Integrating Artificial Intelligence into Credit Risk Assessment (A Comparative Study of Islamic and Conventional Banks)
Authors:
Amal Mohammed Rizk
MBA, College of Business and Management, Fahad Bin Sultan University, Kingdom of Saudi Arabia
doi.org/10.52132/Ajrsp.e.2026.82.1
The purpose of this research is to investigate the ways in which Islamic and conventional banks can incorporate Artificial Intelligence (AI) into credit risk assessment. Based on existing academic literature, industry reports, and regulatory documents, it takes a descriptive–analytical approach.
The study looks into how AI methods like decision trees, machine learning, and neural networks can make credit risk evaluations more accurate, quick, and consistent. It also compares the adoption of AI by Islamic and conventional banks, particularly in terms of ethical principles, transparency, and regulatory requirements.
The prohibition of Riba (interest), the promotion of risk sharing, and the protection of fairness and transparency in financial transactions are all Shariah principles that Islamic banks must adhere to when integrating AI. Data quality, algorithmic bias, explainability, cybersecurity, and institutional readiness are all examined in the study as potential and potential drawbacks of employing AI in a Shariah-compliant setting.
The findings are expected to show that AI can significantly enhance credit risk assessment in both banking models, but successful adoption in Islamic banks requires tailored frameworks for Shariah governance, explainable AI, and ethical data use.
The study offers a conceptual framework for incorporating AI into credit risk assessment that takes into account Islamic ethical values as well as technological efficiency.
Keywords:
Artificial Intelligence; Credit Risk; Islamic Banking; Conventional Banking; Shariah Compliance; Machine Learning
Academic Journal of Research and Scientific Publishing
