Scoring method and device based on multi-dimensional features, computer equipment and storage medium

A technology of multi-dimensional features and feature attributes, applied in the field of big data, can solve problems such as inability to obtain

Pending Publication Date: 2021-03-19
PING AN BANK CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Aiming at the problem that it is currently impossible to obtain user ratings based on the user's use data of enterprise products, a multi-dimensional feature-based scoring method, device, and computer equipment are provided to analyze customer data information to obtain user ratings and improve efficiency. and storage media

Method used

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  • Scoring method and device based on multi-dimensional features, computer equipment and storage medium
  • Scoring method and device based on multi-dimensional features, computer equipment and storage medium
  • Scoring method and device based on multi-dimensional features, computer equipment and storage medium

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Experimental program
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Embodiment 1

[0047] see figure 1 , a scoring method based on multidimensional features of the present embodiment, comprising the following steps:

[0048] S1. Obtain a data set of historical customers.

[0049] Wherein, the data set includes basic attributes and characteristic attributes of each historical customer.

[0050] It should be emphasized that, in order to further ensure the privacy and security of the data in the above-mentioned data set, the above-mentioned data set can also be stored in a node of a block chain.

[0051] In this embodiment, the scoring method based on multi-dimensional features is mainly applicable to the NPS research scenario of credit card customers. Basic attributes can include basic personal information of customers such as age, gender, occupation, place of residence, education, marital status, etc.; based on the life cycle of a credit card (apply card → use card → repayment), it can be divided into three stages (card application stage , card use stage a...

Embodiment 2

[0087] see Figure 4 , a scoring device 1 based on multi-dimensional features in this embodiment includes: an acquisition unit 11 , a division unit 12 , an analysis unit 13 , a construction unit 14 , a processing unit 15 and a scoring unit 16 .

[0088] The acquisition unit 11 is configured to acquire a data set of historical customers, wherein the data set includes basic attributes and characteristic attributes of each historical customer.

[0089] Wherein, the data set includes basic attributes and characteristic attributes of each historical customer.

[0090] It should be emphasized that, in order to further ensure the privacy and security of the data in the above-mentioned data set, the above-mentioned data set can also be stored in a node of a block chain.

[0091] In this embodiment, the scoring method based on multi-dimensional features is mainly applicable to the NPS research scenario of credit card customers. Basic attributes can include basic personal information ...

Embodiment 3

[0125] In order to achieve the above object, the present invention also provides a computer device 2, the computer device 2 includes a plurality of computer devices 2, the components of the scoring device 1 based on multi-dimensional features in Embodiment 2 can be dispersed in different computer devices 2, The computer device 2 can be 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 an independent server, or a server composed of multiple servers) for executing programs. cluster), etc. The computer device 2 of this embodiment at least includes but is not limited to: a memory 21, a processor 23, a network interface 22, and a scoring device 1 based on multidimensional features (refer to Image 6 ). It should be pointed out that, Image 6 Only the computer device 2 is shown with components - but it should be understood that implementing all of the illustrated components is no...

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Abstract

The invention discloses a scoring method and device based on multi-dimensional features, computer equipment and a storage medium, and belongs to the technical field of big data. According to the scoring method based on the multi-dimensional features, historical clients can be divided into a plurality of client sets according to categories of basic attributes in a data set, feature attributes of the clients in all the client sets are analyzed through an initial XGBoost model tree to obtain target feature attributes, and an XGBoost model tree is constructed according to the target feature attributes; a data set is input into an XGBoost model tree for calculation, and a calculation result is analyzed in an equal-frequency binning mode to determine a derogatory threshold. When the data information of the target client is received, the XGBoost model tree is adopted to process the data information to obtain a derogatory probability value, and the score information of the target client is determined based on the derogatory probability value and a derogatory threshold, so that the purpose of quickly, effectively and automatically predicting the score data according to the acquired/acquiredclient data information is achieved.

Description

technical field [0001] The present invention relates to the technical field of big data, in particular to a scoring method, device, computer equipment and storage medium based on multidimensional features. Background technique [0002] Net Promoter Score (NPS) is an index used by companies to measure the possibility that customers will recommend the company to others, and can effectively quantify customer loyalty. The specific calculation method of NPS is as follows: firstly let customers score between 0-10 according to the degree of willingness to recommend, and then divide customers into several categories according to the scoring situation, for example: divide customers into 3 categories, respectively: 9- 10 is divided into recommended category, 7-8 is divided into medium category, 0-6 is divided into derogatory category, and the NPS value is calculated according to the formula = (number of recommenders / total number of samples) × 100% - (number of detractors / total num...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q40/02G06K9/62
CPCG06Q10/04G06Q10/06393G06Q40/03G06F18/24323G06F18/214
Inventor 王雅婷
Owner PING AN BANK CO LTD
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