Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending
importance 4/10
ai assisted
Financial Distress and Bankruptcy Prediction consumer lending stage: research
Problem
Lenders in peer-to-peer lending need to know which machine learning approach best predicts borrower default risk.
Why it matters
Accurate default prediction reduces lender losses in peer-to-peer lending markets.
What the AI did
Compared single classifiers, homogeneous ensembles (XGBoost, GBM, Random Forest) and heterogeneous stacked ensembles on Lending Club data for credit scoring.
Results
Ensemble classifiers outperformed single classifiers, with Stacked Ensemble and XGBoost leading.
Impact
Identifies best-performing algorithms for P2P default prediction on Lending Club data.
Record
| Venue | TEM Journal |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Yanka Aleksandrova |
| Citations | 24 (OpenAlex, at import time) |
| References | 28 |
| Model or method | XGBoost, Stacked Ensemble, Random Forest, GBM, logistic regression, decision tree, multilayer perceptron |
| Organization | not stated in source |
| Primary source | https://doi.org/10.18421/tem101-16 |
| Open access | gold |
| Keywords | credit scoring, peer-to-peer lending, ensemble learning, XGBoost, default risk |
Importance rating
4
real but narrower advance
Algorithm comparison for P2P default prediction on Lending Club data; real but incremental advance for lenders, no scale stated.
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