A new integrated similarity measure for enhancing instance-based credit assessment in P2P lending

Abstract

Instance-based learning has been proved to be effective for credit assessment in Peer-to-peer (P2P) lending. A key challenge of this application is how to measure the similarity of loans, which usually have multiple features gained from different data sources and models. In this paper, a new similarity measure is introduced to effectively integrate the information from different sources and models for credit assessment in P2P lending. Specifically, we firstly deconstructed the characteristics of P2P lending and presented four heterogeneous distance functions, which were generated by different models and information sources, to measure the loans’ similarity. Then, we proposed an integrated similarity measure that combined the above similarities by minimizing their conflicts, which could overcome the bias of the single model and single information source. Finally, we employed the portfolio selection model to develop our investment strategy. Experimental results using real datasets from Prosper demonstrated that our integrated similarity measure improves the performance of the instance-based credit assessment in P2P lending.

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