# PaMIR: Open Benchmark of Public Credit-Default Datasets

arXiv: https://arxiv.org/abs/2610.03259 · HTML: https://mlsanity.com/paper/2610.03259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 18 of 18)

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- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
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- [Benchmarking Distribution Shift in Tabular Data with TableShift](https://mlsanity.com/paper/2312.07577/) (2023)
- [TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation](https://mlsanity.com/paper/2410.20626/) (2024)
- [Heavy-Tailed Diffusion Models](https://mlsanity.com/paper/2410.14171/) (2024)
- [TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks](https://mlsanity.com/paper/2406.19380/) (2024)
- [Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models](https://mlsanity.com/paper/2310.00566/) (2023)
- [Beyond IID: How General Are Tabular Foundation Models, Really?](https://mlsanity.com/paper/2606.30410/) (2026)
- [Label Delay in Online Continual Learning](https://mlsanity.com/paper/2312.00923/) (2023)
- [Foundation Models for Credit Risk Prediction: A Game Changer?](https://mlsanity.com/paper/2605.18147/) (2026)
- [MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains](https://mlsanity.com/paper/2505.14312/) (2025)
- [What's the Price of Monotonicity? A Multi-Dataset Benchmark of Monotone-Constrained Gradient Boosting for Credit PD](https://mlsanity.com/paper/2512.17945/) (2025)
- [textttfindr: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions](https://mlsanity.com/paper/2608.24582/) (2026)

## Abstract (arXiv)

We release PaMIR (Public Arrival-ordered Measurement for Inference in Risk), an open benchmark for credit-default prediction when labels are scarce and arrive late. The field's reference benchmark studies use eight datasets each, only two or four of them public. PaMIR brings together 19 public datasets with binary default labels -- 1.24M loans, firms and card accounts from nine countries -- rebuilt from pinned source snapshots by one leakage-audited recipe and never redistributed; to our knowledge it is the one of its kind as of today. Every model is a single function, scored under a repeated i.i.d. split and a label-delayed stream in which each application is scored on arrival, with AUC reported by label budget; fleet means are withheld unless every dataset is scored. A synthetic-data harness tests generated training rows without letting a generator see held-out rows. This report describes release 0.4.0 of this living benchmark.