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All Journal International Journal of Education, Vocational and Social Science
Ari Muzakir
Information Systems Study Program, Universitas Bina Darma, Palembang, Indonesia
Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Other

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Beyond Accuracy: Leakage-Audited Machine Learning for Explainable Microcredit Risk Screening at Bank Mandiri Puja Malika; Ari Muzakir; Evi Yulianingsih; Maria Ulfa; Nia Oktaviani
International Journal of Education, Vocational and Social Science Vol. 5 No. 04 (2026): International Journal of Education, Vocational and Social Science( IJVESS)
Publisher : Cita konsultindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijevss.v5i04.6418

Abstract

Accuracy can misrank models when at-risk borrowers are rare and missed risk is costlier than unnecessary review. This study evaluates a leakage-audited, explainable machine-learning framework for microcredit risk screening at Bank Mandiri and addresses prior work that rarely links temporal feature availability, class imbalance, and asymmetric error costs. A quantitative cross-sectional experiment used 1,718 anonymized active-facility records from four Palembang microbusiness units. An audit reduced 29 candidate fields to 13 operational predictors. Logistic Regression, Decision Tree, and Random Forest were evaluated through a stratified 80:20 split and training-only fivefold cross-validation. A tuned Decision Tree achieved the highest test accuracy (96.22%; 331/344) but detected 15 of 24 at-risk facilities. Random Forest at a training-selected 0.46 threshold achieved 95.35% accuracy and detected 18 of 24. The accuracy difference was not significant (exact McNemar p = .607). Cost analysis showed that thresholded Random Forest becomes preferable when a false negative costs more than twice a false positive. Account liquidity explained 88.72% of Decision Tree impurity reduction. The study contributes a transparent governance rule connecting leakage control, interpretability, and asymmetric screening costs while preserving human oversight.
Co-Authors Evi Yulianingsih Maria Ulfa Nia Oktaviani Puja Malika