Implementation of Machine Learning in the Credit Risk Management System of Individuals

Abstract

There are many problems in each credit institution. The most important of them is the risk of possible losses in lending. Within the framework of the topic, the studies conducted by other researchers were investigated, from which it was concluded that machine learning tools are often used to optimally solve the above-mentioned problem. Real data on credits were used as a basis for modeling in the work. In this work, based on the available data, several machine learning models were developed, from which the best one was selected, which can contribute to the improvement of the credit risk management process. During the work, the logical connections between data and their interaction with each other were revealed. Then, based on the work done, the appropriate models were built, the quality of which was checked using various tools. The obtained models were compared and the best one was selected. The obtained results are practically applicable and show that each bank and credit organization can develop a better solution based on the large databases they have, which will contribute to curbing credit risk and reducing costs.