Identifying features for detecting fraudulent loan requests on P2P platforms

Abstract

This exploratory study is intended to address the problem of fraudulent loan requests on peer-to-peer (P2P) platforms. We propose a set of features that capture the behavioral characteristics (e.g., learning, past performance, social networking, and herding manipulation) of malevolent borrowers, who intentionally create loan requests to acquire funds from lenders but default later on. We found that using the widely adopted classification methods such as Random Forest and Support Vector Machines, the proposed feature set outperform the baseline feature set in helping detect fraudulent loan requests. Although the performance (e.g., Recall or Sensitivity) is still not up to its optimum, this study demonstrates that by analyzing the transaction records of confirmed malevolent borrowers, it is possible to capture some useful behavioral patterns for fraud detection. Such features and methods would possibly help lenders identify loan request frauds and avoid financial losses.

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