Statistical modelling for home loans and regulatory credit risk capital forecast
Mazibuko, Paulosi Lucky
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In commercial credit institutions, valuation of default is useful for moneylenders such as banks and other companies that make a practice of credit scoring as quantitative research to determine the creditworthiness of an individual or borrower. There are several statistical models that are used in the bank for credit scoring. Logistic and Survival Analysis models are the most-utilised scoring models by lenders, among others. The main intention of this paper is to model and predict the likelihood of non-payment for a mortgage loans in financial institutions. To range these objectives, two statistical approaches, namely Logistic Regression and Survival Analysis, are used to a large dataset of mortgage loans by one of the financial institutions. In this paper, it has been shown that the Survival model is a good method on likelihood of non-payment, in contrast with Logistic Regression. The results of the final modelling for both approaches shows parallel fit in Receiver Operator Characteristic (ROC) with the Logistic Regression model outperforming the Survival model in both training and testing dataset. In prediction of defaulted and non-defaulted results on mortgage loans, Logistic Regression still has better performance than Survival Analysis in both training and testing datasets. In general, the results show that the Survival Analysis method is competitive with the Logistic Regression method traditionally utilised in the financial institutions. Moreover, by methods for a vast, genuine dataset, time reliance was notable that made accessible more precise credit risk scoring and imperative insight into self-motivated market impacts that can educate and upgrade related decision-making.