CN116188177A - Service risk assessment method, device and equipment based on dynamic information quantification - Google Patents
Service risk assessment method, device and equipment based on dynamic information quantification Download PDFInfo
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Abstract
The application relates to a business risk assessment method, a device and equipment based on dynamic information quantification, which can be used in the financial field, and the method comprises the following steps: determining a target service attribute and a target service scale in response to a service handling request; based on the target service attribute, obtaining a plurality of influence factors of the target service, and respectively obtaining dynamic association data of each influence factor; matching the dynamic associated data with corresponding industry reference data to obtain quantitative assignment parameters of all influence factors; generating a plurality of data running tracks by adopting quantitative assignment parameters and dynamic associated data; determining a data security domain corresponding to each data running track according to the target service scale and the matching result; analyzing the fluctuation condition of each data running track in the corresponding data security domain, extracting the abnormal data running trend in each data running track, and generating an event portrait of the target service; and carrying out risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service.
Description
Technical Field
The present disclosure relates to the field of big data technologies, and in particular, to a business risk assessment method, apparatus, and device based on dynamic information quantization.
Background
Currently, in the prior art, a single-point trigger model trained based on industry historical data is mostly adopted to carry out monitoring and evaluation work of business risks. However, since the industry history data is difficult to reflect the change situation of the business risk in real time, the business risk monitoring and evaluating result obtained based on the prior art is poor in timeliness and the accuracy of the business risk monitoring and evaluating result is still to be improved.
Disclosure of Invention
Based on the foregoing, it is necessary to provide a business risk assessment method, device and equipment based on dynamic information quantization.
In a first aspect, the present application provides a business risk assessment method based on dynamic information quantization. The method comprises the following steps:
determining service related information of a target service in response to a service handling request; the service related information comprises a target service attribute and a target service scale;
based on the target service attribute, obtaining a plurality of service influence factors of the target service, and respectively obtaining dynamic associated data of each service influence factor;
Matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result;
generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data;
determining a data security domain corresponding to each data running track according to the target service scale and the matching result;
analyzing fluctuation conditions of the data running tracks in corresponding data safety domains, extracting abnormal data running trends in the data running tracks, and generating event portraits of the target service based on the abnormal data running trends;
and carrying out risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service.
In one embodiment, the dynamic association data includes numeric association data and non-numeric association data; matching the dynamic association data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result, wherein the quantitative assignment parameters comprise:
Acquiring a first assignment parameter corresponding to each non-numerical value type associated data based on a preset quantization assignment standard; matching each numerical type associated data with corresponding industry reference data, and determining a second assignment parameter corresponding to each numerical type associated data according to a matching result; and obtaining the quantized assignment parameters of the business influence factors and the overall assignment parameters of the target business according to the first assignment parameters and the second assignment parameters.
In one embodiment, the determining, according to the target service scale and the matching result, a data security domain corresponding to each data running track includes:
obtaining the development base number of the target service and the monitoring grade of the target service according to the target service scale and the matching result; and determining a data security domain corresponding to each data running track based on the development base and the monitoring level.
In one embodiment, the generating a plurality of data tracks by using the quantization assignment parameter and the dynamic association data includes:
performing quantization assignment on the corresponding dynamic associated data by adopting each quantization assignment parameter, and generating a data running track of each business influence factor based on the quantized and assigned dynamic associated data; and generating the overall running track of the target service based on the development base and the overall assignment parameters.
In one embodiment, the analyzing the fluctuation condition of each data running track in the corresponding data security domain, and extracting the abnormal data running trend in each data running track includes:
extracting abnormal data operation trends in the data operation tracks according to fluctuation conditions of the data operation tracks in corresponding data safety domains and comparison results between the data operation tracks and corresponding reference data operation tracks; the reference data running track is generated based on the industry reference data.
In one embodiment, the generating the event portrayal of the target service based on the abnormal data operation trend includes:
extracting abnormal data in each data running track from the abnormal data running trend, and generating corresponding abnormal alarm information and an abnormal event disposal scheme based on the abnormal data;
integrating the abnormal alarm information and the abnormal event handling scheme to obtain the event portrait of the target service.
In a second aspect, the present application further provides a business risk assessment device based on dynamic information quantization.
