CN113610627A - Data processing method and device for risk early warning - Google Patents
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Abstract
The application discloses a data processing method and device for risk early warning. The method comprises the following steps: acquiring relevant data of an early warning user needing to be credited, namely user data to be processed, performing hierarchical evaluation processing on the user data to be processed through a preset hierarchical evaluation rule, and performing risk evaluation processing on the user data to be processed through a first risk evaluation rule of an internal data source to obtain first evaluation data; carrying out risk evaluation processing on the user data to be processed by utilizing a second risk evaluation rule of an external data source to obtain second evaluation data; and obtaining result risk evaluation data after classifying the first evaluation data and the second evaluation data. The method and the device solve the technical problems of low accuracy and high cost of post-credit early warning in the prior art, improve the accuracy of post-credit risk early warning and reduce the cost of risk early warning.
Description
Technical Field
The application relates to the field of computers, in particular to a data processing method and device for risk early warning.
Background
With the gradual maturity of the automotive financial staging industry chain, post-loan risk early warning also becomes a point that various banks and branch institutions begin to pay attention to in succession. Most risk early warning mechanisms in the market at present are models and rule strategies for running all data sources by a full number of clients whose accounts are not overdue after loan, so that the clients who hit the early warning strategy rules are treated in advance to avoid adverse risks. However, the method needs to run the full-volume loan client, and most of data sources used by the early warning monitoring model and the strategy in the loan need to be charged by external access sources, so that the overall early warning cost is high, and the early warning accuracy is not accurate due to the fact that the data source query is not layered.
In the prior art, the post-credit early warning has the technical problems of low accuracy and high cost.
Content of application
The main purpose of the present application is to provide a data processing method and apparatus for risk early warning, which solve the technical problems of low accuracy and high cost in post-credit early warning in the prior art, so as to improve accuracy of post-credit risk early warning and reduce cost.
In order to achieve the above object, the present application provides a data processing method for risk early warning.
According to a second aspect of the present application, a data processing apparatus for risk pre-warning is presented.
According to a third aspect of the present application, a computer-readable storage medium is presented.
According to a fourth aspect of the present application, an electronic device is presented.
In view of the above, according to a first aspect of the present application, a data processing method for risk pre-warning is provided, including:
acquiring user data to be processed, wherein the user data to be processed is related data of an early warning user needing to be credited;
based on a preset grading evaluation rule, grading risk evaluation processing is carried out on the user data to be processed, and result risk evaluation data are obtained;
and matching an early warning strategy corresponding to the risk assessment data in a preset early warning database based on the result risk assessment data.
Further, based on a preset hierarchical evaluation rule, performing hierarchical risk evaluation processing on the user data to be processed to obtain result risk evaluation data, including:
performing risk assessment processing on the user data to be processed based on a first risk assessment rule utilizing an internal data source to obtain first assessment data;
performing risk evaluation processing on the user data to be processed based on a second risk evaluation rule utilizing an external data source to obtain second evaluation data;
and classifying the first evaluation data and the second evaluation data to obtain result risk evaluation data, wherein the result risk evaluation data comprises low-risk user data and high-risk user data.
Further, based on a first risk assessment rule using an internal data source, performing risk assessment processing on the user data to be processed to obtain first assessment data, including:
acquiring first user data to be processed, wherein the first user data to be processed is the user data to be processed in an internal database;
and performing risk assessment processing on the first to-be-processed user data based on a preset risk assessment model to obtain the first assessment data, wherein the first assessment data comprises high-risk user data, medium-risk user data and low-risk user data.
Further, based on a second risk assessment rule using an external data source, performing risk assessment processing on the user data to be processed to obtain second assessment data, including:
if the first evaluation data is the middle risk user data, second to-be-processed user data is obtained, wherein the second to-be-processed user data is the to-be-processed user data in a first external database of the middle risk user;
performing risk assessment processing on the second user data to be processed to obtain first process risk assessment data;
identifying the first process risk assessment data, and if the first process risk assessment data is medium risk user data, acquiring third to-be-processed user data, wherein the third to-be-processed user data is to-be-processed user data in a second external database of a medium risk user;
performing risk assessment processing on the third user data to be processed to obtain second process risk assessment data;
identifying the second process risk assessment data, and outputting the second assessment data if the second process risk data meets a preset assessment condition, wherein the second assessment data comprises the first process assessment data and the second process assessment data, and the preset assessment condition is that no risk user data exists in the assessment data;
if the second risk process data do not meet the preset evaluation condition, iterating the risk evaluation operation until the preset condition is met, and outputting the second evaluation data.
