Name
Abstract | ||
|---|---|---|
In this paper, we investigate the extent to which features derived from bank statements provided by loan applicants, and which are not declared on an application form, can enhance a credit scoring model for a New Zealand lending company. Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously. We construct a baseline model based solely on the existing scoring features obtained from the loan application form, and a second baseline model based solely on the new bank statement-derived features. A combined feature model is then created by augmenting the application form features with the new bank statement derived features. Our experimental results using ROC analysis show that a combined feature model performs better than both of the two baseline models, and show that a number of the bank statement-derived features have value in improving the credit scoring model. The target data set used for modelling was highly imbalanced, and Naive Bayes was found to be the best performing model, and outperformed a number of other classifiers commonly used in credit scoring, suggesting its potential for future use on highly imbalanced data sets. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Learning | Data mining,Loan,Data set,Naive Bayes classifier,Bank statement,Feature model,Artificial intelligence,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1611.00252 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rory P. Bunker | 1 | 0 | 0.34 |
| Wenjun Zhang | 2 | 37 | 6.79 |
| M. Asif Naeem | 3 | 102 | 19.73 |