Loan classification using logistic regression

Abstract

Objectives. The studied problem of loan classification is particularly important for financial institutions, which must efficiently allocate monetary assets between entities as part of the provision of financial services. Therefore, it is more important than ever for financial institutions to be able to identify reliable borrowers as accurately as possible. At the same time, machine learning is one of the tools for making such decisions. The aim of this work is to analyze the possibility of efficient use of logistic regression for solving the task of loan  classification.

Methods. Based on the logistic regression algorithm using historical data on loans issued, the following  metrics are calculated: cost function, Accuracy, Precision, Recall и  score. Polynomial regression and  principal component analysis are used to determine the optimal set of input data for the being studied logistic regression algorithm.

Results. The impact of data normalization on the final result is estimated, the optimal regularization parameter for solving this problem is determined, the impact of the balance of target values is assessed, the optimal  boundary value for the logistic regression algorithm is calculated, the influence of increasing input indicators by means of filling in missing values and using polynomials of different degrees is considered and the existing set of input indicators is analyzed for redundancy.

Conclusion. The research results confirm that the application of the logistic regression algorithm for solving loan classification problems is appropriate. The use of this algorithm allows to get quickly a working loan  classification tool.