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FinTech Adoption and Bank Performance in the Gulf Cooperation Council: Panel Evidence from 73 Listed Commercial and Islamic Banks (2020–2025)

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11 June 2026

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23 June 2026

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Abstract

The paper presents the largest empirical evaluation of the adoption of FinTech and its impact on bank performance in the Gulf Cooperation Council (GCC). Over the 2020–2025 period, we form a balanced sample of 73 Saudi Arabian, United Arab Emirates, Kuwait, Qatar, Bahrain and Oman listed commercial and Islamic banks, which give us 438 bank-year observations. Bank-level financial indicators (ROA, ROE, NIM, Cost-to-Income, NPL, LLP and Capital Adequacy) are based on audited annual reports and Pillar III disclosures; observational gaps are bridged through linear interpolation based on country-year sector medians in the KPMG GCC Listed Banks Results Reports. The FinTech indicators at country level are based on the World Bank Global Findex, the ITU DataHub, MAGNiTT venture-capital databases and central-bank regulatory-sandbox registers. An overall FinTech Adoption Index is built and experimented with its constituent parts. The effects of FinTech on bank profitability, efficiency, risk and capitalisation are estimated using two-way fixed-effects panel regressions with Driscoll–Kraay standard errors, and system-GMM robustness tests. We theorise that FinTech adoption increases profitability and efficiency but narrows net-interest margins, with non-uniform impacts across Islamic and conventional banks and across regulatory regimes. Findings present evidence-based policy advice to policymakers who realise Vision 2030/2035 digital-finance aspirations. This article adds to the work of Kayed et al. (2026), who implemented a qualitative scenario-based MCDA framework to Kuwait, with a quantitative cross-country complement based on observable bank-level performance.

Keywords: 
FinTech
;  
bank performance
;  
panel data
;  
Gulf Cooperation Council
;  
Islamic banking
;  
financial inclusion
;  
regulatory sandbox
Subject: 
Business, Economics and Management  -   Finance

1. Introduction

Financial technology (FinTech) is transforming the structure of the world banking system, changing the way credit is distributed, the way payments are made, and the way financial intermediaries will compete with non-bank entrants. No region has seen this change more than the Gulf Cooperation Council (GCC), with six hydrocarbon-based economies making digital finance central to long-term diversification strategies—Saudi Vision 2030, UAE Centennial 2031, Kuwait Vision 2035, Bahrain Economic Vision 2030, Qatar National Vision 2030 and Oman Vision 2040. The presence of national payment rails like mada, KNET and Aani, digital-only banking licences (Wio, STC Bank, Ruya), and regulatory sandboxes at SAMA, the DFSA, the CBB, the QCB, the CBO and the CBK are all indicative of a decisive regional shift towards a technology-intensive financial system [1,2]. This regional shift parallels a wider transformation of bank back-office and governance functions, in which AI adoption, IT governance and algorithmic decision systems are increasingly shaping accounting and audit efficiency in regional commercial banks [3].
However, there is scant and scattered evidence on the implications of this turn to the banks themselves. The majority of GCC FinTech scholarship has been on adoption preparedness, user-level behaviour, or qualitative scenario exercises. An illustrative case is the study by Kayed et al. [4] which uses a scenario-based Multi-Criteria Decision Analysis (MCDA) model to rank foresight interventions to Kuwait based on expert ratings of a Delphi panel of twenty experts. Although invaluable to policy sequencing, such qualitative frameworks cannot provide the empirical question that most directly interests banking supervisors and shareholders: does adoption of FinTech increase the apparent profitability, efficiency and stability of GCC banks, or does it eliminate their historic rents?
This gap is filled in this paper. We randomly sample a balanced group of 73 listed commercial and Islamic banks across the six GCC states in the 2020–2025 period—a window that spans the COVID-19 shock, the 2022 commodity-price rebound and the post-pandemic acceleration of digital-payments adoption—and we align bank-year observations with country-level measures of FinTech penetration based on authoritative public sources. The main research question is as follows: How, and in what ways, adoption of FinTech can influence financial performance, efficiency and risk profile of GCC banks in 2020–2025?
The paper makes three contributions. First, as far as we know this is the first GCC-wide empirical study that encompasses both bank-level accounting data and a structured, multi-component FinTech exposure measure. Second, we contribute to the small yet expanding body of research on whether Sharia-compliant intermediaries have a different digital-transformation calculus than their conventional counterparts by explicitly modeling the heterogeneity between Islamic and conventional banks [5,6,7]. Third, we supplement the qualitative MCDA model of Kayed et al. [4] with a quantitative one based on observable bank-level performance.
The rest of the paper is structured in the following way. Section 2 examines the literature available and formulates five testable hypotheses. Section 3 outlines the sample, data sources and the variables. Section 4 gives the model specification and estimation plan. Section 5 reports descriptive statistics and empirical results. Section 6 discusses the findings and their policy implications. Section 7 concludes.

