Browsing by Author "Alsanousi, Hessa"

Now showing 1 - 4 of 4
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    Understanding customer behaviours toward the use of electronic banking given customer characteristics and financial portfolios
    (Center for Promoting Ideas, 2021-01-31) Alsanousi, Hessa; Al Barrak, Najla; Moulitsas, Irene; Filippone, Salvatore
    The evolution of electronic banking demonstrates the need for research regarding demographics and banking preferences. In this study, financial data was collected from three Kuwaiti banks. The data included customer characteristics, product portfolios, and usage information in all electronic banking channels. This data was used to predict the use of electronic channels, treated as dependent variables in our study, based on individual customer’s information, that is treated as independent variables. Machine learning (ML) techniques, specifically multinomial logistic regression, were used to handle the data bearing in mind that these techniques would bring the most benefit to financial analysts and bankers. The results showed that one can determine the preferred electronic banking channel for each customer by knowing some of their characteristics and financial portfolio.
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    Using Big Data to compare classification models for household credit rating in Kuwait
    (Inderscience, 2021-03-13) Albarrak, Najla; Alsanousi, Hessa; Moulitsas, Irene; Filippone, Salvatore
    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
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    Using Big Data to compare classification models for household credit rating in Kuwait
    (Springer, 2021-09-10) Albarrak, Najla; Alsanousi, Hessa; Moulitsas, Irene; Filippone, Salvatore
    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.
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    Using client’s characteristics and their financial products to predict their usage of banking electronic channels
    (Springer, 2021-10-27) Alsanousi, Hessa; Albarrak, Najla; Moulitsas, Irene; Filippone, Salvatore
    Technology innovation and its impact on the progress of electronic banking establish the requirement for this research regarding customer demographics, their financial portfolio and banking preferences. In this research, banking financial data were collected from three Kuwaiti banks. The data included usage information in all electronic banking channels for each customer, their characteristics and financial portfolio. The aim of this study is to predict the customer use of electronic channels, treated as dependent variables, considering individual customer information, which is treated as independent variables. To bring the most benefit to bankers and financial analysts, machine learning techniques (ML), specifically multinomial logistic regression, were used to deal with the data from cleaning to analysing. The results disclosed that banks can determine the preferred electronic banking channel for each of their customers by knowing some information about their characteristics and financial product portfolio.