The device comprises:
The service information acquisition module is used for responding to the service handling request and determining service related information of the target service; the service related information comprises a target service attribute and a target service scale;
the dynamic data acquisition module is used for acquiring a plurality of service influence factors of the target service based on the target service attribute and respectively acquiring dynamic associated data of each service influence factor;
the assignment parameter acquisition module is used for matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantized assignment parameters of the business influence factors according to the matching result;
the running track generation module is used for generating a plurality of data running tracks by adopting the quantitative assignment parameters and the dynamic associated data;
the data security domain determining module is used for determining the data security domain corresponding to each data running track according to the target service scale and the matching result;
the event portrait generation module is used for analyzing the fluctuation condition of each data running track in the corresponding data security domain, extracting the abnormal data running trend in each data running track and generating the event portrait of the target service based on the abnormal data running trend;
And the evaluation result output module is used for carrying out risk evaluation on the target service based on the event portrait to obtain a risk evaluation result of the target service.
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 which when executing the computer program performs the steps of:
determining service related information of a target service in response to a service handling request; the service related information comprises a target service attribute and a target service scale; based on the target service attribute, obtaining a plurality of service influence factors of the target service, and respectively obtaining dynamic associated data of each service influence factor; matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result; generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data; determining a data security domain corresponding to each data running track according to the target service scale and the matching result; analyzing fluctuation conditions of the data running tracks in corresponding data safety domains, extracting abnormal data running trends in the data running tracks, and generating event portraits of the target service based on the abnormal data running trends; and carrying out risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
determining service related information of a target service in response to a service handling request; the service related information comprises a target service attribute and a target service scale; based on the target service attribute, obtaining a plurality of service influence factors of the target service, and respectively obtaining dynamic associated data of each service influence factor; matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result; generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data; determining a data security domain corresponding to each data running track according to the target service scale and the matching result; analyzing fluctuation conditions of the data running tracks in corresponding data safety domains, extracting abnormal data running trends in the data running tracks, and generating event portraits of the target service based on the abnormal data running trends; and carrying out risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
determining service related information of a target service in response to a service handling request; the service related information comprises a target service attribute and a target service scale; based on the target service attribute, obtaining a plurality of service influence factors of the target service, and respectively obtaining dynamic associated data of each service influence factor; matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result; generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data; determining a data security domain corresponding to each data running track according to the target service scale and the matching result; analyzing fluctuation conditions of the data running tracks in corresponding data safety domains, extracting abnormal data running trends in the data running tracks, and generating event portraits of the target service based on the abnormal data running trends; and carrying out risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service.
The service risk assessment method, device and equipment based on dynamic information quantification firstly respond to a service handling request to determine service related information of a target service; the service related information includes a target service attribute and a target service size. And then, based on the target service attribute, obtaining a plurality of service influence factors of the target service, and respectively obtaining dynamic associated data of each service influence factor. And then, matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining the quantized assignment parameters of each business influence factor according to the matching result. And then, generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data. And then, according to the target service scale and the matching result, determining a data security domain corresponding to each data running track. And then analyzing the fluctuation condition of each data running track in the corresponding data security domain, extracting the abnormal data running trend in each data running track, and generating the event portrait of the target service based on the abnormal data running trend. And finally, performing risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service. According to the method and the system for evaluating the risk of the target business, the matching result between the dynamic associated data of each business influence factor of the target business and the corresponding industry reference data is analyzed in real time, comprehensive evaluation of the development trend of the target business from multiple dimensions is achieved, the risk change condition of the target business can be reflected timely, timeliness and accuracy of the risk evaluation result of the target business are guaranteed, and the efficiency of risk evaluation of the target business can be effectively improved.
Drawings
FIG. 1 is a flow chart of a business risk assessment method based on dynamic information quantization in one embodiment;
FIG. 2 is a flow diagram of a particular manner in which quantization assignment parameters and overall assignments are obtained in one embodiment;
FIG. 3 is a flowchart illustrating a specific manner of determining a data security domain corresponding to each data operation track in one embodiment;
FIG. 4 is a flow chart illustrating a specific way of generating a data trace in one embodiment;
FIG. 5 is a flow diagram of a particular manner in which event portraits for a target service are obtained in one embodiment;
FIG. 6 is a block diagram of a business risk assessment device based on dynamic information quantization in one embodiment;
FIG. 7 is an internal block 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.
Currently, in the prior art, a single-point trigger model trained based on industry historical data is mostly adopted to carry out monitoring and evaluation work of business risks. However, since the industry history data is difficult to reflect the change situation of the business risk in real time, the business risk monitoring and evaluating result obtained based on the prior art is poor in timeliness and the accuracy of the business risk monitoring and evaluating result is still to be improved.
