Predicting Peer to Peer Lending Loan Risk Using Classification Approach

Abstract

Technological innovations have affected all sectors of life, especially, the financial sector with the emergence of financial technology. One of them is marked by the emergence of Peer-to-Peer Lending ("P2P Lending). Credit Risk Management is essential to P2P Lending as it directly affects business results, therefore it is important for P2P Lending to predict borrowers with the highest probability to become good or bad loans based on their profile or characteristics. In the experiments, five classification algorithms are used, which are Gradient Boosted Trees, Naïve Bayes, Random Forest, Decision Tree and Logistic Regression. The result is two modelling performed well that is Random Forest with accuracy 93.38% and Decision Tree with 92.35%.