Loan Default Risk Prediction System in Online Loan Services using Machine Learning and Streamline - Yosi; Jose, Septian; Celvin Andrean; Sarmila; Weiskhy Steven Dharmawan, (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
The rapid development of information technology has driven innovation in the financial sector, particularly in the field of credit lending services. However, the increasing number of credit lending services also leads to a higher risk of default, which can lead to financial losses for lenders. This study aims to develop a loan default risk prediction system using machine learning algorithms, namely Naïve Bayes and K-Nearest Neighbor (KNN), implemented through the Streamlit framework. This study applies a quantitative method with a data mining approach based on the CRISP-DM framework, utilizing the German Credit dataset consisting of variables such as age, occupation, housing, savings account, loan amount, and purpose. The models were evaluated using a confusion matrix to measure accuracy. The results show that the Naïve Bayes algorithm achieved the highest accuracy (86.4%) in predicting loan decisions, followed by KNN (60%). The developed Streamlit-based application provides interactive visualizations, training models, and prediction features, enabling users to assess credit risk efficiently. This system is expected to help financial institutions identify potential defaulters more accurately and improve the overall performance of credit lending services.