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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL SISTEM INFORMASI BISNIS JURNAL DIVERSITA Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan Jurnal Ilmiah Ilmu Pemerintahan Journal of Information Systems and Informatics Jurnal Inovasi Pendidikan MH Thamrin Journal of Economics and Business UBS Sosio-Didaktika: Social Science Education Journal Jurnal Locus Penelitian dan Pengabdian JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA Jurnal Indonesia Sosial Teknologi Jurnal Pengabdian Masyarakat dan aplikasi Teknologi
Iwan Purwanto
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Religion Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Neuroscience Social Sciences Other

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Journal : Journal of Information Systems and Informatics

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A Decision Support Model for Online Lending Creditworthiness Using Comparative Personality Indicators Purwanto, Iwan; Sari, Syandra
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1478

Abstract

The rise of online lending platforms has improved financial accessibility but also increased credit default risk due to information asymmetry and limited borrower profiling. Traditional creditworthiness models rely primarily on financial and demographic data, which often fail to capture behavioral characteristics. This study proposes a decision support model for creditworthiness prediction by integrating personality indicators from the Big Five Personality Traits and the California Psychological Inventory (CPI). The framework incorporates these personality-based features into a machine learning-driven system alongside traditional borrower data. Psychological indicators are quantified and assessed using multiple classification models to evaluate their impact on predictive performance. The model's effectiveness is measured using metrics such as accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Empirical results show a significant improvement in prediction accuracy, with the AUC rising from 0.74 in the baseline model to 0.87 after including personality features. A comparative analysis reveals the relative contributions of each personality framework, demonstrating that personality indicators enhance predictive performance over traditional models. These findings emphasize the value of incorporating behavioral factors, supporting the development of more effective and sustainable credit risk assessment systems.
Co-Authors Agustian, Panggih Amanda, Shavira Putri Aris Puji Widodo Azmi, Qori'a Ta'rifajrin Didien Suhardini E. Shintadewi Julian Faishal Erlangga Fathul Jannah, Aisyah Gunawan Tjahjadi Heni Sulistiani Hidayatullah, Raza Syarif Ibrahim, Fahmi Alfin Ifan Kurniawan Iveline Anne Marie Kiki Prawiroredjo Novia Rahmawati Parjito Parjito R Rizal Isnanto Ratnaningsih, Sita Sari, Syandra Sulistiarini, Raidah Syairul Bahar Syaripulloh Ulfah Fajarini