Fintech and Sustainable Finance

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

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 41336

Special Issue Editors


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Guest Editor
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Interests: financial data science; graphical models; network models; financial networks; systemic risk; financial risk management; fintech risk management; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Economics and Management, University of Pavia, 27100 Pavia, Italy
Interests: econometrics; applied & mathematical statistics; financial econometrics; network econometrics; fintech credit risk analysis; financial networks; systemic risk analysis

Special Issue Information

Dear Colleagues,

Current financial market structures and investment strategies are mainly driven by the ultimate goal of financial returns, without considering the impact on society. As evidenced by recent and past financial crises, losses suffered by market participants do not only affect investors, financial institutions, and markets. Instead, some of these losses trigger severe impacts outside the financial system thereby disrupting the activities of many environmental, social, and government structures. This Special Issue seeks to solicit original ideas and research materials on the development of innovative tools and instruments for technological financial platforms, institutions, and markets to address social, environmental, and governance problems alongside financial returns when making investment decisions.

Prof. Dr. Paolo Giudici
Dr. Daniel Felix Ahelegbey
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. 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

  • environmental risk
  • social risk
  • governance risk
  • financial systems
  • impact investing
  • sustainability
  • capital management
  • impact evaluation
  • ESG finance
  • Fintech risk management

Published Papers (9 papers)

