CN113538154B - Risk object identification method and device, storage medium and electronic equipment - Google Patents
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Abstract
The disclosure relates to the technical field of computers, and in particular relates to a method and a device for identifying a risk object, a storage medium and electronic equipment. The method comprises the following steps: dividing the collected transaction data into a plurality of index dimensions, and setting monitoring indexes in the index dimensions; establishing a sub-evaluation model corresponding to each index dimension according to the monitoring index in each index dimension; integrating the obtained multiple sub-evaluation models according to a preset integration rule to obtain a target evaluation model; and identifying a target risk object from the risk objects to be evaluated based on the target evaluation model. The method and the system take transaction data as a data source, describe multidimensional monitoring indexes and corresponding risk assessment models, and have the characteristics of wide data source and high quality; and an integrated model obtained by integrating the sub-evaluation models established based on the index dimensions is used for identifying risk objects, and has high identification accuracy and high reusability.
Description
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to a risk object identification method, a risk object identification device, a computer storage medium, and an electronic apparatus.
Background
In many situations, the existence of risk objects has had an impact on the production and life of people, for example, illegal funding brings a great threat to the property safety of people. Therefore, whether to timely prevent the generation of the risk behaviors and timely screen the risk objects becomes a non-trivial problem.
In the related art, a supervised machine learning model is used for predicting risks, and is characterized by a basic composition unit of the model, so that a large amount of tag data is needed, namely, which accounts are known to be risk objects in advance, and meanwhile, the risk behaviors of the risk objects can be obtained; in addition, the data disclosed through the network often has the problems of low accuracy, low timeliness, messy data and the like, which necessarily affects the accuracy of recognition of the recognition model obtained based on the data training.
It should be noted that the information of the present invention in the above background section is only for enhancing understanding of the background of the present disclosure, and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure aims to provide a risk object identification method and device, a computer storage medium and electronic equipment, so that identification accuracy and reusability of a risk object identification model are improved at least to a certain extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided a method of identifying a risk object, including: dividing the collected transaction data into a plurality of index dimensions, and setting monitoring indexes in the index dimensions; establishing a sub-evaluation model corresponding to each index dimension according to the monitoring index in each index dimension; integrating the obtained multiple sub-evaluation models according to a preset integration rule to obtain a target evaluation model; and identifying a target risk object from the risk objects to be identified based on the target evaluation model.
In an exemplary embodiment of the disclosure, the establishing a sub-evaluation model corresponding to each index dimension according to the monitored index in each index dimension includes:
according to a preset construction rule, constructing a structure of a sub-evaluation model corresponding to each index dimension;
and setting parameter values of the corresponding sub-evaluation model structures according to the monitoring indexes in the index dimensions to obtain sub-evaluation models corresponding to the index dimensions.
In an exemplary embodiment of the present disclosure, the sub-evaluation model is a decision tree model, and the establishing a sub-evaluation model corresponding to each index dimension according to the monitored index in each index dimension includes: determining the depth, the node number and the node hierarchy relation of the corresponding decision tree model according to the attribute of each index dimension and the number of monitoring indexes in each index dimension, wherein the monitoring indexes in each index dimension are used as nodes of the corresponding decision tree model; setting node risk probability of each decision tree model; and determining a corresponding node threshold according to the quantile distribution of each monitoring index.
In an exemplary embodiment of the present disclosure, the number of the target evaluation models is a plurality;
And integrating the plurality of sub-evaluation models according to a preset integration rule to obtain a target evaluation model, wherein the method comprises the following steps: determining the number of the target evaluation models according to the attribute of the plurality of index dimensions, wherein the number of the target evaluation models is smaller than the number of the sub-evaluation models; and carrying out grouping integration processing on the sub-evaluation models to obtain target evaluation models with the number.
In an exemplary embodiment of the disclosure, the identifying a target risk object from the risk objects to be identified based on the target assessment model includes:
Respectively inputting monitoring indexes corresponding to any risk object to be identified into each target evaluation model, and outputting a plurality of evaluation scores; acquiring the highest evaluation score in the plurality of evaluation scores as a risk score; and comparing the risk score with a risk threshold, and determining a risk object to be identified corresponding to the risk score larger than the risk threshold as the target risk object.