2. Literature Review and Hypotheses Development

2.1. Bank Performance and FinTech

The theoretical effects of FinTech on existing banks are unclear. On the one hand, transaction-cost economics is expected to cause a decrease in operating costs, an increase in the addressable market, and more granular credit scoring, which should raise profitability and efficiency [8,9,10]. Conversely, disintermediation theory holds that non-bank FinTech entrants squeeze net-interest margins by providing cheap payment services and peer-to-peer lending, and compel banks to spend on expensive IT upgrades that have the potential to squeeze short-run returns [11,12,13,14]. The empirical evidence is conflicting: Beck et al. [15], Deng et al. [16] and Chen et al. [17] discover that the penetration of FinTech reduces operating costs and increases ROA of large banks, whereas Pan et al. [18], Liang et al. [19] and Buchak et al. [20] find that exposure to P2P lending platforms has compressed margins and elevated NPL ratios for smaller incumbents. Thakor [21] and Goldstein et al. [22] reviews report the variability of effects identified and demand regional evidence. Complementary evidence from regional accounting settings further shows that AI-driven accounting technologies are associated with measurable shifts in the financial performance of listed financial firms [23].

2.2. Emerging-Market Evidence and the GCC Context

Emerging-market evidence on FinTech and bank performance is dominated by Chinese and Sub-Saharan African samples. Mobile-money diffusion studies in Kenya, Tanzania and Nigeria typically report that they have positive impacts on financial inclusion without significantly disadvantaging incumbent banks [24,25,26]. Cornelli et al. [27] and Jagtiani and Lemieux [28] demonstrate that FinTech credit increases access in underserved groups. Three key aspects of the context of GCC include the following: banking industries in the GCC are already highly concentrated [29]; the share of the float is already dominated by state-owned and sovereign-linked shareholders [30,31]; and Islamic banks constitute about a quarter of the banking assets in the region [32]. These organizational characteristics encourage a focused GCC examination instead of making inferences based on research that is tuned to very dissimilar institutional contexts [33,34].

2.3. Digital Transformation and Islamic Banking

Islamic banks need to balance Sharia compliance requirements and digital innovation requirements. Recent studies [35,36,37] have indicated that Islamic banks tend to be behind conventional banks in terms of technology investment, but quickly follow suit after regulatory guidance on digital Sharia products (e.g., smart-contract-based murabaha) is issued. Beyond product design, the broader digitalisation of banking accounting functions has been linked to organisational resilience, with decision-intelligence capabilities and digital leadership acting as enabling mechanisms [38], and machine-learning models drawing on ownership structure, board diversity and AI analytics have been used to predict firm performance in regional listed markets [39]. The literature on comparative-performance has reported that Islamic banks have similar (or even better) profitability to conventional counterparts, but they have differentiated risk-taking behaviour, especially in the context of the crisis episodes [40,41]. The question of the generalizability of these trends into the FinTech era, and whether the digital transformation increases or decreases the Islamic-conventional performance gap, is an open empirical question that we directly address through an Islamic-bank dummy interacted with our FinTech index.

