Risk scoring model construction method, device, storage medium and terminal

A technology of risk scoring and construction methods, applied in the field of devices, risk scoring model construction methods, storage media and terminals, can solve the noise interference of APP activity rate and conversion rate, remove abnormal accounts with low timeliness and accuracy, and false registered users Demand stimulation and other issues to achieve the effect of improving the quality of existing users, fast computing speed, and improving timeliness

Inactive Publication Date: 2018-07-10
CHINA PING AN LIFE INSURANCE CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing abnormal account identification methods are passive identification mechanisms such as post-event analysis, and the timeliness and accuracy of clearing abnormal accounts are low, which leads to great noise interference in the evaluation and improvement of APP activity rate and conversion rate by false registered users. Reduc

Method used

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  • Risk scoring model construction method, device, storage medium and terminal
  • Risk scoring model construction method, device, storage medium and terminal
  • Risk scoring model construction method, device, storage medium and terminal

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Experimental program
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Embodiment 2

[0108] Image 6 It shows a composition structure diagram of the apparatus for constructing the risk scoring model provided by the embodiment of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown.

[0109] In the embodiment of the present invention, the device for constructing the risk scoring model is used to realize the above-mentioned figure 1 , figure 2 , image 3 , Figure 4 The method for constructing the risk scoring model described in the embodiments may be a software unit, a hardware unit, or a combination of software and hardware built into the terminal.

[0110] refer to Image 6 , the construction device of the risk scoring model includes:

[0111] A sample library construction module 61, configured to construct a blacklist sample library and a whitelist sample library according to preset account data, wherein the blacklist sample library includes abnormal accounts, and the whi...

Embodiment 3

[0137] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for constructing the risk scoring model in Embodiment 1 is implemented. To avoid repetition, it is not repeated here repeat. Alternatively, when the computer program is executed by the processor, the functions of each module / unit in the risk scoring model construction device in Embodiment 2 are realized, and to avoid repetition, details are not repeated here.

Embodiment 4

[0139] Figure 7 It is a schematic diagram of a terminal provided by an embodiment of the present invention, and the terminal includes but is not limited to a server and a mobile terminal. like Figure 7 As shown, the terminal 7 of this embodiment includes: a processor 70 , a memory 71 and a computer program 72 stored in the memory 71 and operable on the processor 70 . When the processor 70 executes the computer program 72, it realizes the steps in the embodiment of the method for constructing the above-mentioned risk scoring model, for example figure 1 shown in steps S101 to S104, figure 2 Steps S1021 to S1025 described in the embodiment, image 3 Steps S1031 to S1033 described in the embodiment and Figure 4 Steps S105 to S108 described in the embodiment; or, when the processor 70 executes the computer program 72, the functions of the various modules / units in the above-mentioned risk scoring model construction device embodiment are realized, for example Image 6 The fu...

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Abstract

The present invention provides a risk scoring model construction method. The construction method includes the following steps that: a blacklist sample library and a whitelist sample library are constructed according to preset account data, wherein the blacklist sample library includes abnormal accounts, and the whitelist sample library includes normal accounts; cluster training is performed on theabnormal accounts in the blacklist sample library and the normal accounts in the whitelist sample library on the basis of a gradient boosting decision tree (GBDT) algorithm, and abnormal account classification features are screened out; the abnormal account classification features are trained on the basis of a random forest (RF) algorithm, and a contribution degree corresponding to each abnormalaccount classification feature is obtained; and a risk scoring model is constructed according to the abnormal account classification features and the contribution degrees corresponding to the abnormalaccount classification features, and the risk scoring model is used for identifying the abnormal accounts. The risk scoring model constructed by the method of the invention improves the timeliness ofclearing the abnormal accounts, reduces noise interferences caused by the abnormal accounts, and improves the calculation precision of many indicators of APPs.

Description

technical field [0001] The invention belongs to the technical field of communication, and in particular relates to a method, device, storage medium and terminal for constructing a risk scoring model. Background technique [0002] At present, there are a large number of abnormal false registered users in the life insurance APP, and these false registered users have behaviors such as swiping activity and swiping activities. The APP activity rate is the ratio of the number of times the APP is logged on to the total number of users, and the conversion rate is the ratio of the number of customers acquired to the number of users who purchase products. Existing abnormal account identification methods are passive identification mechanisms such as post-event analysis, and the timeliness and accuracy of clearing abnormal accounts are low, which leads to great noise interference in the evaluation and improvement of APP activity rate and conversion rate by false registered users. Reduc...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q40/08G06K9/62
CPCG06Q10/0635G06Q40/08G06F18/24323
Inventor 于洋刘杰马宁谢波孙家棣
Owner CHINA PING AN LIFE INSURANCE CO LTD
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