Responsible risk management is central to banking ethics. With the 1999 publication of the Basel Committee's proposal, Basel II, for a New Capital Accord to replace the 1988 agreement, Basel I, an attempt has been made to address the problem of correlating banks' risk management with their capital requirements. The Basel II framework, finalised in June 2004, is designed to improve risk management by using models based on past performance to help set the amount of capital banks are required to (...) hold by regulators, with the purpose of improving the efficiency of capital allocation. The objectives of this study are to investigate how banks generally, but particularly those located in China, could improve their risk management systems and what the implications of these new regulations are for them. Three relevant propositions were formulated, namely, Basel II will improve risk management; Basel II will improve capital allocation efficiency; and compliance with advanced risk management systems is biased in favour of the large banks. Evidence was assembled with which to evaluate these three propositions by gathering relevant primary data by means of a representative survey of Chinese banking executives involved in risk management. The findings strongly support the first two of the above propositions and partly support the third proposition. (shrink)

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners’ knowledge status over time by analyzing the learners’ exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the knowledge (...) components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners’ knowledge status and questions. A context-aware attentive knowledge query network model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners’ interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners’ knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners’ knowledge status like other models but also model learners’ forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms. (shrink)