Predicting loan default risk is a crucial task in the Fintech industry, where lenders aim to minimize their financial losses due to unpaid loans. In this paper, we present a machine learning approach for predicting loan default risk using a dataset of borrower information from a leading Fintech lender. We explore a machine learning technique namely logistic regression and evaluate its performance in predicting loan default risk. Our results show that the logistic regression model achieves 89% accuracy in predicting loan default risk. We also identify key factors affecting loan default risk, which can inform lending decisions and risk management strategies in the Fintech industry. Our study highlights the importance of accurate loan default risk prediction in Fintech and provides insights into the use of machine learning techniques for this task.