| Summary: | Credit scoring is a feasibility test system to provide financing with the aim of reducing the risk of default on mortgage financing (KPR). This study analyze the characteristics of customers of PT Bank XYZ and design a credit scoring model for mortgage financing. The data used are the demographics and quality of financing from January 2014 to December 2017. This study compared several methods namely descriptive analysis, Weight of Evidence (WoE) Information Value (IV) method, logistic regression analysis with imbalance data and logistic regression analysis with Synthetic Minority Over sampling Technique (SMOTE) to overcome unbalanced data problems between non default and default customers. The results of the descriptive and WoE IV method compared the logistic regression analysis are relatively different because they analyze the effect of each independent X variable on dependent Y partially without considering the interaction of each variable. The credit scoring model with unbalanced data has higher accuracy and sencitivity than the credit scoring model with the SMOTE method. However, specificity of the credit scoring model by using unbalanced data is lower than the credit scoring model with the SMOTE method. In this study, credit scoring model was created to mitigate credit risk by avoiding customers who have greater default opportunities so the credit scoring model chosen is a higher specificity, namely the credit scoring model using the SMOTE method.
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