Makokha, Christopher WatitwaKube, AnandaNgesa, Oscar2024-05-272024-05-272024-05https://doi.org/10.4236/jdaip.2024.122009https://ir-library.ku.ac.ke/handle/123456789/27902ArticlePeer-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%.enCredit-ScoringClusteringClassificationNeural NetworksArticle