Open AccessCredit scoring model using MARS method to comply with FSA regulation
Author(s) -
Astri Afrilia,
A Joharudin,
Muhammad Zaky,
Bintang Budiman,
Mega Fauziah
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1869/1/012135
Subject(s) - multivariate adaptive regression splines , credit rating , mars exploration program , process (computing) , selection (genetic algorithm) , business , finance , financial services , computer science , actuarial science , regression analysis , econometrics , economics , artificial intelligence , bayesian multivariate linear regression , machine learning , physics , astronomy , operating system
Financial Service Authority (FSA) 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, the credit rating system needs to be renewed. Statistical methods can help to include permits and environmental impact in the 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 have been formulated has type I error of 0.00% and type II error of 0.54%. Furthermore, the authors believe that this model can be used to asses potential debtors’ credit rating while adhering to the policy of Sustainable Finance.