Name
Abstract | ||
|---|---|---|
Credit operations are indispensable in the organizational development of financial institutions. However, misconduct in these operations occurs, and this can lead to financial loss. These consequences are caused by incorrectly granting credit or incorrectly assigning customer ratings and can compromise a credit portfolio. The result shows that support vector machine is the most commonly used classifier for credit scores, and while the system performs well, it does not apply approaches with collateral. The analysis includes 84 studies in this article to propose using statistical methodology to conduct a meta-analysis to compare the results of classification methods. It shows some cases that consider various probability distributions and also survival data. It also elaborates that collateral is not the first approach for credit scoring. The credit scoring system can then give several starting credit scores according to the classifier the user wants to use. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JSYST.2019.2937552 | IEEE SYSTEMS JOURNAL |
Keywords | DocType | Volume |
Support vector machines, Systematics, Decision support systems, Banking, Statistical analysis, Bayes methods, Basel Accords, Bayesian network (BN), collateral, credit scoring, decision support system, neural network (NN), support vector machine (SVM) | Journal | 14 |
Issue | ISSN | Citations |
3 | 1932-8184 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Germanno Teles | 1 | 0 | 0.68 |
| JOEL J. P. C. RODRIGUES | 2 | 3484 | 341.72 |
| Kashif Saleem | 3 | 214 | 21.58 |
| Sergei A. Kozlov | 4 | 0 | 0.68 |