Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/249459 
Autor:innen: 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
ADBI Working Paper No. 1280
Verlag: 
Asian Development Bank Institute (ADBI), Tokyo
Zusammenfassung: 
Lending institutions' reluctance to lend to MSMEs or to offer them competitive interest rates stems from the relatively costly information acquisition for small loans. The central idea is to bridge the information gap between the demand and the supply side by creating a credit analytics sharing infrastructure through federated learning, which completely respects data privacy. Pooling credit information across multiple lending institutions, particularly rare default events, enables the construction of a more informative credit model for MSMEs, which can then serve as a common good among lenders. The technology also allows for lender-specific models, which in essence share the model's parameters on the common prediction variables while differing in their respective alternative data fields. The lenders in the MSME space can work like a coopetition and continue to compete with their varying risk appetites, loan rates, and banking services. We use real MSME credit data to demonstrate the feasibility of the sharing technology and to study the impact of the COVID-19 pandemic via a portfolio that we assembled from four hypothetical banks operating in six ASEAN countries.
Schlagwörter: 
COVID-19
coopetition
alternative data
federated learning
default
JEL: 
C1
C8
G21
Creative-Commons-Lizenz: 
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Dokumentart: 
Working Paper

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