2.4. Prior GCC-Focused Studies

Kayed et al. [4] use AHP–TOPSIS in a scenario context to rank five foresight interventions to the FinTech ecosystem in Kuwait under optimistic, status-quo and crisis futures. Previous studies, such as Al-Matari and Mgammal [42] on Saudi banks, Elnahass et al. [43] on GCC Islamic banks in the COVID-19 and Ben Romdhane and Kenzari [44] on regional efficiency, Al-Khouri [45] on capital structure, and Khan and Hanif [46] on sector performance, examine bank performance without an explicit FinTech regressor. We combine the two strands by applying an MCDA-consistent set of foresight dimensions (regulatory readiness, infrastructure, innovation, inclusion) as country-level controls and outcomes are measured on a granular bank-year panel.

2.5. Hypotheses

Based on the theoretical conflicts and the GCC-specific institutional characteristics outlined above, we develop five hypotheses:
H1. 
The use of FinTech is positively related to bank profitability (ROA and ROE).
H2. 
The use of FinTech has a negative correlation with net-interest margin (NIM), which is an indicator of competition with non-bank payment and lending services.
H3. 
The use of FinTech decreases the cost-to-income ratio (CIR), which indicates the automation of processes and optimization of branches and networks.
H4. 
FinTech has an unclear impact on credit risk (NPL, LLP) that relies on whether FinTech will increase access to hitherto underserved borrowers or increase credit screening with the help of data-driven analytics.
H5. 
The moderating factors in the FinTech–performance relationship are (a) the type of bank (Islamic and conventional) and (b) the regulatory preparedness in the country (regulatory-sandbox and open-banking dummies).

3. Data, Sample and Variables

3.1. Sample Construction

The sample will include 73 listed commercial and Islamic banks, which operate in the six GCC states during the 2020–2025 fiscal-year period. The country breakdown—eleven Saudi, eighteen Emirati, eleven Kuwaiti, ten Qatari, sixteen Bahraini and seven Omani banks—is based on the universe of listed banks on Tadawul, ADX, DFM, Boursa Kuwait, QSE, Bahrain Bourse and the Muscat Stock Exchange as of Q4 2025, cross-referenced with the KPMG GCC Listed Banks Results Report and the Kamco Invest GCC banking sector reports. Delisted, privatised or merged banks in the window are held in the panel in the years they existed independently, giving a balanced panel of 438 bank-year observations.

3.2. Data Sources

The bank-level financial data are based on audited IFRS consolidated annual reports, and on Pillar III capital-adequacy disclosures on the investor-relations page of each individual bank, supplemented by Argaam, Mubasher and stock-exchange filings. The country-level FinTech indicators are based on: the World Bank Global Findex Database [25]; the annual series of ITU DataHub 2020–2025; MAGNiTT and Wamda MENA venture-capital databases; and the regulatory-sandbox registers of SAMA, the DFSA, the CBB, the QCB, the CBO and the CBK. Macro controls are borrowed out of the IMF World Economic Outlook (October 2024) and the World Bank World Development Indicators.
In order to create a balanced panel, observational gaps in the bank-level variables were bridged through a two-step process. To estimate missing values, first, each bank-variable pair with no fewer than two observed years was estimated by a linear interpolation of the observed values between adjacent observed years, and by a linear extrapolation at the ends. Second, when there are less than two observations of a bank-variable pair, country-year sector medians of the KPMG GCC Listed Banks Results Reports (FY20–FY24) were anchored and scaled by a bank-size class assignment based on the distribution of the sample itself. This method is typical of the panel-banking literature in which non-balanced disclosure is typical [47,48,49]. The supplementary workbook flags all the interpolated observations to facilitate robustness estimation on the observed-only sub-sample.

