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 have usually multiple features gained from different data sources and models. In this paper, a new similarity measure is proposed to effectively integrate the information from different sources and models for credit assessment in P2P lending. Specifically, we firstly deconstruct the characteristics of P2P lending and present four heterogeneous distance functions to measure the loans’ similarity, which are generated by different models and information sources. Then, we propose an integrated similarity measure that combines the above similarities by minimizing their conflicts, which overcomes the bias of the single model and single information source. Finally, we employ the portfolio selection model to develop our investment strategy. Experimental results using real datasets from Prosper demonstrate that our integrated similarity measure improves the performance of the instance-based credit assessment in P2P lending.