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15 pages, 457 KiB

Open AccessArticle

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 5 | Viewed by 5730

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

Open AccessArticle

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 12 | Viewed by 5737

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

Open AccessArticle

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 - 7 Jun 2022

Cited by 12 | Viewed by 7390

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

Open AccessArticle

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 - 2 Jun 2022

Cited by 4 | Viewed by 3663

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

Open AccessArticle

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 18 | Viewed by 11284

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

Open AccessArticle

Statistical Modelling of Downside Risk Spillovers

by Daniel Felix Ahelegbey

FinTech 2022, 1(2), 125-134; https://doi.org/10.3390/fintech1020009 - 1 Apr 2022

Viewed by 2084

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

Open AccessArticle

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 13 | Viewed by 4729

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

Open AccessArticle

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 3213

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

Open AccessSystematic 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 13 | Viewed by 13817

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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