CN117437004A - Risk identification method and device for resource borrowing service and computer equipment - Google Patents
Risk identification method and device for resource borrowing service and computer equipment Download PDFInfo
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Abstract
The application relates to a risk identification method, a risk identification device and computer equipment for resource borrowing service, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring resource information of a resource borrowing object of a resource borrowing service, and credit data of the resource borrowing object; acquiring a historical resource transfer value corresponding to the resource information and a resource value variation; obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation; and inputting the risk evaluation data and the credit data into a resource risk evaluation model which is trained in advance and deployed in the cloud server, and acquiring a risk identification result of a resource borrowing service of a resource borrowing object through the resource risk evaluation model. By adopting the method, the risk identification efficiency of the resource borrowing service can be improved.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a risk identification method, apparatus, and computer device for resource borrowing service.
Background
With the development of the financial business field, the resource borrowing business is an important core business in a resource management organization, and the resource borrowing business is a business performed between the resource management organization and a resource borrowing object. In order to avoid the loss of the resource management mechanism, the resource or the related information thereof owned by the resource borrowing object is usually required to be evaluated, and the resource borrowing is performed on the resource transfer application object meeting the risk evaluation condition.
In the existing method for evaluating the risk of the resource by the resource borrowing object, service personnel of a resource management mechanism usually apply information to the resource borrowing provided by the resource borrowing object, and perform risk identification evaluation on the resource owned by the resource borrowing object so as to judge that the resource is a resource with high risk.
However, the applicant finds that in the implementation process, the method for performing risk identification on the resource borrowing service by manpower in the prior art has a problem of low efficiency.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a risk identification method, apparatus and computer device for resource borrowing service capable of providing risk identification efficiency.
In a first aspect, the present application provides a risk identification method for a resource borrowing service, where the method includes:
acquiring resource information of a resource borrowing object of a resource borrowing service, and credit data of the resource borrowing object;
acquiring a historical resource transfer value corresponding to the resource information and a resource value variation;
obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation;
and inputting the risk evaluation data and the credit data into a resource risk evaluation model which is trained in advance and deployed in the cloud server, and acquiring a risk identification result of a resource borrowing service of a resource borrowing object through the resource risk evaluation model.
In one embodiment, obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation includes:
respectively cleaning the historical resource transfer value and the resource value variation to obtain a cleaned historical resource transfer value and a cleaned resource value variation;
respectively carrying out standardization processing on the washed historical resource transfer value and the washed resource value variation to obtain a standardized historical resource transfer value and a standardized resource value variation;
and obtaining risk evaluation data corresponding to the resource borrowing object based on the historical resource transfer value after the standardization processing and the resource value change quantity after the standardization processing.
In one embodiment, cleaning the historical resource transfer value and the resource value variation respectively to obtain a cleaned historical resource transfer value and a cleaned resource value variation, including:
respectively carrying out missing value processing on the historical resource transfer value and the resource value variation to obtain a historical resource transfer value after missing value processing and a resource value variation after missing value processing;
and respectively carrying out abnormal value processing on the historical resource transfer value after the missing value processing and the resource value change after the missing value processing to obtain a cleaned historical resource transfer value and a cleaned resource value change.
In one embodiment, obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation includes:
acquiring a first evaluation index corresponding to the historical resource transfer value and a second evaluation index corresponding to the resource value variation;
and weighting the historical resource transfer value based on the first evaluation index, and weighting the resource value variation based on the second evaluation index to obtain risk evaluation data corresponding to the resource borrowing object.
In one embodiment, obtaining resource information of a resource borrowing object of a resource borrowing service includes:
responding to input operation of borrowing service personnel, and acquiring identity information of a resource borrowing object corresponding to the input operation;
acquiring historical resource transfer information matched with the identity information from a resource information server, wherein a historical account carries resources;
and obtaining the resource information of the resource borrowing object based on the historical resource transfer information and the historical account carrying resources.