In order to solve the problems in the prior art, the method and the device are based on the concept of dynamic information quantification, based on big data application, various types of dynamic information affecting service risk are quantified into specific numerical values, and according to each specific numerical value obtained after the dynamic information is quantified, splitting, quantifying and mapping analysis are performed on various types of dynamic information related to the service risk, so that time nodes with abnormal dynamic information are accurately positioned based on analysis results, the change situation of the service risk is reflected in real time, the multi-dimensional evaluation and control of the development process and the overall development trend of the service risk are realized, the timeliness and the accuracy of the service risk evaluation result are further guaranteed, and the processing cost of the service risk is effectively reduced through the early pre-judgment of the occurrence nodes of the service risk.
The business risk assessment method based on dynamic information quantification can be applied to server execution. The data storage system can store data which the server needs to process; the data storage system can be integrated on a server, and can also be placed on a cloud or other network servers; the server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 1, a business risk assessment method based on dynamic information quantification is provided, and the method is applied to a server for explanation, and comprises the following steps:
step S110, responding to a service handling request, and determining service related information of a target service; the service related information includes a target service attribute and a target service size.
In this step, the service transaction request refers to a service transaction request issued by any event main body for transacting a certain service; the service related information of the target service refers to the acquired service information related to the target service after the target service corresponding to the service handling request is determined; the target service attribute refers to service related information which can be used for classifying the target service into the corresponding classification category and represents the attribute of the target service; the target service scale refers to service related information representing the scale of the target service, which can be used for determining a monitoring level corresponding to the target service.
In practical applications, the event main body may be a natural person having a business handling requirement or an unnatural person having a business handling requirement, for example, a company, an enterprise, or an organization having a business handling requirement.
Step S120, based on the target business attribute, a plurality of business influence factors of the target business are obtained, and dynamic associated data of each business influence factor are respectively obtained.
In this step, the target service attribute refers to service related information that can be used to classify the target service into a corresponding classification category and characterize the attribute of the target service; the plurality of business influencing factors of the target business refer to a plurality of business influencing factors which possibly influence the development of the target business, and the plurality of business influencing factors can be divided into two major business influencing factors and minor business influencing factors based on the influence degree of the plurality of business influencing factors on the development of the target business; the dynamic association data of each business-affecting factor refers to association data corresponding to each business-affecting factor and dynamically changing with the passage of time, and the main body for generating the dynamic association data of the type may be the event main body for issuing a business handling request for handling a business.
In practical applications, the correspondence between the service influencing factors and the dynamic associated data may be a one-to-many relationship, that is, one service influencing factor corresponds to multiple dynamic associated data. For example, if it is known that one of the business impact factors is personal information and an event main body that issues a business transaction request for transacting a business is a natural person, the dynamic association data corresponding to the business impact factor of the personal information may be association data that dynamically changes with time, such as occupation, age, resident area, academic, home background, surrounding policy, living environment, asset condition, and balance condition of the natural person; the primary and secondary business impact factors may correspond to different impact weight indices, respectively; the dynamic association data can be directly provided by an event main body, or can be obtained by inquiring corresponding information of the event main body in a data information system such as a proxy wage system, a business registration system, a networking check system and the like; furthermore, the dynamic association data may be obtained based on a policy support of the location of the event main body, a background investigation of the related industry of the event main body, a regional climate, an economic environment, and other preset data collection conditions.
And step S130, matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result.
In this step, the dynamic association data, that is, the dynamic association data of each service influencing factor, refers to the association data corresponding to each service influencing factor and dynamically changing with the passage of time; industry reference data refers to industry reference data which is obtained by sampling a large number of similar events in the same area and can be used as a matching reference of each dynamic associated data; the matching result refers to a matching result between the dynamic associated data and corresponding industry reference data; the quantized assignment parameters of the business influence factors are assignment parameters which are determined according to the matching result between the dynamic associated data and the corresponding industry reference data and are used for carrying out quantized assignment on the business influence factors respectively, and the corresponding relation between the business influence factors and the quantized assignment parameters is one-to-one, namely one business influence factor corresponds to one quantized assignment parameter.