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15 pages, 457 KiB  
Article
Explaining the Factors Affecting Customer Satisfaction at the Fintech Firm F1 Soft by Using PCA and XAI
by Mohan Khanal, Sudip Raj Khadka, Harendra Subedi, Indra Prasad Chaulagain, Lok Nath Regmi and Mohan Bhandari
FinTech 2023, 2(1), 70-84; https://doi.org/10.3390/fintech2010006 - 19 Jan 2023
Cited by 4 | Viewed by 4021
Abstract
The most significant and rapidly expanding fintech services in Nepal are provided by several fintech firms. Customer satisfaction must be compared side by side even if every organization has made an effort to expand the usage of services. Many studies have concentrated on [...] Read more.
The most significant and rapidly expanding fintech services in Nepal are provided by several fintech firms. Customer satisfaction must be compared side by side even if every organization has made an effort to expand the usage of services. Many studies have concentrated on evaluating the impact of various factors on customer satisfaction, but significantly fewer studies have been conducted to explore the factors and focus of machine learning. Based on the planned behavioural theory (TPB), the study is concentrated on exploring and evaluating customer satisfaction on a different stimulus offered by F1 Soft (a fintech firm in nepal), customers’ loyalty and the compatibility they gain through the company’s services. By exploring various factors affecting customer satisfaction by using principal component analysis (PCA) and explainable AI (XAI), the study explored the eight factors (customer service, compatibility, ease of use, assurance, loyalty intention, technology perception, speed and firm’s innovativeness) which affect customer satisfaction individually. Furthermore, by using support vector machine (SVM) and logistic regression (LR), the major contributing factors are explained with local interpretable model-agnostic explanation (LIME) and Shapley additive explanations (SHAP). SVM holds the training accuracy of 89.13% whereas LR achieves 87.88%, and both algorithms show that compatibilty issues consider the major contributing factor for customer satisfaction. Contributing toward different dimensions, determinants, and the results of customer satisfaction in fintech, the study suggests how fintech companies must integrate factors affecting customer satisfaction in their system for further process development. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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12 pages, 618 KiB  
Article
Financial Inclusion, Fintech, and Income Inequality in Africa
by Biruk Birhanu Ashenafi and Yan Dong
FinTech 2022, 1(4), 376-387; https://doi.org/10.3390/fintech1040028 - 16 Nov 2022
Cited by 7 | Viewed by 3968
Abstract
Financial inclusion and Fintech have revolutionized the financial sector and fundamentally changed how we store, save, borrow, transfer, and invest money. This paper investigates the impact of financial inclusion and Fintech on income inequality using waves of survey data for 2011, 2014, and [...] Read more.
Financial inclusion and Fintech have revolutionized the financial sector and fundamentally changed how we store, save, borrow, transfer, and invest money. This paper investigates the impact of financial inclusion and Fintech on income inequality using waves of survey data for 2011, 2014, and 2017 across 39 African countries. By using pooled ordinary least square and two-stage least square (2sls) estimation methods, we obtain three key findings. First, institutional factors such as political stability, control of corruption, and government effectiveness determine Fintech and financial inclusion. Second, Fintech encourages individuals to have a formal bank account, thereby promoting financial inclusion. Third, financial inclusion and Fintech exacerbate income inequality. The direct implication of our findings is that policymakers make tradeoffs whether they seek to achieve higher inclusion and Fintech or to reduce income inequality. We highlight that a pro-poor financial sector development is vital. Easing the bottleneck in obtaining loans, offering agriculture-based Fintech services, and improving digital literacy are important steps to gain the most out of inclusion and Fintech in reducing income inequality. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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10 pages, 502 KiB  
Article
Fintech Strategies of Islamic Banks: A Global Empirical Analysis
by Ahmet Faruk Aysan, Abdelilah Belatik, Ibrahim Musa Unal and Rachid Ettaai
FinTech 2022, 1(2), 206-215; https://doi.org/10.3390/fintech1020016 - 07 Jun 2022
Cited by 9 | Viewed by 4657
Abstract
As new digitalization strategies storm the banking industry, banks which are behind the technological curve may struggle to keep pace. This is a well-known challenge in the Islamic banking sector in particular; however, this research shows that little is being done in order [...] Read more.
As new digitalization strategies storm the banking industry, banks which are behind the technological curve may struggle to keep pace. This is a well-known challenge in the Islamic banking sector in particular; however, this research shows that little is being done in order to achieve unified digitalization in operations. The 2020 Global Islamic Bankers Survey (GIBS) from CIBAFI sought opinions and data from 101 Islamic banks, which outlined both their institutions’ adoption of financial technology and their awareness of existing technologies. In addition, several technology trends—such as AI, machine learning, DLTs, and P2P lending—were analyzed separately in order to understand how they may be implemented within Islamic banking. This paper performed different statistical procedures to answer these research questions via correlation analysis and one-way ANOVA. The data were compiled and analyzed using SPSS software. In doing so, this study clarified the perspective of Islamic banks on digital transformation and answered whether Islamic banks are taking the right direction in terms of their digitalization strategies. Interestingly, most newly developing technologies have a low implementation level in Islamic banking operations globally, with the exception of mobile banking, which already has a vast global infrastructure. The results may serve as a warning to Islamic banks to invest more capital and energy in the developing fields of financial technologies in order to keep abreast of their conventional banking counterparts. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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11 pages, 537 KiB  
Article
Regulated LSTM Artificial Neural Networks for Option Risks
by David Liu and An Wei
FinTech 2022, 1(2), 180-190; https://doi.org/10.3390/fintech1020014 - 02 Jun 2022
Cited by 2 | Viewed by 2900
Abstract
This research aims to study the pricing risks of options by using improved LSTM artificial neural network models and make direct comparisons with the Black–Scholes option pricing model based upon the option prices of 50 ETFs of the Shanghai Securities Exchange from 1 [...] Read more.