In one embodiment, the training manner of the resource risk evaluation model includes:
acquiring sample risk evaluation data and sample credit data of a sample object, and sample risk identification results aiming at the sample object;
inputting sample risk evaluation data and sample credit data into a resource risk evaluation model to be trained, and acquiring a predicted risk identification result of a resource borrowing service of a sample object through the resource risk evaluation model;
and training the resource risk evaluation model based on the difference between the sample risk recognition result and the predicted risk recognition result.
In a second aspect, the present application further provides a risk identification device for resource borrowing service, where the device includes:
the resource and credit acquisition module is used for acquiring the resource information of the resource borrowing object of the resource borrowing service and the credit data of the resource borrowing object;
the transfer value and transformation amount acquisition module is used for acquiring a historical resource transfer value and a resource value variation corresponding to the resource information;
the risk evaluation data acquisition module is used for acquiring risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation;
the risk identification result acquisition module is used for inputting the risk evaluation data and the credit data into a resource risk evaluation model which is trained in advance and deployed in the cloud server, and acquiring a risk identification result of the resource borrowing object through the resource risk evaluation model.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.
The risk identification method, the risk identification device and the computer equipment of the resource borrowing service acquire the resource information of the resource borrowing object of the resource borrowing service and the credit data of the resource borrowing object; acquiring a historical resource transfer value corresponding to the resource information and a resource value variation; obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation; and inputting the risk evaluation data and the credit data into a resource risk evaluation model which is trained in advance and deployed in the cloud server, and acquiring a risk identification result of a resource borrowing service of a resource borrowing object through the resource risk evaluation model. Compared with the prior art, the risk identification method and the risk identification device have the advantages that the risk identification result of the resource borrowing service can be obtained by inputting the risk evaluation data and the credit data of the resource borrowing object to the resource risk evaluation model which is deployed in the cloud server and trained in advance, so that the risk identification result of the resource borrowing service can be effectively and accurately obtained, the risk evaluation audit can be avoided manually, and the risk identification efficiency of the resource borrowing service can be improved.
Drawings
FIG. 1 is a flow chart of a risk identification method of a resource borrowing service in one embodiment;
FIG. 2 is a flowchart illustrating steps for obtaining risk assessment data corresponding to a resource borrowing object in one embodiment;
fig. 3 is a flowchart illustrating a step of obtaining resource information of a resource borrowing object of a resource borrowing service according to an embodiment;
FIG. 4 is a flowchart illustrating a training step of the resource risk assessment model in one embodiment;
FIG. 5 is a flowchart of a risk identification method of a resource borrowing service in another embodiment;
FIG. 6 is a block diagram illustrating a risk identification device for a resource borrowing service in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In an exemplary embodiment, as shown in fig. 1, a risk identification method of a resource borrowing service is provided, and this embodiment is illustrated by applying the method to a terminal, where it is understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of: the method comprises the following steps: s102 to S108, wherein:
s102, acquiring resource information of a resource borrowing object of a resource borrowing service and credit data of the resource borrowing object.
The resource borrowing service may be a service that the resource borrowing object performs resource borrowing to the resource management mechanism. The resource information may be information of resources owned by the resource borrowing object. The resource information may be asset information of a resource borrowing object. The credit data may be data characterizing the credit of the resource borrowing object. The credit data may be a credit rating or a credit value of the resource borrowing object in the resource management authority, etc.
For example, object information of a resource borrowing object applying for the resource borrowing service may be obtained, for example, identity information of the resource borrowing object may be obtained, and the identity information may be a displacement identity of the resource borrowing object. The identity information may be identity information uploaded by a service person of the resource borrowing service through the terminal when the resource borrowing service is applied. The server may obtain the resource information of the resource borrowing object in the resource borrowing service according to the object information of the resource borrowing object. Meanwhile, the server can also acquire the credit data of the resource borrowing object according to the object information of the resource borrowing object. Credit data may be obtained from historical resource interaction services.
S104, obtaining a historical resource transfer value corresponding to the resource information and a resource value variation.
The historical resource transfer value may be a resource transfer value in a resource interaction service of the resource borrowing object history. The resource value change may be a historical resource value change, and may be a historical change of a resource owned by the resource borrowing object.
