A Meta-Analysis Evaluating the Performance of Machine Learning Models on Probability of Loan Default
DOI:
https://doi.org/10.47611/jsrhs.v11i2.2726Keywords:
Machine Learning, Statistical Analysis, Mortage LendingAbstract
There has been a recent increase in the implementation of machine learning algorithms to predict the credit risk of prospective loan applicants. This meta-analysis aims to contribute to the small but growing research on the effects of algorithmic lending. Specifically, we compare the performance of the Logistic Regression (LR) model and Random Forest (RF) model in predicting loan default (PD). Using the area under the receiver operating characteristic curve as a measure of aggregate machine learning model performance, we ultimately find convincing evidence that the RF model is more accurate than the logit model in PD (p-value=0.029, α = 0.01). These results have major implications for banks and financial firms as mortgage lending transitions into the FinTech era.
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