Data mining application for financial decision optimization at risk
| dc.contributor.author | Koçoğlu, Enes | |
| dc.contributor.author | Ersöz, Filiz | |
| dc.date.accessioned | 2024-09-29T16:31:09Z | |
| dc.date.available | 2024-09-29T16:31:09Z | |
| dc.date.issued | 2021 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description.abstract | Inancial decisions can add value to the existence of businesses or individuals, as well as a wrongfinancial decision can cause businesses to cease to exist. Hence, financial decision or financialassumptions are vital for businesses or individuals. In financial assumptions, risk refers to theprobability of losing as a result of an investment made in an asset. Measures can be taken againstpossible risks in the future through financial assumptions. In this study, the Logistic RegressionAnalysis (LR), one of the traditional methods, and the machine learning algorithm, Support VectorMachines (SVM) technique, which is one of the new approaches, are compared in the loaning process.It is aimed to determine the importance of the compared methods, the accuracy of the model, theestimation power of the model, the estimation performance of the model, the determination of theimportance of the independent variables that affect the non-repayment of the loan, and the superiorityof the techniques. According to the analysis results, the SVM technique is superior to the LRtechnique in calculating accuracy rate and prediction rate, and the LR technique is superior to theSVM technique in assumption performance calculation. The most significant variable in the SVMtechnique is \"Lending policy\", the most significant variable in the LR technique is \"Interest rate\", thesecond significant variable is \"Interest rate\" in the SVM technique, and \"Lending Policy\" as the secondimportant variable in the LR technique. It is seen that the third most crucial variable in the twotechniques is the \"Income\" variable. The determination of the SVM technique as the more importantvariable of the loan policy is deemed more suitable to the opinion of the banking expert. Detectingmore realistic results of the SVM technique compared to the LR technique has shown the superiorityof the SVM technique. | en_US |
| dc.identifier.doi | 10.46519/ij3dptdi.950062 | |
| dc.identifier.endpage | 209 | en_US |
| dc.identifier.issn | 2602-3350 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.startpage | 195 | en_US |
| dc.identifier.trdizinid | 441280 | en_US |
| dc.identifier.uri | https://doi.org/10.46519/ij3dptdi.950062 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/441280 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/11204 | |
| dc.identifier.volume | 5 | en_US |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | International Journal of 3D Printing Technologies and Digital Industry | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.title | Data mining application for financial decision optimization at risk | en_US |
| dc.type | Article | en_US |
