Abstract
With the development of big data and artificial intelligence, it is more and more common to apply machine learning algorithms to financial risk control. However, the current research work still lack the integration of prior rule knowledge and big data algorithms. This paper proposes a novel risk control model, which firstly uses the financial industry's prior rules to conduct risk control identification, and rapid screens data of rise that is easy to judge. Then, we use machine learning algorithms such as SVM to train and learn financial big data. The combination of the two makes full use of prior risk control support for fast and effective data judgment and machine learning for efficient and real-time monitoring of financial dynamic changes.
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