Predicting Bank Loan Risks Using Machine Learning Algorithms

Alsaleem, Maan Y.; Hasoon, Safwan O.

doi:10.33899/csmj.2020.164686

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…XGBoost is eXtreme Gradient Boosting. It was designed by Chen Tianqi, which is also an improved algorithm for gradient boosting [24]. It improves the loss function.…”

Section: Methodsmentioning

confidence: 99%

“…Causes a conditional offset problem. CatBoost improves the statistics and introduces the prior distribution item and its corresponding weight on the original basis, reducing the impact of variables with fewer categories in the classification variables on the data; secondly, it can effectively reduce noise [24]. Another improvement of CatBoost is improving the traditional gradient estimation method to an ordered boosting method, which will obtain an unbiased gradient estimation, reduce the gradient estimation error, reduce the over-fitting problem, and finally improve the model generalization [31,32].…”

Section: Methodsmentioning

confidence: 99%

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Fraud detection under the unbalanced class based on gradient boosting

Alothman

Talib

Mohammed

2022

EEJET

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Credit fraud modeling is an important topic covered by researchers. Overdue risk management is a critical business link in providing credit loan services. It directly impacts the rate of return and the bad debt percentage of lending organizations in this sector. Credit financial services have benefited the general public as a result of the development of the mobile Internet, and overdue risk control has evolved from the manual judgment that relied on rules in the past to a credit model built using a large amount of customer data to predict the likelihood of customers becoming delinquent. When creating a credit rating model, the emerging nature of the credit samples makes the minority class sample score very few; that is, when a large number of actual samples are obtained, this causes machine learning models to be biased towards the majority class when training. Traditional data balancing methods can reduce the bias of models to the majority category when the data is relatively unbalanced rather than excessive. Gradient boosting algorithms (XGBoost and CatBoost) are proposed in this paper to model highly unbalanced data to detect credit fraud. To find hyperparameters and determine the accuracy of the minority class as an optimization function of the model, Bayesian optimization is used to increase the model's accuracy for the minority class. The paper was tested with real European credit card fraud data. The results were compared to traditional machine learning (decision trees and logistic regression) and the performance of the bagging algorithm (random forest). For comparison, the traditional data balancing method (Oversample) is used

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“…XGBoost is eXtreme Gradient Boosting. It was designed by Chen Tianqi, which is also an improved algorithm for gradient boosting [24]. It improves the loss function.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fraud detection under the unbalanced class based on gradient boosting

Alothman

Talib

Mohammed

2022

EEJET

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“…This research carries significant implications for enhancing the risk assessment and sustainability of online lending platforms. In (Alsaleem & Hasoon, 2020), the research objective was to assess the performance of various machine learning algorithms in classifying bank loan risks. With the increasing importance of bank loans and the challenges posed by the abundance of borrower data, this research aimed to aid banks in making informed grant decisions.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Model to Assess the Credit Risk in Egyptian Banks

Fathy,

Marie,

Yehia

2024

المجلة العلمية للبحوث والدراسات التجارية

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In the realm of financial and banking institutions, the art of forecasting and assessing banking risks holds paramount significance. Preserving the financial stability of banks is contingent upon adept risk management, a cornerstone in enhancing overall bank performance. Moreover, the effectiveness of financial and banking institutions can be gauged by their ability to systematically evaluate and mitigate risks. Among these risks, the assessment of banking credit risks looms large in contemporary times, given the heightened necessity for decision-makers to anticipate the likelihood of loan defaults. However, one formidable challenge persists: the inadequate assessment of banking credit risks. This challenge stems from the multifaceted factors that influence risk assessment and the soundness of credit decisions. In response to this pressing issue, our research presents a predictive model employing machine learning (ML) algorithms. Our objective is to facilitate informed credit decision-making and safeguard the financial assets of banks. In pursuit of this aim, we employed five machine learning classification algorithms: Artificial Neural Networks (ANN), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT) and XGBoost (XGB). To ensure the robustness of our study, we utilized a real-world dataset gleaned from the historical records of a prominent Egyptian bank. Subsequently, we assessed the performance of our model based on key metrics such as accuracy, precision, recall, and the f1 score. The results showed that XGB exhibited the highest accuracy, underlining the potential for ML algorithms to revolutionize the assessment of banking credit risks.

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“…Notably, they observed that multilayer perceptron (MLP) outperformed the random forest, naive Bayes (NB), and DTJ48 algorithms in categorizing bank loan risks. The evaluation of the model's performance was conducted using traditional metrics on a dataset comprising 1000 loans and their corresponding repayment status [11].…”

Section: Related Researchmentioning

confidence: 99%

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data

Alagic,

Zivic,

Kadusic

et al. 2024

MAKE

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The number of loan requests is rapidly growing worldwide representing a multi-billion-dollar business in the credit approval industry. Large data volumes extracted from the banking transactions that represent customers’ behavior are available, but processing loan applications is a complex and time-consuming task for banking institutions. In 2022, over 20 million Americans had open loans, totaling USD 178 billion in debt, although over 20% of loan applications were rejected. Numerous statistical methods have been deployed to estimate loan risks opening the field to estimate whether machine learning techniques can better predict the potential risks. To study the machine learning paradigm in this sector, the mental health dataset and loan approval dataset presenting survey results from 1991 individuals are used as inputs to experiment with the credit risk prediction ability of the chosen machine learning algorithms. Giving a comprehensive comparative analysis, this paper shows how the chosen machine learning algorithms can distinguish between normal and risky loan customers who might never pay their debts back. The results from the tested algorithms show that XGBoost achieves the highest accuracy of 84% in the first dataset, surpassing gradient boost (83%) and KNN (83%). In the second dataset, random forest achieved the highest accuracy of 85%, followed by decision tree and KNN with 83%. Alongside accuracy, the precision, recall, and overall performance of the algorithms were tested and a confusion matrix analysis was performed producing numerical results that emphasized the superior performance of XGBoost and random forest in the classification tasks in the first dataset, and XGBoost and decision tree in the second dataset. Researchers and practitioners can rely on these findings to form their model selection process and enhance the accuracy and precision of their classification models.

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Predicting Bank Loan Risks Using Machine Learning Algorithms

Cited by 11 publications

References 9 publications

Fraud detection under the unbalanced class based on gradient boosting

Fraud detection under the unbalanced class based on gradient boosting

An Intelligent Model to Assess the Credit Risk in Egyptian Banks

Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data

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