Prediction Model for Loan Default Using Machine Learning

Abstract

Business firms and households sometimes seek for extra-funding to fulfill certain needs. The demand which arises from the need of extra funds is fulfilled by the credit market. Banks and others financial lending institutions are the key players in this market (Gaigaliene and Cesnys, 2018). Loan is one of the most important products of most financial institutions. All financial lenders try to find effective business strategies for persuading customers to apply for loans. However, there are some borrowers who default in loan payments (Begum and Deniz, 2019). During a loan term, default may occur when the borrower fails to make required payments. Therefore, an assessment of a borrower's default risk over time is essential to enable timely risk management. Credit officers determine whether borrowers can fulfill their requirements using manually analysis of borrower's credit history. In the last decade, this trend has changed over time with technological advancement (Rehman, 2017).In recent years, financial lending institutions are using automated loan default models as credit risk scoring tools when granting loans to potential borrowers (Bao et al., 2019). Machine Learning (ML) algorithms have been applied to assess the credit risk of borrowers in financial lending institutions (Djeundj and Crook, 2018). Reliable models for credit risks play an important role in loss control and revenue maximization (Luo and Nie, 2016). Earlier research treated loan default prediction as a binary classification problem, where a loan is classified as either creditworthy or non-creditworthy (Rosenberg and Gleit, 1994). Linear Discriminant Analysis (LDA) and logistic regression (LR) are two most popular tools for constructing credit scoring models (Wiginton, 1980). Subsequently, other classification algorithm such as, Artificial neural networks (ANN) Gulsoy and Kulluk (2019) support vector machines (SVM) Alaka et al. (2018), decision trees (DT) Liu et al. (2015), and Bayesian classifier (BC) Carta et al. (2020), have been used to estimate borrowers' probability of default. Recently, time-to-default modeling has attracted increasing research interest (Dirick et al., 2017). Time-to-default data fall into the category of lifetime data in general, which is commonly analyzed by survival analysis (SA) (Malekipirbazari and Aksakalli, 2015). In loan prediction, two types of errors inevitably lead to inefficiency in prediction

Default Prediction for Loan Lenders Using Machine Learning Algorithms

“…The results presented above explained the overall models performances with and without Early_R attribute, since this research is focus on finding the significant impact of Early_R on models performances as well as determining best predictive model as shown in Table 1 and Table 2. Our research outputs are similar to work of (Awuza et al, 2022: Malekipirbazari & Aksakali, 2015. Table 1 shows a general models performance improvement for all base learner algorithms with respect to the five performance metrics compared to Table 2 with exception of SVM which have same performance rate for both instance.…”

“…Early repayment was identified and used as a factor that could greatly minimize classification error in a research by (Awuza et al 2022). However, their work uses only three algorithms in models building with Logistic regression proven to be the best classification model.…”

“…In loan prediction, two types of errors inevitably lead to inefficiency in prediction accuracy i.e. the rejection of a true credit worthy applicant (also known as a "false positive"), and the non-rejection of a false credit worthy applicant (also known as "false negative") (Awuza et al, 2022). Most loan default prediction models revolve around the minimization of one or both of these errors (Nok, 2017).…”

“…However, they could not identify the factors that significantly minimize the errors of classifying good borrowers as bad ones or bad borrowers as good ones i.e. false positive and false negative rate (Awuza et al, 2022, Malekipirbazari & Aksakalli, 2015and Begum & Deniz, 2019. Jiang et al, (2019) identified the "early loan repayment" attribute as a factor that could drastically minimize classification errors.…”

“…However, they could not use it as an attribute in developing their prediction model because of its exclusion in the dataset they collected. However, this attribute was applied in a research by (Awuza et al, 2022) but the result could not be comprehensive as only fewer algorithms were used for classification.…”