In Saudi fintech, AI tools that automate advice, fraud detection and credit scoring are linked to broader financial access, whereas personalized AI banking does not increase access; however, results are based on a small cross-sectional survey and do not establish causality.

AI-Powered Financial Services and Access in Saudi Arabia’s Fintech Sector: Advancing Vision 2030

Shahzeb Muhammad, Ayyaz Mehmood Khan, Muhammad Rizwan · December 10, 2025 · Journal of Cognitive Computing and Extended Realities

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A survey-based SEM analysis of 194 Saudi fintech stakeholders finds AI-powered robo-advisory, fraud detection, and credit-scoring services are associated with improved financial access, while AI-driven personalized banking shows no significant association with access.

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Grounded into Innovation Diffusion Theory and Technology Acceptance Model, the purpose of this study was to evaluate the impact of AI-powered financial services on financial access in the Saudi Arabian fintech sector.To achieve this aim, the research employed SEM analysis on the collected data from 194 employees working in the departments related to AI-based services, staff members of fintech firms, and owners of small enterprises who use digital financial solutions in Riyadh, Jeddah, and Dammam.The results reveal that AI-based robo-advisory platforms, fraud detection, and credit scoring services significantly improved financial access demonstrating that AI adoption in financial services can play a transformative role in promoting inclusion and reducing barriers for underserved populations whereas AI-based personalized banking solutions showed insignificant impact suggesting that while personalization may enhance user satisfaction or loyalty, it does not directly translate into increased access to financial services.In practical terms, the findings imply that fintech companies and financial institutions should prioritize AI-enabled services as a means of expanding access to professional financial advice which requires a multi-stakeholder approach, where fintech firms, regulators, and policymakers collaborate to maximize the benefits of AIpowered financial services while minimizing associated risks.Further research should be carried out adopting longitudinal design and mixed methodology to study the role of emerging technologies such as blockchain-based identity verification, AI-driven insurance, or decentralized finance platforms on financial access.

Summary

Main Finding

AI-powered fintech services—specifically robo-advisory platforms, AI-driven fraud detection, and AI-based credit scoring—significantly improve financial access in Saudi Arabia, while AI-enabled personalized banking solutions do not have a significant effect on access.

Key Points

The study is grounded in Innovation Diffusion Theory and the Technology Acceptance Model.
Sample: 194 respondents drawn from employees in AI-related departments, fintech staff, and small-enterprise owners using digital financial solutions in Riyadh, Jeddah, and Dammam.
Method: Structural Equation Modeling (SEM) applied to cross‑sectional survey data.
Positive, significant effects found for:
- Robo-advisory platforms (increase professional financial advice availability)
- AI-based fraud detection (reduce barriers/risks to using services)
- AI-based credit scoring (improve credit access for underserved groups)
No significant effect for AI-based personalized banking on financial access (may affect satisfaction/loyalty rather than access).
Policy recommendation: coordinated, multi-stakeholder approach (fintechs, regulators, policymakers) to scale benefits while managing risks.
Research recommendation: future studies using longitudinal and mixed-methods designs and examining emerging technologies (blockchain identity, AI insurance, DeFi).

Data & Methods

Design: Cross-sectional survey study.
Participants: N = 194 employees and users connected to fintech and AI-based financial services in three major Saudi cities.
Analytical approach: Structural Equation Modeling to test relationships between adoption/use of AI services and financial access outcomes.
Limitations implicit in design: cross-sectional data (limits causal inference), modest sample size, geographic concentration (Riyadh, Jeddah, Dammam), and no reported effect magnitudes in the summary.

Implications for AI Economics

Access and inclusion: AI tools that reduce information asymmetries (credit scoring) or lower service costs/risks (robo-advisors, fraud detection) can materially expand financial access and alter market participation.
Credit markets: AI-based credit scoring may reallocate credit access—potentially expanding lending to underserved borrowers but raising questions about model fairness, data bias, and adverse selection dynamics.
Competition and structure: Successful AI services could lower entry barriers for fintechs while pressuring incumbents to adopt AI, affecting market concentration and pricing.
Regulatory economics: Need for regulation balancing innovation and consumer protection (model-risk governance, data privacy, explainability, anti-discrimination).
Welfare and distributional effects: Empirical work should measure welfare gains across population subgroups to assess whether AI narrows or widens inequality in access.
Research agenda: stronger causal evidence (longitudinal/quasi‑experimental), mixed-methods to capture user experiences and institutional constraints, and evaluation of emerging tech interactions (blockchain identity, AI insurance, DeFi) on market functioning and social welfare.

Assessment

Paper Typecorrelational Evidence Strengthlow — Cross-sectional survey data analyzed with SEM provides associations but cannot support causal claims; small, non-random sample, self-reported measures, potential selection and common-method biases, and limited statistical power reduce confidence that observed relationships reflect causal effects on financial access. Methods Rigorlow — The authors use structural equation modeling (an appropriate tool for testing hypothesized relationships) but rely on a convenience sample of 194 respondents from three Saudi cities, with no experimental or quasi-experimental design, limited information on sampling frame, measurement validation, or robustness checks reported; sample size is marginal for SEM and raises concerns about estimate stability. SampleCross-sectional survey of 194 respondents comprising employees in AI-related departments, fintech staff, and small-enterprise owners who use digital financial solutions, sampled from Riyadh, Jeddah, and Dammam (Saudi Arabia). Themesadoption innovation inequality GeneralizabilityLimited to fintech sector actors and small-business users rather than general population or bank customers, Geographically restricted to three major Saudi cities (Riyadh, Jeddah, Dammam); excludes rural areas and other countries, Small convenience sample (n=194) limits external validity, Findings rely on self-reported perceptions and usage rather than objective measures of access or outcomes, Cross-sectional design limits inference to other time periods or causal dynamics

Claims (6)

Claim	Direction	Outcome	Confidence & Evidence	Details
AI-based robo-advisory platforms, fraud detection, and credit scoring services significantly improved financial access. Consumer Welfare	positive	financial access (access to financial services)	Reading fidelity high Study strength medium	n=194 0.3
AI-based personalized banking solutions showed an insignificant impact on financial access. Consumer Welfare	null_result	financial access (access to financial services)	Reading fidelity high Study strength medium	n=194 0.3
AI adoption in financial services can play a transformative role in promoting inclusion and reducing barriers for underserved populations. Consumer Welfare	positive	financial inclusion / reduction of access barriers	Reading fidelity high Study strength low	n=194 0.15
Fintech companies and financial institutions should prioritize AI-enabled services as a means of expanding access to professional financial advice, requiring a multi-stakeholder approach (fintech firms, regulators, policymakers) to maximize benefits and minimize risks. Consumer Welfare	positive	expansion of access to professional financial advice (financial access)	Reading fidelity high Study strength speculative	n=194 0.05
The study employed structural equation modeling (SEM) on survey data from 194 employees (AI-related departments), fintech staff, and small enterprise owners in Riyadh, Jeddah, and Dammam. Research Productivity	null_result	methodology / sample description	Reading fidelity high Study strength high	n=194 0.5
Further research should adopt longitudinal designs and mixed methodologies to study the role of emerging technologies (e.g., blockchain-based identity verification, AI-driven insurance, decentralized finance platforms) on financial access. Research Productivity	positive	research on financial access effects of emerging technologies	Reading fidelity high Study strength speculative	not reported 0.05