The application of machine learning in bank credit rating prediction and risk assessment

Dai, Zongrui; Zhang, Yuchen; Li, Aya; Qian, Guobin

doi:10.1109/icbaie52039.2021.9389901

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…For [8], researchers studied companies loan default prediction and applied machine learning algorithms for classification to demonstrate the superiority of Random forest. Moreover, [9] examined credit risk assessment and confirmed that SVM have good accuracy while applied to a company's dataset limited to three features. They also studied the impact of the company daily income to its credit score.…”

Section: Related Work: Companies Loan Default Predictionmentioning

confidence: 77%

“…Moreover, commercial banks can experience significant losses due to default payments on loans, particularly in cases where large amounts are involved, such as financing investment projects. However, there is limited research on this topic [8] [9], with most studies focusing on personal loans rather than investment loans for companies. Therefore, this research will specifically address the issue of corporate loans.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Machine Learning-based Model for Corporate Loan Default Prediction

BERRADA,

BARRAMOU,

ALAMI

2024

IJACSA

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As the core business of the banking system is to lend money and then get it back, loan default is one of the most crucial issues for commercial banks. With data analysis and artificial intelligence, extracting valuable information from historical data, to lower their losses, banks would be able to classify their customers and predict the probability of credit repayment instead of relying on traditional methods. As most actual research is focused on individuals' loans, the novelty of the present paper is to treat corporate loans. Its main objective is to propose a model to address the problem using selected machine learning algorithms to classify companies into two classes to be able to predict loan defaulters. This paper delves into the Corporate Loan Default Prediction Model (CLD PM), which is designed to forecast loan defaults in corporations. The model is grounded in the CRISP-DM process, commencing with comprehending corporate requirements and implementing classification techniques. The data acquisition and preparation phase are critical in testing the selected algorithms, which involve Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, XGBoost, and Adaboost. The model's efficacy is assessed using various metrics, namely Accuracy, Precision, Recall, F1 score, and AUC. Subsequently, the model is scrutinized using an actual dataset of loans for Moroccan real estate firms. The findings reveal that the Random Forest and XGBoost algorithms outperformed the others, with every metric surpassing 90%. This was accomplished by utilizing SMOTE as an oversampling method, given the dataset's imbalance. Furthermore, when concentrating on financial statements, selecting the five most significant financial ratios and the company's age, Random Forest was adept at predicting defaulters with good results: accuracy of 90%, precision of 75%, recall of 50%, F1 score of 60% and AUC of 77%.

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Section: Related Work: Companies Loan Default Predictionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

BERRADA,

BARRAMOU,

ALAMI

2024

IJACSA

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“…Supervised learning is also used for logistical learning, where instead of a regression model, a categorical probability distribution is to be learned. Supervised learning has seen considerable success in many areas, from credit-rating models [68] to scientific fields [69].…”

Section: Supervised Learningmentioning

confidence: 99%

Quantum Machine Learning: from physics to software engineering

Melnikov¹,

Kordzanganeh²,

Alodjants³

et al. 2023

Preprint

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Quantum machine learning (QML) is a new, rapidly growing, and fascinating area of research where quantum information science and quantum technologies meet novel machine learning and artificial intelligent facilities. A comprehensive analysis of the main directions of current QML methods and approaches is performed in this review. The aim of our work is twofold. First, we show how classical machine learning approach can help improve the facilities of quantum computers and simulators available today. It is most important due to the modern noisy intermediate-scale quantum (NISQ) era of quantum technologies. In particular, the classical machine learning approach allows optimizing quantum hardware for achieving desired quantum states by implementing quantum devices. Second, we discuss how quantum algorithms and quantum computers may be useful for solving keystone classical machine learning tasks. Currently, quantum-inspired algorithms, which use a quantum approach to classical information processing, represent a powerful tool in software engineering for improving classical computation capacities. In this work, we discuss various quantum neural network capabilities that can be implemented in quantum-classical training algorithms for variational circuits. It is expected that quantum computers will be involved in routine machine learning procedures. In this sense, we are showing how it is essential to elucidate the speedup problem for random walks on arbitrary graphs, which are used in both classical and quantum algorithms. Quantum technologies enhanced by machine learning in fundamental and applied quantum physics, as well as quantum tomography and photonic quantum computing, are also covered.

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“…The superior accuracies indicate that Stationary Mahalanobis kernel SVM is a novel kernel appropriate for Chinese credit risk estimation. In 2021, Dai et al proposed a combinational method to figure out three features used in training methods [14]. And they compare three traditional credit assessment methods: random forest, SVM, and gradient boosted classification, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Support Vector Machine for Credit System: The Effect of Parameter Optimization and Training Sample

Ling

Liu²

2023

TNS

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The credit assessment system is an essential part of modern financial institutions, and most of them have adopted different models to perform the task according to their specific needs. Support Vector Machine has been widespread and proved an efficient classifier, especially for relatively small datasets in recent years. When using SVM, data processing, choosing an appropriate kernel function, and tuning parameters can largely affect its performance. The most popular kernel function of SVM is the Radial basis function (RBF), and its main parameters are the regularization parameter, C, and the kernel coefficient, . Our study based on the South German credit dataset demonstrates that parameter optimization and an appropriate ratio of the size of the training dataset to the size of the testing dataset could significantly improve the performance of SVM.

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The application of machine learning in bank credit rating prediction and risk assessment

Cited by 8 publications

References 10 publications

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

Quantum Machine Learning: from physics to software engineering

Support Vector Machine for Credit System: The Effect of Parameter Optimization and Training Sample

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