Purpose

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Design/methodology/approach

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

Findings

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

Originality/value

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

中文翻译：

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使用机器学习技术对海湾合作委员会伊斯兰银行进行信用模式预测和检测

目的

银行信用是指对银行履行财务义务的能力的评价。它是对银行财务健康状况、稳定性和风险管理能力的评估。本文旨在研究对于评估伊斯兰银行信用度至关重要的信用评级模式，从而评估其行业的稳定性。

设计/方法论/途径

为了实现预期目标，利用和评估了三种不同的机器学习算法。本研究首先使用决策树机器学习算法作为基学习器，与集成决策树和随机森林进行深入比较。随后，部署 Apriori 算法来发现影响银行信用评级的最重要属性。为了评估先前阐明的模型，应用了十倍交叉验证方法。该方法涉及将数据集分割成十份，其中九份用于训练，一份用于测试，或者可改变十次。这种方法旨在减轻学习和培训阶段可能出现的任何潜在偏见。在此过程之后，将评估准确性并在混淆矩阵中进行描述，如方法部分所述。

发现

这项调查的结果表明，随机森林机器学习算法的性能优于其他算法，在预测信用评级方面达到了令人印象深刻的 90.5% 的准确率。值得注意的是，我们的研究揭示了贷存比作为影响信用评级预测的主要属性的重要性。此外，这项研究还揭示了其他对所研究的测量有强烈影响的关键银行特征。本文的研究结果证明贷存比似乎是影响信用评级预测的最纯粹的银行属性。此外，存款资产比和利润分享投资账户比率标准在信用评级预测中被认为是有效的，股权结构标准被视为信用评级预测中的基本银行属性之一。

原创性/价值

这些发现为理解强烈影响银行业信用评级预测的属性提供了重要证据。这项研究的独特贡献在于揭示了以前文献中未记录的模式，拓宽了我们对该领域的理解。