Further, performing risk assessment processing on the second user data to be processed to obtain first process risk assessment data, including:
judging whether the second user data to be processed meets a preset query condition, wherein the query preset condition is that a user corresponding to the second user data to be processed calls the first external database within a preset time period, and the query preset condition comprises the step of;
if the second user data to be processed meets the preset query condition, calling the recent historical data corresponding to the second user data to be processed;
if the second user data to be processed does not meet the preset query condition, calling new data of the user corresponding to the second user data to be processed;
and performing risk assessment processing on the second user data to be processed to obtain first process risk assessment data.
Further, based on the result risk assessment data, matching an early warning strategy corresponding to the risk assessment data in a preset early warning database, including:
and identifying the risk assessment data, and if the risk assessment data is high-risk user data, outputting risk early warning data of the user corresponding to the risk assessment data.
According to a second aspect of the present application, a data processing apparatus for risk early warning is provided, which is characterized by comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring user data to be processed, and the user data to be processed is related data of an early warning user needing to be credited;
the grading evaluation module is used for carrying out grading risk evaluation processing on the user data to be processed based on a preset grading evaluation rule to obtain result risk evaluation data;
and the risk early warning module is used for matching an early warning strategy corresponding to the risk assessment data in a preset early warning database based on the result risk assessment data.
Further, a hierarchical evaluation module comprising:
the first risk evaluation module is used for carrying out risk evaluation processing on the user data to be processed based on a first risk evaluation rule utilizing an internal data source to obtain first evaluation data;
the second risk evaluation module is used for carrying out risk evaluation processing on the user data to be processed based on a second risk evaluation rule utilizing an external data source to obtain second evaluation data;
and the classification module is used for classifying the first evaluation data and the second evaluation data to obtain the result risk evaluation data, wherein the result risk evaluation data comprises low-risk user data and high-risk user data.
According to a third aspect of the present application, a computer-readable storage medium is provided, which stores computer instructions for causing the computer to execute the above-mentioned data processing method for risk pre-warning.
According to a fourth aspect of the present application, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the above-mentioned data processing method for risk pre-warning.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the method, related data of a pre-warning user needing to be credited, namely user data to be processed, is obtained, and the user data to be processed is subjected to hierarchical evaluation processing according to a preset hierarchical evaluation rule, wherein the user data to be processed is subjected to risk evaluation processing according to a first risk evaluation rule of an internal data source, so that first evaluation data is obtained; carrying out risk evaluation processing on the user data to be processed by utilizing a second risk evaluation rule of an external data source to obtain second evaluation data; the risk early warning of the user is realized by classifying the first evaluation data and the second evaluation data to obtain result risk evaluation data, matching an early warning strategy corresponding to the risk evaluation data according to the risk evaluation result data, evaluating the data of the internal data source firstly and then evaluating the data with the external data source by carrying out hierarchical evaluation on the user, so that the technical problems of low accuracy and high cost of post-credit early warning in the prior art are solved, the accuracy of the post-credit risk early warning is improved, and the cost of the risk early warning is reduced.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
fig. 1 is a schematic flowchart of a data processing method for risk early warning provided in the present application;
fig. 2 is a schematic flowchart of a data processing method for risk early warning provided in the present application;
fig. 3 is a schematic flowchart of a data processing method for risk early warning provided in the present application;
fig. 4 is a schematic structural diagram of a data processing apparatus for risk early warning provided in the present application;
fig. 5 is a schematic structural diagram of another data processing apparatus for risk early warning provided in the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In this application, the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings. These terms are used primarily to better describe the present application and its embodiments, and are not used to limit the indicated devices, elements or components to a particular orientation or to be constructed and operated in a particular orientation.
Moreover, some of the above terms may be used to indicate other meanings besides the orientation or positional relationship, for example, the term "on" may also be used to indicate some kind of attachment or connection relationship in some cases. The specific meaning of these terms in this application will be understood by those of ordinary skill in the art as appropriate.
