Name
Title | ||
|---|---|---|
Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques. |
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. |
Year | Venue | Field |
|---|---|---|
2017 | ICTAI | Econometrics,Decision tree,Loan,Bayesian inference,Peer-to-peer,Computer science,Artificial intelligence,Sampling (statistics),Financial market,Machine learning,Credit risk,Market research |
DocType | Citations | PageRank |
Conference | 2 | 0.42 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Eduardo Boiko Ferreira | 1 | 2 | 0.42 |
| Jean Paul Barddal | 2 | 140 | 16.77 |
| Heitor Murilo Gomes | 3 | 155 | 17.36 |
| Fabrício Enembreck | 4 | 274 | 38.42 |