A Multimetric Evaluation of Ensemble and Neural Models for Imbalanced Credit Scoring on the German Credit Dataset
Abstract
Credit scoring models must balance global discrimination, minority-class detection, probability quality, and computational efficiency, particularly when default cases are less frequent than non-default cases. This study benchmarks Logistic Regression, Random Forest, XGBoost, and a Multilayer Perceptron using the German Credit Dataset, which contains 1,000 borrower records. All models were evaluated using a consistent preprocessing, partitioning, and imbalance-aware training protocol. Performance was assessed through ROC-AUC, PR-AUC, Balanced Accuracy, precision, recall, F1-score, Brier Score, and computational time. The results indicate moderate predictive performance across the evaluated models. XGBoost achieved the highest global discrimination with a ROC-AUC of 0.764, whereas the MLP obtained the highest PR-AUC of 0.378 and the lowest Brier Score of 0.123, indicating the best native probability quality among the evaluated models. No post-hoc probability calibration was applied. However, the MLP produced lower recall at the selected classification threshold. Logistic Regression required the least computation but recorded the lowest ROC-AUC and PR-AUC. Exploratory SHAP analysis was used to identify influential borrower attributes. These benchmark-specific findings demonstrate that model selection depends on the intended decision objective rather than architectural complexity alone.
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BibTeX
@article{rino2026multimetric,
title = {{A Multimetric Evaluation of Ensemble and Neural Models for Imbalanced Credit Scoring on the German Credit Dataset}},
author = {Rino and Aditiya Hermawan and Elfindah Princes and Satria Abadi and Umarkhon Mukhamedov},
journal = {Journal of Information Systems and Informatics},
year = {2026},
month = oct,
doi = {10.63158/journalisi.v8i5.1825},
url = {https://doi.org/10.63158/journalisi.v8i5.1825},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Multimetric Evaluation of Ensemble and Neural Models for Imbalanced Credit Scoring on the German Credit Dataset
AU - Rino
AU - Aditiya Hermawan
AU - Elfindah Princes
AU - Satria Abadi
AU - Umarkhon Mukhamedov
PY - 2026
DA - 2026/10/24
JO - Journal of Information Systems and Informatics
DO - 10.63158/journalisi.v8i5.1825
UR - https://doi.org/10.63158/journalisi.v8i5.1825
AB - Credit scoring models must balance global discrimination, minority-class detection, probability quality, and computational efficiency, particularly when default cases are less frequent than non-default cases. This study benchmarks Logistic Regression, Random Forest, XGBoost, and a Multilayer Perceptron using the German Credit Dataset, which contains 1,000 borrower records. All models were evaluated using a consistent preprocessing, partitioning, and imbalance-aware training protocol. Performance was assessed through ROC-AUC, PR-AUC, Balanced Accuracy, precision, recall, F1-score, Brier Score, and computational time. The results indicate moderate predictive performance across the evaluated models. XGBoost achieved the highest global discrimination with a ROC-AUC of 0.764, whereas the MLP obtained the highest PR-AUC of 0.378 and the lowest Brier Score of 0.123, indicating the best native probability quality among the evaluated models. No post-hoc probability calibration was applied. However, the MLP produced lower recall at the selected classification threshold. Logistic Regression required the least computation but recorded the lowest ROC-AUC and PR-AUC. Exploratory SHAP analysis was used to identify influential borrower attributes. These benchmark-specific findings demonstrate that model selection depends on the intended decision objective rather than architectural complexity alone.
SN - 2656-5935
SN - 2656-4882
ER -