Ranking Prior Alignment for Credit Risk Modeling: When Do External Priors Matter?
We propose Ranking Prior Alignment, a model-agnostic framework that distills external ranking priors (from domain experts, teacher models, or LLMs) into any scoring model via a temperature-scaled KL divergence loss.
ProofPaper ↗
Key points
- Cold-start credit scoring -- deploying models with scarce labeled data, weak features, or minimal capacity -- is a recurring problem in financial machine learning.
- When a new lending product launches, labeled default data is scarce, feature pipelines are immature, and models must be deployed with minimal capacity to avoid overfitting.
- On an industrial dataset of over 1.5M merchants, MIL alignment achieves 7/7 positive evaluation cells at 3K bags (1 ID + 3 OOT + 3 degradation metrics; peak Delta AUC = +0.020 on OOT-1), and XGBoost ablation achieves 9/9 positive metrics at 300 bags.
- We further observe that alignment gains exhibit an inverse-scaling pattern: benefits grow as data abundance N, model capacity C, and feature quality Q decrease, helping practitioners decide when to invest in prior annotation.
Sources (1)
- [1]Ranking Prior Alignment for Credit Risk Modeling: When Do External Priors Matter?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:11 AM
We propose Ranking Prior Alignment, a model-agnostic framework that distills external ranking priors (from domain experts, teacher models, or LLMs) into any scoring model via a temperature-scaled KL divergence loss.
Cold-start credit scoring -- deploying models with scarce labeled data, weak features, or minimal capacity -- is a recurring problem in financial machine learning.
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