In an exemplary embodiment of the disclosure, the inputting the monitoring index corresponding to any risk object to be identified into each target evaluation model, outputting a plurality of evaluation scores includes: and outputting the evaluation score output by any target evaluation model as the average value of the scores output by the sub-evaluation models corresponding to any target evaluation model.
In an exemplary embodiment of the disclosure, the index dimension includes a transfer-in transaction ratio for the private account, a transfer-out transaction ratio for the private account, a cashback of a preset dedicated resource, a scatter transfer-in, a concentrate transfer-out resource, a concentrate transfer-in, and a scatter transfer-out resource, and corresponds to the first decision tree model, the second decision tree model, the third decision tree model, the fourth decision tree model, and the fifth decision tree model, respectively; the first target evaluation model corresponds to the enterprise account and serves as a risk behavior collection account, the second target evaluation model corresponds to the enterprise account and serves as a risk behavior refund account, and the third target evaluation model corresponds to the enterprise account and serves as a risk behavior collection account and a refund account.
In an exemplary embodiment of the present disclosure, the integrating the obtained multiple sub-assessment models according to a preset integration rule to obtain a target assessment model includes: combining the first decision tree model with a fourth decision tree model to obtain the first target evaluation model; combining the second decision tree model with a fifth decision tree model to obtain the second target evaluation model; and taking the third decision tree model as the third target evaluation model.
In one exemplary embodiment of the present disclosure, before dividing the collected transaction data into a plurality of index dimensions and setting the monitoring index in the plurality of index dimensions, the method further comprises: transaction data is collected from the transaction facility and is subjected to cleaning processing.
According to one aspect of the present disclosure, there is provided an identification system of a risk object, the system comprising: the system comprises an index setting module, a data processing module and a data processing module, wherein the index setting module is used for dividing collected transaction data into a plurality of index dimensions and setting monitoring indexes in the plurality of index dimensions; the model building module is used for building a sub-evaluation model corresponding to each index dimension according to the monitoring index in each index dimension; the model integration module is used for integrating the plurality of sub-evaluation models according to a preset integration rule to obtain a target evaluation model; and the object identification module is used for identifying a target risk object from the risk objects to be identified based on the target evaluation model.
According to one aspect of the present disclosure, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the method of identifying a risk object according to any of the above.
According to one aspect of the present disclosure, there is provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of identifying a risk object of any of the above via execution of the executable instructions.
According to the risk object identification method in the exemplary embodiment of the disclosure, monitoring indexes of multiple dimensions are set based on transaction data, sub-assessment models corresponding to the dimensions of the indexes are established, and therefore the multiple sub-assessment models are integrated and processed into a target assessment model, and the target assessment model is used for risk object identification. On one hand, the collected transaction data is used as a data source to describe a multi-dimensional monitoring index, and the transaction data can be directly obtained from a related transaction mechanism and has the characteristics of wide source, high timeliness and high quality; meanwhile, the collected transaction data is used as sample data of a construction model, rather than known risk objects, so that modeling can be realized without knowing which objects are risk objects and taking enough risk objects as samples; on the other hand, sub-assessment models corresponding to different index dimensions are integrated into a target assessment model, and the accuracy of risk object identification is high; on the other hand, the dividing of the index dimension, the determination of the monitoring index in the index dimension and the setting of the model parameters in the process of constructing the model can be combined with the manual actual business experience to adjust the corresponding parameters, so that the constructed model has better business interpretation.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which:
FIG. 1 illustrates a flowchart of a method of identifying a risk object according to an exemplary embodiment of the present disclosure;
FIG. 2 illustrates a sub-assessment model creation flow diagram according to an exemplary embodiment of the present disclosure;
FIG. 3 illustrates a sub-assessment model (decision tree model) modeling schematic in accordance with an exemplary embodiment of the present disclosure;
FIG. 4 illustrates a model integration flow diagram according to an exemplary embodiment of the present disclosure;
FIG. 5 illustrates a flowchart for identifying a target risk object based on a target assessment model according to an exemplary embodiment of the present disclosure;
FIG. 6 illustrates a schematic diagram of a risk object identification system according to an exemplary embodiment of the present disclosure;
FIG. 7 illustrates a schematic diagram of a storage medium according to an exemplary embodiment of the present disclosure; and
Fig. 8 shows a block diagram of an electronic device according to an exemplary embodiment of the present disclosure.
