Credit scoring for P2P lending: Difference between revisions
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<sup>(a)</sup> Department of Economics and Management Sciences, University of Pavia, Italy. | <sup>(a)</sup> 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. | |||
Revision as of 15:17, 24 April 2023
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.
