A Data Driven Model for predicting Loan Approval Using Machine Learning Approaches
Salaheldin, Y., Abdelhady, S., Abdallah, R., Fawzy, A., Mohamed, M. (2025). A Data Driven Model for predicting Loan Approval Using Machine Learning Approaches. EKB Journal Management System, 4(1), 2271-2289. doi: 10.21608/erurj.2025.291674.1149
Yasser Salaheldin; Shady Abdelhady; Reham Abdallah; Anton Magdy Fawzy; Mazen Alaa Mohamed. "A Data Driven Model for predicting Loan Approval Using Machine Learning Approaches". EKB Journal Management System, 4, 1, 2025, 2271-2289. doi: 10.21608/erurj.2025.291674.1149
Salaheldin, Y., Abdelhady, S., Abdallah, R., Fawzy, A., Mohamed, M. (2025). 'A Data Driven Model for predicting Loan Approval Using Machine Learning Approaches', EKB Journal Management System, 4(1), pp. 2271-2289. doi: 10.21608/erurj.2025.291674.1149
Salaheldin, Y., Abdelhady, S., Abdallah, R., Fawzy, A., Mohamed, M. A Data Driven Model for predicting Loan Approval Using Machine Learning Approaches. EKB Journal Management System, 2025; 4(1): 2271-2289. doi: 10.21608/erurj.2025.291674.1149

A Data Driven Model for predicting Loan Approval Using Machine Learning Approaches

ERU Research Journal
Volume 4, Issue 1, January 2025, Page 2271-2289  XML PDF (645.46 K)
Document Type: Review article
DOI: 10.21608/erurj.2025.291674.1149
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Authors
Yasser Salaheldin email orcid 1; Shady Abdelhadyorcid 2; Reham Abdallahorcid 2; Anton Magdy Fawzy2; Mazen Alaa Mohamed2
1Artificial Intelligence Department, Faculty of Computers and Information, Sadat Academy for Management sciences (SAMS), 1, Maadi Cornich, Maadi, Cairo Governorate, Egypt
2Business Technology Department, Faculty of Management, Economics and Business technology, Egyptian Russian University, Cairo, Egypt.
Abstract
Predicting loan approval is an essential task for banks and financial institutions, as it entails assessing the risk and profitability of lending money to potential borrowers. Loan approval processes in financial institutions are often complex and time-consuming, relying on manual assessments that can be biased and inconsistent. These difficulties make it more difficult to properly evaluate risks and guarantee ethical lending practices. To address these issues, this research proposes an approach rooted in machine learning to anticipate the approval status of loans for applicants based on their personal and financial characteristics. The research depends on a dataset consisting of 5000 loan applicants with 14 attributes, acquired from Kaggle. We assess the model using three classification techniques such as decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Metrics like accuracy, precision, recall, and F1-score were used to assess the performance of the models. The SVM model demonstrates an accuracy of 96%, while the decision tree achieves 92% and KNN attains 86%. According to these findings, we conclude that using the SVM model is a reliable and effective method for predicting loan approval status.
Keywords
Loan Approval; Machine Learning; Decision Tree; Neural Network; Classification
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