{
  "abstract": "The use of run-off triangles (ROTs) is a common industry practice in estimating the loss given default (LGD) risk parameter when predicting credit losses in banking. We benchmark this industry practice using credit card data against a more sophisticated (though classical) regression-based approach, which is able to leverage various types of input variables in producing loan-level LGD-estimates. This regression-based approach can demonstrably recover the typical characteristics of the 'U-shaped' empirical LGD-distribution, which the ROT-based approach cannot do. First, we critically review the ROT-based approach and identify multiple demerits using data-driven diagnostics. We then estimate a two-stage regression-based LGD-model and favourably assess the model performance of each component (or 'stage'). Finally, we aggregate the LGD-estimates produced by each approach over time, and compare each time series to the mean empirical loss rate over time. The ROT-based aggregates diverge substantially from the empirical rate over most time periods, whilst the regression-based aggregates follow the empirical trends much closer. These results underscore the greater prediction accuracy of the regression-based LGD-model, relative to the ROT-based one. By implication, the former approach is probably better than the latter ROT-based approach when estimating the LGD under the IFRS 9 accounting framework, which prioritises accuracy.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.05926",
  "author_total": 4,
  "authors": [
    {
      "name": "Arno Botha"
    },
    {
      "name": "Henko Crewe"
    },
    {
      "name": "Marcel Muller"
    },
    {
      "name": "Janette Larney"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/95arr8d5y8x61.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.05926",
  "fulltext_url": null,
  "id": "95arr8d5y8x61",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.05926",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-fin.RM"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2003|Credit Risk and Financial Regulations"
    ]
  ],
  "openalex": "W7220712692",
  "pdf_url": "https://arxiv.org/pdf/2610.05926",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/95arr8d5y8x61.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:16Z",
      "native_id": "2610.05926",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.05926"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Comparing two approaches for modelling the loss given default of credit cards: Run-off triangles vs regression",
  "title_text": "Comparing two approaches for modelling the loss given default of credit cards: Run-off triangles vs regression",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:16Z",
  "url": "https://research.draconitic.com/paper/95arr8d5y8x61",
  "venue": "arXiv",
  "versions": []
}