Byanjankar, A., Heikkila, M. and Mezei, J. (2015) Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach. Proceedings-2015 IEEE Symposium Series on Computational Intelligence (SSCI 2015), Cape Town, 7-10 December 2015, 719-725.
https://doi.org/10.1109/SSCI.2015.109

has been cited by the following article:

• TITLE: A Hybrid Approach for Predicting Probability of Default in Peer-to-Peer (P2P) Lending Platforms Using Mixture-of-Experts Neural Network

  AUTHORS: Christopher Watitwa Makokha, Ananda Kube, Oscar Ngesa

  KEYWORDS: Credit-Scoring, Clustering, Classification, Neural Networks

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.12 No.2, April 26, 2024

  ABSTRACT: Peer-to-peer (P2P) lending offers an alternative way to access credit. Unlike established lending institutions with proven credit risk management practices, P2P platforms rely on numerous independent variables to evaluate loan applicants’ creditworthiness. This study aims to estimate default probabilities using a mixture-of-experts neural network in P2P lending. The approach involves coupling unsupervised clustering to capture essential data properties with a classification algorithm based on the mixture-of-experts structure. This classic design enhances model capacity without significant computational overhead. The model was tested using P2P data from Lending Club, comparing it to other methods like Logistic Regression, AdaBoost, Gradient Boosting, Decision Tree, Support Vector Machine, and Random Forest. The hybrid model demonstrated superior performance, with a Mean Squared Error reduction of at least 25%.