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Forecasting Loan Default in Europe with Machine Learning

Author

Listed:
  • Luca Barbaglia
  • Sebastiano Manzan
  • Elisa Tosetti

Abstract

We use a dataset of 12 million residential mortgages to investigate the loan default behavior in several European countries. We model the default occurrence as a function of borrower characteristics, loan-specific variables, and local economic conditions. We compare the performance of a set of machine learning algorithms relative to the logistic regression, finding that they perform significantly better in providing predictions. The most important variables in explaining loan default are the interest rate and the local economic characteristics. The existence of relevant geographical heterogeneity in the variable importance points at the need for regionally tailored risk-assessment policies in Europe.

Suggested Citation

  • Luca Barbaglia & Sebastiano Manzan & Elisa Tosetti, 2023. "Forecasting Loan Default in Europe with Machine Learning," Journal of Financial Econometrics, Oxford University Press, vol. 21(2), pages 569-596.
    • Handle: RePEc:oup:jfinec:v:21:y:2023:i:2:p:569-596.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbab010
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    More about this item

    Keywords

    big data; credit risk; loan default; machine learning; regional analysis;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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