In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Detailed Description
Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known structures, methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, or in one or more software-hardened modules, or in different networks and/or processor devices and/or microcontroller devices.
In the related art in the field, the risk object is predicted and identified through the supervised machine learning model, and the model is characterized by basic constituent units of the model, so that a large amount of tag data is needed to be possessed, namely, which accounts are known to be risk objects, and meanwhile, the recent transaction flowing water of the accounts is acquired, and the model training can be performed in a targeted manner based on the model, so that the risk objects are identified. In a related technology, a multidimensional portrait of a risk object is depicted based on data disclosed by a network, the similarity between the risk object to be identified and the depicted multidimensional portrait is calculated, and a target risk object is identified according to the similarity.
Accordingly, the method for identifying the risk object in the related art has the following defects: on the one hand, in an actual scene, a single institution (such as a bank) is difficult to master a large number of risk objects (such as illegal funding enterprise accounts), even if an individual institution discriminates a certain number of risk objects in its own system, a model constructed only for a small number of samples is difficult to popularize to other institutions, and the universality of the model is difficult to realize; on the other hand, the network disclosure data has low accuracy, low timeliness and disordered data, and the risk objects are described by simply processing the data based on the data, so that the accuracy and timeliness of the obtained multidimensional image are difficult to ensure, and the identification rate of the risk objects is low.
As one example of identifying risk objects, preventing illegal funding is required that many institutions (such as banks, securities companies, insurance companies, trust investment companies, and fund management companies) can accurately screen illegal funding enterprises in time to avoid threat to property security of people.
Based on this, in an exemplary embodiment of the present disclosure, there is first provided a risk object identification method. Referring to fig. 1, the method for identifying a risk object includes the following steps:
step S110: dividing the collected transaction data into a plurality of index dimensions, and setting monitoring indexes in the plurality of index dimensions;
Step S120: establishing a sub-evaluation model corresponding to each index dimension according to the monitoring index in each index dimension;
step S130: according to a preset integration rule, integrating the obtained multiple sub-evaluation models to obtain a target evaluation model;
step S140: and identifying the target risk object from the identification objects to be evaluated based on the target evaluation model.
According to the risk object identification method in the embodiment, on one hand, collected transaction data is taken as a data source to describe multi-dimensional monitoring indexes, and the transaction data can be directly obtained from related transaction institutions and has the characteristics of wide sources, high timeliness and high quality; meanwhile, the collected transaction data is used as sample data of a construction model, rather than known risk objects, so that modeling can be realized without knowing which objects are risk objects and taking enough risk objects as samples; on the other hand, sub-evaluation models corresponding to different index dimensions are integrated into a target evaluation model, so that the accuracy of risk object identification can be improved; on the other hand, the division of the index dimension, the determination of the monitoring index in the index dimension and the parameter setting of the model in the process of constructing the model can be combined with the manual actual business experience to adjust the corresponding parameters, so that the constructed model has better business interpretation.
A method of identifying a risk object in an exemplary embodiment of the present disclosure is described below with reference to fig. 1.
In step S110, the collected transaction data is divided into a plurality of index dimensions, and monitoring indexes in the plurality of index dimensions are set.
In an exemplary embodiment of the disclosure, the index dimension is obtained by dividing transaction data, is determined according to attributes of enterprise accounts, and is used for describing the dimension of the accounts, and taking the identification of illegal fund collecting accounts as an example, the index dimension comprises five dimensions of a transfer-in transaction ratio of a private account, a transfer-out transaction ratio of the private account, a cashback of preset exclusive resources, a scattered transfer-in and concentrated transfer-out resources and a concentrated transfer-in and scattered transfer-out resources; the monitoring indexes refer to index objects for describing the dimension transaction data in each dimension, the number of the monitoring indexes can be determined according to the actual risk objects to be identified, for example, the number of the monitoring indexes can be 5, 8, 10, 15, etc., and the number of the monitoring indexes in each dimension is not particularly limited in the disclosure. For example, under the index dimension "private account transfer transaction duty ratio", eight monitoring indexes may be set, which are respectively account past X time to private transfer account number, account past X time to private transfer account number of transactions, account past X time to private transfer account amount of transactions, enterprise past X time to private transfer account amount of transactions, enterprise past X time to private transfer account amount of transactions, account past X time to private transfer account number duty ratio, account past X time to private transfer account number of transactions duty ratio, and account past X time to private transfer account amount of transactions duty ratio.
