P2P Lending Platforms Bankruptcy Prediction Using Fuzzy SVM with Region Information

Menghan Wang · Xiaolin Zheng · Mengying Zhu · Zhongkai Hu

12 citations0/0 files storedIEEE International Conference on e-Business Engineering (ICEBE)

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Financial Distress and Bankruptcy Prediction FinTech, Crowdfunding, Digital Finance Evaluation and Optimization Models

Abstract

P2P Online lending has enjoyed exponential growth with multifold increases across all main indicators such as the number of customers, market volumes, and business turnovers. However, the P2P lending industry is flawed due to low quality of risk control. In this paper, we focus on Chinese P2P lending platforms and propose a novel method named FSVM-RI, which uses fuzzy SVM algorithm with region information to predict platform bankruptcy. Experiments on real-world datasets show that our proposed method exploits the region information and yields higher classification rate than other state-of-the-art classifiers when outliers and missing values exist in the dataset.

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References

2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 2011What's in a Picture?: Evidence of Discrimination from Prosper.com 2005A new fuzzy support vector machine to evaluate credit risk 2012Herding behavior in online P2P lending: An empirical investigation 2010What’s in a Picture?2008Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach 2010Fuzzy Support Vector Machine for bankruptcy prediction

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