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Auxiliary decision making method and system, server and storage medium

A technology to assist decision-making and business, applied in the computer field, it can solve the problems of poor effect, inability to push the required customers, low efficiency of target customer selection, etc., and achieve the effect of high accuracy

Pending Publication Date: 2021-01-05
CHINA CONSTRUCTION BANK
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, this method has certain shortcomings: too much reliance on the business experience of business personnel in the financial industry leads to low efficiency in screening target customers, and once new businesses appear or customer groups change, activities will not be accurately pushed to required customers. , resulting in poor promotional effects, and business personnel need to re-explore and accumulate experience

Method used

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  • Auxiliary decision making method and system, server and storage medium
  • Auxiliary decision making method and system, server and storage medium
  • Auxiliary decision making method and system, server and storage medium

Examples

Experimental program
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Effect test

Embodiment 1

[0029] figure 1 It is a flow chart of the decision-making assistance method provided by Embodiment 1 of the present application. This embodiment is applicable to the situation of accurately screening target customers in financial scenarios and pushing target business activities to target customers. This method can be implemented by the decision-making assistance system Execution, the system can be implemented in the form of software and / or hardware, and can be integrated on a server.

[0030] Such as figure 1 As shown, the method of assisting decision-making specifically includes the following processes:

[0031] S101. In response to a trigger operation of starting a target business activity, use a pre-trained auxiliary decision-making model to screen out target customers from a set of all customers.

[0032] When institutions in the financial industry (such as banks) launch some target business activities (such as credit card promotion activities, installment payment activi...

Embodiment 2

[0039] figure 2 It is a schematic flow chart of the method for assisting decision-making provided in Embodiment 2 of the present application. This embodiment is optimized on the basis of the above-mentioned embodiments, and the process of pre-training the auxiliary decision-making model is added. See figure 2 , the method includes:

[0040] S201. Construct an initial auxiliary decision-making model, use customer sample training to initially train the auxiliary decision-making model in turn, and adjust hyperparameters of the initial auxiliary decision-making model during the training process to obtain at least three target auxiliary decision-making models.

[0041] In this embodiment of the application, the initial auxiliary decision-making model is a gradient descent tree model; the preset hyperparameters of the auxiliary decision-making model include the maximum depth of the gradient descent tree, the learning rate, and the maximum number of gradient descent trees. What ne...

Embodiment 3

[0053] image 3 It is a schematic flow chart of the method for assisting decision-making provided in the third embodiment of the present application. This embodiment is optimized on the basis of the above-mentioned embodiments, and the process of adding customer sample collection is added. See image 3 , the method includes:

[0054] S301. Obtain an initial customer sample set uploaded by business personnel, a target business type, and a target business department to which the target business belongs.

[0055] Wherein, the target business is exemplarily a credit card business, and the target business department is a credit card handling department. It should be noted that the target service may also be other services, which are not specifically limited here.

[0056] S302. Determine whether the number of positive samples and the number of negative samples in the initial customer sample set reach a preset threshold.

[0057] Wherein, the preset threshold can be determined ac...

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Abstract

The embodiment of the invention discloses an auxiliary decision making method and system, a server and a storage medium. The method comprises the steps: selecting a preset number of target auxiliary decision making models from at least three target auxiliary decision making models obtained through training, and carrying out the fusion of the selected target auxiliary decision making models, and obtaining a final auxiliary decision making model; in response to a trigger operation of starting a target business activity, screening out a target customer from the full customer set by using a pre-trained assistant decision model; pushing the target business activity to the screened target clients. In the embodiment of the invention, the assistant decision model is obtained by adopting a model fusion mode, so that the accuracy of screening the users by the assistant decision model is higher, and the assistant decision model is used for screening the target customers, thereby preventing the salesman from screening the target customers based on business experience; therefore, the business personnel without business experience can efficiently and accurately screen the target clients, and thus the related activities can be accurately pushed to the clients with requirements.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technologies, and in particular, to a decision-making assistance method, system, server, and storage medium. Background technique [0002] In the financial industry, some activities are often promoted to customers to increase customer stickiness. However, due to the need to protect customer information, business personnel cannot provide the information of seed customers in the bank to advertisers outside the banking system for customer analysis and precise advertising when promoting activities. Business personnel in the financial industry can only rely on their own business experience to grab target customers to be marketed from the database for event promotion. [0003] However, this method has certain shortcomings: too much reliance on the business experience of business personnel in the financial industry leads to low efficiency in screening target customers, and once new bu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q40/02G06N5/00
CPCG06Q30/0255G06Q40/02G06N5/01G06Q40/03
Inventor 凌晓蔚
Owner CHINA CONSTRUCTION BANK
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