It should be noted that the number of the monitoring indexes included in each index dimension may be the same or different, and may be adjusted according to the actual requirement, which is not limited in this disclosure.
In an exemplary embodiment of the present disclosure, transaction data may also be collected from a transaction facility, such as collecting transaction flows of an enterprise account (including account billing transactions and account billing transactions) from a bank, before the collected transaction data is divided into a plurality of index dimensions and monitoring indexes in the plurality of index dimensions are set; and, data cleaning is carried out on the collected transaction data. The time length and frequency for collecting transaction data can be set according to practical situations, for example, in the last year, the collecting period is daily, and the method comprises the time length and frequency for collecting transaction data; the data cleaning refers to a process of rechecking and checking data, and aims to delete repeated information, correct existing errors and provide data consistency.
In step S120, a sub-evaluation model corresponding to each index dimension is established according to the monitored index in each index dimension.
In an exemplary embodiment of the present disclosure, a structure and parameter values of a sub-evaluation model are constructed according to monitoring indexes in each index dimension, wherein the sub-evaluation model corresponds to the index dimension one by one. Specifically, fig. 2 shows a sub-evaluation model creation flowchart according to an exemplary embodiment of the present disclosure, and as in fig. 2, a process of the sub-evaluation model includes the steps of:
step S210, constructing a structure of a sub-evaluation model corresponding to each index dimension according to a preset construction rule;
In an exemplary embodiment of the present disclosure, a sub-evaluation model corresponding to an index dimension may be constructed according to the attribute and the number of the monitored index in the index dimension. Optionally, in the preset structures of a plurality of sub-evaluation models, directly calling the available sub-evaluation model structure of the current index dimension according to the attribute and the number of the monitoring indexes; optionally, a sub-evaluation model corresponding to the current index dimension may be generated in response to a model building operation (e.g., a selection operation, an input operation) of the operation user.
Step S220, setting parameter values of the corresponding sub-evaluation model structures according to the monitoring indexes in the index dimensions to obtain sub-evaluation models corresponding to the index dimensions.
In an exemplary embodiment of the present disclosure, parameter values of corresponding sub-evaluation models are set according to monitoring indexes in each index dimension; taking a sub evaluation model as a decision tree model as an example, determining the depth, the node number and the node hierarchy relation of the decision tree model according to the attribute and the number of the monitoring indexes in each index dimension; the monitoring index in the index dimension can be used as a node corresponding to the decision tree model, the node risk probability of the decision tree model is set, and finally the node threshold in the decision tree model is calculated according to the monitoring index, so that a complete decision tree model is obtained.
The process of constructing the sub-assessment model (e.g., decision tree model) is described in detail below, taking the identification of illegal payees as an example. Fig. 3 shows a modeling schematic diagram of a sub-evaluation model (decision tree model) according to an exemplary embodiment of the present disclosure, as shown in fig. 3, taking the above-mentioned setting of eight monitoring indexes under the index dimension "account transfer into transaction ratio for private account" as an example, the eight monitoring indexes are respectively account past X time to account number of private transfers, account past X time to account number of transactions into private transfers, account past X time to account transaction amount of private transfers, enterprise past X time to account number of transactions into pen average transaction amount of private transfers, account past X time to account number of private transfers into account ratio, account past X time to account number of transactions into private transfers into account ratio, and account past X time to account number of transactions into private transfers ratio.
