This application leverages deep learning to predict the loan status of applicants, categorizing them as either "Fully Paid" or "Charged Off." Fully Paid: The applicant has successfully repaid the loan amount, along with the interest, within the stipulated time frame. Charged Off: The applicant has failed to make timely installment payments for an extended period, resulting in loan default.
To use this app, simply provide the necessary loan and borrower details, and let the model predict the loan status. Our app’s predictive capabilities are powered by advanced deep learning techniques, ensuring high accuracy and reliability.
Dataset link is provided in the Credits section.
This dataset is sourced from LendingClub, a leading US peer-to-peer lending company based in San Francisco, California. LendingClub was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. It is currently the world’s largest peer-to-peer lending platform.
In this case study, we explore the application of deep learning techniques to predict the loan status of borrowers. This project demonstrates how advanced machine learning methods can address real-world business problems, particularly in risk analytics within banking and financial services. By understanding and utilizing this data, we aim to minimize the risk of financial loss when lending to customers. Completing this case study will help build a solid foundation in deep learning and risk analytics, which are highly valued skills in the industry.