DATA MINING APPLICATION FOR FINANCIAL DECISION OPTIMIZATION AT RISK

Financial decisions can add value to the existence of businesses or individuals, as well as a wrong financial decision can cause businesses to cease to exist. Hence, financial decision or financial assumptions are vital for businesses or individuals. In financial assumptions, risk refers to the probability of losing as a result of an investment made in an asset. Measures can be taken against possible risks in the future through financial assumptions. In this study, the logistic regression analysis (LR) method, one of the traditional methods, and the machine learning algorithm support vector machines (SVM) method, 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, the estimation power of the model, the estimation performance of the model, the determination of the importance of the independent variables that affect the non-repayment of the loan, and the superiority of the methods. According to the analysis results, the SVM method is superior to the LR method in calculating accuracy rate and prediction rate, and the LR method is superior to the SVM method in assumption performance calculation. The most significant variable in the SVM method is "Lending policy", the most significant variable in the LR method is "Interest rate", the second significant variable is "Interest rate" in the SVM method, and "Lending Policy" as the second important variable in the LR method. It is seen that the third most crucial variable in the two methods is the "Income" variable. The determination of the SVM method as the more important variable of the loan policy is deemed more suitable to the opinion of the banking expert. Detecting more realistic results of the SVM method compared to the LR method has shown the superiority of the SVM method.

Keywords:

Support Vector Machines, Logistic Regression Data Mining, Decision Making Under Risk,

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