For example, a historical resource transfer value of the resource borrowing object in the historical resource interaction business may be determined from the resource information, and a resource value variation of the resource borrowing object may be determined. Thus, it can evaluate whether the resource borrowing object has stable resources or not through the historical resource transfer value and the resource value variation. For example, if the resource of the resource borrowing object is unstable, the resource borrowing risk of the resource borrowing object may be considered high. Wherein whether unstable or not can be determined by a preset stability criterion.
And S106, obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation.
The risk evaluation data may be data for evaluating a risk of a resource borrowing service of the resource borrowing object.
For example, the risk evaluation data corresponding to the resource borrowing object may be determined according to the historical resource transfer value and the resource value variation. For example, the risk evaluation data may be a risk evaluation value, and the risk evaluation value may be obtained by performing numerical conversion on the historical resource transfer value and the resource value variation, respectively, as the risk evaluation value. As an example, the historical resource transfer value and the resource value variation may be respectively subjected to score conversion according to a preset score conversion function, to obtain converted scores, and weighted according to preset weights, to obtain risk evaluation data.
As an example, the historical resource transfer value and the resource value variation amount may be input to a trained risk evaluation data acquisition model, and the risk evaluation data may be output through the risk evaluation data acquisition model. The risk evaluation data acquisition model can be a neural network model, and can be obtained by training a sample historical resource transfer value, a sample resource value variation and sample risk evaluation data.
S108, inputting the risk evaluation data and the credit data into a resource risk evaluation model which is trained in advance and deployed in the cloud server, and acquiring a risk identification result of a resource borrowing service of a resource borrowing object through the resource risk evaluation model.
The resource risk evaluation model may be a model for acquiring a risk identification result. The resource risk assessment model may be a neural network model. The resource risk evaluation model can be obtained by training sample risk evaluation data, sample credit data and sample risk identification results. For example, the resource risk evaluation model meeting the training completion condition can be obtained by adjusting parameters of the resource risk evaluation model according to the sample risk recognition result and the prediction recognition result obtained by predicting the sample risk evaluation data and the sample credit data. The risk identification result may be a resource borrowing risk level of a resource borrowing service; for example, the risk level of the resource borrowing service may be classified into 5 levels, and the risk identification result may be a specific level of risk, for example, the risk identification result may be that the resource borrowing service of the resource borrowing object is at 3 levels of risk.
By way of example, the risk evaluation data and the credit data may be input to a resource risk evaluation model trained in advance deployed in a cloud server, the cloud server may perform cloud computing, and prediction of a risk recognition result may be performed by the resource risk evaluation model, to obtain a risk recognition result of a resource borrowing service of a resource borrowing object. In this way, the risk identification can be effectively and accurately carried out on the resource borrowing service of the resource borrowing object, so that the efficiency of carrying out the risk identification on the resource borrowing service can be improved
In this embodiment, resource information of a resource borrowing object of a resource borrowing service and credit data of the resource borrowing object are obtained; acquiring a historical resource transfer value corresponding to the resource information and a resource value variation; obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation; and inputting the risk evaluation data and the credit data into a resource risk evaluation model which is trained in advance and deployed in the cloud server, and acquiring a risk identification result of a resource borrowing service of a resource borrowing object through the resource risk evaluation model. Compared with the prior art, the risk identification result of the resource borrowing service can be obtained by inputting the risk evaluation data and the credit data of the resource borrowing object into the resource risk evaluation model which is deployed in the cloud server and trained in advance, so that the risk identification result of the resource borrowing service can be effectively and accurately obtained, manual risk evaluation and audit can be avoided, and the risk identification efficiency of the resource borrowing service can be improved.
In an exemplary embodiment, as shown in fig. 2, according to the historical resource transfer value and the resource value variation, risk evaluation data corresponding to the resource borrowing object is obtained, including S202 to S206, where:
s202, respectively cleaning the historical resource transfer value and the resource value variation to obtain a cleaned historical resource transfer value and a cleaned resource value variation;
s204, respectively carrying out standardization processing on the washed historical resource transfer value and the washed resource value variation to obtain a standardized historical resource transfer value and a standardized resource value variation;
s206, obtaining risk evaluation data corresponding to the resource borrowing object based on the standardized historical resource transfer value and the standardized resource value variation.
