Analysis and prediction of loan payments by machine learning algorithms in banking sector

Credit risk management plays a vital role for survival of banks, therefore it is not only important, but also very critical to predict and manage the loan application and allocation process. It is neccessary for the bank to understand whether it is going to be a non-performing or performing credit before granting the approval based on customer behaviour. As a result, it is aimed to offer a better and healthier credit appropriation by presenting the predictive analysis provided by machine learning algorithms. By the help of this project, it will be possible to manage the credit application process in more conscious and error free way. In this study, it is aimed to estimate whether the credits of the consumers are going to be non-performing or performing and whether the credit loan allocations are applied in a controlled and proper way, or not. Applying the prediction analysis in the study in an efficient way is the main expectation of the project before making any decisions about credit, increasing the performance and efficiency, correct usage of the resources economically are going to be some of the results og the model that is built at the end of this work.