The Relevance of Soft Information for Predicting Small Business Credit Default: Evidence from a Social Bank

Simon Cornée ()
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Simon Cornée: CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique

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Abstract: Using a unique, hand-collected database of 389 small loans granted by a French social bank dealing with genuinely small, informationally opaque businesses (mainly social enterprises), our study highlights the relevance of including soft information (especially on management quality) to improve credit default prediction. Comparing our findings with those of previous studies also reveals that the more opaque the borrower, the higher the predictive value of soft information in comparison with hard. Finally, a cost-benefit analysis shows that including soft information is economically valuable once collection costs have been accounted for, albeit to a moderate extent.

Keywords: Social Banking; Credit Rating; Relationship Lending; Credit Default Prediction (search for similar items in EconPapers)
Date: 2019-07
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Citations: View citations in EconPapers (1)

Published in Journal of Small Business Management, 2019, 57 (3), pp.699-719. ⟨10.1111/jsbm.12318⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-02477820

DOI: 10.1111/jsbm.12318

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