(2024-05) Makokha, Christopher Watitwa; Kube, Ananda; Ngesa, Oscar
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%.