Insider Trading Detection Algorithm in Industrial Chain Based on Logistics Time Interval Characteristics

Insider trading behavior is becoming increasingly prevalent with the rapid development of the industrial chain. Insider trading refers to the illegal behavior of conducting insider trading by obtaining insider information. The existing insider trading detection methods of industrial chain do not consider the problems of inefficient industrial chain data characteristics and long trading time span, resulting in poor algorithm effect. Therefore, in order to solve the above problems, this paper proposes an algorithm for detecting insider trading in the industrial chain based on logistics time interval characteristics. Firstly, aiming at the problem of inefficiency of industrial chain data characteristics, this algorithm proposes a logistics index construction method for describing the whole process of insider trading behavior; Secondly, aiming at the problem of long time span of transaction, a dynamic sliding window method is proposed; Finally, the isolation forest algorithm is improved to identify the abnormal data. Verified under the real data set, the results show that compared to using the isolation forest methods, the F1 value of the insider trading behavior detection problem of the industry chain can be improved by 20.68% by using the logistics time interval feature.