Operation behavior risk identification method and device, computer equipment and storage medium

Abstract

The invention relates to an operation behavior risk identification method and device, computer equipment and a storage medium. The method comprises the steps of obtaining operation data, and performing feature data extraction on the operation data to obtain first feature data; performing statistical analysis on the first feature data according to the dimensions of the risk objects to obtain secondfeature data, the second feature data including feature data of behavior features of the risk objects; according to the second feature data, determining an exception identification result of each behavior feature of each risk object; determining a risk index of each risk object and a risk characteristic of each risk object according to the abnormal identification result; and outputting a risk identification result of each risk object, wherein the risk identification result comprises a risk index of each risk object and a risk characteristic of each risk object. By adopting the method, abnormal data in the operation process can be identified, an enterprise is helped to discover operation behavior risks in time, abnormal points and risk objects are positioned, and thus the enterprise is helped to control losses.

Description

technical field [0001] The present application relates to the field of logistics technology, in particular to a method, device, computer equipment and storage medium for identifying risks in operation behaviors. Background technique [0002] With the development of Internet technology and big data technology, data has become the basis for operation process monitoring, operation quality control and financial settlement in the field of logistics operations. Data sources are mainly divided into two types. One is manual data entry by operators operating the operating system, such as the sender’s phone number, sender’s address, recipient’s address, and other information entered by recipients in the parcel collection process. The second is the log record data of the operation operation, such as the operator scanning the package to confirm that the package has been picked up, and the time of picking up the package, the person who picked up the package, and other information recorde...

AI Technical Summary

Problems solved by technology

However, there is also a greater risk

The risk is mainly reflected in the falsification of data, that is, the data recorded by the system may be true or false

Especially for data that will affect financial settlement, operators may resort to various methods to falsify data in order to obtain more profits

Because logistics companies do not have a strong awareness of risk control, this kind of data falsification to obtain profits has developed into an industry, and there are even special software to help with data falsification, which has caused huge losses to logistics companies

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