Proceedings ArticleDOI
Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques
Luís Ferreira,Jean Paul Barddal,Heitor Murilo Gomes,Fabrício Enembreck +3 more
- pp 175-181
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TLDR
This work wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016, and analysis how supervised classification models and techniques to handle class imbalance impact creditworthiness prediction rates shows that sampling techniques outperform ensembles and cost sensitive approaches.Abstract:
Peer-to-peer (P2P) lending is a global trend of financial markets that allow individuals to obtain and concede loans without having financial institutions as a strong proxy. As many real-world applications, P2P lending presents an imbalanced characteristic, where the number of creditworthy loan requests is much larger than the number of non-creditworthy ones. In this work, we wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016. We analyze how supervised classification models and techniques to handle class imbalance impact creditworthiness prediction rates. Ensembles, cost-sensitive and sampling methods are combined and evaluated along logistic regression, decision tree, and bayesian learning schemes. Results show that, in average, sampling techniques outperform ensembles and cost sensitive approaches.read more
Citations
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Journal ArticleDOI
Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
TL;DR: This work assesses the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending and reveals that the machine learning alternative is superior in terms of not only classification performance but also explainability.
Journal ArticleDOI
Peer to Peer (P2P) Lending Problems and Potential Solutions: A Systematic Literature Review
TL;DR: This study aims to identify problems in P2P Lending and present alternative technical and non-technical solutions to the problems and finds a rich picture, creates a table of problem identification and alternative solutions.
Journal ArticleDOI
Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams
TL;DR: This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI), which consists of a long-term static classifier to handle gradual and multiple dynamic classifiers to handle sudden concept drift.
Proceedings ArticleDOI
Metric Learning from Imbalanced Data
TL;DR: In this paper, a new Mahalanobis metric learning algorithm (IML) is proposed to deal with class imbalance in the metric learning problem, where the number of positive examples is much smaller than the negatives.
Journal ArticleDOI
Risk-Return modelling in the P2P lending market: Trends, Gaps, Recommendations and future directions
Miller Janny Ariza-Garzon,María-del-Mar Camacho-Miñano,María Jesús Segovia-Vargas,Javier Arroyo +3 more
TL;DR: A bibliometric and systematic analysis is performed on the academic literature published during the last decade on P2P lending to identify the main research trends and find potential gaps that limit stakeholders' use of research proposals.
References
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