In practical application, a specific way of matching the dynamic association data with the corresponding industry reference data may be to confirm the data coincidence between the dynamic association data and the corresponding industry reference data, i.e. confirm whether the values corresponding to the dynamic association data and the corresponding industry reference data are consistent; on the premise that the business influence factors comprise two major types of main business influence factors and minor business influence factors, and the main business influence factors and the minor business influence factors respectively correspond to different influence weight indexes, if the matching results of the dynamic association data corresponding to the main business influence factors and the minor business influence factors and corresponding industry reference data are required to be obtained based on the matching results of the dynamic association data corresponding to the main business influence factors and the minor business influence factors, quantitative assignment parameters of the influence factors are also required to be obtained, and the influence weight indexes of the main business influence factors or the minor business influence factors corresponding to the matching results are also required to be considered.
And step S140, generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data.
In the step, the quantized assignment parameters refer to assignment parameters for performing quantized assignment on various business influence factors; the dynamic association data is association data which corresponds to each business influence factor and can dynamically change along with the time; the plurality of data running tracks are generated based on dynamic associated data corresponding to each business influence factor and assignment parameters for carrying out quantitative assignment on each business influence factor.
In practical application, the plurality of data running tracks may include data running track related information for representing hydropower loss related to the target service, dynamic change condition of funds account related to the target service, production cycle change condition of the target service, and the like.
And step S150, determining a data security domain corresponding to each data running track according to the target service scale and the matching result.
In the step, the target service scale refers to the relevant information which can be used for determining the monitoring level corresponding to the target service and represents the scale of the target service; the matching result refers to a matching result between the dynamic associated data and corresponding industry reference data; each data running track refers to a plurality of generated data running tracks based on dynamic associated data corresponding to each business influence factor and assignment parameters for carrying out quantitative assignment on each business influence factor; the data security domain corresponding to each data running track is a data security domain for determining a data security upper limit and a data security lower limit corresponding to each data running track, and in view of dynamic association data and corresponding industry reference data corresponding to each business influence factor, a matching result between the dynamic association data and the corresponding industry reference data can change dynamically over time, and on the basis, the generated data security domain corresponding to each data running track can be in a wave shape according to the matching result between the dynamic association data and the corresponding industry reference data.
Step S160, analyzing fluctuation conditions of each data running track in the corresponding data security domain, extracting abnormal data running trends in each data running track, and generating event portraits of the target service based on the abnormal data running trends.
In this step, the fluctuation condition of each data running track in the corresponding data security domain may include the number of times that each data running track touches the data security upper limit and the data security lower limit of the corresponding data security domain in the corresponding data security domain, the fluctuation amplitude of each data running track in the corresponding data security domain, the overall fluctuation trend of each data running track in the corresponding data security domain, and the like, which represent the data fluctuation related information of the fluctuation condition of each data running track in the corresponding data security domain; abnormal data operation trends in the data operation tracks refer to the extracted corresponding abnormal data operation trends based on abnormal fluctuation generated by the data operation tracks in the corresponding data security domain; the event portrayal of the target service refers to the event portrayal corresponding to the generated target service based on the abnormal fluctuation generated by each data running track in the corresponding data security domain and the extracted corresponding abnormal data running trend.
In practical application, the event portrait of the target service may include event portrait related information of the target service, such as an overall operation trend evaluation level of the target service, an external risk evaluation level of the target service, an internal risk evaluation level of the target service, an industry reference data matching degree, an asset and event matching degree evaluation, an abnormal operation trend evaluation result of the target service, a development potential evaluation result of the target service, a development prediction result of the target service, and the like.
Step S170, performing risk assessment on the target business based on the event portrait to obtain a risk assessment result of the target business.
In the step, event portrayal, namely event portrayal of a target service, refers to event portrayal corresponding to the generated target service based on abnormal fluctuation generated by each data running track in a corresponding data security domain and the extracted corresponding abnormal data running trend; performing risk assessment on the target service, namely assessing the risk of developing the target service based on the related information represented by the event portrait corresponding to the target service; the risk evaluation result of the target service refers to that the risk of the developed target service is evaluated based on the related information represented by the event portrait corresponding to the target service, and the obtained risk evaluation result of the developed target service is obtained.
