Credit Scoring Model Based on HMM/Baum-Welch Method

Badreddine Benyacoub (), Souad ElBernoussi (), Abdelhak Zoglat () and Mohamed Ouzineb ()
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Badreddine Benyacoub: Institut National de Statistique et d’Economie Appliquée
Souad ElBernoussi: University Mohammed V
Abdelhak Zoglat: University Mohammed V
Mohamed Ouzineb: Institut National de Statistique et d’Economie Appliquée

Computational Economics, 2022, vol. 59, issue 3, No 9, 1135-1154

Abstract: Abstract Credit scoring becomes an important task to evaluate an applicant by a banker. Many tools are available for making initial lending decisions. This paper presents a Hidden Markov Model (HMM ) for credit scoring, and uses Baum-Welch method; an iterative procedure approach; for building a set of credit scoring models. We introduce HMM/Baum-Welch model: a tool developed to explore a good accurate model for classification problems. There are two phases in this model: learned an initial model from training data using HMM, and re-estimating HMM parameters by an iterative process using Baum-Welch algorithm. The proposed model is successfully applied to a real credit problem, and the application procedure is illustrated through two data sets: German and Australian. The criteria used to evaluate the performance of different resulting models are the accuracy and AUC (area under the ROC curve). The experiment of this model, shows that, HMM with Baum-Welch approach can improve the pattern classification performance in credit scoring.

Keywords: Credit scoring; Hidden markov model(HMM); Baum-Welch; Credit risk (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10614-021-10122-9

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