3rd European International Conference on Industrial Engineering and Operations Management

ANALYSIS OF CREDIT SCORING USING PARTICLE SWARM OPTIMIZATION ALGORITHM IN LOGISTIC REGRESSION MODEL

Ulfa Rahmani, Sukono Sukono, Riaman Riaman, Subiyanto Subiyanto & Abdul Talib Bon
Publisher: IEOM Society International
0 Paper Citations
1 Views
1 Downloads
Track: Graduate Student Paper Competition
Abstract

Banking is a financial institution that has very important role in an economic and trade activity that is useful for channeling funds in the form of loans to the public who need fresh funds for business in the hope of helping improve the people's economy. In the process of lending, bank are often faced with a risk known as credit risk or problem loans. Therefore, the credit rating was analyzed by using the Particle Swarm Optimization algorithm in the Logistic Regression model. It was determined by using feasibility parameters of prospective debtors based on past data variables held by prospective debtors. This risk can be overcome by using a scoring system that is owned by each bank. In this paper, the data used is data on financial service cooperatives in Indonesia. Of the eight factors were analyzed, there are six factors that have a significant effect on the risk of default, namely including the age of debtors, family dependents, the amount of savings, the value of collateral, given the credit limit, and the loan term.

Published in: 3rd European International Conference on Industrial Engineering and Operations Management, Pilsen, Czech Republic

Publisher: IEOM Society International
Date of Conference: July 23-26, 2019

ISBN: 978-1-5323-5949-1
ISSN/E-ISSN: 2169-8767