This research aims to study the pricing risks of options by using improved LSTM artificial neural network models and make direct comparisons with the Black–Scholes option pricing model based upon the option prices of 50 ETFs of the Shanghai Securities Exchange from 1 January 2018 to 31 December 2019. We study an LSTM model, a mathematical option pricing model (BS model), and an improved artificial neural network model—the regulated LSTM model. The method we adopted is first to price the options using the mathematical model—i.e., the BS model—and then to construct the LSTM neural network for training and predicting the option prices. We further form the regulated LSTM network with optimally selected key technical indicators using Python programming aiming at improving the network’s predicting ability. Risks of option pricing are measured by MSE, RMSE, MAE and MAPE, respectively, for all the models used. The results of this paper show that both the ordinary LSTM and the traditional BS option pricing model have lower predictive ability than the regulated LSTM model. The prediction ability of the regulated LSTM model with the optimal technical indicators is superior, and the approach adopted is effective. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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9 pages, 215 KiB  
Article
How Do Fintechs Impact Banks’ Profitability?—An Empirical Study Based on Banks in China
by Shuli Lv, Yangran Du and Yong Liu
FinTech 2022, 1(2), 155-163; https://doi.org/10.3390/fintech1020012 - 23 May 2022
Cited by 11 | Viewed by 8158
Abstract
The rapid development of Fintechs has brought opportunities and challenges to the profitability of banks. In this paper, we theoretically expound how Fintechs impact on banks’ profitability, then we establish the Error Correction Model (ECM) and combine this with the Granger causal relation [...] Read more.
The rapid development of Fintechs has brought opportunities and challenges to the profitability of banks. In this paper, we theoretically expound how Fintechs impact on banks’ profitability, then we establish the Error Correction Model (ECM) and combine this with the Granger causal relation test based on the data of the Industrial and Commercial Bank of China (ICBC) in 2011–2020. The research results show the following findings: (1) banks’ profitability (ROE) has a cooperative relationship with the development of Fintechs (FTI), banks’ assets (TA), the profitability of interest-bearing assets (NIM), credit risks (NPL) and cost control (CTI). (2) Fintechs have a “U”-shaped impact on the banks’ profitability. In the initial stages, Fintechs impact the business of banks, which reduces the profitability of banks; the advantages of Fintechs gradually increase in the middle and later stages, and the profitability gradually increases. (3) The assets of banks (TA) and the profitability of interest-bearing assets (NIM) change in the same direction as banks’ profitability (ROE), while credit risks (NPL) and cost control (CTI) change in the opposite direction from ROE. (4) The level of bank profitability and the development of Fintechs are Granger causes of each other, the size of the bank’s assets is the Grange reason for the increase in profitability and the increase in profitability is the Granger cause for the improvement of NIM and the decline in NPL. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
10 pages, 1077 KiB  
Article
Statistical Modelling of Downside Risk Spillovers
by Daniel Felix Ahelegbey
FinTech 2022, 1(2), 125-134; https://doi.org/10.3390/fintech1020009 - 01 Apr 2022
Viewed by 1693
Abstract
We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application [...] Read more.
We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application examines whether downside risk connections among the major stock markets are merely anecdotal or provide a signal of contagion and the nature of sensitivity among major equity markets during the global financial crisis and the coronavirus pandemic. The result showed that the COVID-19 crisis recorded the historically highest spike in the downside risk interconnectedness among the major equity market indices, suggesting higher financial market vulnerability in the coronavirus pandemic than during the global financial crisis. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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9 pages, 459 KiB  
Article
Shapley Feature Selection
by Alex Gramegna and Paolo Giudici
FinTech 2022, 1(1), 72-80; https://doi.org/10.3390/fintech1010006 - 25 Feb 2022
Cited by 9 | Viewed by 3808
Abstract
Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, [...] Read more.
Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, based on Shapley values, to provide more accurate information for feature selection. We test our proposal in a real setting, which concerns the prediction of the probability of default of Small and Medium Enterprises. Our results show that the integrated approach may indeed prove fruitful to some feature selection methods, in particular more parsimonious ones like LASSO. In general the combination of approaches seems to provide useful information which feature selection algorithm can improve their performance with. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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9 pages, 427 KiB  
Article
Crypto Asset Portfolio Selection
by Daniel Felix Ahelegbey, Paolo Giudici and Fatemeh Mojtahedi
FinTech 2022, 1(1), 63-71; https://doi.org/10.3390/fintech1010005 - 21 Feb 2022
Viewed by 2608
Abstract
The aim of this paper is to propose a portfolio selection methodology capable to take into account asset tail co-movements as additional constraints in Markowitz model. We apply the methodology to the observed time series of the 10 largest crypto assets, in terms [...] Read more.
The aim of this paper is to propose a portfolio selection methodology capable to take into account asset tail co-movements as additional constraints in Markowitz model. We apply the methodology to the observed time series of the 10 largest crypto assets, in terms of market capitalization, over the period 20 September 2017–31 December 2020 (1200 daily observations). The results indicate that the portfolios selected considering tail risk are more diversified and, therefore, more resilient to financial shocks. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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11 pages, 792 KiB  
Systematic Review
Fintech, Digitalization, and Blockchain in Islamic Finance: Retrospective Investigation
by Ibrahim Musa Unal and Ahmet Faruk Aysan
FinTech 2022, 1(4), 388-398; https://doi.org/10.3390/fintech1040029 - 17 Nov 2022
Cited by 7 | Viewed by 8167
Abstract
The increasing interest in Fintech, Blockchain, and Digitalization in Islamic Finance created a new area in the literature, requiring a systematic review of these academic publications. The scope of the analysis is limited to journal articles to understand the trends in the indexed [...] Read more.
The increasing interest in Fintech, Blockchain, and Digitalization in Islamic Finance created a new area in the literature, requiring a systematic review of these academic publications. The scope of the analysis is limited to journal articles to understand the trends in the indexed journals. Results are categorized into three sections, Islamic banks’ digitalization, Blockchain and Crypto Assets research, and Islamic non-bank financial institutions’ digitalization. Islamic fintech has great potential mainly because of the overlapping norms of Shariah and fintech, making it easier to implement technological disruption into Islamic finance. Moreover, the trust shift to Islamic finance could be merged with the opportunities of fintech and increase the potential of Islamic fintech even more. Full article
(This article belongs to the Special Issue Fintech and Sustainable Finance)
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