Narrow Results

This study explores the status of the online risk disclosure practices on the listed Islamic banks in the Egyptian exchange market. Manual content analysis was employed as a research approach to examine the practices of the online risk disclosure for the three listed Islamic banks in Egypt, and based on a prior study, it developed a risk index composed of 10 main categories and a total of 61 sub-items as a research tool. The empirical analysis presents that all listed Islamic banks in the Egyptian exchange market have websites and all these banks report risk information in their published online reports (full annual report and full financial report). Furthermore, the results provide confirmatory evidence that there is a high adherence by all listed Islamic banks in Egypt to the mandatory risk disclosure requirements on their websites, while there is a low level of voluntary risk disclosure on the websites of these banks as well; in addition, the study pointed out that Al Baraka Bank has the highest average (67.2%) of online risk disclosure level among all Islamic banks, followed by Faisal Islamic Bank with an average of 65.5%, and finally, Abu Dhabi Islamic Bank came in the last place with an average of 60.6%. What’s more, the yielded data show that the total average level of online risk disclosure of the Islamic listed banks in Egypt is 64.5%. Finally, the results of this study outline that there are no significant differences between the levels of online risk disclosure at the level of the common categories for all Islamic listed banks in Egypt. The limitations of this study are as follows: the way the content analysis was conducted; in its reliance on the websites and the published online reports for examining risk disclosure information; due to its focus on the financial and non-financial risks; due to its focus on Islamic listed banks in Egypt; and due to its focus on the context of the Egyptian environment.

The emergence of FinTech has reshaped the financial sector landscape worldwide, while its potential effects are still far from clear. This chapter investigates the influence of FinTech on diversification strategies of traditional banks in China between 2012 and 2018. To this end, we construct measures of diversification from the two main perspectives taken into account so far in the literature: income-based indicators and asset-based indicators. The Peking University Digital Financial Inclusion Index is used to measure FinTech development in China; the methodological approach is instrumental quantile regression. The overall results establish a significant and positive link between FinTech and bank income diversification during the sample period, especially for national shareholding commercial banks. However, asset diversification strategies are not sensitive to FinTech for all bank tiers in China.

Big data and artificial intelligence (AI) assist businesses with decision-making. They help companies create new products and processes or improve existing ones. As the amount of data grows exponentially and data storage and computing power costs drop, AI is predicted to have great potentials for banks. This chapter discusses the implications of big data and AI for the banking industry. First, we provide background on big data and AI. Second, we identify areas in which banks can benefit from big data and AI, and evaluate their applications for the banking industry. Third, we discuss the implications of big data and AI for regulatory compliance and supervision. Last, we conclude with the limitations and challenges facing the use of big-data based AI.

The Kerala model of cooperatives has won international attention for its achievement in building sustainable models. Collective actions have been facilitated by the people to build up a socially, economically, and environmentally feasible society. The credit sector has built up considerable financial assistance and support for the people to sustain their livelihood. Driven by the need for detailed examination, this study highlights the cases of three cooperative banks, namely Kodiyathoor Service Co-operative Bank, Kizhathadiyoor Service Co-operative Bank, and Peringandur Service Co-operative Bank, which have built a sustainable model in the cooperative sector. This study is based on primary and secondary data collected to understand the process and practices in cooperatives. This study is also a contribution towards a cooperative model for sustainable growth in banks.

This study examines the implications of a bank’s activity mix and funding strategy for its liquidity management as defined in the Basel III accords or when they create more liquidity as measured by Berger and Bouwman (2009). Using an international sample of 624 banks in 65 countries, we find that, at low levels of noninterest income and nondeposit funding, there could be some risk diversification benefits in increasing these shares; however, at higher levels of noninterest income and nondeposit funding shares, additional increases result in higher illiquidity. Finally, better accounting disclosure will improve the effect on bank liquidity by both bank’s activity mix and funding strategy, but at the same time, they worsen the bank liquidity by stricter capital stringency and more market power.

Customer classification is one of the major tasks in customer relationship management. Customers have both static characteristics and dynamic behavioral features. To apply both kinds of data to conduct comprehensive analysis can enhance the reasonability of customer classification. In this paper, customer dynamic data is clustered using a hybrid genetic algorithm and then is combined with customer static data to give reasonable customer segmentation by using neural network technique. A novel classification method which considers both the static and dynamic data of customers is proposed. Applying the proposed method in a bank's datasets can obviously improve the accuracy of customer classification comparing with traditional methods where only static data is used.

Big data and artificial intelligence (AI) assist businesses with decision-making. They help companies create new products and processes or improve existing ones. As the amount of data grows exponentially and data storage and computing power costs drop, AI is predicted to have great potentials for banks. This chapter discusses the implications of big data and AI for the banking industry. First, we provide background on big data and AI. Second, we identify areas in which banks can benefit from big data and AI, and evaluate their applications for the banking industry. Third, we discuss the implications of big data and AI for regulatory compliance and supervision. Last, we conclude with the limitations and challenges facing the use of big-data based AI.