Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute
Asian Institute of Research, Journal Publication, Journal Academics, Education Journal, Asian Institute

Economics and Business

Quarterly Reviews

ISSN 2775-9237 (Online)

asian institute research, jeb, journal of economics and business, economics journal, accunting journal, business journal, managemet journal
asian institute research, jeb, journal of economics and business, economics journal, accunting journal, business journal, managemet journal
asian institute research, jeb, journal of economics and business, economics journal, accunting journal, business journal, managemet journal
asian institute research, jeb, journal of economics and business, economics journal, accunting journal, business journal, managemet journal
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open access

Published: 25 July 2022

Predicting Household Resilience Before and During Pandemic with Classifier Algorithms

Ndari Surjaningsih, Hesti Werdaningtyas, Faizal Rahman, Romadhon Falaqh

Central Bank of Indonesia, Indonesia

asian institute research, jeb, journal of economics and business, economics journal, accunting journal, business journal, management journal

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doi

10.31014/aior.1992.05.03.437

Pages: 75-81

Keywords: Default Event, Household Resilience, Vulnerability, Machine Learning

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.

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