Furthermore, the terms "mounted," "disposed," "provided," "connected," and "sleeved" are to be construed broadly. For example, "connected" may be a fixed connection, a detachable connection, or a unitary construction; can be a mechanical connection, or an electrical connection; may be directly connected, or indirectly connected through intervening media, or may be in internal communication between two devices, elements or components. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
Fig. 1 is a schematic flowchart of a data processing method for risk early warning provided in the present application, and as shown in fig. 1, the method includes the following steps:
s101: acquiring user data to be processed, wherein the user data to be processed is related data of an early warning user needing to be credited;
wherein, the user needing the early warning in the loan is the client who has already paid out the bill but is not overdue after the loan is put.
S102: based on a preset grading evaluation rule, grading risk evaluation processing is carried out on the user data to be processed, and result risk evaluation data are obtained;
fig. 2 is a schematic flowchart of a data processing method for risk early warning provided in the present application, and as shown in fig. 2, the method includes the following steps:
s201: performing risk assessment processing on the user data to be processed based on a first risk assessment rule utilizing an internal data source to obtain first assessment data;
acquiring first to-be-processed user data, wherein the first to-be-processed user data is to-be-processed user data in an internal database;
and performing risk evaluation processing on the first to-be-processed user data based on a preset risk evaluation model to obtain the first evaluation data, wherein the first evaluation data comprises high-risk user data, medium-risk user data and low-risk user data.
The method comprises the steps that through a first risk assessment rule based on an internal data source, the full-amount client assessment of the paid-out bill which is not overdue after loan is divided into a low-risk high-quality client group, a high-risk client group and a medium-risk (unknown risk) client group, and due to the fact that data in the internal data source are not comprehensive, the full-amount user is difficult to carry out comprehensive and accurate risk assessment, and further risk assessment is carried out on users in the medium-risk client group. The low-risk passenger group does not need early warning, the high-risk passenger group carries out early warning processing, and the passenger group with uncertain risk enters a lower-layer data source to continue evaluation.
S202: performing risk evaluation processing on the user data to be processed based on a second risk evaluation rule utilizing an external data source to obtain second evaluation data;
fig. 3 is a schematic flowchart of a data processing method for risk early warning provided in the present application, and as shown in fig. 3, the method includes the following steps:
s301: if the first evaluation data is intermediate risk user data, acquiring second to-be-processed user data;
the second to-be-processed user data is to-be-processed user data in a first external database of the risk-suffering user;
s302: performing risk assessment processing on the second user data to be processed to obtain first process risk assessment data;
judging whether the second user data to be processed meets a preset query condition, wherein the query preset condition is that a user corresponding to the second user data to be processed calls the first external database within a preset time period, and the query preset condition comprises the step of;
if the second user data to be processed meets the preset query condition, calling the recent historical data corresponding to the second user data to be processed;
if the second user data to be processed does not meet the preset query condition, calling new data of the user corresponding to the second user data to be processed;
and performing risk assessment processing on the second user data to be processed to obtain first process risk assessment data.
S303: identifying the first process risk assessment data, and if the first process risk assessment data is intermediate risk user data, acquiring third to-be-processed user data;
wherein the third to-be-processed user data is to-be-processed user data in a second external database of the risk-suffering user;
and the external database selects the external database based on the rule with high cost performance and high identification risk, sets the query sequence according to the rule, selects the external database with high cost performance and high identification risk for risk evaluation, and then selects the external database with poor cost performance and low identification risk for risk evaluation.
S304: performing risk assessment processing on the third user data to be processed to obtain second process risk assessment data;
judging whether the third user data to be processed meets a preset query condition, and if so, calling the latest historical data corresponding to the third user data to be processed;
if the third user data to be processed does not meet the preset query condition, calling new data of the user corresponding to the third user data to be processed;
and setting a query time period for each layer of external database, classifying the new and old customers by judging whether the query time period is exceeded or not, calling the data corresponding to the latest query by the old customer, and querying the data corresponding to the new customer in the external database based on the characteristic information of the customer by the new customer.
S305: identifying the second process risk assessment data, and outputting the second assessment data if the second process risk data meets a preset assessment condition;
the second evaluation data comprises the first process evaluation data and the second process evaluation data, and the preset evaluation condition is that no risk user data exists in the evaluation data;
s306: if the second risk process data do not meet the preset evaluation condition, iterating the risk evaluation operation until the preset condition is met, and outputting the second evaluation data.
