Abstract
Big data technology has had a significant impact on new business and financial services: for example, GPS and Bluetooth inspire location-based services, and search and web technologies motivate online shopping, reviews, and payments. These business services have become more connected than ever, and as a result, financial frauds have become a significant challenge. Therefore, combating financial risks in the big data era requires breaking the borders of traditional data, algorithms, and systems. An increasing number of studies have addressed these challenges and proposed new methods for risk detection, assessment, and forecasting. As a key contribution, we categorize these works in a rational framework: first, we identify the data that can be used to identify risks. We then discuss how big data can be combined with the emerging tools to effectively learn or analyze financial risk. Finally, we highlight the effectiveness of these methods in real-world applications. Furthermore, we stress on the importance of utilizing multi-channel information, graphs, and networks of long-range dependence for the effective identification of financial risks. We conclude our survey with a discussion on the new challenges faced by the financial sector, namely, deep fake technology, adversaries, causal and interpretable inference, privacy protection, and microsimulations.
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