The Performance of Multivariate Adaptive Regression Splines (MARS) in the Selection Process of Potential Debtors to Comply with Sustainable Finance Policy
Astri Afrilia, S.Si., M.Stat (a), Agus Joharudin, MIntBus (b), Dr. Muhammad Zaky, M.Si (b), Budi Budiman, M.Si., M.Ag (b), Meli Fauziah, S.Pd., M.A (b)

a) UIN Sunan Gunung Djati Bandung, Jalan AH Nasution 105, Bandung 40614, Indonesia
*) astriafrilia88[at]uinsgd.ac.id
b) UIN Sunan Gunung Djati Bandung, Jalan AH Nasution 105, Bandung 40614, Indonesia

Abstract

Financial Service Authority (OJK) introduced a new policy on Sustainable Finance to financial institutions such as Banks. It is currently a hot issue that needs to be implemented in the selection process of potential debtors. Consequently, credit rating system needs to be renewed. Statistical methods can help to include permits and environmental impact in selection process. Thus, this study intends to formulate a credit rating model for productive debtors. This study used a quantitative method using Multivariate Adaptive Regression Splines (MARS). Our study’s significant finding is that the credit rating model for productive debtors that has been formulated has type I error of 0.00% and type II error of 15.78%. Furthermore, The authors believe that this model can be used to asses potential debtors’ credit rating while adhering to the policy of Sustainable Finance.

Keywords: Credit rating, Multivariate Adaptive Regression Splines, Sustainability finance.

Topic: Mathematics