Loan Default Prediction Using Machine Learning : a Case of Mobile Based Lending

Abstract

Over the last decade, digital credit has been the fastest growing financial innovation in Kenya. This has largely been attributed to by technological innovations and mobile phone penetration enabling expanded access to financial services to individuals who were previously unbanked. Overall access to formal financial services now stands at 83%, up from 67% in 2016, and 88% of the adult population has access to a mobile money account (KFSD , 2019). Precise credit risk assessment also known as loan default prediction is crucial to the functioning of lending institutions. Traditional credit score models are constructed with demographic characteristics, historical payment data, credit bureau data and application data. In online mobile based lending, borrower’s fraudulent risk is higher. Hence, credit risk models based on machine learning algorithms provide a higher level of accuracy in predicting default. The main objective of this project is to predict loan default by applying machine learning algorithms. The proposed methodology involves data collection , data pre-processing , data analysis , model selection and performance evaluation . This project takes data of previous customers to whom on a set of parameters loan were approved. The machine learning model is then trained on that record to get accurate results. The main machine learning algorithms applied are logistic regressions, naïve bayes and decision trees. The performance of the machine learning models are then compared using performance metrics and the best machine learning algorithm is selected to predict the loan default.