Machine Learning-Based Prediction Model for Loan Status Approval

Suliman Mohamed Fati

Abstract

Loan approval in financial organizations is one of the challenges that affect the operational financial process due to the inaccurate estimation or the lack of information. Thus, the banks aim to minimize the credit risks by assessing the loan status through an intensive evaluation process to avoid unforeseen issues. Therefore, loan prediction based on the given and collected information is very important in this regard. Data mining, particularly Machine learning, is a promising direction to give accurate and on-time decisions to approve/disapprove the loans. The main goal of this work is to investigate the loan prediction process by applying different machine learning algorithms. The proposed methodology starts with data pre-processing to clean the data, remove outliers, and find the correlation between the features to find the most noteworthy feature. Then, three machine-learning algorithms will be trained and tested: Logistic Regression, Decision Tree, and Random Forest. The novelty of this research can be represented by comparing three machine-learning algorithms to find the most accurate prediction. The experimental results showed the superiority of Logistic Regression on the other two algorithms in terms of accuracy precision, Recall, F1, and Area under the curve (AUC). The decision tree algorithms also underwent Receiver operating characteristic (ROC), which demonstrated the ability of Logistic Regression to predict the loan status under different thresholds.

 

Keywords: loan approval, machine learning algorithm, logistic regression, data mining, prediction model.


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References


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