Early-Warning Stress Testing for Consumer Loans Based on Dynamic Macroeconomic Scenario Generation

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Published: 2026-02-14
Keywords:
Stress testing, consumer credit, Macroeconomic simulation, scenario analysis, default forecasting

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Louis Bernard
Department of Economics, Université PSL (Paris Sciences et Lettres), Paris, 75005, France
Manon Giroux
Department of Economics, Université PSL (Paris Sciences et Lettres), Paris, 75005, France
Nicolas Faure
Department of Economics, Université PSL (Paris Sciences et Lettres), Paris, 75005, France
Sophie Lambert
Department of Economics, Université PSL (Paris Sciences et Lettres), Paris, 75005, France

Abstract

This study proposes a dynamic stress-testing framework for consumer-finance portfolios by generating forward-looking macroeconomic scenarios using a vector autoregression model combined with Monte Carlo simulation. The scenarios are fed into a panel logistic-regression engine trained on 4.2 million loan accounts from 2015-2024. Using 1,000 simulated macro paths, the model quantifies stressed default probabilities up to 12 months ahead. Results show that under the 5% most adverse scenarios, 90-day delinquency rates rise by 28.9-46.2%, depending on product type. The model achieves an ROC-AUC of 0.82 in out-of-sample stress periods and captures 72.5% of actual default surges during the 2020 downturn. This approach offers a systematic tool for scenario-driven risk assessment in consumer lending.

Article Details

Issue

Vol. 2 No. 1 (2026)

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Articles

How to Cite

Early-Warning Stress Testing for Consumer Loans Based on Dynamic Macroeconomic Scenario Generation. (2026). Journal of Science, Innovation & Social Impact, 2(1), 139-145. https://sagespress.com/index.php/JSISI/article/view/88

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