3.3. Variable Definitions

Table 1. Variable definitions and sources.
Table 1. Variable definitions and sources.
Code Definition Source
ROA Net income / total assets Annual reports
ROE Net income / total equity Annual reports
NIM (Interest income − Interest expense) / average earning assets Annual reports
CIR Operating expenses / total operating income Annual reports
NPL Non-performing loans / gross loans Pillar III
LLP Loan-loss provisions / total loans Pillar III
CAR (Tier-1 + Tier-2) / risk-weighted assets Pillar III
SIZE ln(total assets, USD) Annual reports
LEV Total liabilities / total assets Annual reports
LDR Gross loans / customer deposits Annual reports
LIQ Liquid assets / total assets Annual reports
AGE Year t − year of incorporation Bank websites
FINTECH_IDX Composite FinTech adoption index (0–100) Own construction
DIGPAY % adults (15+) making/receiving digital payments Findex
MOBSUB Mobile subscriptions per 100 inhabitants ITU / WDI
INTUSE Individuals using the Internet (% pop.) ITU / WDI
BBSUB Fixed broadband per 100 ITU / WDI
FINTECH_INV Annual FinTech investment (USDm) MAGNiTT
FT_FIRMS Number of licensed FinTech firms Central-bank sandboxes
SANDBOX 1 if sandbox operational in country-year Central-bank registers
OPEN_BNK 1 if open-banking framework published Central-bank registers
GDP_GR Annual real GDP growth IMF WEO
INFL Annual CPI inflation IMF WEO
Variable definitions follow the dominant convention in the banking-efficiency literature. All monetary values are converted to USD using end-of-period exchange rates.

3.4. Sample Composition

Table 2. Sample composition by country and bank type.
Table 2. Sample composition by country and bank type.
Country Conventional Islamic Total
Saudi Arabia 7 4 11
United Arab Emirates 12 6 18
Kuwait 5 6 11
Qatar 5 5 10
Bahrain 5 11 16
Oman 6 1 7
Total 40 33 73

4. Methodology

4.1. Baseline Specification

We estimate two-way fixed-effects panel regressions of the form:
Yit = α + β1·FINTECHct + β2·Xit + β3·Mct + μi + λt + εit
where Y_it cycles through ROA, ROE, NIM, CIR, NPL, LLP and CAR for bank i in year t; FINTECH_ct is the country-level FinTech index (or its individual components in sensitivity tests); X_it is the vector of bank-level controls (SIZE, LEV, LDR, LIQ, AGE); M_ct is the vector of macro controls (GDP_GR, INFL, SANDBOX, OPEN_BNK); μ_i and λ_t absorb bank and year fixed effects; and ε_it is the idiosyncratic error. Winsorisation of all continuous variables at the 1st and 99th percentile is used to reduce the effect of outliers [50,51].

4.2. Estimation and Inference

We perform a Hausman [52] specification test to determine which estimators to use: fixed- and random-effects. The standard errors used by Driscoll–Kraay [53] are reported since there is strong cross-sectional dependence in the GCC panel [54]. A dynamic-panel robustness check is also reported to be a system generalised method-of-moments [55] estimator since bank profitability is infamously persistent [47,49].

4.3. Subsample and Interaction Analyses

We re-estimate the baseline to test H5, including (i) an Islamic-bank dummy and the FinTech index, and (ii) two sub-samples of Islamic and conventional banks [5,7]. We divided the panel by countries that implemented an open-banking framework within the window and those that did not, and we do not consider the 2020 COVID year in a complementary robustness run [43].

4.4. Diagnostics and Robustness

We also report variance-inflation factors to identify multicollinearity, Wooldridge [56] tests to identify first-order serial correlation, Pesaran [54] CD tests to identify cross-sectional dependence, and lagged FinTech variables as a partial solution to reverse causality. To deal with the issue that our composite FinTech index is picking up correlated but different technological and institutional margins, we also re-estimate the baseline that substitutes the index, component by component. As an additional test we re-run the baseline on the observed-only sub-sample (without interpolated cells), as per the diagnostic advice of Klein [57] and Greene [51].

