the form of diversification of investment in stocks that can withstand future possible crises in the market. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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13 pages, 2311 KiB  
Article
The Impact of the COVID-19 Pandemic on the Music Industry
by Yuechu Hu and Jong-Min Kim
FinTech 2022, 1(4), 399-411; https://doi.org/10.3390/fintech1040030 - 02 Dec 2022
Cited by 1 | Viewed by 3817
Abstract
The COVID-19 pandemic ravaged the world, not only threatening people’s health but also impacting various industries. This paper will focus on the impact of the pandemic on the music industry, specifically on live and recorded music. To help determine how the COVID-19 pandemic [...] Read more.
The COVID-19 pandemic ravaged the world, not only threatening people’s health but also impacting various industries. This paper will focus on the impact of the pandemic on the music industry, specifically on live and recorded music. To help determine how the COVID-19 pandemic has impacted both live and recorded music, we will analyze the log-returns of stock data of three companies representative of the music industry: Live Nation Entertainment, Tencent Music Entertainment, and Warner Music Group. We also provide descriptive statistics related to the log-returns of stock data of the three companies and calculate the correlation coefficients of the log returns for these companies using three correlation methods (Pearson correlation test, Kendall correlation test, and Spearman correlation) before and after the pandemic. From stock price charts, we observed a negative relationship between the stock indices of both live and recorded music during the early pandemic period. However, we found that there was no correlation in the log-returns of both live and recorded music company stocks after the COVID-19 vaccination became widely available, despite their being a slight positive correlation from the results. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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Review

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13 pages, 1331 KiB  
Review
Factors Affecting Fintech Adoption: A Systematic Literature Review
by Egi Arvian Firmansyah, Masairol Masri, Muhammad Anshari and Mohd Hairul Azrin Besar
FinTech 2023, 2(1), 21-33; https://doi.org/10.3390/fintech2010002 - 28 Dec 2022
Cited by 27 | Viewed by 13136
Abstract
The rise of financial technology (fintech) has been one of the substantial changes in the financial landscape driven by technological advancements and the global financial crisis. This paper employs the systematic literature review (SLR) technique to review recent literature on fintech adoption or [...] Read more.
The rise of financial technology (fintech) has been one of the substantial changes in the financial landscape driven by technological advancements and the global financial crisis. This paper employs the systematic literature review (SLR) technique to review recent literature on fintech adoption or acceptance employing the Scopus database (2019–2022). The final reviewed documents are sixteen journal articles published by various journals from different country contexts and theoretical backgrounds. Several inclusion criteria were used to filter those selected documents. One crucial criterion is the journal continuity in the Scopus index, which assures the quality of the published scholarly works. This criterion selection is expected to represent this paper’s novelty. The study reveals various determinants derived from the theories used by the fintech researchers. However, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are the most used theoretical foundations. Additionally, trust, financial literacy, and safety are other factors developed by previous researchers and are significant determinants of fintech adoption. Besides, these results suggest that future studies on fintech adoption develop a genuine construct since fintech keeps progressing, and so does the customers’ behavior. Full article
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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