#5109. Incorporating multilevel macroeconomic variables into credit scoring for online consumer lending

July 2026publication date
Proposal available till 18-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Management of Technology and Innovation;
Computer Networks and Communications;
Computer Science Applications;
Marketing;
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Abstract:
Booming online consumer lending encounters high credit risk and thus needs well-designed credit scoring models. As a supplementary data source, multilevel macroeconomic variables (MVs), which means mixed regions, data frequency, and lags of MVs, are combined with application data for credit scoring. Moreover, we propose a Bayesian MV selection and lag optimization method to handle the highly correlated MVs and capture flexible lag effects. Validated on a real-world dataset from an anonymous online consumer lending platform, the empirical results confirm that multilevel MVs are significant determinants of consumer credit risk. The comparison of classification performance further implies the superiority of incorporating multilevel MVs and the proposed MV selection and lag optimization method relative to benchmarks. Gradient boosting decision tree-based approaches provide significantly better performance than industry benchmarks while maintaining interpretability via Shapley additive explanations values.
Keywords:
Consumer credit risk; Credit scoring; FinTech; Macroeconomic variables; Online consumer lending

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