Predicting Household Resilience Before and During Pandemic with Classifier Algorithms

Abstract

One of the lessons learned from the global financial crisis in 2008 was raising attention to monitoring and maintaining household vulnerability, particularly household credit risk, by using the default rate as the indicator. The indicator would be worsening at the economic recession, likewise, recently happened caused by the pandemic. The default event has a complex nonlinearity relationship among the determinants. To tackle the complex relationship, this study suggests exploiting machine learning approach in modeling the probability of default, especially the individual and ensemble classifiers. Therefore, this study aims to investigate changes of the Indonesian household financial resilience before and during the pandemic, supported by the individual-level data of the Financial Information Service System. This study finds that the ensemble classifiers, notably extreme gradient boosting, have a more predominant performance than the individual classifiers. The best model, then has the feature importance analysis to identify the variable pattern in explaining the default event periodically which reveals the pattern changes before and during the pandemic. The cost of debt/repayment capability and the policy mix is significant in explaining the default event. At the same time, the project location feature weakens in discriminating the target class.