Firstly, when a decision tree model is constructed, determining the depth and the node number of a decision tree according to the fact that the attribute of an index dimension is' the transaction ratio of a private account, and the number of monitoring indexes in the index dimension is eight, and the decision tree structure is shown in figure 3;
Secondly, determining the hierarchical relation of each node (monitoring index) according to the distinguishing degree of each monitoring index, for example, selecting the monitoring index from the root node of the decision tree, taking the most distinguished 'account past X time versus private stations such as account number' as the root node, and then sequentially selecting the monitoring index as a leaf node according to the distinguishing degree of the monitoring index until the monitoring index in the index dimension is completely selected, so as to obtain a complete decision tree model structure;
Next, according to the monitoring index in the index dimension, the node risk probability in the decision tree model structure is set, wherein the node risk probability may be automatically set according to a preset node risk probability rule, or the node risk probability may be set in response to a selection or input operation by the user, as shown in fig. 3, p=0.05, p=0.1, and so on. The setting principle of the node risk probability of the child node can be smooth growth, and the node risk probability of the decision tree model going down is larger, as shown in fig. 3, of course, the corresponding node risk probability can also be set according to actual requirements, and the disclosure is not limited in particular;
finally, determining the corresponding node threshold according to the quantiles of the monitoring indexes, for example, calculating the quantile distribution of each monitoring index, and taking 90% of the quantiles as the node threshold of the corresponding monitoring index. The score refers to a numerical point that divides a probability distribution range of a random variable into several equal parts, and the scheme can use 90% score of each monitoring index as a node threshold.
In the process of constructing the decision tree model, from determining the monitoring index under each index dimension to setting the node arrangement and the parameter value in the preset construction rule, the decision tree model is determined based on the actually collected transaction data, the attribute of the enterprise account and the actual service requirement, and the corresponding parameter adjustment can be performed by combining with the manual actual service experience in the process, so that the obtained decision tree model has more accurate identification result and better service interpretation.
Step S130: and integrating the plurality of sub-evaluation models according to a preset integration rule to obtain a target evaluation model.
In an exemplary embodiment of the present disclosure, the number of target evaluation models is a plurality, and the number of target evaluation models is less than the number of sub-evaluation models. Fig. 4 shows a model integration flow diagram, as in fig. 4, according to an exemplary embodiment of the present disclosure, the process including the steps of:
Step S410: determining the number of target evaluation models according to the attribute of the plurality of index dimensions;
In exemplary embodiments of the present disclosure, which sub-assessment models may be combined may be determined based on the attributes of the index dimension, thereby determining the number of resulting target assessment models.
For example, taking illegal funding enterprise identification as an example, five index dimensions, namely "account transfer transaction ratio for private account, cashback of preset dedicated resources, scattered transfer to concentrated transfer resources, concentrated transfer to scattered transfer resources", respectively correspond to the first decision tree model, the second decision tree model, the third decision tree model, the fourth decision tree model and the fifth decision tree model, and because the combination of the monitoring index, namely "account transfer transaction ratio for private account" and the monitoring index, namely "scattered transfer to concentrated transfer resources", can reflect the risk of the industrial account as illegal funding collection account ", the first evaluation model is obtained by combining the decision tree model 1 corresponding to the account transfer transaction ratio for private account and the decision tree model 4 corresponding to the monitoring index, namely" scattered transfer to concentrated transfer resources ", and has better service interpretation. Similarly, the combination of the monitoring index of the "duty ratio of the transfer transaction to the private account" and the monitoring index of the "centralized transfer to the scattered transfer resources" can reflect the risk of the enterprise account as the illegally funded return account ", so that the decision tree model 2 and the decision tree model 5 are combined to obtain a second target evaluation model, the monitoring index of the" preset cashing of the exclusive resources "reflects the risk of the enterprise account as the risk behavior collection and return account", and the decision tree model 3 is used as a third target evaluation model, so that the 5 sub-evaluation models are integrated into 3 target evaluation models.
The risk of each combined index (such as 'business account as illegally funded collection account') can be more accurately reflected by the target evaluation model obtained by integrating the sub-evaluation models, so that the risk object identification accuracy of the target evaluation model is improved.
Step S420: the sub-evaluation models are subjected to a grouping integration process to obtain target evaluation models having the number determined in step S410.
