Frequency Model of Credit Payment using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression

Authors

  • Ikacipta Mega Ayuputri Department of Statistics, Faculty of Mathematics, Computing, and Data Science Institut Teknologi Sepuluh Nopember, 60111 Surabaya, Indonesia
  • Nur Iriawan Department of Statistics, Faculty of Mathematics, Computing, and Data Science Institut Teknologi Sepuluh Nopember
  • Pratnya Paramitha Oktaviana Department of Statistics, Faculty of Mathematics, Computing, and Data Science Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.11113/matematika.v34.n3.1143

Abstract

In distributing funds to customers as credit, multi-finance companies have two necessary risks, i.e. prepayment risk, and default risk. The default risk can be minimized by determining the factors that affect the survival of customers to make credit payment, in terms of frequency of credit payments by customers that are distributed geometry. The proposed modelling is using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression. The best model of this research is modelling using Bayesian Geometric Regression method because it has lower DIC values than Bayesian Mixture Geometric Regression. Modelling using Bayesian Geometric Regression show the significant variables are marital status, down payment, installment length, length of stay, and insurance.

Downloads

Published

2018-12-31

How to Cite

Ayuputri, I. M., Iriawan, N., & Oktaviana, P. P. (2018). Frequency Model of Credit Payment using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression. MATEMATIKA: Malaysian Journal of Industrial and Applied Mathematics, 34(3), 103–113. https://doi.org/10.11113/matematika.v34.n3.1143