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All Journal CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia Indonesian Journal of Multidisciplinary Science Jurnal Locus Penelitian dan Pengabdian
Mulyati , Heti
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Religion Aerospace Engineering Humanities Automotive Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences

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Journal : Indonesian Journal of Multidisciplinary Science

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The Effect of Digitalization and Human Intervention in Credit Decisions on the Risk of Late Payments in Multifinance Companies PT. XYZ Nopendi, Ferlan; Priyarsono, Priyarsono; Mulyati , Heti
Indonesian Journal of Multidisciplinary Science Vol. 4 No. 11 (2025): Indonesian Journal of Multidisciplinary Science
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/ijoms.v4i11.1170

Abstract

This study analyzes the impact of digitalization and human intervention in credit decisions on the risk of late payments in multifinance companies PT. XYZ. The digital transformation in the financial industry has shifted the creditworthiness assessment process from a manual system to a fully automated one. The study used a quantitative method with binary logistic regression analysis on data from 533 credit contracts divided into two periods: January–April 2021 (196 contracts with human intervention) and June–September 2021 (337 contracts without human intervention). The variables analyzed included tenor, digital score, effective interest, monthly income, and credit expert involvement in First Payment Default (FPD). The results showed that digital scores had a significant influence on the probability of FPD, with an odds ratio of 1.852 (p = 0.009), meaning that every increase of one unit in digital score increased the probability of FPD by 85.2%. In contrast, the variables of tenor, interest, income, and credit expert intervention did not show a significant influence on FPD. The Chi-square test confirmed that there was no significant difference in FPD levels between credits with and without human intervention (p = 0.148). The model has a classification accuracy of 96.4%, with an AUC value of 0.738, indicating a fairly good discriminating ability. Strict implementation of Know Your Customer (KYC) plays an important role in risk mitigation. These findings provide managerial implications that the digital underwriting system can be relied upon as the main tool in the credit assessment process, supporting the decision of PT. 
Co-Authors Alfatiani, Nitra Aprilia , Bunga Kaila Eko Ruddy Cahyadi Kurniati, Pat Nopendi, Ferlan Nuralamsyah , Irfan Priyarsono, Priyarsono Rostiani , Rita