For example, the history resource transfer value and the resource value change amount may be subjected to cleaning processing, respectively, for example, the history resource transfer value and the resource value change amount may be subjected to missing value processing, abnormal value processing, etc., respectively, and the history resource transfer value and the resource value change amount after cleaning may be obtained. In this way, the historical resource transfer value and the resource value variation are respectively cleaned, so that the accuracy of determining the risk evaluation data can be improved.
The washed historical resource transfer value and the washed resource value change amount can be respectively standardized, for example, the washed historical resource transfer value and the washed resource value change amount can be uniformly converted, so that the historical resource transfer value and the washed resource value change amount are in the same magnitude or meet the standardized value interval. For example, the post-cleaning historical resource transfer value and the post-cleaning resource value change amount may be weighted to obtain the post-normalization historical resource transfer value and the post-normalization resource value change amount.
According to the historical resource transfer value after the standardization processing and the resource value change quantity after the standardization processing, the risk evaluation data corresponding to the resource borrowing object can be determined. Therefore, the accuracy of the risk evaluation data can be improved, and the accuracy of the risk identification result can be further ensured.
In this embodiment, after the historical resource transfer value and the resource value variation are subjected to the cleaning treatment and the normalization treatment, the risk evaluation data corresponding to the resource borrowing object can be accurately obtained based on the historical resource transfer value after the normalization treatment and the resource value variation after the normalization treatment, so that the accuracy of the risk evaluation data can be improved.
In an exemplary embodiment, the cleaning process is performed on the historical resource transfer value and the resource value variation respectively, to obtain a cleaned historical resource transfer value and a cleaned resource value variation, which includes:
respectively carrying out missing value processing on the historical resource transfer value and the resource value variation to obtain a historical resource transfer value after missing value processing and a resource value variation after missing value processing;
and respectively carrying out abnormal value processing on the historical resource transfer value after the missing value processing and the resource value change after the missing value processing to obtain a cleaned historical resource transfer value and a cleaned resource value change.
For example, the missing value processing may be performed on the historical resource transfer value and the resource value variation, respectively, to obtain a historical resource transfer value after the missing value processing and a resource value variation after the missing value processing. For example, if there is a missing value in the historical resource transfer value, the missing value may be complemented based on other data in the historical resource transfer value to complete the missing value processing of the historical resource transfer value.
The historical resource transfer value after the missing value processing and the resource value change after the missing value processing can be respectively subjected to abnormal value processing to obtain the cleaned historical resource transfer value and the cleaned resource value change. For example, whether or not an abnormal value exists in the resource numerical value change amount may be determined, and as an example, whether or not an abnormal value exists may be determined by a standard change amount corresponding to the resource numerical value change amount, and if an abnormal value exists in the resource numerical value change amount, the abnormal value processing may be performed on the resource numerical value change amount, and the abnormal value of the resource numerical value change amount may be corrected.
In this embodiment, missing value processing and abnormal value processing are performed on the historical resource transfer value and the resource value variation, so as to obtain a cleaned historical resource transfer value and a cleaned resource value variation. Thus, accurate historical resource transfer values and resource value variation can be obtained, so that the accuracy of the historical resource transfer values and the resource value variation can be improved, and further, the accuracy of risk assessment can be improved.
In an exemplary embodiment, obtaining risk evaluation data corresponding to a resource borrowing object according to a historical resource transfer value and a resource value variation, includes:
acquiring a first evaluation index corresponding to the historical resource transfer value and a second evaluation index corresponding to the resource value variation;
and weighting the historical resource transfer value based on the first evaluation index, and weighting the resource value variation based on the second evaluation index to obtain risk evaluation data corresponding to the resource borrowing object.
The first evaluation index may be an index for evaluating a historical resource transfer value. The second evaluation index may be an index for evaluating the resource numerical value variation amount. The first evaluation index and the second evaluation index may each be an index obtained by an algorithm model or a function.
