PEMODELAN PROBABILITY OF DEFAULT PORTOFOLIO PEMBIAYAAN BERSAMA FINTECH LENDING DAN MULTI FINANCE:STUDI KASUS BANK ABC

Abstract

This research is to find the appropriate credit scoring model technique to build the default model based on the joint financing schemes product between bank and fintech lending and multi finances that conducted by bank ABC. The credit scoring model to be compared using traditional approach, logistic regression against machine learning technique. This research is case study in Bank ABC’s portfolio starting April 2019 up to December 2022 and will be classified into default or non-default so the model can predict the possibility of customer default during the period. The analysis conducted based on variables from application and transaction data that not breaching the confidentiality of personal data in Bank ABC. Furthermore, the analysis only applicable for joint financing schemes product to fintech lending and multi finances that already have operated in Indonesia only. The significant variable to model the probability of default for joint financing schemes between bank and fintech lending or multi finances are tenure, loan purpose, interest amount, job description, home city, age and declared income. Furthermore, the analysis showed that the traditional technique logistic regression had higher accuracy compared to machine learning using decision tree in the case study.