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), one of the traditional methods, and the machine learning algorithm, Support Vector Machines (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, 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 techniques. According to the analysis results, the SVM technique is superior to the LR technique in calculating accuracy rate and prediction rate, and the LR technique is superior to the SVM technique in assumption performance calculation. The most significant variable in the SVM technique is "Lending policy", the most significant variable in the LR technique is "Interest rate", the second significant variable is "Interest rate" in the SVM technique, and "Lending Policy" as the second important variable in the LR technique. It is seen that the third most crucial variable in the two techniques is the "Income" variable. The determination of the SVM technique 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 technique compared to the LR technique has shown the superiority of the SVM technique.

In trade, the risks taken may increase the expected income; however, they may also cause large amounts of losses as well. Banks transfer the capital and the deposits they collect from their clients to the individuals or institutions in need of profit, taking certain risks into account. One of the important risks taken in this process of capital transfer is the market's change in interest or profit share rates. If the bank transfers the deposit collected with a certain commitment to the market at a lower rate, it will make a loss. Models for predicting future interest or profit share rates gain importance for preventing this situation. The aim of this study is to determine which variables will be taken into account for the loan interest rate that banks will offer to their customers during the lending process, and to create a machine learning model that can predict the loan interest rate that the bank will offer to its customers by using these variables. Multiple Linear Regression analysis was performed to demonstrate the relationship between the variables selected based on the literature review, expert opinions, and the interest rate. In order to facilitate decision-makers in practice, Random Forests, Decision Trees, K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) algorithms from machine learning algorithms were compared by using the prediction model. Accuracy Rate, Cohen's Kappa, Precision, Sensitivity, and F-Measure measurements were used to compare the algorithms used in the study. According to the analysis results, it was observed that the Random Forest algorithm was more successful on the first model consisting of weekly data. The Decision Tree algorithm succeeded more on the second model consisting of monthly data prediction performance. In the model consisting of weekly data, USD Selling Price, Stock Index (BIST100), and Central Bank Gold Reserve from the Multiple Linear Regression variables were found significant in affecting the interest rate.