Illustratively, the historical resource transfer value may be normalized, and the first evaluation index may be obtained for the normalized historical resource transfer value; the resource numerical value variation may be normalized, and the second evaluation index may be obtained for the normalized resource numerical value variation. The historical resource transfer values can be weighted according to the first evaluation index, and the resource value variation is weighted according to the second evaluation index, so that risk evaluation data corresponding to the resource borrowing object can be obtained.
In the embodiment, a first evaluation index corresponding to a historical resource transfer value and a second evaluation index corresponding to a resource value change are obtained; the historical resource transfer values are weighted based on the first evaluation index, and the resource value change amount is weighted based on the second evaluation index, so that risk evaluation data corresponding to the resource borrowing object can be obtained, and accuracy of acquiring the risk evaluation data can be guaranteed.
In an exemplary embodiment, as shown in fig. 3, obtaining resource information of a resource borrowing object of a resource borrowing service includes:
s302, responding to input operation of borrowing service personnel, and acquiring identity information of a resource borrowing object corresponding to the input operation;
s304, acquiring historical resource transfer information matched with the identity information and historical account carried resources from a resource information server;
s306, obtaining the resource information of the resource borrowing object based on the historical resource transfer information and the historical account carrying resources.
The identity information may be unique information for identifying the resource borrowing object, and the identity information may be necessary information for the resource borrowing object to transact the resource borrowing service. The resource information server may be a cloud server for storing resource information.
For example, the identification information of the resource borrowing object input in the input operation may be obtained in response to the input operation of the service personnel of the resource borrowing. The historical resource transfer information and the historical account carried resource matched with the identity information of the resource borrowing object can be obtained from the cloud server for storing the information resource, so that the historical resource transfer information and the historical account carried resource of the resource borrowing object are obtained. Further, the resource information of the resource borrowing object can be obtained according to the historical resource transfer information and the historical account carrying resources.
In the embodiment, in response to an input operation of a borrowing service person, acquiring identity information of a resource borrowing object corresponding to the input operation; acquiring historical resource transfer information matched with the identity information from a resource information server, wherein a historical account carries resources; based on the historical resource transfer information and the historical account carrying resources, the resource information matched with the resource borrowing object can be obtained, so that the accuracy of risk identification on the resource borrowing object can be improved.
In an exemplary embodiment, as shown in fig. 4, the training manner of the resource risk assessment model includes:
s402, sample risk evaluation data, sample credit data and sample risk identification results of the sample object are obtained;
s404, inputting sample risk evaluation data and sample credit data into a resource risk evaluation model to be trained, and acquiring a predicted risk recognition result of a resource borrowing service of a sample object through the resource risk evaluation model;
s406, training a resource risk evaluation model based on the difference between the sample risk recognition result and the prediction risk recognition result.
The sample risk evaluation data may be risk evaluation data used as a sample in model training. The sample credit data may be credit data used as a sample in model training. The sample risk assessment data and the sample credit data may each be training set data. The sample object may be an object corresponding to the data for which model training is performed. The sample risk identification result may be a true value of the risk identification result of the sample object, e.g. a true risk level of the sample object. The predicted risk recognition result may be a risk recognition result predicted in the training process of the resource risk evaluation model.
For example, sample risk assessment data, sample credit data, and sample risk recognition results for a sample object for model training may be obtained for the sample object. Sample risk evaluation data and sample credit data can be input into a resource risk evaluation model to be trained, and a predicted risk recognition result of the resource borrowing business of the sample object is predicted and obtained through the resource risk evaluation model to be trained. Further, the resource risk evaluation model can be trained through the difference between the sample risk recognition result and the predicted risk recognition result, so that the difference between the sample risk recognition result and the predicted risk recognition result meets the difference condition, and the training of the resource risk evaluation model is completed.
In the embodiment, sample risk evaluation data and sample credit data are input into a resource risk evaluation model to be trained, and a predicted risk recognition result of a resource borrowing service of a sample object is obtained through the resource risk evaluation model; based on the difference between the sample risk identification result and the predicted risk identification result, training the resource risk evaluation model, and obtaining the trained resource risk evaluation model, so that the accuracy of acquiring the risk identification result by the resource risk evaluation model can be improved.
