Analysis of Individual Loan Defaults Using Logit under Supervised Machine Learning Approach

Abstract

Financial institutions have a large amount of data on their borrowers, which can be used to predict the probability of borrowers defaulting their loan or not. Some of the models that have been used to predict individual loan defaults include linear discriminant analysis models and extreme value theory models. These models are parametric in nature since they assume that the response being investigated takes a particular functional form. However, there is a possibility that the functional form used to estimate the response is very different from the actual functional form of the response. The purpose of this research was to analyze individual loan defaults in Kenya using the logistic regression model. The data used in this study was obtained from equity bank of Kenya for the period between 2006 to 2016. A random sample of 1000 loan applicants whose loans had been approved by equity bank of Kenya during this period was obtained. Data obtained was on the credit history, purpose of the loan, loan amount, nature of the saving account, employment status, sex of the applicant, age of the applicant, security used when acquiring the loan and the area of residence of the applicant (rural or urban). This study employed a quantitative research design, it deals with individual loans defaults as group characteristics of a borrower. The data was pre-processed by seeding using R- Software and then split into training dataset and test data set. The train data was used to train the logistic regression model by employing Supervised machine learning approach. The R-statistical software was used for the analysis of the data. The test data set was used to do cross-validation of the developed logistic model which later was used for analysis prediction of individual loan defaults. This study focused on the analysis of individual loan defaults in Kenya using the logistic regression model in Machine learning. The logistic regression model predicted 303 defaults from train data set, 122 non-defaults and misclassified loans were 56 and 69. The model had an accuracy of 0.7727 with the train data and 0.7333 with the test data. The logistic regression model showed a precision of 0.8440 and 0.8244 with the train and test data respectively. The performance of the model with both the train and test data was illustrated using a plot of train errors and test errors against sample size on the same axes. The plot showed that the performance of the model increases with an increase in sample size. The study recommended the use of logistic regression in conjunction with supervised machine learning approach in loan default prediction in financial institutions and also more research should be carried out on ensemble methods of loan defaults prediction in order to increase the prediction accuracy.

Classification of Non-Performing Financing Using Logistic Regression and Synthetic Minority Over-sampling Technique-Nominal Continuous (SMOTE-NC)

“…A study by Agbemava et al used data from microfinance institutions in Accra Ghana and analyzed using binomial logistic regression shows that six factors have a significant effect on the quality of financing: marital status, dependents, type of collateral, financing period, and type of financing with an accuracy of 86.67% [5]. Another study by Obare et al analyzed the NPF customers in Kenya using the logistic Regression method shows that the history of financing, the purpose of financing, the amount of financing, the nature of the deposit account, occupation, gender, age, type of collateral and place of residence has an accuracy of 73.33% [6]. Another study by Wibowo et al compares several methods of Weight of Evidence (WoE), Information Value (IV), logistic regression with imbalanced data, and logistic regression with SMOTE to overcome the problem of imbalanced data between the number of good credit status and bad credit status in the Islamic banks in Indonesia.…”