And the second risk process data does not meet the preset condition, namely after the evaluation is carried out based on the external data source of the current level, the intermediate risk guest group exists in the evaluation result, the external data source of the next level is called to repeatedly execute the operation until the intermediate risk guest group does not exist in the evaluation result or the external data source reaches the preset external data source upper limit, and the second evaluation data is output.
S203: and classifying the first evaluation data and the second evaluation data to obtain the result risk evaluation data.
And when the risk objective group does not exist in the evaluation result or the external data source reaches the preset external data source upper limit, classifying the first evaluation data and the second evaluation data, combining all high-risk users in the first evaluation data and the second evaluation data, combining all low-risk users in the first evaluation data and the second evaluation data, and obtaining the middle-risk users after the evaluation is finished.
S103: and matching an early warning strategy corresponding to the risk assessment data in a preset early warning database based on the result risk assessment data.
And early warning processing is carried out on the high-risk users, manual evaluation prompt information is output to the medium-risk users, and the low-risk users are stored.
Fig. 4 is a schematic structural diagram of a data processing apparatus for risk early warning provided in the present application, and as shown in fig. 4, the apparatus includes:
the data acquisition module 41 is configured to acquire user data to be processed, where the user data to be processed is related to an early warning user that needs to perform credit;
the grading evaluation module 42 is used for carrying out grading risk evaluation processing on the user data to be processed based on a preset grading evaluation rule to obtain result risk evaluation data;
and a risk early warning module 43, which matches an early warning strategy corresponding to the risk assessment data in a preset early warning database based on the result risk assessment data.
Fig. 5 is a schematic structural diagram of another data processing apparatus for risk early warning provided in the present application, and as shown in fig. 5, the apparatus includes:
the first risk assessment module 51 is configured to perform risk assessment processing on the user data to be processed based on a first risk assessment rule using an internal data source to obtain first assessment data;
the second risk assessment module 52 is configured to perform risk assessment processing on the user data to be processed based on a second risk assessment rule using an external data source, so as to obtain second assessment data;
and a classifying module 53, configured to perform classification processing on the first evaluation data and the second evaluation data to obtain the result risk evaluation data, where the result risk evaluation data includes low-risk user data and high-risk user data.
The specific manner of executing the operations of the units in the above embodiments has been described in detail in the embodiments related to the method, and will not be elaborated herein.
In summary, in the present application, relevant data of a user needing to perform an early warning in credit, that is, user data to be processed, is obtained, and the user data to be processed is subjected to hierarchical evaluation processing according to a preset hierarchical evaluation rule, wherein the user data to be processed is subjected to risk evaluation processing by using a first risk evaluation rule of an internal data source, so as to obtain first evaluation data; carrying out risk evaluation processing on the user data to be processed by utilizing a second risk evaluation rule of an external data source to obtain second evaluation data; the risk early warning of the user is realized by classifying the first evaluation data and the second evaluation data to obtain result risk evaluation data, matching an early warning strategy corresponding to the risk evaluation data according to the risk evaluation result data, and evaluating the data of the internal data source and then evaluating the data with the external data source by evaluating the user in a layered manner.
Filtering mass customers through an internal data source, performing inline data supplement risk portrait on the customers through an external data source, evaluating filtered medium-risk customer groups for the internal data source, and further identifying low-risk, medium-risk and high-risk customer groups; the intermediate risk client group continues to call the external data source, the intermediate risk client group is further evaluated, and other risk client groups stop calling; the process is continuously advanced until the risk of the user can be accurately depicted.
The method and the device solve the technical problems of low accuracy and high cost of post-credit early warning in the prior art, improve the accuracy of post-credit risk early warning and reduce the cost of risk early warning.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
It will be apparent to those skilled in the art that the various elements or steps of the present application described above may be implemented by a general purpose computing device, centralized on a single computing device or distributed across a network of multiple computing devices, or alternatively, may be implemented by program code executable by a computing device, such that the program code may be stored in a memory device and executed by a computing device, or may be implemented by individual integrated circuit modules, or by a plurality of modules or steps included in the program code as a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A data processing method for risk early warning is characterized by comprising the following steps:
acquiring user data to be processed, wherein the user data to be processed is related data of an early warning user needing to be credited;
based on a preset grading evaluation rule, grading risk evaluation processing is carried out on the user data to be processed, and result risk evaluation data are obtained;
and matching an early warning strategy corresponding to the risk assessment data in a preset early warning database based on the result risk assessment data.