In an exemplary embodiment of the present disclosure, according to the number of target evaluation models determined in step S410, the sub-evaluation models are subjected to corresponding grouping integration processing, so as to obtain target evaluation models. The evaluation score output by any target evaluation model is the average value of the scores output by the sub-evaluation models corresponding to any target evaluation model, that is, the evaluation score output by the first target evaluation model integrated by the decision tree model 1 and the decision tree model 2 is the average value of the scores output by the two decision tree models.
According to the scheme, based on the fact that full transaction data are used as data sources, from the division of index dimension and the determination of monitoring index in the index dimension to the parameter setting of the model in the process of constructing the model, corresponding adjustment can be carried out by combining with manual actual business experience, and the risk identification accuracy of the model is improved.
Step S140: and identifying the target risk object from the risk objects to be identified based on the target evaluation model.
In an exemplary embodiment of the present disclosure, fig. 5 shows a flowchart of identifying a target risk object based on a target assessment model according to an exemplary embodiment of the present disclosure, as in fig. 5, the process includes:
Step S510, respectively inputting monitoring indexes corresponding to any risk object to be identified into each target evaluation model, and outputting a plurality of evaluation scores; then obtaining the highest evaluation score in the plurality of evaluation scores as a risk score;
Step S520, comparing the risk score with a risk threshold, and determining a risk object to be identified corresponding to the risk score larger than the risk threshold as a target risk object;
in an exemplary embodiment of the present disclosure, the risk threshold may be set according to an actual situation of the risk object to be identified, which is not particularly limited by the present application.
Based on the method, the collected transaction data is taken as a data source to describe multidimensional monitoring indexes, and the transaction data can be directly obtained from related transaction institutions, so that the method has the characteristics of wide source, high timeliness and high quality; meanwhile, the collected transaction data is used as sample data of a construction model, rather than known risk objects, so that modeling can be realized without knowing which objects are risk objects and taking enough risk objects as samples; the sub-assessment models corresponding to different index dimensions are integrated into a target assessment model, so that the accuracy of risk object identification can be improved; in addition, the division of index dimension, the determination of monitoring index in index dimension and the parameter setting of the model in the process of constructing the model can be combined with manual actual business experience to adjust corresponding parameters, so that the constructed model has better business interpretation.
Furthermore, in an exemplary embodiment of the present disclosure, a risk object identification system is also provided. Referring to fig. 6, the risk object identification system 600 may include an index setting module 610, a model building module 620, a model integration module 630, and an object identification module 640. In particular, the method comprises the steps of,
The index setting module 610 is configured to divide the collected transaction data into a plurality of index dimensions, and set a monitoring index in the plurality of index dimensions;
The model building module 620 is configured to build a sub-evaluation model corresponding to each index dimension according to the monitoring index in each index dimension;
The model integration module 630 is configured to integrate the obtained multiple sub-evaluation models according to a preset integration rule to obtain a target evaluation model;
the object recognition module 640 is configured to recognize a target risk object from the risk objects to be recognized based on the target evaluation model.
In an exemplary embodiment of the present disclosure, the model creation module 620 may further include:
the model structure construction unit is used for constructing the structure of the sub-evaluation model corresponding to each index dimension according to a preset construction rule;
and the parameter setting unit is used for setting parameter values of the corresponding sub-evaluation model structures according to the monitoring indexes in the index dimensions to obtain sub-evaluation models corresponding to the index dimensions.
In an exemplary embodiment of the present disclosure, the model structure constructing unit may include:
The decision tree structure construction unit is used for determining the depth, the node number and the node hierarchy relation of the corresponding decision tree model according to the attribute of each index dimension and the number of the monitoring indexes in each index dimension, wherein the monitoring indexes in each index dimension are used as the nodes of the corresponding decision tree model;
the node risk probability setting unit is used for setting the node risk probability of each decision tree model;
and the node threshold determining unit is used for determining a corresponding node threshold according to the quantile distribution of each monitoring index.
In an exemplary embodiment of the present disclosure, the number of target evaluation models is a plurality; the model integration module 630 may further include:
The number determining unit is used for determining the number of target evaluation models according to the attributes of the plurality of index dimensions, wherein the number of the target evaluation models is smaller than that of the sub-evaluation models;
And the integration unit is used for carrying out grouping integration processing on the sub-evaluation models to obtain the target evaluation models with the determined number determined by the number determination unit.
