PaMIR (Public Arrival-ordered Measurement for Inference in Risk) is an open benchmark for credit‑default prediction that addresses the challenge of scarce and late‑arriving labels. It aggregates 19 public datasets—totaling 1.24 million loans, firms, and card accounts from nine countries—into a single, leakage‑audited recipe that is never redistributed. Models are evaluated via repeated i.i.d. splits and a label‑delayed stream, with AUC reported by label budget, and a synthetic‑data harness tests training rows without exposing held‑out data.
arXiv:2608. 08202v1 Announce Type: new Abstract: Data-centric curation pipelines frequently rely on model confidence scores to flag and filter noisy or mislabeled training instances.
arXiv:2605.18147v3 Announce Type: replace Abstract: Predictive models are central to credit risk management, where the accuracy of default-probability and loss estimates affects the profitability of...
arXiv:2609.30316v1 Announce Type: cross Abstract: Language models used in financial backtests suffer from look-ahead bias, as a model trained on text published after the study period has already obse...
arXiv:2608. 02664v1 Announce Type: cross Abstract: Deploying ML models in regulated decision-making (credit underwriting, fraud detection, loan approval) requires demonstrating fairness and robustness to auditors without exposing model weights or customer data.
arXiv:2608.24582v1 Announce Type: cross Abstract: Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regres...
The paper presents a locked, multi‑signal audit protocol designed to detect supervision drift in credit‑risk models that use proxy labels. It comprises five layers—transfer performance, an oracle‑gap probe, a calibration diagnostic, feature‑label stability, and a synthetic positive control—each with predefined thresholds and decision rules. Applied to a public LendingClub dataset, the protocol shows stable ranking, small oracle gaps, and identifies a prevalence and probability‑scale mismatch that recalibration largely mitigates, though its root cause remains unclear.
arXiv:2606. 03305v1 Announce Type: new Abstract: Benchmark contamination, where evaluation examples appear in a model's training data, threatens the validity of LLM assessment.
arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.
The study evaluates how scientific explanations influence experimental forecasts by comparing paired predictions that share the same intervention, forecaster, and outcome but differ in description, matched explanation, and donor context. Across 336 prospective states, 12 Tox21 endpoints, and 24 OpenML tasks, the predictive credit of explanations was largely inconclusive, with key metrics such as ROC AUC interval-score and point-equivalence rules unmet. While certain model variants (e.g., DeepSeek V4 Pro) reduced drift and improved some error metrics, overall matched point-accuracy gains and natural-explanation credit remained unconfirmed at the tested donor resolutions.
arXiv:2605.24564v2 Announce Type: replace Abstract: Backtesting large language models (LLMs) on historical financial data is unreliable when their pre-training data include the evaluated events. An L...
arXiv:2610.08869v1 Announce Type: cross Abstract: Regulated credit scoring requires scores monotone non-decreasing in every exposure input. Deployed pipelines -- hand-crafted monotone aggregates feed...
arXiv:2606. 18479v1 Announce Type: new Abstract: Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood.