PaMIR 开源基准发布：19 个公开信用违约数据集统一评测
PaMIR: Open Benchmark of Public Credit-Default Datasets
做风控或信用建模的可以看看，19 个公开数据集、124 万条记录，统一防泄漏流程，比以往只用 2-4 个公开数据集的基准全多了。
arXiv 论文 2610.03259 发布 PaMIR 基准 0.4.0 版本，针对标签稀少且延迟到达的信用违约预测场景。基准汇集 19 个公开数据集，覆盖 124 万条来自 9 个国家的贷款、企业和信用卡账户记录，通过统一的防泄漏流程从固定源快照重建。评测要求每个模型按申请到达时点打分，AUC 按标签预算分组报告，并包含一个防止生成器接触保留数据的合成数据测试框架。
PaMIR: Open Benchmark of Public Credit-Default Datasets
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.
