Machine Learning Models for Predicting Financially Vigilant Low-Income Households


Kannan, Rathimala and Shing, Khor Woon and Ramakrishnan, Kannan and Ong, Hway Boon and Alamsyah, Andry (2022) Machine Learning Models for Predicting Financially Vigilant Low-Income Households. IEEE Access, 10. pp. 70418-70427. ISSN 2169-3536

[img] Text
Restricted to Repository staff only

Download (3MB)


The COVID-19 pandemic has adversely affected households’ lives in terms of social and economic factors across the world. The Malaysian government has devised a number of stimulus packages to combat the pandemic’s effects. Stimulus packages would be insufficient to alleviate household financial burdens if they did not target those most affected by lockdowns. As a result, assessing household financial vigilance in the case of crisis like the COVID-19 pandemic is crucial. This study aimed to develop machine learning models for predicting and profiling financially vigilant households. The Special Survey on the Economic Effects of Covid-19 and Individual Round 1 provided secondary data for this study. As a research methodology, a cross-industry standard process for data mining is followed. Five machine learning algorithms were used to build predictive models. Among all, Gradient Boosted Tree was identified as the best predictive model based on F-score measure. The findings showed machine learning approach can provide a robust model to predict households’ financial vigilances, and this information might be used to build appropriate and effective economic stimulus packages in the future. Researchers, academics and policymakers in the field of household finance can use these recommendations to help them leverage machine learning.

Item Type: Article
Uncontrolled Keywords: Economics, Machine learning, Government, COVID-19, Biological system modeling, Pandemics, Predictive models
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Management (FOM)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 01 Aug 2022 07:08
Last Modified: 01 Aug 2022 07:08


Downloads per month over past year

View ItemEdit (login required)