Using Big Data to compare classification models for household credit rating in Kuwait - Albarrak, Najla; Alsanousi, Hessa; Moulitsas, Irene; Filippone, Salvatore (Springer, 2021)
Credit rating risks have become the backbone of bank performance. They are the reflection of the current status of the bank and the milestone for future planning. A good credit assessment can better anticipate expected losses and will minimize unexpected losses from accumulating. Given advancements in technology as well as the big data available within banks about customers in an oil country such as Kuwait, a built-in model to help in-household credit scoring is at management’s decision. Compared with 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: Credit rating model; Credit risk; Technology; Conventional banking; Islamic banking; Classification models; Household customers; Machine learning; Logistic regression; Fine decision tree; Linear support vector machines; Kernel Naïve Bayes; RUSBoosted