Enterprise risk early warning method and device, equipment and readable storage medium

A risk warning and enterprise technology, applied in the field of equipment and readable storage media, devices, and enterprise risk warning methods, can solve problems such as difficult to cover cases and easy to produce omissions.

Pending Publication Date: 2020-12-29
PING AN ASSET MANAGEMENT CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing way of judging whether a company has a default risk is that risk control personnel use expert experience and reasoning logic to make risk judgments based on various announcement information of the company. Due to the limitations of the experience and perspective coverage of different professionals, it is difficult to Covering all cases, it is easy to miss; with the continuous development of machine learning algorithms, more and more machine learning algorithms are applied to

Method used

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  • Enterprise risk early warning method and device, equipment and readable storage medium
  • Enterprise risk early warning method and device, equipment and readable storage medium
  • Enterprise risk early warning method and device, equipment and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054]The embodiment of the present invention provides an enterprise risk early warning method, such asfigure 1 As shown, the method specifically includes the following steps:

[0055]Step S101: Receive an early warning instruction; wherein, the early warning instruction includes: information of an enterprise to be alerted.

[0056]Specifically, before step S101, the method further includes:

[0057]Step A1: Receive enterprise information; wherein, the enterprise information includes: basic information of multiple enterprises and related information between each enterprise;

[0058]Step A2: Draw the nodes representing each enterprise, and set the basic information of each enterprise as node attributes; wherein the node attributes include: the enterprise risk value set for the enterprise in advance;

[0059]The greater the enterprise risk value of an enterprise, the greater the probability that the enterprise has a default risk;

[0060]Step A3: Draw edges between each node according to the associatio...

Embodiment 2

[0090]The embodiment of the present invention provides an enterprise risk early warning device, such asfigure 2 As shown, the device specifically includes the following components:

[0091]The receiving module 201 is used to receive an early warning instruction; wherein the early warning instruction includes: information of the enterprise to be alerted;

[0092]The determining module 202 is used to determine the risk transmission path from the node characterizing the enterprise to be warned to the node characterizing the risk enterprise from the preset enterprise association graph; wherein, each node in the risk transmission path passes through the edge Connect sequentially, and the number of edges included in each risk transmission path is not greater than the preset maximum path constraint value;

[0093]The calculation module 203 is configured to, for a risk transmission path, according to the node attribute of each node and the edge attribute of each edge, according to the preset risk tr...

Embodiment 3

[0112]This embodiment also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including independent servers, or A server cluster composed of multiple servers), etc. Such asimage 3As shown, the computer device 30 in this embodiment at least includes but is not limited to: a memory 301 and a processor 302 that can be communicably connected to each other through a system bus. It should be pointed out thatimage 3Only the computer device 30 with components 301-302 is shown, but it should be understood that it is not required to implement all of the illustrated components, and more or fewer components may be implemented instead.

[0113]In this embodiment, the memory 301 (readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), Re...

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PUM

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Abstract

The invention discloses an enterprise risk early warning method and device, equipment and a readable storage medium. The method comprises the steps of receiving an early warning instruction; wherein the early warning instruction comprises information of a to-be-warned enterprise; determining a risk conduction path from a node representing the to-be-warned enterprise to each node representing a risk enterprise from a preset enterprise association graph; for one risk conduction path, calculating a risk influence value of a risk enterprise in the risk conduction path on the to-be-warned enterprise according to the node attribute of each node, the edge attribute of each edge and a preset risk conduction model; calculating a risk assessment value of the to-be-warned enterprise according to therisk influence value corresponding to each risk conduction path and a first preset algorithm; judging whether the risk assessment value is greater than a preset threshold value or not, and if so, sending a risk early warning message to a specified terminal; according to the invention, enterprise risk early warning can be carried out more accurately.

Description

Technical field[0001]The invention relates to the technical field of intelligent decision-making, in particular to an enterprise risk early warning method, device, equipment and readable storage medium.Background technique[0002]In recent years, domestic corporate bonds have begun to break the law of redemption, with default incidents, and the number of defaults has shown an increasing trend. Therefore, risk control in the bond market and early warning of bond issuers with credit risks to avoid major losses for investors have become more and more important. The existing method of judging whether a company has a default risk is that risk control personnel use expert experience and reasoning logic to make risk judgments based on various announcements of the company. Due to the limitations of the experience and perspective coverage of different professionals, it is difficult to Covering all cases, it is easy to omit; with the continuous development of machine learning algorithms, more a...

Claims

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Application Information

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IPC IPC(8): G06Q10/06
CPCG06Q10/0635
Inventor 张乐情王昊罗水权刘剑张骅
Owner PING AN ASSET MANAGEMENT CO LTD
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