CN113837868A - Passenger group layering system and method - Google Patents
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Abstract
The invention relates to the technical field of customer classification, and particularly discloses a customer group layering system and a method, wherein the system comprises a customer basic information acquisition module, an association module and a processing module, and the method comprises the following steps: a basic information acquisition step, which is to acquire the client characteristic information of the service type to be handled by the client; and an evaluation step, namely evaluating the rejection probability and the customer level of the customer according to the customer characteristic information of the customer to obtain the corresponding rejection probability and the corresponding customer level. According to the method and the device, the client levels are determined through the processing module, so that the artificial interference is avoided, and meanwhile, no matter new clients or old clients, the clients can be layered accurately as long as corresponding information can be acquired, so that the clients can be layered accurately, the client groups are layered accurately, and the problems of artificial interference and inaccurate layering of new clients are avoided.
Description
Technical Field
The invention relates to the technical field of customer classification, in particular to a customer group layering system and a customer group layering method.
Background
In the process of credit business application, the client needs to be audited, and the client can know before loan more comprehensively and accurately by the method so as to judge the credit qualification of the client. Because the credit business application client group has relatively large difference, if the unified auditing standard is executed on the whole client group, if the auditing standard is too strict, a plurality of high-quality clients can be rejected by mistake, but if the auditing standard is too relaxed, some bad clients can exist, and the client group can be layered by considering the bad clients, so that different client groups can be subjected to differential approval, and the approval efficiency and the approval accuracy are improved.
At present, the existing client group layering generally comprises two types of layering of a user-defined rule and layering of a clustering algorithm, wherein the user-defined rule layering is to divide the client group by defining some rules according to business experiences by an approver, and the method is simple, efficient and easy to operate but too depends on the business experiences of the approver, so that the human factor is too heavy; the layering through the clustering algorithm can effectively reduce the man-made interference, but the method is complex, the accuracy of the layering of new customers is not high, and meanwhile, the operability is not strong.
Based on this, a system and a method for layering a customer group are needed, which can accurately layer the customer group and avoid the problems of artificial interference and inaccurate layering of new customers.
Disclosure of Invention
The invention aims to provide a guest group layering system and a guest group layering method, which can accurately layer a guest group and avoid the problems of artificial interference and inaccurate layering of new guests.
In order to achieve the above object, a technical solution of the present invention provides a guest group hierarchical system, including:
the client basic information acquisition module is used for acquiring the information of the service types handled by the clients to obtain the client characteristic information;
the association module is prestored with an association strategy for associating with the corresponding information classification model according to the service type;
and the processing module is used for calling a pre-stored association strategy which is associated with the corresponding information classification model according to the service type, matching the strategy to the corresponding information classification model, inputting the client characteristic information into the corresponding information classification model, evaluating the rejection probability and the client level of the client and outputting the corresponding rejection probability and the client level of the client.
The principle and the effect of the scheme are as follows: the method comprises the steps of obtaining corresponding client characteristic information according to the service type to be handled by a client, matching the corresponding client characteristic information to an information classification model corresponding to the service type according to a pre-stored association strategy which is associated with the corresponding information classification model according to the service type, inputting the corresponding client characteristic information into the information classification model, taking the client rejection probability and the client level as output, achieving the evaluation of the client rejection probability and the client level, and then generating a corresponding evaluation report.
According to the method and the device, the corresponding information classification models and the client characteristic information used as the input information of the information classification models are matched through the service types to be handled by the clients, then the rejection probability and the client levels of the clients are evaluated through the information classification models, different service types correspond to different information classification models, accurate layering can be carried out on the clients handling different service types, meanwhile, manual interference is avoided, and meanwhile, the clients can be accurately layered as long as corresponding information can be obtained regardless of new clients or old clients.
Certainly, the customers can be accurately layered through the output of the customer level, the staff can know the customer level corresponding to the customer, the position of the level where the customer is located can be more accurately known through the rejection probability of the customer, after all, the rejection probability of the customer at the same customer level is also high or low, and the customer level of the customer can be further known.
Further, the client characteristic information acquisition module comprises an information acquisition module, an information preprocessing module and an information reprocessing module;
the information acquisition module is used for acquiring all client information provided by a client;
the information preprocessing module is used for processing, analyzing and screening all information of the client and obtaining standard information of the client;
and the information reprocessing module is used for screening the client standard information according to the service types to be handled by the client and obtaining the client characteristic information.
