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Credit scoring analysis using pseudo nearest neighbor
| Content Provider | Scilit |
|---|---|
| Author | Pratiwi, H. Mukid, M. A. Hoyyi, A. Widiharih, T. |
| Copyright Year | 2019 |
| Description | Journal: Journal of Physics: Conference Series Credit scoring is one of the crucial task and a core responsibility for financial institutions in their risk management. This study aims to apply the pseudo nearest neighbour (PNN) method as a tool to identify which prospective borrowers are eligible for their loan proposals. If a new borrower has characteristics closer to a good historical borrower then the loan proposal is worthy to approval. But if not, the proposed loan will be refused. The historical data in this paper are credit data from a national bank in Indonesia. The characteristics of historical debtors consist of age, amount of a child, length time of business, income, loans amount, and the period of credit. The best classification of k-NN is using k = 1, because it makes the smallest error 1,89%. While the best classification of PNN is using k = 13 with the smallest error 20,75%. Based on total accuracy of classification shows that the credit classification of debtors using k-NN is more appropriate than PNN. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1217/1/012100/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1217/1/012100 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1217 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-05-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Hardware and Architecture |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
