CREDIT MANAGEMENT ANALYSIS TO PREDICT EARLY NON-PERFORMING LOANS AT PT. BANK NEGARA INDONESIA (PERSERO) TBK
DOI:
10.54443/ijerlas.v5i4.3615Published:
2025-07-27Downloads
Abstract
The purpose of this study is to improve credit quality in designing a customer loan default prediction system so that it can reduce the possibility of huge losses in banking in order to produce a model that has high accuracy and recall rate at PT. Bank Negara Indonesia (Persero) Tbk. The entire population in this study was sampled as many as 467 customers. Using the random forest method, the results obtained are: 1) descriptive analysis shows that the variables of customer credit history, payment ratio, age, collateral value, and affiliate balances influence the occurrence of bad debt, 2) the prediction model identifies patterns of customers at risk of bad debt, 3) the application of the credit model appropriately can reduce the level of bad debt.
Keywords:
Bad Debt Credit Management Credit RiskReferences
Dwihandayani, D. (2017). Analisis Kinerja Non Performing Loan (Npl) Perbankan Di Indonesia
Dan Faktor-Faktor Yang Mempengaruhi NPL. Jurnal Ilmiah Ekonomi Bisnis, 22(3), 228985.
Firdaus, R., & Ariyanti, M. (2009). Manajemen perkreditan bank umum teori, masalah, kebijakan dan aplikasinya lengkap dengan analisis kredit. ALFABETA.
Ismanto, E., & Novalia, M. (2021). Komparasi Kinerja Algoritma C4.5, Random Forest, dan Gradient Boosting untuk Klasifikasi Komoditas. Techno.Com, 20(3), 400–410. https://doi.org/10.33633/tc.v20i3.4576
Pahlevi, O.-, Amrin, A.-, & Handrianto, Y.-. (2023). Implementasi Algoritma Klasifikasi Random Forest Untuk Penilaian Kelayakan Kredit. Jurnal Infortech, 5(1), 71–76. https://doi.org/10.31294/infortech.v5i1.15829
Putri, Y. M. W., & Akmalia, A. (2016). Pengaruh CAR, NPL, ROA dan LDR Terhadap
Penyaluran Kredit Pada Perbankan. Journal Balance, XIII(2), 82– 93, ISSN Print: 1693-9352,
e-ISSN: 2614-820X.
Zhang, W., Yan, S., Li, J., Tian, X., & Yoshida, T. (2022). Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data. Transportation Research Part E: Logistics and Transportation Review, 158, 102611. https://doi.org/10.1016/j.tre.2022.102611.
License
Copyright (c) 2025 Hardiansyah, Nazaruddin, Muhammad Anggia Muchtar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Downloads
How to Cite