All the information of the client provided by the client is not in accordance with the standard, and the problems of unclear information content, repeated information and the like may exist, so that the corresponding information can be standardized through the processes of processing, analyzing and screening all the information of the client, and the subsequent information processing can be more accurate. Meanwhile, according to the service types to be handled by the client, the information required by the corresponding types is screened out, so that the subsequent information processing is simpler, more convenient and quicker.
Further, the information preprocessing module comprises an information extraction module and an information conversion module;
the information extraction module is used for extracting information with repeated content and information with unclear content in all information of the client;
and the information conversion module is used for carrying out standardized conversion on the extracted information.
Information with repeated content and unclear content is removed through information extraction, and then the information is subjected to standardized conversion, so that the correspondingly obtained information can be quickly identified and processed by a corresponding information classification model.
The system further comprises a storage module, a client level and a client level management module, wherein the storage module stores examination and approval process information which corresponds to the client levels one to one;
and the approval process display module is used for calling the approval process information corresponding to the client level according to the client level and displaying the approval process information.
The examination and approval process information is set so that each client level has corresponding examination and approval process information, and therefore after the corresponding client level is obtained, the client can be examined and approved in a targeted manner, the client of each client level can obtain a fair examination and approval process, the difference examination and approval of the client of different client levels is achieved, and the examination and approval efficiency and accuracy are improved.
The technical scheme of the invention also provides a guest group layering method, which comprises the following steps:
a basic information acquisition step, which is to acquire the client characteristic information of the service type to be handled by the client;
and an evaluation step, namely evaluating the rejection probability and the customer level of the customer according to the customer characteristic information of the customer to obtain the corresponding rejection probability and the corresponding customer level.
The principle and the effect of the scheme are as follows: firstly, acquiring the characteristic information of a client to be handled by the client, and then evaluating the rejection probability and the client level of the client according to the characteristic information of the client to obtain the corresponding rejection probability and the corresponding client level of the client.
According to the method, the client rejection probability and the client level of the client can be evaluated as long as the client provides corresponding client characteristic information no matter whether the new client or the old client, the client level of the client can be accurately determined, manual operation is avoided, manual interference is reduced, and meanwhile, the client group can be accurately layered.
Further, the evaluating step includes:
calling a pre-stored association strategy which is associated with the corresponding information classification model according to the service type to be handled by the client, and matching the information classification model corresponding to the service type to be handled by the client;
and inputting the customer characteristic information into a corresponding information classification model, evaluating the rejection probability and the customer level of the customer, and outputting the corresponding rejection probability and the customer level of the customer.
The corresponding information classification models are matched according to the service types to be handled by the clients, and the rejection probability and the client levels of the clients are determined by using the information classification models, so that the client levels of the clients handling different service types can be accurately determined, and meanwhile, the client levels are more targeted.
Further, the basic information acquiring step includes:
an information acquisition step, which is to acquire all client information submitted by a client;
the information preprocessing step, processing, analyzing and screening all the information of the client to obtain the standard information of the client;
and an information reprocessing step, namely screening the client standard information according to the service types to be handled by the client to obtain the client characteristic information.
All information of the client is processed, analyzed and screened, and the information is preprocessed, so that the information provided by the client can be well processed, the client information is standardized, and the information can be processed more quickly by a later system. And then, data information used by the model in the system is screened out through reprocessing, so that the model is more accurate in evaluation.
Further, the information preprocessing step includes:
an information extraction step, which is to extract information with repeated content and information with unclear content from all the information of the client;
and an information conversion step of performing standardized conversion on the extracted information.
Through information extraction and conversion, information with repeated content and unclear content is extracted, the information after extraction is subjected to standardization processing, the problem that information with repeated content and unclear content in information submitted by a client can be well solved and extracted, preliminary integration of the information is completed, and then the information is converted into a mode which can be input by a model, so that later evaluation is facilitated.
Further, the method also comprises the following steps:
and an approval process information display step, namely calling out approval process information matched with the client level from the server according to the output client level, and displaying the approval process information.
The different client levels correspond to different approval process information, so that each client level has corresponding approval process information, and after the corresponding client level is obtained, the client can be approved in a targeted manner, so that the client of each client level can obtain a fair approval process, the difference approval of the client of different client levels is realized, and the approval efficiency and accuracy are improved.
Drawings
Fig. 1 is a logic block diagram of a guest group hierarchy system according to a first embodiment of the present invention.
Fig. 2 is a flowchart of a guest group layering method according to a first embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
An embodiment substantially as shown in figure 1: a guest group layering system comprises a guest basic information acquisition module, an association module, a processing module, a storage module and an approval process display module.
The client characteristic information acquisition module comprises an information acquisition module, an information preprocessing module and an information reprocessing module.
