Laith T. Khrais

Type of the article: Research Article

Abstract
The increasing complexity of insurance fraud in Jordan has unveiled inadequacies of traditional detection mechanisms, calling for advanced technologies. This study investigates drivers and inhibitors of machine learning adoption for fraud detection within Jordan’s insurance sector, with a focus on institutional readiness, ethical concerns, and supporting regulations. By applying quantitative and exploratory research design, Partial Least Squares Structural Equation Modeling serves as an approach to analyze data collected from 291 practitioners of fraud detection, data science, and insurance compliance in the industry.
Findings show that both existing fraud detection efforts (coefficient = 0.42, p = 0.012) and knowledge of machine learning (coefficient = 0.55, p = 0.009) have favorable impacts on adoption likelihood, which underlines the relevance of bureau experience and informed professional culture. By contrast, major adoption deterrents such as limited IT capability, budgetary constraints, and moral concerns about fairness and clarity (coefficient = –0.40 and –0.38, respectively) unfavorably decrease adoption intention.
Regulatory encouragement has a two-fold role: it has a direct promoting effect on adoption (coefficient = 0.47, p = 0.011) and a buffering effect on negative ethical concerns (interaction = 0.36, p = 0.025) and adoption barriers (interaction = –0.28, p = 0.032). Perceived efficacy also mediates between awareness/experience on the one hand and adoption decisions on the other (coefficients = 0.51 and 0.44, p < 0.05).
The results demonstrate successful incorporation of machine learning into fraud detection as depending on the clarity of regulations, ethical protections, and institutional readiness, rather than on technical capability itself.

The integration of insurance technology with e-commerce in Saudi Arabia is a key driver of financial and technological advancement, aligning with Vision 2030, the national strategy for economic diversification and digital transformation. This study examines the technological factors influencing this integration, assessing both enablers and barriers, including application programming interfaces, artificial intelligence, real-time risk assessment, cybersecurity, outdated infrastructure, and regulatory alignment. A quantitative approach was employed, gathering data from 253 professionals in Saudi Arabia’s insurance and e-commerce sectors, including financial managers handling underwriting and investment, compliance officers ensuring regulatory compliance, information technology specialists overseeing system integration and cybersecurity, and policymakers shaping industry regulations. Structural equation modeling revealed that application programming interfaces (β = 0.78, p = 0.020), artificial intelligence (β = 0.70, p = 0.025), and real-time risk assessment (β = 0.62, p = 0.030) significantly facilitate integration, while cybersecurity vulnerabilities (β = 0.57, p = 0.035), outdated infrastructure (β = 0.54, p = 0.040), and regulatory misalignment (β = 0.57, p = 0.035) pose major barriers. Additionally, government incentives (β = 0.51, p = 0.040) and workforce expertise (β = 0.49, p = 0.035) influence adoption outcomes. The findings highlight the need for regulatory harmonization, enhanced cybersecurity, financial support, and workforce training to facilitate seamless integration and ensure the long-term sustainability of insurance technology in Saudi Arabia’s evolving digital economy.

Type of the article: Research Article

Abstract
The emergence of Islamic finance has positioned Sukuk as a moral substitute for traditional bonds. However, misalignment with International Financial Reporting Standard 9, especially in Jordan, erodes investor confidence and reduces integration into world markets. This paper attempts to quantitatively evaluate how classification difficulties under International Financial Reporting Standard 9 affect investment strategies, decision-making, and market attractiveness of Sukuk within Jordan’s financial system.
Data were collected from a stratified sample of 346 finance professionals from banks, investment businesses, insurance companies, and regulatory authorities. Each participant had at least three years of work experience and suitable academic credentials. Utilizing partial least squares structural equation modeling, the survey was carried out between September 2024 and January 2025. The results indicate that classification issues have a significant adverse effect, reducing investment strategy efficacy by 46% (β = –0.46, p < 0.01), decision-making clarity by 37% (β = –0.37, p < 0.05), and Sukuk attractiveness by 52% (β = –0.52, p < 0.001). These significant effects are reinforced by vigorous diagnostics of the model, with variance inflation factor measures between 1.15 and 1.23, and by superb fit indices of the model, such as a standardized root mean square residual of 0.06 and a comparative fit index of 0.95.
The results underline the need for a coordinated international classification system and the structural influence of regulatory inconsistencies on Sukuk viability. Promoting openness, restoring investor confidence, and enabling wider acceptance in foreign markets all depend on aligning Islamic financial instruments with global reporting standards.

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.

Type of the article: Research Article

Abstract
Aligning environmental objectives with economic performance is an ongoing challenge in the renewable energy transition, especially in emerging solar markets facing operational inefficiencies and uneven policy implementation. This paper examines the environmental and economic efficiency of large-scale solar energy conversion projects by employing a Material Flow Cost Accounting methodology based on ISO 14051 standards. Based on the operating and financial performance data of 83 solar energy conversion projects (46 from India and 37 from Jordan) covering the period 2017–2023, avoidable energy loss costs due to dust settling, heat stress, grid curtailment, and plant downtime have been estimated and quantified cumulatively in both settings. The findings indicate a technical efficiency of 88.4% and 77.9% with an average energy loss potential of 128 and 274 gigawatt-hours per year in both settings of Jordan and India, respectively, causing an economic loss potential of 9.2% and 18.7%, respectively, collectively amounting to a financial loss potential of about 54.6 million annually. Systematic plant maintenance and coordinated use in electric grids increased output potential by about 16% and lowered costs by 13%, with efficient management options collectively leading to an 11.5% increase in financial returns in Jordan and a 19.3% boost in India’s financial performance. Based on findings, MFCA methodology is indeed capable of interlinking environmental protection with economic performance for efficient, sustainable energy policymaking within developing nations.