Credit scoring for P2P lending: Difference between revisions
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|citation_keywords=Clustering, Credit Scoring, Factor Models, FinTech, P2P Lending, Segmentation | |citation_keywords=Clustering, Credit Scoring, Factor Models, FinTech, P2P Lending, Segmentation | ||
|citation_publisher=Top Italian Scientists | |citation_publisher=Top Italian Scientists | ||
|citation_pdf_url=https://en.wiki.topitalianscientists.org/images/7/72/Credit_scoring_for_P2P_lending.pdf | |||
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Revision as of 07:59, 25 April 2023
| Published |
| April 25, 2023 |
| Title |
| Credit scoring for P2P lending |
| Authors |
| Daniel Felix Ahelegbey, Paolo Giudici |
| Downloads |
Daniel Felix Ahelegbey, Paolo Giudici(a)
(a) Department of Economics and Management Sciences, University of Pavia, Italy.
Abstract
This paper shows how to improve the measurement of credit scoring by means of factor clustering. The improved measurement applies, in particular, to small and medium enterprises (SMEs) involved in P2P lending. The approach explore the concept of familiarity which relies on the notion that, the more familiar/similar things are, the more close they are in terms of functionality or hidden characteristics (latent factors that drive the observed data). The approach uses singular value decomposition to extract the factors underlying the observed financial performance ratios of the SMEs. We then cluster the factors using the standard k-mean algorithm. This allows us to segment the heterogeneous population into clusters with more homogeneous characteristics. The result shows that clusters with relatively fewer number of SMEs produce a more parsimonious and interpretable credit scoring model with better default predictive performance.
