Real-time Predictive Analysis of Loan Risk with Intelligent Monitoring and Machine Learning Technique

Credit risk management in modern financial organizations is based on assessing and recognizing the potential default risk of borrowers prior to making loans and determining the default probability of borrowers. This research uses the random forest technique to develop a loan default prediction model based on historical loan data from banks and other financial institutions. This technique outperforms other classification algorithms, such as decision trees and logistic regression, in terms of accuracy and recall rate, according to the experimental data. Additionally, the dataset’s feature rankings are determined by applying the random forest machine learning technique. The banking sector can better predict loan risk and control pre-lending risk by acquiring features that have a significant impact on loan risk.