In an exemplary embodiment, a resource borrowing service person collects various data of a resource borrowing object to be evaluated, and can process the various parameter data through cloud computing, wherein the processing process is mainly to input a resource risk evaluation model according to the various data, and the service person can obtain an evaluation result, give an evaluation opinion and identify a high risk resource. As shown in fig. 5, the risk identification method for resource borrowing service includes the steps: s501, collecting asset data by business personnel; s502, inputting data into a resource risk evaluation model of a cloud server; s503, outputting an evaluation result through the cloud server.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a risk identification device for the resource borrowing service, which is used for realizing the risk identification method of the resource borrowing service. The implementation scheme of the solution provided by the device is similar to the implementation scheme described in the above method, so the specific limitation in the embodiments of the risk identification device for one or more resource borrowing services provided below may refer to the limitation of the risk identification method for the resource borrowing service hereinabove, and will not be repeated herein.
In an exemplary embodiment, as shown in fig. 6, there is provided a risk identification device for a resource borrowing service, including: a resource and credit acquisition module 610, a transfer value and transformation amount acquisition module 620, a risk assessment data acquisition module 630, and a risk identification result acquisition module 640, wherein:
a resource and credit acquisition module 610, configured to acquire resource information of a resource borrowing object of a resource borrowing service and credit data of the resource borrowing object;
a transfer value and transformation amount obtaining module 620, configured to obtain a historical resource transfer value and a resource value variation corresponding to the resource information;
the risk evaluation data obtaining module 630 is configured to obtain risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation;
the risk identification result obtaining module 640 is configured to input risk evaluation data and credit data to a resource risk evaluation model that is trained in advance and deployed in a cloud server, and obtain a risk identification result of a resource borrowing object through the resource risk evaluation model.
In one exemplary embodiment, the risk evaluation data acquisition module includes a cleaning processing unit, a normalization processing unit, and a risk evaluation data determination unit.
The cleaning processing unit is used for respectively cleaning the historical resource transfer value and the resource value variation to obtain a cleaned historical resource transfer value and a cleaned resource value variation. The standardized processing unit is used for respectively carrying out standardized processing on the history resource transfer value after cleaning and the resource value change after cleaning to obtain the history resource transfer value after standardized processing and the resource value change after standardized processing. The risk evaluation data determining unit is used for obtaining risk evaluation data corresponding to the resource borrowing object based on the historical resource transfer value after the standardized processing and the resource value variation after the standardized processing.
In one exemplary embodiment, the cleaning processing unit includes a missing value processing unit and an outlier processing unit.
The missing value processing unit is used for respectively carrying out missing value processing on the historical resource transfer value and the resource value variation to obtain a historical resource transfer value after missing value processing and a resource value variation after missing value processing. The abnormal value processing unit is used for respectively carrying out abnormal value processing on the historical resource transfer value after the missing value processing and the resource value change after the missing value processing to obtain the cleaned historical resource transfer value and the cleaned resource value change.
In an exemplary embodiment, the risk evaluation data acquisition module includes an evaluation index determination unit and a weighting unit.
The evaluation index determination unit is used for acquiring a first evaluation index corresponding to the historical resource transfer value and a second evaluation index corresponding to the resource value variation. The weighting unit is used for weighting the historical resource transfer value based on the first evaluation index, and weighting the resource value variation based on the second evaluation index to obtain the risk evaluation data corresponding to the resource borrowing object.
In one exemplary embodiment, the resource and credit acquisition module includes an identity information acquisition unit, an information matching unit, and a resource information determination unit.
The identity information acquisition unit is used for responding to the input operation of the borrowing service personnel and acquiring the identity information of the resource borrowing object corresponding to the input operation. The information matching unit is used for acquiring historical resource transfer information matched with the identity information from the resource information server and carrying resources by the historical account. The resource information determining unit is used for obtaining resource information of the resource borrowing object based on the historical resource transfer information and the historical account carrying resources.
In an exemplary embodiment, the apparatus further comprises a sample acquisition module, a prediction result acquisition module, and a training module.