2. The data processing method of claim 1, wherein the step of performing hierarchical risk assessment processing on the user data to be processed based on a preset hierarchical assessment rule to obtain result risk assessment data comprises:
performing risk assessment processing on the user data to be processed based on a first risk assessment rule utilizing an internal data source to obtain first assessment data;
performing risk evaluation processing on the user data to be processed based on a second risk evaluation rule utilizing an external data source to obtain second evaluation data;
and classifying the first evaluation data and the second evaluation data to obtain result risk evaluation data, wherein the result risk evaluation data comprises low-risk user data and high-risk user data.
3. The data processing method of claim 2, wherein performing risk assessment processing on the user data to be processed based on a first risk assessment rule using an internal data source to obtain first assessment data comprises:
acquiring first user data to be processed, wherein the first user data to be processed is the user data to be processed in an internal database;
and performing risk assessment processing on the first to-be-processed user data based on a preset risk assessment model to obtain the first assessment data, wherein the first assessment data comprises high-risk user data, medium-risk user data and low-risk user data.
4. The data processing method of claim 2, wherein performing risk assessment processing on the user data to be processed based on a second risk assessment rule using an external data source to obtain second assessment data comprises:
if the first evaluation data is the middle risk user data, second to-be-processed user data is obtained, wherein the second to-be-processed user data is the to-be-processed user data in a first external database of the middle risk user;
performing risk assessment processing on the second user data to be processed to obtain first process risk assessment data;
identifying the first process risk assessment data, and if the first process risk assessment data is medium risk user data, acquiring third to-be-processed user data, wherein the third to-be-processed user data is to-be-processed user data in a second external database of a medium risk user;
performing risk assessment processing on the third user data to be processed to obtain second process risk assessment data;
identifying the second process risk assessment data, and outputting the second assessment data if the second process risk data meets a preset assessment condition, wherein the second assessment data comprises the first process assessment data and the second process assessment data, and the preset assessment condition is that no risk user data exists in the assessment data;
if the second risk process data do not meet the preset evaluation condition, iterating the risk evaluation operation until the preset condition is met, and outputting the second evaluation data.
5. The data processing method of claim 4, wherein performing risk assessment processing on the second to-be-processed user data to obtain first process risk assessment data comprises:
judging whether the second user data to be processed meets a preset query condition, wherein the query preset condition is that a user corresponding to the second user data to be processed calls the first external database within a preset time period, and the query preset condition comprises the step of;
if the second user data to be processed meets the preset query condition, calling the recent historical data corresponding to the second user data to be processed;
if the second user data to be processed does not meet the preset query condition, calling new data of the user corresponding to the second user data to be processed;
and performing risk assessment processing on the second user data to be processed to obtain first process risk assessment data.
6. The data processing method of claim 1, wherein matching, based on the resulting risk assessment data, an early warning policy corresponding to the risk assessment data in a preset early warning database comprises:
and identifying the risk assessment data, and if the risk assessment data is high-risk user data, outputting risk early warning data of the user corresponding to the risk assessment data.
7. A data processing apparatus for risk early warning, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring user data to be processed, and the user data to be processed is related data of an early warning user needing to be credited;
the grading evaluation module is used for carrying out grading risk evaluation processing on the user data to be processed based on a preset grading evaluation rule to obtain result risk evaluation data;
and the risk early warning module is used for matching an early warning strategy corresponding to the risk assessment data in a preset early warning database based on the result risk assessment data.
8. The data processing apparatus of claim 7, wherein the hierarchical evaluation module comprises:
the first risk evaluation module is used for carrying out risk evaluation processing on the user data to be processed based on a first risk evaluation rule utilizing an internal data source to obtain first evaluation data;
the second risk evaluation module is used for carrying out risk evaluation processing on the user data to be processed based on a second risk evaluation rule utilizing an external data source to obtain second evaluation data;
and the classification module is used for classifying the first evaluation data and the second evaluation data to obtain the result risk evaluation data, wherein the result risk evaluation data comprises low-risk user data and high-risk user data.
9. A computer-readable storage medium storing computer instructions for causing a computer to execute the data processing method for risk pre-warning according to any one of claims 1 to 6.
10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the data processing method for risk pre-warning as claimed in any one of claims 1 to 6.
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