{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Rino"
    },
    {
      "name": "Aditiya Hermawan"
    },
    {
      "name": "Elfindah Princes"
    },
    {
      "name": "Satria Abadi"
    },
    {
      "name": "Umarkhon Mukhamedov"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fkd9bg05fge93.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1825",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1825",
  "id": "fkd9bg05fge93",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1825",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "1702|Imbalanced Data Classification Techniques"
    ]
  ],
  "openalex": "W7223266663",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1825/1050",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/fkd9bg05fge93.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:45Z",
      "native_id": "634d23b440ab48d186c039a74c70fd86",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1825"
    },
    {
      "first_seen": "2026-10-30T13:44:45Z",
      "native_id": "6f482f127ae5478daf8230eb9f3be7c5",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1825"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "lcc:QA1-939",
      "subfield": "general"
    }
  ],
  "title": "A Multimetric Evaluation of Ensemble and Neural Models for Imbalanced Credit Scoring on the German Credit Dataset",
  "title_text": "A Multimetric Evaluation of Ensemble and Neural Models for Imbalanced Credit Scoring on the German Credit Dataset",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:45Z",
  "url": "https://research.draconitic.com/paper/fkd9bg05fge93",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}