In an exemplary embodiment of the present disclosure, the object recognition module 640 may further include:
The evaluation score determining unit is used for respectively inputting the monitoring indexes corresponding to any risk object to be identified into each target evaluation model and outputting a plurality of evaluation scores;
A risk score acquisition unit configured to acquire a highest evaluation score among the plurality of evaluation scores as a risk score;
the data comparison unit is used for comparing the risk score with the risk threshold value and determining a risk object to be identified corresponding to the risk score larger than the risk threshold value as a target risk object.
In an exemplary embodiment of the disclosure, the index dimension includes a transfer-in transaction ratio for the private account, a transfer-out transaction ratio for the private account, a cashback of a preset dedicated resource, a scatter transfer-in, a concentrate transfer-out resource, a concentrate transfer-in, and a scatter transfer-out resource, and corresponds to the first decision tree model, the second decision tree model, the third decision tree model, the fourth decision tree model, and the fifth decision tree model, respectively;
The first target evaluation model corresponds to the enterprise account and serves as a risk behavior collection account, the second target evaluation model corresponds to the enterprise account and serves as a risk behavior refund account, and the third target evaluation model corresponds to the enterprise account and serves as a risk behavior collection account and a refund account.
In an exemplary embodiment of the present disclosure, the identification system of the risk object further comprises a data acquisition module for acquiring transaction data from the transaction facility.
In an exemplary embodiment of the present disclosure, the risk object identification system further includes a data cleansing module for cleansing the transaction data collected by the data collection module.
Since each functional module of the risk object identification system of the exemplary embodiment of the present disclosure is the same as that in the embodiment of the present invention of the risk object identification method, a detailed description thereof is omitted herein.
It should be noted that although in the above detailed description several modules or units of an identification system of risk objects are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, in exemplary embodiments of the present disclosure, a computer storage medium capable of implementing the above-described method is also provided. On which a program product is stored which enables the implementation of the method described above in the present specification. In some possible embodiments, the various aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above-described method according to an exemplary embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 800 according to such an embodiment of the present disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 8, the electronic device 800 is embodied in the form of a general purpose computing device. Components of electronic device 800 may include, but are not limited to: the at least one processing unit 810, the at least one storage unit 820, a bus 830 connecting the different system components (including the storage unit 820 and the processing unit 810), and a display unit 840.
Wherein the storage unit stores program code that is executable by the processing unit 810 such that the processing unit 810 performs steps according to various exemplary embodiments of the present disclosure described in the above section of the present specification.
The storage unit 820 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 8201 and/or cache memory 8202, and may further include Read Only Memory (ROM) 8203.
Storage unit 820 may also include a program/utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 830 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 800, and/or any device (e.g., router, modem, etc.) that enables the electronic device 800 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 850. Also, electronic device 800 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 over bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 800, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are only schematic illustrations of processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (11)
1. A method of identifying a risk object, comprising:
dividing the collected transaction data into a plurality of index dimensions, and setting monitoring indexes in the index dimensions; the index dimension is determined according to the attribute of the enterprise account;
establishing a sub-evaluation model corresponding to each index dimension according to the monitoring index in each index dimension;
Integrating the obtained multiple sub-evaluation models according to a preset integration rule to obtain a target evaluation model;
identifying a target risk object from risk objects to be identified based on the target evaluation model;
wherein the number of the target evaluation models is a plurality of; and integrating the plurality of sub-evaluation models according to a preset integration rule to obtain a target evaluation model, wherein the method comprises the following steps:
determining the number of the target evaluation models according to the attribute of the index dimensions;
And carrying out grouping integration processing on the sub-evaluation models to obtain target evaluation models with the number, wherein the number of the target evaluation models is smaller than that of the sub-evaluation models.
2. The method of claim 1, wherein the creating a sub-evaluation model corresponding to each of the index dimensions according to the monitored index in each of the index dimensions comprises:
according to a preset construction rule, constructing a structure of a sub-evaluation model corresponding to each index dimension;
and setting parameter values of the corresponding sub-evaluation model structures according to the monitoring indexes in the index dimensions to obtain sub-evaluation models corresponding to the index dimensions.