The detailed acquisition module is used for acquiring all client information provided by the client. In this embodiment, when a client needs to handle a certain loan transaction type, the client submits much information to the system, such as client identification card information, academic certificates, personal property information, recent consumption lists, credit investigation information, payroll and the like, in order to be qualified for the loan.
And the information preprocessing module is used for processing, analyzing and screening all information of the client and obtaining the standard information of the client.
The information preprocessing module comprises an information extraction module and an information conversion module.
And the information extraction module is used for extracting the information with repeated content and the information with unclear content in all the information of the client.
In this embodiment, by extracting the information with unclear content and repeated content, the information submitted by the client can be integrated and combed, for example, a plurality of pieces of client identification card information corresponding to the information submitted by the client appears, and meanwhile, some of the repeated client identification card information is still fuzzy, which means that the information extraction module in the system firstly extracts the fuzzy client identification card information, and then extracts the rest client identification card information, and only one piece of client identification card information is left.
And the information conversion module is used for carrying out standardized conversion on the extracted information.
In the embodiment, some information submitted by the client cannot be directly identified by the system, and each information is converted into a form which can be identified by the system through information conversion, so that the subsequent information processing is facilitated. For example, the recent consumption list submitted by the client is a picture, and only the information in the list is identified, so that the electronic list is formed.
And the information reprocessing module is used for screening the client standard information according to the service types to be handled by the client to obtain the client characteristic information.
All the information submitted by the client is not useful information, and the required information is determined according to the type of the service to be handled by the client, so that the client standard information provided by the client is screened and matched with the corresponding information. For example, the information submitted by the client includes a, B, C1, C2, C3, D, E1, E2, F and D, where C1, C2 and C3 are information with the same content, E1 and E2 are information with the same content, and the content of C2 is unclear, during the preprocessing, C2 is extracted through data extraction, C1 or C2, E1 or E2 is extracted, and in this embodiment, the obtained client standard information is a, B, C1, D, E2, F and D, and then the obtained client standard information is reprocessed, i.e., normalized, and the converted client characteristic information obtained through this step is a +, B, C1, E2, F and D-.
And the association module is prestored with an association strategy for associating with the corresponding information classification model according to the service type.
And the processing module is used for calling a pre-stored association strategy which is associated with the corresponding information classification model according to the service type, matching the strategy to the corresponding information classification model, inputting the client characteristic information into the corresponding information classification model, evaluating the rejection probability and the client level of the client and outputting the corresponding rejection probability and the client level of the client.
In this embodiment, the rejected customer probability is divided into Z probability intervals, where Z is 1 … … n, the levels of the customer hierarchy correspond to the probability intervals one by one, for example, when Z is 5, 5 probability intervals of the rejected customer probability are respectively 0 to 20%, 21% to 40%, 41% to 60%, 61% to 80%, and 81% to 100%, the customer levels are five levels, the probability interval corresponding to the first level is 0 to 20%, the probability interval corresponding to the second level is 21% to 40%, the probability interval corresponding to the third level is 41% to 60%, the probability interval corresponding to the fourth level is 61% to 80%, and the probability interval corresponding to the fifth level is 81% to 100%. For example, the customer hierarchy output by the customer is the fourth level, the rejection probability of the customer is 78%, it can be known that the customer hierarchy where the customer is located is the fourth level, the rejection probability of the actual customer in the probability interval corresponding to the fourth level is 78%, it can be accurately known that the rejection probability of the customer a is still high in the fourth level, and thus the rejection probability is more strict in later approval than that of the customer a in the fourth level but less than 78%.
The corresponding models in this embodiment include logistic regression, random forest, GBDT, XGBoost, LightGBM, and the like.
And the storage module is used for storing the approval process information which corresponds to the client levels one by one.
And the approval process display module is used for calling the approval process information corresponding to the client level according to the client level and displaying the approval process information.
Each customer level corresponds to one piece of approval process information, so that corresponding approval can be carried out according to the customer level of the customer. For example, the first level corresponds to a class a approval process, the second level corresponds to a class B approval process, the third level corresponds to a class C approval process, the fourth level corresponds to a class D approval process, the fifth level corresponds to a class F approval process, and the customer level output by the customer a is the fourth level, so that the class D approval process corresponding to the fourth level is called and displayed, and the worker performs corresponding approval according to the corresponding approval process.
As shown in fig. 2, the present embodiment further discloses a guest group layering method, including the following steps:
and a basic information acquisition step, namely acquiring the client characteristic information of the service type to be handled by the client.
