Issue #1 (Volume 21 2026)

Type of the article: Research Article

Abstract
The study examines the impact of liquidity risk and operational risk on the financial stability in both commercial and Islamic banks in Jordan. The study utilizes secondary data covering a period of ten years, from 2012 to 2022, as it offers a holistic and uninterrupted period that includes the key regulatory, economic, and structural changes in Jordan’s banking sector. The study employs panel data analysis to assess these relationships. Liquidity risks are measured using the cash-to-asset ratio and the liabilities-to-deposit ratio, while operational risks are assessed through two indicators: the operational cost-to-income ratio and the operational cost-to-total-assets ratio. The findings reveal that the liabilities-to-deposit ratio, as represented by liquidity risk, and the operating cost-to-income ratio, as represented by operational risks, have significant negative impacts on the financial stability of conventional banks, which emphasizes the need for effective cost and liquidity management. In Islamic banks, financial stability improves significantly due to liquidity indicators: higher liabilities-to-deposit ratios and cash-to-asset ratios, highlighting how essential liquidity is. The operational risk indicators have no impact on Islamic banks. Moreover, control variables such as return on assets (ROA) positively influence stability in conventional and Islamic banks. However, the stability of conventional and Islamic banks faces negative influences from their size, which indicates that bigger banks could become exposed to operational risks and financial vulnerabilities. The research demonstrates that financial stability elements operate differently between conventional and Islamic banking systems. Financial institutions, together with governments, need to establish solutions to fill these gaps.

Type of the article: Research Article

Abstract
The balance between stability and profitability in banking systems has gained renewed urgency as rising interest rates, persistent inflation, and credit risks reshape the global financial landscape. Regulators, such as the IMF, ECB, and OECD, emphasize that while robust capital buffers are indispensable for resilience, excessive capitalization may constrain lending. In contrast, weak asset quality undermines returns regardless of capital strength. Against this backdrop, this article aims to explore how capital adequacy and asset quality jointly influence bank profitability. The analysis uses IMF Financial Soundness Indicators for 133 countries over 2010–2024 and applies two-way fixed-effects panel regressions with Driscoll-Kraay robust inference. The results reveal a consistently concave relationship: Tier 1 capital to assets is positively related to return on assets (ROA) with diminishing returns, though the turning point lies at an implausible 161.7%. In contrast, Tier 1 capital to risk-weighted assets shows an economically plausible peak around 26.3%, with gains tapering beyond that level. Within typical ranges (15-20% RWA), a one percentage point increase in capital is associated with a 0.06-0.03-point rise in ROA, but additional accumulation yields little benefit. Asset quality exerts a strong negative influence, with a 1-point increase in non-performing loans lowering ROA by 0.04-0.05 points, while liquidity remains statistically insignificant. These findings highlight that capital deepening contributes to profitability only up to moderate levels, and that poor asset quality can offset the benefits of stronger capital buffers, underscoring the need for integrated regulatory approaches to stability and performance.

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.