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. We systematically evaluate several such methods and uncover a structural failure mode: in a natural retraining cycle, models whose accuracy improves while recall collapses create an illusion of improvement that leads practitioners to believe the system is getting better when, in fact, its rejection quality -- the ability to correctly screen out defaulters -- is deteriorating. We then propose a controlled explora
This research is published as AI systems become increasingly integrated into critical financial decision-making processes, highlighting the urgent need for robust evaluation methods.
A strategic reader needs to understand that current AI evaluation metrics can obscure critical functional failures, potentially leading to increased financial risk and misallocation of capital.
The understanding of AI model performance in credit scoring shifts from simple accuracy metrics to a focus on the real-world impact of rejection quality, necessitating new evaluation paradigms.
- · AI ethicists
- · Risk management firms
- · Regulators
- · Specialized AI auditing tools
- · Financial institutions relying solely on basic accuracy metrics
- · AI model developers ignoring rejection quality
- · Consumers unfairly rejected by flawed systems
Financial institutions may face increased scrutiny for their AI-driven credit scoring models and potentially revise their evaluation methodologies.
New regulatory guidelines and industry standards could emerge specifically addressing the 'illusion of improvement' in AI systems used for high-stakes decisions.
Public trust in AI systems for financial services may erode if these structural failures become widespread, prompting a demand for greater transparency and explainability.
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Read at arXiv cs.LG