Paper Title
Using Big Data to Compare Classification Models for Household Credit Rating in Kuwait

Credit rating risks have become the main indicator of bank performance. They are the reflection of the current status of the bank and an important milestone for future planning. An effective credit assessment can better anticipate expected losses and will minimize unexpected losses from accumulating. In an oil country such as Kuwait, advancements in technology as well as the big data available within banks about customers can lead to a built-in credit assessment model. This built-in model can work to help in-household credit scoring at the decision of a financial institution’s management. Compared to the current ‘black box’ rating models, we did a comparison between different classification models for two types of banking: conventional and Islamic. The classification models are as follows: Logistic Regression, Fine Decision Tree, Linear Support Vector Machines, Kernel Naïve Bayes, and RUSBoosted. Sufficiently, the last could be used to classify banks household customers and determine their default cases. Keywords - Classification Models, Conventional Banking, Credit Rating, Household Customers, Islamic Banking