The basic information acquiring step specifically comprises:
and an information acquisition step, namely acquiring all the information of the client submitted by the client.
And an information preprocessing step, namely processing, analyzing and screening all information of the client to obtain client standard information.
The information preprocessing step specifically comprises the following steps:
and an information extraction step, namely extracting the information with repeated content and the information with unclear content in all the information of the client.
And an information conversion step of performing standardized conversion on the extracted information.
And an information reprocessing step, namely screening the client standard information according to the service types to be handled by the client to obtain the client characteristic information.
And an evaluation step, namely evaluating the rejection probability and the customer level of the customer according to the customer characteristic information of the customer to obtain the corresponding rejection probability and the corresponding customer level.
The evaluation step specifically comprises:
calling a pre-stored association strategy which is associated with the corresponding information classification model according to the service type to be handled by the client, and matching the information classification model corresponding to the service type to be handled by the client;
and inputting the customer characteristic information into a corresponding information classification model, evaluating the rejection probability and the customer level of the customer, and outputting the corresponding rejection probability and the customer level of the customer.
And an approval process information display step, namely calling out approval process information matched with the client level from the server according to the output client level, and displaying the approval process information.
The above are merely examples of the present invention, and the present invention is not limited to the field related to this embodiment, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein too much, and those skilled in the art can know all the common technical knowledge in the technical field before the application date or the priority date, can know all the prior art in this field, and have the ability to apply the conventional experimental means before this date, and those skilled in the art can combine their own ability to perfect and implement the scheme, and some typical known structures or known methods should not become barriers to the implementation of the present invention by those skilled in the art in light of the teaching provided in the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (9)
1. A guest group hierarchy system, comprising:
the client basic information acquisition module is used for acquiring the information of the service types handled by the clients to obtain the client characteristic information;
the association module is prestored with an association strategy for associating with the corresponding information classification model according to the service type;
and the processing module is used for calling a pre-stored association strategy which is associated with the corresponding information classification model according to the service type, matching the strategy to the corresponding information classification model, inputting the client characteristic information into the corresponding information classification model, evaluating the rejection probability and the client level of the client and outputting the corresponding rejection probability and the client level of the client.
2. A guest group hierarchy system according to claim 1, wherein: the client characteristic information acquisition module comprises an information acquisition module, an information preprocessing module and an information reprocessing module;
the information acquisition module is used for acquiring all client information provided by a client;
the information preprocessing module is used for processing, analyzing and screening all information of the client and obtaining standard information of the client;
and the information reprocessing module is used for screening the client standard information according to the service types to be handled by the client to obtain the client characteristic information.
3. A guest group hierarchy system according to claim 2, wherein: the information preprocessing module comprises an information extraction module and an information conversion module;
the information extraction module is used for extracting information with repeated content and information with unclear content in all information of the client;
and the information conversion module is used for carrying out standardized conversion on the extracted information.
4. A guest group hierarchy system according to claim 3, wherein: the system also comprises a storage module, a client and a client management module, wherein the storage module stores approval process information which corresponds to the client levels one to one;
and the approval process display module is used for calling the approval process information corresponding to the client level according to the client level and displaying the approval process information.
5. A method for layering a group of passengers, comprising the steps of:
a basic information acquisition step, which is to acquire the client characteristic information of the service type to be handled by the client;
and an evaluation step, namely evaluating the rejection probability and the customer level of the customer according to the customer characteristic information of the customer to obtain the corresponding rejection probability and the corresponding customer level.
6. The method of claim 5, wherein: the evaluating step includes:
calling a pre-stored association strategy which is associated with the corresponding information classification model according to the service type to be handled by the client, and matching the information classification model corresponding to the service type to be handled by the client;
and inputting the customer characteristic information into a corresponding information classification model, evaluating the rejection probability and the customer level of the customer, and outputting the corresponding rejection probability and the customer level of the customer.
7. The method of claim 6, wherein: the basic information acquiring step includes:
an information acquisition step, which is to acquire all client information submitted by a client;
the information preprocessing step, processing, analyzing and screening all the information of the client to obtain the standard information of the client;
and an information reprocessing step, namely screening the client standard information according to the service types to be handled by the client to obtain the client characteristic information.
8. The method of claim 7, wherein: the information preprocessing step comprises:
an information extraction step, which is to extract information with repeated content and information with unclear content from all the information of the client;
and an information conversion step of performing standardized conversion on the extracted information.
9. The method of claim 8, further comprising the steps of:
and an approval process information display step, namely calling out approval process information matched with the client level from the server according to the output client level, and displaying the approval process information.
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