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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Ivan Michael Siregar
Harapan Bangsa Institute of Technology
Computer Science & IT

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A Hybrid Scoring and Machine Learning Framework for Quantitative Modeling of Customer Payment Potential Cindy Kristiya Himawan; Kevin Sebastian Brilian; Ivan Michael Siregar
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 4 (2026): Agustus 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i4.3534

Abstract

Debt collection generates extensive interaction data collection actions, contact outcomes, and communication history that is traditionally recorded only qualitatively for documentation, limiting its analytical value. This study aims to develop a scoring-based framework that converts qualitative collection data into quantitative indicators for data-driven decision-making. The method begins with a rule-based scoring mechanism to encode collection actions and interaction history into a total score reflecting customer payment potential, then extends this baseline using machine learning to learn feature importance and improve prediction accuracy. Model performance is evaluated using AUC ROC and Precision@K for payment likelihood, MAE and RMSE for payment time prediction, and recovery rate for business impact. Results show that the hybrid model significantly improves prioritization efficiency and recovery outcomes compared to conventional qualitative methods, demonstrating how operational data can be systematically quantified and integrated with predictive analytics for smarter collection strategies.
Co-Authors Cindy Kristiya Himawan Kevin Sebastian Brilian