ISCSR Research Publishing
Economic Policy and Enterprise Development
Research Article Published October 9, 2026 Article eped20260005

Causal Shortcut Mitigation for Credit Default Prediction under Market Regime Shift

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Abstract

Credit default prediction models may rely on unstable shortcuts such as temporary income patterns, regional lending policies, platform-specific user behavior, or short-term macroeconomic signals. These correlations can be useful during training but may fail when market conditions change. This study investigates causal shortcut mitigation for credit risk prediction under regime shift. We propose a Causal Risk Stabilization Model (CRSM), which separates persistent borrower risk indicators from shortcut-sensitive variables by combining environment-wise feature instability testing, causal regularization, and counterfactual covariate reweighting. The model was trained on 1.26 million loan records from 2018 to 2024, including borrower demographics, repayment history, debt ratio, credit utilization, employment type, loan purpose, and regional economic indicators. Three testing environments were constructed: pre-pandemic lending, pandemic-period lending, and post-rate-hike lending. Compared with XGBoost and a standard multilayer perceptron, CRSM improved out-of-time AUC from 0.713 to 0.768 and reduced default-risk calibration error from 0.084 to 0.047. In high-volatility regions, the model reduced false approval cases by 15.2% while maintaining comparable approval coverage. These findings suggest that causal shortcut mitigation can improve credit model stability when borrower behavior and macroeconomic conditions change.

Keywords
Credit default predictioncausal regularizationshortcut mitigationfinancial riskregime shiftrobust machine learning
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Publication details
Journal
Economic Policy and Enterprise Development
Volume
1 (2026)
Issue
1 · Forthcoming issue
Article number
eped20260005
License
CC BY 4.0