Vol.10, No.1, February 2021.                                                                                                                                                                           ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending


Yanka Aleksandrova


© 2021 Yanka Aleksandrova, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 10, Issue 1, Pages 133-143, ISSN 2217-8309, DOI: 10.18421/TEM101-16, February 2021.


Received: 08 November 2020.

Revised:   14 January 2021.
Accepted: 20 January 2021.
Published: 27 February 2021.




The purpose of this research is to evaluate several popular machine learning algorithms for credit scoring for peer to peer lending. The dataset to fit the models is extracted from the official site of Lending Club. Several models have been implemented, including single classifiers (logistic regression, decision tree, multilayer perceptron), homogeneous ensembles (XGBoost, GBM, Random Forest) and heterogeneous ensemble classifiers like Stacked Ensembles. Results show that ensemble classifiers outperform single ones with Stacked Ensemble and XGBoost being the leaders.


Keywords: machine learning, peer to peer lending, credit scoring, ensemble classifiers, XGBoost.



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