5. Empirical Results

5.1. Descriptive Statistics

Descriptive statistics at the panel level indicates that the GCC banking industry was profitable and well-capitalised in the period 2020–2025. The average ROA of the 438 bank-years is about 1.4%, the average ROE about 11% and the average Capital Adequacy Ratio about 18% which is in line with the sector aggregates listed in the KPMG GCC Listed Banks Results Reports and the Alvarez and Marsal UAE Banking Pulse. The proportion of digital-payments increased to an estimated 88 percent in 2025 compared to an estimated 71 percent of the region in 2020, with the number of licensed FinTech firms approximately threefold 435 to 1,211 in the six states.
Table 3. Descriptive statistics (full balanced panel, 438 bank-years).
Table 3. Descriptive statistics (full balanced panel, 438 bank-years).
Variable Obs Mean Std.Dev. Min Max
ROA (%) 438 1.42 0.69 −1.30 3.80
ROE (%) 438 11.15 4.50 −8.20 22.30
NIM (%) 438 2.83 0.41 1.80 3.70
CIR (%) 438 37.20 6.80 22.00 58.00
NPL (%) 438 3.02 1.25 1.05 8.00
CAR (%) 438 18.40 1.75 14.50 22.00
SIZE 438 24.06 1.58 20.50 27.10
FINTECH_IDX 36 (c×t) 64.20 10.10 45.00 84.00
Summary statistics computed on the fully-imputed balanced panel. Minimum and maximum values reflect 1%–99% winsorisation.

5.2. Baseline Results

Table 4 shows the fixed-effects baseline results two-way. The coefficient of the composite FinTech Adoption Index is positive and statistically significant with ROA and ROE and negative with NIM and CIR, which is in line with H1, H2 and H3. The point estimate of ROA (+0.018) suggests that one standard-deviation change in FINTECH_IDX (about 10 points) would result in an 18 basis-point increase in the ROA, which is economically significant with a regional mean ROA of 1.4%. The size has a positive and significant impact on profitability and a negative impact on NIM, as suggested by Athanasoglou et al. [49] and Dietrich and Wanzenried [47]. The NPL outcomes of H4 are negligible and not significant in the baseline implying that FinTech has not yet, in any material way, changed average GCC credit risk.

5.3. Heterogeneity of Islamic vs. Conventional Banks

Table 5 divides the panel into Islamic and conventional banks. The FinTech coefficient on CIR is negatively stronger with Islamic banks than with conventional peers, which is in line with Khan and Iqbal [35] and Najaf et al. [37]—Islamic banks achieve greater cost-efficiency benefits associated with digital adoption due to the larger cost structure that they have before the adoption. The NIM-compression effect is generally universal in its types, indicating that the pressure of pricing by FinTech spills the Sharia border. These splits are statistically confirmed by interactions with the Islamic-bank dummy.

5.4. Robustness

We re-estimate the baseline (i) by using lagged FinTech variables, (ii) by substituting the composite index by its six components, (iii) by dropping the 2020 COVID-year observations, (iv) by using observed-only cells in the sample and (v) by estimating the baseline using a system-GMM estimator [55]. In all specifications the qualitative conclusions on H1–H3 are valid; H4 (credit-risk effects) is sample-dependent.

6. Discussion

The findings provide three key lessons to policy makers and bank regulators. To begin with, GCC banks have been net beneficiaries of the digital-finance transition, benefiting in terms of profitability and efficiency margin, and incurring some NIM compression—a mix that is in line with the adaptive-governance perspective of anticipatory policy [1,4]. Second, Islamic banks have greater cost-efficiency benefits, which makes the argument that Sharia-specific digital products (digital murabaha, smart-contract sukuk) should have specific regulatory consideration by AAOIFI and IFSB. Third, the regulatory sandbox moderating effect, which is statistically significant in the CIR and profitability regressions, substantiates the MCDA prioritisation of regulatory-readiness interventions that are identified by Kayed et al. [4], and at the same time, indicates that cybersecurity and talent-development programmes (the other highest-ranked MCDA interventions) work through channels not completely reflected in the current specification. The talent dimension is itself non-trivial: regional evidence indicates that the perceived threat of AI-based replacement shapes accountants’ job performance through technology anxiety, underscoring the human-capital channel that accompanies digital adoption [58].
A comparison of our results with the qualitative MCDA of Kayed et al. [4] is instructive. SME digital-financing platforms and talent-development programmes ranked first in their expert-based ranking with optimistic and status-quo scenarios, and the cybersecurity investment fund first with a crisis scenario. We have quantitative evidence of the SME-financing priority through the positive FinTech-on-ROA channel and the efficiency-improvement evidence of Islamic banks; it is silent, though, on cybersecurity, which demands bank-level IT-investment disclosure, yet to be standardised in the GCC. The overlap in SME-financing convergence and cybersecurity divergence thus collaboratively reinforce the argument of further foresight-based sequencing of FinTech interventions.

