Modelling the probability of household default for conventional and Islamic banking. - AlBarrak, Nijlah Saleh (Cranfield University, 2021)
Credit rating risks have become a main indicator of the performance of banks. An effective credit assessment can make it easier to anticipate losses and manage it. Based on regulators, banks' credit exposure must have a risk weight that are assigned as a way of calculating the expected loss for each customer. In a country like Kuwait, whose economy is based on oil, advances in technology and increases in the amount of data available about banking customers have made it possible to develop a built-in credit default probability model that is more effective than the standard fixed risk model that is currently in use. Having a robust and fair model under the control of a central bank will increase supervision and make the internal risk weight framework more reliable, bringing it into line with regulations. Our aim in this study is to come up with an internal credit rating system that uses machine learning to calculate the risk weights for different households. We aim to create models that will be approved by the Central Bank of Kuwait and be acceptable to other banks. Our objectives are to develop a customized model for each of the banks under study; to account for different types of banking (conventional and Islamic); to produce different models for the different types of loans granted, to generate a general model for the Central Bank as well for each type of loan. We compared different classification models for conventional and Islamic banks. The classification models were as follows: Logistic Regression, Fine Decision Tree, Linear Support Vector Machines, Kernel Naïve Bayes, Bagging, AdaBoostM1, and RUSBoosted. Ensemble models were used to classify the customers who took our household loans at the different banks and determine the likelihood that they would default. This led to the development of further mechanisms for assessing the models at the central bank, such as Gentelboost, Logitboost and Robustboost. The following were adopted as key performance measures: AUC curve, confusion matrix, running time, and k-fold. This ensured that the prediction models in this critical field were well structured. Our findings met our expectations. The key performance measures for comparing the different models show that ensemble models are the most suitable. The parameters selected for this study varied in importance depending on the prediction model. We found that several new parameters were significant and as influential as we expected. One of the key contributions that we made was that we used a substantial amount of data; this can be considered a contribution in itself. This was also the first time that a credit rating model was developed for Islamic banks. We provided an internal model for each of the six banks we studied, as well as a robust system for the central banks. The results of our work show that banks should reduce the credit risk for each customer from 75% to 30% or less. The results should also enhance the role of the central banks given that it provides a robust system, with new and undiscovered variables. This system can be used to calculate the credit default probability. Therefore, there will be efficient supervision of the banks' internal systems, ensuring the reliability of the banks. This will also be considered in central banks through periodic stress testing that adds new impact factors for predicting default cases. A periodic run of the systems will help to prevent customers from defaulting unexpectedly. In conclusion, our models could be passed to other governmental financial bodies in Kuwait.
Keywords: Credit rating; credit risk; technology; conventional banking; Islamic banking; classification models; machine learning