A comparison of random forest and logistic regression model in credit scoring of rural households

Hong Nhung Do () and Michel Simioni ()
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Hong Nhung Do: NEU - National Economics University [Hanoï, Vietnam]
Michel Simioni: UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro - Montpellier SupAgro - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement

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Abstract: Many banks currently use the logistic regression model to do credit scoring to give loans to customers. This paper compares the random forest and logistic regression methods to support the financial analysis functions of the predictive tool for credit scoring. We use the data provided by the Vietnam Access Resource to Household Survey (VARHS), which contains 3,530 households in the year 2014 in 12 provinces of Vietnam. Results show that random forest proved to be a better accurate predictive tool than the logistic regression method. This suggests banks use the random forest to predict potential lenders based on the existing client dataset resulting in saving time and cost to find potential clients.

Keywords: Random forest; Logistic regression; VARHS; Credit assessment; Machine learning (search for similar items in EconPapers)
Date: 2021-08-03
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Published in 23rd Malaysian Finance Association International Conference 2021 (MFAIC 2021), Malaysian Finance Association, Aug 2021, Gelugor, Universiti Sains Malaysia, Malaysia

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