3. The method according to claim 2, wherein the sub-evaluation model is a decision tree model, and the establishing a sub-evaluation model corresponding to each index dimension according to the monitored index in each index dimension includes:
Determining the depth, the node number and the node hierarchy relation of the corresponding decision tree model according to the attribute of each index dimension and the number of monitoring indexes in each index dimension, wherein the monitoring indexes in each index dimension are used as nodes of the corresponding decision tree model;
Setting node risk probability of each decision tree model;
and determining a corresponding node threshold according to the quantile distribution of each monitoring index.
4. The method according to claim 1, wherein the identifying a target risk object from risk objects to be identified based on the target evaluation model comprises:
Respectively inputting monitoring indexes corresponding to any risk object to be identified into each target evaluation model, and outputting a plurality of evaluation scores;
acquiring the highest evaluation score in the plurality of evaluation scores as a risk score;
And comparing the risk score with a risk threshold, and determining a risk object to be identified corresponding to the risk score larger than the risk threshold as the target risk object.
5. The method of claim 4, wherein the inputting the monitoring index corresponding to any risk object to be identified into each of the target evaluation models, and outputting a plurality of evaluation scores, respectively, includes:
And outputting the evaluation score output by any target evaluation model as the average value of the scores output by the sub-evaluation models corresponding to any target evaluation model.
6. The identification method of claim 3, wherein the index dimension includes a transfer-in transaction ratio for the private account, a transfer-out transaction ratio for the private account, a cashback of a preset dedicated resource, a scattered transfer-in and concentrated transfer-out resource, a concentrated transfer-in and scattered transfer-out resource, and corresponds to the first decision tree model, the second decision tree model, the third decision tree model, the fourth decision tree model and the fifth decision tree model, respectively;
the first target evaluation model corresponds to the enterprise account and serves as a risk behavior collection account, the second target evaluation model corresponds to the enterprise account and serves as a risk behavior refund account, and the third target evaluation model corresponds to the enterprise account and serves as a risk behavior collection account and a refund account.
7. The method of identifying according to claim 6, wherein the integrating the obtained plurality of sub-evaluation models according to a preset integration rule to obtain a target evaluation model includes:
combining the first decision tree model with a fourth decision tree model to obtain the first target evaluation model;
combining the second decision tree model with a fifth decision tree model to obtain the second target evaluation model;
and taking the third decision tree model as the third target evaluation model.
8. The identification method of any one of claims 1 to 7, wherein prior to dividing the collected transaction data into a plurality of index dimensions and setting a monitoring index in the plurality of index dimensions, the method further comprises:
transaction data is collected from the transaction facility and is subjected to cleaning processing.
9. A system for identifying a risk object, the system comprising:
The system comprises an index setting module, a data processing module and a data processing module, wherein the index setting module is used for dividing collected transaction data into a plurality of index dimensions and setting monitoring indexes in the plurality of index dimensions; the index dimension is determined according to the attribute of the enterprise account;
The model building module is used for building a sub-evaluation model corresponding to each index dimension according to the monitoring index in each index dimension;
The model integration module is used for integrating the plurality of sub-evaluation models according to a preset integration rule to obtain a target evaluation model;
The object identification module is used for identifying a target risk object from risk objects to be identified based on the target evaluation model;
wherein the number of the target evaluation models is a plurality of; the model integration module is configured to perform:
determining the number of the target evaluation models according to the attribute of the index dimensions;
And carrying out grouping integration processing on the sub-evaluation models to obtain target evaluation models with the number, wherein the number of the target evaluation models is smaller than that of the sub-evaluation models.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements a method of identifying a risk object according to any of claims 1 to 8.
11. An electronic device, comprising:
a processor; and
A memory for storing executable instructions of the processor;
Wherein the processor is configured to perform the method of identifying a risk object of any of claims 1 to 8 via execution of the executable instructions.
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| CN116187768B (en) * | 2023-04-26 | 2023-07-18 | 浙江电力交易中心有限公司 | Risk assessment and protection method suitable for green electricity market |
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