Purpose: This study aims to develop a unique methodology to construct a bank’s financial technology (Fintech) score, which captures the degree of digitalization of a bank’s operations. Using the Fintech score as the proxy, this study investigates the effect of Fintech on the operating performance of the top largest Islamic bank.Design/methodology/approach: The methodology used measures the link between the degree of digitization of a bank and its operational performance. This study applies the three-degree polynomial of regression to the largest Islamic bank in which the explanatory variable is the natural logarithm of Fintech score, and the response variable is common operating performance measure. To check the sensitivity of the estimates to the sample size and assumptions’ violation, this study has applied Bootstrapping and Bayesian processes to the three-degree polynomial regressions.Findings: The study estimates from 2007 to 2021 show that the relationship between the operating performance of the Islamic banks and the Fintech is nonlinear and strongly significant: operating returns increase with the increasing level of Fintech, whereas the operating returns decrease with the increasing Fintech variance. At an aggregate level, this study attributes a significant rise in internet coverage to the emergence of Fintech in the Middle East region.Originality/value: This study constructs an implicit measure of Fintech that measures the adoption of Fintech by the bank and, consequently, offers the technology to their customers for higher use satisfaction. This study finds that Fintech is linked to the operating performance in a nonlinear fashion, in which Fintech and Fintech variance have the opposite effect on operating performance: Fintech increases the operating profitability, whereas Fintech variance decreases the operating profitability of a bank.

Purpose: The purpose of this study is to evaluate the global developments in the area of fintech solutions by analyzing Islamic and Conventional banks core accounting and market analysis IFIs and their impact on financial inclusion within its core markets.Design/methodology/approach: The authors collect and analyze annual accounting and market Data of the top ten largest Islamic banks and the top ten US Conventional banks, in terms of Total Asset and Market Capitalization, from Bloomberg Data.Findings: The analysis of Bloomberg data shows higher risk-return for Islamic banks–except ROE Market measure that we suggest-than US conventional banks. Nonetheless, Islamic banking grew faster than conventional banking over the period 2006–2021. As a business model, we find that Islamic banks take more credit with more than seventy percent of their profit from loans, while US conventional banks struggle to reach seventy percent interest rate ratio. The authors’ research documents that Fintech and digitalization are driving Islamic finance growth during financial and economic downturns.Research limitations/implications: FinTech data is not available for banks, further insights of analysis on FinTech and Innovations in the banking sectors.Practical implications: Islamic banks continuously innovate to satisfy the users of their services and Fintech is opportune to innovation. This study could be interesting for both practitioners and academics wishing to understand and compare Islamic and conventional banking futures.Social implications: The authors compared two banking systems, the US and Islamic Banks, which could be useful for users to differentiate between those banking operations.Originality/value: The authors collected accounting and market data from Bloomberg of top 10 Islamic and top 10 US Conventional banks from 2006 to 2021 to examine Risk-Return, Growth and Business Model of those banks. The authors propose a new Risk-Return measure ROE-Market and its volatility.

Purpose: Research and development (R&D) is increasingly considered to be a key driver of economic growth. The relationship between these variables is commonly examined using linear models and thus relies only on single-point estimates. Against this background, this paper provides new evidence on the impact of R&D on economic growth using a machine learning approach that makes it possible to go beyond single-point estimation.Design/methodology/approach: The authors use the kernel regularized least squares (KRLS) approach, a machine learning method designed for tackling econometric models without imposing arbitrary functional forms on the relationship between the outcome variable and the covariates. The KRLS approach learns the functional form from the data and thus yields consistent estimates that are robust to functional form misspecification. It also provides pointwise marginal effects and captures non-linear relationships. The empirical analyses are conducted using a sample of 101 countries over the period 2000–2020.Findings: The estimates indicate that R&D expenditure and high-tech exports positively and significantly influence economic growth in a non-linear manner. The authors also find a positive and statistically significant relationship between economic growth and greenhouse gas emissions. In both cases, the effects are higher for upper-middle-income and high-income countries. These results suggest that a substantial effort is needed to green economic growth. Internet access is found to be an important factor in supporting economic growth, especially in high-income and middle-income countries.Practical implications: This paper contributes to underlining the importance of investing in R&D to support growth and shows that the disparity between countries is driven by the determinants of economic growth (human capital in R&D, high-tech exports, Internet access, economic freedom, unemployment rate and greenhouse gas emissions). Moreover, since the authors find that R&D expenditure and greenhouse gas emissions are positively associated with economic growth, technological progress with green characteristics may be an important pathway for green economic growth.Originality/value: This paper uses an innovative machine learning method to provide new evidence that innovation supports economic growth.

Purpose: – This paper aims to find the effects of regulatory and supervisory policies on bank risk-taking. The same regulation and supervision have different effects on bank risk-taking depending on influence factors. These factors were considered and a sample of the largest European banks from France, Germany, UK, Italy, Spain and Greece was used over the period 2005-2011. Design/methodology/approach: – In this paper, the author analyses the effects of regulation and supervision on risk-taking. The author uses a sample of the biggest banks from six European countries (France, UK, Germany, Italy, Spain and Greece) over the period 2005-2011. Because the applicable entry of IFRS was in 2005, thus data of European banks are not available before this date. For each country in the sample, the 10 largest banks (defined by total assets) that lend money to firms were identified. The author does not include central banks or postal banks, which generally do not lend money to firms and are described as non-banking institutions (La Porta et al., 2002). Findings: – It was found that restrictions on bank activities, supervisors’ power and capital adequacy decrease risk-taking. Thus, regulation and supervision enhance bank’s stability. While, deposit insurance increases the risk due to its association to moral hazard. Finally, it was found that strengthening regulatory and supervisory framework raises the risk-taking and weakens the stability of European banks. Originality/value: – The author contributes to existing empirical analyses in three ways. First, the existing literature has drawn a lot of attention on US banks. However, the purpose of this paper is to examine the biggest banks of three European leaders (France, Germany and UK) and three more European countries influenced by the recent crisis (Spain, Italy and Greece) over the period 2005-2011. Second, most studies focus mainly on the relationship between regulation and profitability, yet seldom on the relationship between regulation, supervision and risk-taking. The author focuses on this relationship. Third, this study applies the two-step dynamic panel data approach suggested by Blundell and Bond (1998) and also uses dynamic panel generalized method of moments (GMM) method to address potential problems. The two-step GMM estimator that the author uses is generally the most efficient.