Credit Risk Classification in Peer-to-Peer Marketplaces: The Nexus of Neural Network Approach

Baah Alexander, Tan Zhongming, Ding Guoping, Albert Henry Ntarmah, Asare Evans Kwabena


Financial innovation in recent years have prominently contributed to the growth of Peer-to-Peer lending marketplaces allowing individual and businesses to secure loans on a common internet-based network. Similar to the ‘bricks and mortar’ banking system, online lending is coupled with the problem of information asymmetry. Borrower risk assessment has henceforth become the major concerns of various platforms that aim to reducing information imbalance towards mitigating credit risk. In this article, authors compared two learning algorithms – Logistic regression and Artificial Neural Network to classify borrowers based on loan repayment schedule. We revealed that both approaches were robust in classifying late borrowers with logistic regression being 0.02% more robust than Neural Network. Regarding variable relative importance, gender is considered the least important variable whereas terms-of-repayment is the most important variable affecting borrowers’ intention to pay off loans. Even though our study contributes to existing literature, it is however not limited to determining factors that may affect lenders’ investment decision in social lending.

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Copyright (c) 2019 Baah Alexander, Tan Zhongming, Ding Guoping, Albert Henry Ntarmah, Asare Evans Kwabena

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Business and Economic Research  ISSN 2162-4860

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