A Factor Clustering Approach To Credit Scoring

This paper investigates how factor clustering-based approach to segment and estimate a statistical-based credit score for 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