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Credit Risk Analysis in Peer-to-Peer Lending System

Vinod Kumar L · S Natarajan · S Keerthana · K M Chinmayi

46 citations0/0 files storedIEEE International Conference on Knowledge Engineering and Applications (ICKEA)

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Financial Distress and Bankruptcy Prediction Microfinance and Financial Inclusion FinTech, Crowdfunding, Digital Finance

Abstract

This research paper aims to analyze the credit risk involved in peer-to-peer (P2P) lending system of “LendingClub” Company. The P2P system allows investors to get significantly higher return on investment as compared to bank deposit, but it comes with a risk of the loan and interest not being repaid. Ensemble machine learning algorithms and preprocessing techniques are used to explore, analyze and determine the factors which play crucial role in predicting the credit risk involved in “LendingClub” publicly available 2013–2015 loan applications dataset. A loan is considered “good” if it's repaid with interest and on time. The algorithms are optimized to favor the potential good loans whilst identifying defaults or risky credits.

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References

2007A neural network approach for credit risk evaluation 2015A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring 2011A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine 2009Credit Risk Management: Basic Concepts, Financial Risk Components, Rating Analysis, Models, Economic and Regulatory Capital

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