Enhancing bank's profitability by applying machine learning techniques on financial data to intelligently predict customer behaviour towards the use of electronic channels. - Alsanousi, Hessa Abdulaziz (Cranfield University, 2021)
Technology is evolving rapidly, and this represents a huge prospect for investment in business. In the banking industry, new technologies have led to the creation of new electronic communication channels for customers. Therefore, banks need to do consider how to make the most of these channels. This will help them create prediction models to implement better strategies and improve their decision making. In the long run, this will help them to decrease costs and increase revenues. The aim of this study was to determine the appropriate electronic channels for specific customers based on their information. It used a big data prediction model to predict the best online channel for banking customers. The point of the model was to help banks understand their customers’ preferences, thereby increasing customer satisfaction, productivity, and profits. I obtained a substantial amount of financial data from a minimum of 100,000 customers from ten local banks in Kuwait. The data covered a period of ten years. The independent variables I studied were age, gender, number of current accounts, number of savings accounts, number of deposit accounts, income, number of consumer loans, number of instalment loans, credit card limit, outstanding credit card balance, length of relationship with the bank, continent, and nationality. The dependent variables were call centres, websites, and mobile applications. Given the size and type of data, I used machine learning. I used the Statistics and Machine Learning Toolbox, Financial Toolbox, and other functions in MATLAB to run a multinomial logistic regression analysis. I examined four different methods for analyzing the data and chose the one that was most appropriate: multinomial logistic regression. I also considered whether there was any correlation between the independent variables. I discovered one significant correlation between credit card limit and outstanding credit card balance. I explored this finding further by considering the time of execution and the key performance measures. Consequently, I decided to keep both variables in my study. I also studied the relation between the volume of training data and the accuracy of the model to see how sensitive the models were to variations in data size. The results confirmed that my method performed stably across the different sample sizes. I addressed the issue of overfitting by running a sub-sample regression and comparing it to the original model.my results indicated that the model was not overfitted. I differentiated between conventional banks and Islamic banks. This distinction had not been made by previous studies, and my outcomes provided more information about the difference between Islamic banks and conventional banks. I found that there were very few differences between the customers of conventional banks and those of Islamic banks. However, I found that male customers of Islamic banks tended to use internet more often than female customers, who tended to use the mobile applications. I also found that older customers tended to use call centres as their primary way of communicating with their bank. The results showed that clients with more current accounts and savings accounts were more likely to use mobile applications. However, one unexpected finding was that clients with more deposit accounts were more likely to use the internet or call centres rather than mobile applications. The findings also demonstrated that clients with higher incomes were more likely to use mobile applications than other communication platforms. In a couple of banks, customers with more loans tended to use call centres as their primary means of communication. The type of loan had no significant impact on their choices. Also, I found that customers who had been with their bank for longer were more likely to use call centres as their primary communication source. Finally, in a few banks, the results showed that a client’s continent or nationality had no significant impact on their preference for a particular communication channel. These are very important findings that could change how banks operate. They will have a positive influence on decision-making and strategies in the banking industry.
Keywords: MATLAB; banking data; banking prediction model; mobile banking; online banking; multinominal logisitic regression