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

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dc.contributor.authorMakokha, Christopher Watitwa
dc.contributor.authorKube, Ananda
dc.contributor.authorNgesa, Oscar
dc.date.accessioned2024-05-27T08:22:47Z
dc.date.available2024-05-27T08:22:47Z
dc.date.issued2024-05
dc.descriptionArticleen_US
dc.description.abstractPeer-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%.en_US
dc.identifier.urihttps://doi.org/10.4236/jdaip.2024.122009
dc.identifier.urihttps://ir-library.ku.ac.ke/handle/123456789/27902
dc.language.isoenen_US
dc.subjectCredit-Scoringen_US
dc.subjectClusteringen_US
dc.subjectClassificationen_US
dc.subjectNeural Networksen_US
dc.titleA Hybrid Approach for Predicting Probability of Default in Peer-to-Peer (P2P) Lending Platforms Using Mixture-of-Experts Neural Networken_US
dc.typeArticleen_US
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