Ranking Prior Alignment Improves Cold-Start Credit Scoring in Low-Data Regimes
Preprint introduces model-agnostic framework that incorporates external ranking priors via temperature-scaled KL divergence to regularize credit models when labeled data…
- Framework unifies neural and tree-based models with single loss combining task objective and decaying KL term from external priors.
- Gains are largest in cold-start settings with scarce data or weak features, following an inverse-scaling pattern.
- Method requires no external model at inference and tolerates annotation noise up to 50 percent in tree-based version.
Cold-Start Credit Risk Challenge
Deploying credit-scoring models for new lending products frequently encounters scarce labeled defaults, immature feature pipelines and the need for low-capacity models to prevent overfitting. Standard approaches rely on the same limited data, creating demand for external regularization grounded in domain knowledge.
The preprint proposes Ranking Prior Alignment as a solution. It distills ranking information from domain experts, teacher models or large language models into any scoring model through a temperature-scaled Kullback-Leibler divergence loss.
Method and Implementation
The unified formulation is L = L_task + gamma(t) * KL(P_agent || P_model), where gamma follows an exponential decay schedule. This approach is model-agnostic, demonstrated across neural MIL attention, XGBoost with custom objective, LightGBM and logistic regression.
No external model is needed at inference time. The tree-based instantiation tolerates annotation noise up to eta = 0.5. Four different teacher architectures confirm that benefits are independent of the prior source.
Experimental Results
On an industrial dataset exceeding 1.5 million merchants, MIL alignment achieved positive results in all seven evaluation cells at 3K bags, with peak out-of-time AUC improvement of 0.020. The XGBoost version delivered positive outcomes across all nine metrics at 300 bags.
Cross-dataset validation on the public Amex benchmark produced positive results in all five folds, with average AUC lift of 0.041. Alignment gains exhibit an inverse-scaling pattern: improvements grow as labeled data N, model capacity C and feature quality Q decline.
Practical Implications
The observed inverse scaling helps practitioners decide when to invest in prior annotation. Benefits are most pronounced precisely in the cold-start scenarios where conventional regularization struggles.
While the preprint supplies promising empirical support on both proprietary and public data, full verification awaits peer-reviewed publication and independent replication. The abstract-only capture limits assessment of implementation details and robustness across additional financial datasets.
