Credit Scoring Refinement using Optimized Logistic Regression

SUTRISNO, HENDRI and Halim, Siana (2017) Credit Scoring Refinement using Optimized Logistic Regression. In: ICSIIT 2017, 29-09-2017 - 29-09-2017, Denpasar - Indonesia.

[img] PDF
Download (3491Kb)
    PDF (cek plagiasi)
    Download (88Kb) | Preview
      PDF (peerreview)
      Download (1062Kb) | Preview


        A poor credit scoring model will give a poor power for predicting defaulted loan. There are many approaches for modeling the default prediction, such as classical logistic regression and Bayesian logistics regression. In this paper, we applied both classical logistic regression and AUC (Area under Curved) optimized using Nelder-Mead Algorithm for refining a credit scoring model that has already been used for several years by an International bank in Indonesia. Both classical logistics regression and AUC optimized method perform well in improving the model, but logistic regression still better in some aspects. AUC Optimized model has higher AUC than logistic regression model but has lower Kolmogorov-Smirnov Score (KS-Score)

        Item Type: Conference or Workshop Item (Paper)
        Uncontrolled Keywords: Credit scoring, logistics regression, Nelder-Mead Algorithm, AUC optimization
        Subjects: H Social Sciences > HA Statistics
        Divisions: Faculty of Industrial Technology > Industrial Engineering Department
        Depositing User: Admin
        Date Deposited: 22 Feb 2018 18:17
        Last Modified: 16 Jan 2019 11:44
        URI: http://repository.petra.ac.id/id/eprint/17788

        Actions (login required)

        View Item