Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.

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Trade-based Money Laundering, a new form of money laundering using international trade as a signboard, always appears along with speculative capital movement which has been accepted as the most concerned and consensus incentive giving rise to the collapse of the financial market. Unfortunately, preventing money laundering is very difficult since money laundering always has a plausible trade characterization. To reach this goal, supervision for regulator and financial institutions aims to effectively monitor micro entities’ behavior in financial markets. The main purpose of this paper is to establish a monitoring method including accurate recognition and classified supervision for Trade-based Money Laundering by means of knowledge-driven multi-class classification algorithms associated with macro and micro prudential regulation, such that the model can forecast the predicted class from the concerned management areas. Based on empirical data from China, we demonstrate the application and explain how the monitor method can help to improve management efficiency in the financial market.

PurposeSocial media commerce provides a convenient way for users to share information and interact with each other. Few studies, however, have examined the effect of marketing messages and consumer engagement behaviors on the economic performance of marketing. This study, therefore, explored the economic performance of social media in terms of marketing messages and consumer engagement.Design/methodology/approachUsing ordinary least squares regression and data collected from Weibo and Maoyan, this study analyzed the effects among marketing messages, consumer engagement and movie ticket sales.FindingsThe results indicated that marketing messages on Weibo had a positive effect on box office revenues, while consumer engagement behavior (whether personal or interactive) did not affect box office revenues. The results suggested that marketing messages on social media have more salient effects for predicting economic performance than consumer engagement behaviors.Originality/valueThis study underscores the importance of social media in consumer purchasing behavior. The findings also extend the literature related to commerce and product message design on social media platforms.

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