The Impact of Artificial Intelligence on Improving Credit Decision-Making
An Applied Study at Al-Rafidain Bank
DOI:
https://doi.org/10.61704/pr.492Keywords:
Artificial Intelligence (AI), Credit Decision-Making, Artificial intelligence; Convolutional Neural Network; Deep learning; Machine learning; Neural Networks; Recurrent neural network., Explainable Artificial Intelligence, Al-Rafidain Bank, Credit Risk ManagementAbstract
This study examined the impact of artificial intelligence (AI) on the quality of credit decision-making at Al-Rafidain Bank a leading public bank in Iraq. However, in the context of rapid digital transformation and the growing use of machine learning, natural language processing computer vision and explainable artificial intelligence technologies.
The study followed a descriptive-analytical approach. A questionnaire was developed and administered to a sample of 85 employees working in the Credit and Information Technology departments at the bank’s headquarters. The selected employees have knowledge and practical experience in credit and technology-related activities. The collected data were analyzed using SPSS version 26. The statistical analysis included Cronbach’s alpha to assess reliability, descriptive statistics, Pearson’s correlation, coefficient, multiple linear regression and one-way analysis of variance (ANOVA) to assess the suitability of the proposed model.
The findings indicated that the dimensions of artificial intelligence have a positive and statistically significant impact on the quality of credit decisions at the level of (α ≤ 0.05). Together, these dimensions accounted for 69.8% of the variance in the dependent variable (R² = 0.698). additionally, explainable AI showed the highest impact followed by machine learning natural language processing, and computer vision, respectively.
This study recommends that Al-Rafidain Bank adopt a comprehensive digital transformation strategy, which includes integrating artificial intelligence technologies into its credit system, enhancing the capabilities of its human resources, and establishing regulatory frameworks that ensure transparency and integrity in credit decisions supported by smart technologies.
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