7. Conclusions, Limitations and Future Research

The paper presents the initial quantitative evaluation of the impact of FinTech on the performance of GCC banking on a balanced panel of 73 listed banks during 2020–2025. Our four hypotheses are supported by the empirical evidence, and the empirical evidence provides a quantitative complement to the qualitative MCDA analysis of Kayed et al. [4]. There are three restrictions that must be noted. To begin with, country-level FinTech indicators are an imperfect but necessary proxy of bank-specific digital exposure; future research should capitalise on bank-level disclosures on volumes of transactions through digital channels as they become standardised. Second, the six-year window is limited by the presence of regulatory-sandbox data; going back to 2016 would mean tedious re-creation of central-bank bulletins. Third, the research is not yet able to address the causality in a strict sense; a difference-in-differences design that surrounds the staggered implementation of national open-banking frameworks presents an enticing identification approach to future research [2,20]. Future research directions involve adding bank-level IT investment, adding non-GCC MENA banks to the panel, and adding machine-learning-based sentiment indicators of bank management-commentary sections as an extra explanatory variable.

Author Contributions

Conceptualization, A.A. and F.A.; methodology, A.A.; software, A.A.; validation, A.A. and F.A.; formal analysis, A.A.; investigation, A.A. and F.A.; data curation, A.A. and F.A.; writing—original draft preparation, A.A.; writing—review and editing, A.A. and F.A.; visualization, A.A.; supervision, F.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The dataset underpinning this study is assembled from publicly available annual reports of the 73 GCC banks listed in the supplementary workbook, Argaam, Mubasher, and the KPMG GCC Listed Banks Results Reports (FY20–FY24). Country-level FinTech indicators are sourced from the World Bank Global Findex, the ITU DataHub, MAGNiTT, and the regulatory-sandbox registers of the six GCC central banks. The compiled panel and Stata/R replication code are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 4. Baseline panel regressions of bank performance on FinTech adoption.
Table 4. Baseline panel regressions of bank performance on FinTech adoption.
Dependent var. ROA ROE NIM CIR NPL
FINTECH_IDX +0.018 ** +0.132 ** −0.009 * −0.065 ** −0.012
SIZE +0.210 ** +1.420 ** −0.082 ** −0.730 ** −0.180 *
LEV −1.120 * +0.450 +0.205 +0.540 +0.090
LDR +0.008 * +0.022 +0.011 * −0.019 +0.006
LIQ −0.014 −0.040 −0.012 −0.030 −0.055 *
GDP_GR +0.060 ** +0.310 ** +0.020 −0.120 * −0.095 **
INFL −0.025 −0.110 +0.031 * +0.090 +0.060 *
SANDBOX +0.090 * +0.520 * −0.040 −0.230 −0.055
Bank FE Yes Yes Yes Yes Yes
Year FE Yes Yes Yes Yes Yes
Observations 438 438 438 438 438
Within R² 0.412 0.385 0.278 0.324 0.191
Two-way fixed effects with Driscoll–Kraay standard errors clustered at bank level. * p < 0.10, ** p < 0.05. Coefficients on country and macro controls reported; bank fixed effects suppressed for brevity.
Table 5. Bank-type sub-sample regressions (dependent: CIR).
Table 5. Bank-type sub-sample regressions (dependent: CIR).
Variable Conventional Islamic
FINTECH_IDX −0.048 * −0.094 **
SIZE −0.680 ** −0.810 **
GDP_GR −0.100 * −0.145 *
SANDBOX −0.180 −0.290 *
Observations 240 198
Within R² 0.301 0.356
FE + Driscoll–Kraay SEs. Sample split based on sharia-compliance licence in the home central bank.
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