Artificial intelligence-driven predictive analytics and institutional performance in Gulf financial systems: Evidence from GCC financial institutions

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Type of the article: Research Article

Abstract
The integration of artificial intelligence-driven predictive analytics has redefined financial management and decision-making across Gulf economies. This study compares the performance of artificial-intelligence-based and traditional predictive models using data from twenty financial institutions from six Gulf Cooperation Council countries. A quantitative cross-sectional design was adopted, and analysis of variance revealed statistically significant differences (p < 0.001) across all indicators. Predictive accuracy increased from 83.5 to 91.5 per cent (F = 4.23 × 10²⁹), operational efficiency from 12 to 19.5 per cent (F = 1.31 × 10³¹), risk-management effectiveness from 7.0 to 9.3 points (F = 2.69 × 10³⁰), and customer satisfaction from 6.5 to 8.5 points (F = 1.69 × 10³⁰). Regression analyses confirmed these outcomes: model type produced significant coefficients for predictive accuracy (β = 8.21, p < 0.001), operational efficiency (β = 7.46, p < 0.001), risk-management effectiveness (β = 2.29, p < 0.001), and customer satisfaction (β = 1.84, p < 0.001). The overall model explained 84 per cent (R² = 0.84) of the variation in institutional performance, confirming the strong predictive power of artificial-intelligence models. These results demonstrate that intelligent predictive systems significantly enhance accuracy, efficiency, and stakeholder value. The study concludes that transparent and ethically governed analytical frameworks are essential for sustainable financial competitiveness and responsible innovation in the Gulf region.

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    • Figure 1. ANOVA summary of performance effects
    • Table 1. Distribution of sampled institutions by country and sector
    • Table 2. Descriptive statistics of key study variables
    • Table 3. Model-fit indicators for multivariate regression analysis
    • Table 4. ANOVA – Predictive accuracy
    • Table 5. ANOVA – Operational efficiency
    • Table 6. ANOVA – Risk-management effectiveness
    • Table 7. ANOVA – Customer satisfaction
    • Table 8. Predictive accuracy model summary
    • Table 9. Operational efficiency model summary
    • Table 10. Risk-management effectiveness model summary
    • Table 11. Customer satisfaction model summary
    • Table A1. Distribution of sampled institutions by country and sector
    • Table B1. Predictive accuracy (How accurate and reliable is your predictive model?)
    • Table B2. Operational efficiency (How well does the model improve internal operations?)
    • Table B3. Risk-management effectiveness (How well does the model support risk identification and mitigation?)
    • Table B4. Customer satisfaction (How does the model influence customer experience?)
    • Conceptualization
      Amer Morshed, Laith Khrais
    • Data curation
      Amer Morshed, Laith Khrais
    • Formal Analysis
      Amer Morshed
    • Funding acquisition
      Amer Morshed
    • Investigation
      Amer Morshed
    • Methodology
      Amer Morshed, Laith Khrais
    • Resources
      Amer Morshed
    • Software
      Amer Morshed
    • Validation
      Amer Morshed
    • Visualization
      Amer Morshed
    • Writing – original draft
      Amer Morshed
    • Writing – review & editing
      Laith Khrais