The sample acquisition module is used for acquiring sample risk evaluation data, sample credit data and sample risk identification results for the sample object. The prediction result acquisition module is used for inputting the sample risk evaluation data and the sample credit data into a resource risk evaluation model to be trained, and acquiring a prediction risk identification result of the resource borrowing service of the sample object through the resource risk evaluation model. The training module is used for training the resource risk evaluation model based on the difference between the sample risk recognition result and the prediction risk recognition result.
The modules in the risk identification device of the resource borrowing service can be all or partially implemented by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store resource information and credit data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a risk identification method for resource borrowing services.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use, and processing of the related data are required to meet the related regulations.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A risk identification method for resource borrowing services, the method comprising:
acquiring resource information of a resource borrowing object of a resource borrowing service and credit data of the resource borrowing object;
acquiring a historical resource transfer value and a resource value variation corresponding to the resource information;
obtaining risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation;
and inputting the risk evaluation data and the credit data into a resource risk evaluation model which is trained in advance and deployed in a cloud server, and acquiring a risk identification result of a resource borrowing service of the resource borrowing object through the resource risk evaluation model.
2. The method of claim 1, wherein the obtaining risk assessment data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation includes:
respectively cleaning the historical resource transfer value and the resource value variation to obtain a cleaned historical resource transfer value and a cleaned resource value variation;
respectively carrying out standardization processing on the washed historical resource transfer value and the washed resource value variation to obtain a standardized historical resource transfer value and a standardized resource value variation;
and obtaining risk evaluation data corresponding to the resource borrowing object based on the historical resource transfer value after the standardization processing and the resource value change after the standardization processing.
3. The method according to claim 1, wherein the cleaning the historical resource transfer value and the resource value variation respectively to obtain a cleaned historical resource transfer value and a cleaned resource value variation comprises:
respectively carrying out missing value processing on the historical resource transfer value and the resource value variation to obtain a historical resource transfer value after missing value processing and a resource value variation after missing value processing;
and respectively carrying out abnormal value processing on the historical resource transfer value after the missing value processing and the resource value change after the missing value processing to obtain the historical resource transfer value after cleaning and the resource value change after cleaning.
4. A method according to any one of claims 1 to 3, wherein the obtaining risk assessment data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation includes:
acquiring a first evaluation index corresponding to the historical resource transfer value and a second evaluation index corresponding to the resource value variation;
and weighting the historical resource transfer value based on the first evaluation index, and weighting the resource value variation based on the second evaluation index to obtain risk evaluation data corresponding to the resource borrowing object.
5. The method of claim 1, wherein the obtaining the resource information of the resource borrowing object of the resource borrowing service comprises:
responding to input operation of borrowing service personnel, and acquiring identity information of a resource borrowing object corresponding to the input operation;
acquiring historical resource transfer information matched with the identity information from a resource information server, wherein a historical account carries resources;
and obtaining the resource information of the resource borrowing object based on the historical resource transfer information and the historical account carrying resources.
6. The method according to claim 1, wherein the training mode of the resource risk assessment model comprises:
acquiring sample risk evaluation data, sample credit data of a sample object and sample risk identification results aiming at the sample object;
inputting the sample risk evaluation data and the sample credit data into a resource risk evaluation model to be trained, and acquiring a predicted risk identification result of a resource borrowing service of the sample object through the resource risk evaluation model;
and training the resource risk evaluation model based on the difference between the sample risk recognition result and the prediction risk recognition result.
7. A risk identification device for a resource borrowing service, the device comprising:
the resource and credit acquisition module is used for acquiring the resource information of the resource borrowing object of the resource borrowing service and the credit data of the resource borrowing object;
the transfer value and transformation amount acquisition module is used for acquiring a historical resource transfer value and a resource value variation corresponding to the resource information;
the risk evaluation data acquisition module is used for acquiring risk evaluation data corresponding to the resource borrowing object according to the historical resource transfer value and the resource value variation;
the risk identification result acquisition module is used for inputting the risk evaluation data and the credit data into a resource risk evaluation model which is deployed in a cloud server and trained in advance, and acquiring a risk identification result of the resource borrowing object through the resource risk evaluation model.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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