Firstly, responding to a service handling request, and determining service related information of a target service; the service related information includes a target service attribute and a target service size. And then, based on the target service attribute, obtaining a plurality of service influence factors of the target service, and respectively obtaining dynamic associated data of each service influence factor. And then, matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining the quantized assignment parameters of each business influence factor according to the matching result. And then, generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data. And then, according to the target service scale and the matching result, determining a data security domain corresponding to each data running track. And then analyzing the fluctuation condition of each data running track in the corresponding data security domain, extracting the abnormal data running trend in each data running track, and generating the event portrait of the target service based on the abnormal data running trend. And finally, performing risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service. According to the method and the system for evaluating the risk of the target business, the matching result between the dynamic associated data of each business influence factor of the target business and the corresponding industry reference data is analyzed in real time, comprehensive evaluation of the development trend of the target business from multiple dimensions is achieved, the risk change condition of the target business can be reflected timely, timeliness and accuracy of the risk evaluation result of the target business are guaranteed, and the efficiency of risk evaluation of the target business can be effectively improved.
For the specific manner of obtaining the quantization assignment parameter and the overall assignment, in one embodiment, as shown in fig. 2, the dynamic association data includes numerical association data and non-numerical association data; the step S130 specifically includes:
step S210, based on a preset quantization assignment standard, acquiring a first assignment parameter corresponding to each non-numerical value type associated data.
In the step, preset quantization assignment criteria are assignment criteria used for performing quantization assignment on various non-numerical value type associated data respectively; each non-numerical type associated data, namely, each non-numerical type associated data, can comprise associated data stored in information carriers such as characters, pictures and the like; the first assignment parameters corresponding to the non-numerical association data refer to the first assignment parameters corresponding to the non-numerical association data.
In practical application, the preset quantization assignment standard may be an assignment standard respectively formulated for various non-numerical related data based on the degree of influence of various non-numerical related data on related events and analysis conditions of related industry case information by experts in the corresponding fields. Further, on the premise that the event main body is a natural person and certain non-numerical related data is an academic of the natural person, assignment can be performed on the academic of different grades by adopting a preset fixed quantitative score, for example, the doctor academy is-10 points, the filling academy is-8 points and the family academy is-6 points.
Step S220, matching each numerical type associated data with corresponding industry reference data, and determining a second assignment parameter corresponding to each numerical type associated data according to the matching result.
In this step, each numerical type association data, that is, each numerical type association data; industry reference data refers to industry reference data which is obtained by sampling a large number of similar events in the same area and can be used as a matching reference of various numerical value type associated data; the matching result refers to the matching result between various numerical value type associated data and corresponding industry reference data; the second assignment parameters corresponding to the numerical association data refer to the second assignment parameters corresponding to the numerical association data.
And step S230, obtaining the quantized assigned parameters of each business influence factor and the overall assigned parameters of the target business according to the first assigned parameters and the second assigned parameters.
In the step, the first assignment parameters refer to the first assignment parameters corresponding to various non-numerical value type associated data respectively; the second assignment parameters refer to the second assignment parameters corresponding to the various numerical value type associated data respectively; the quantization assignment parameters of the business influence factors refer to assignment parameters which are correspondingly obtained and used for carrying out quantization assignment on the business influence factors based on the first assignment parameters corresponding to the non-numerical type associated data and the second assignment parameters corresponding to the numerical type associated data; the overall assignment parameters of the target service are obtained based on the respective corresponding influence weights of all service influence factors of the target service and the quantized assignment parameters of all service influence factors.
In practical application, the basis for acquiring the overall assignment parameters of the target service may include the target service scale in addition to the influence weights corresponding to the service influence factors of the target service and the quantized assignment parameters of the service influence factors. Assuming that two business influence factors exist in the target business based on the target business attribute, the business influence factors a (corresponding to the standard assignment variable A) and the business influence factors B (corresponding to the standard assignment variable B) are respectively obtained, and the influence weight ratio corresponding to each of the business influence factors a and the business influence factors B is 4:6, obtaining the monitoring grade corresponding to the business influence factor a as a according to the target business scale In (a) The monitoring grade corresponding to the business influence factor b is b High height ，a In (a) And b High height The quantization assigned parameter corresponding to each is A In (a) % and B High height And (c%) quantifying the assigned business impact factor a=a×a In (a) And (3) quantifying assigned business influence factors b=B.times.B High height In%, the overall assignment parameter of the target service=the quantized assigned service influencing factor a+the quantized assigned service influencing factor b=a×a In (a) ％+B*B High height ％。
According to the embodiment, the quantitative assignment parameters of the business influence factors and the overall assignment parameters of the target business are determined based on the assignment parameters corresponding to the dynamic association data of different types, so that the efficiency of quantitative assignment for the dynamic association data for generating a plurality of data running tracks is effectively improved, and further the efficiency of risk assessment for the target business is effectively ensured.
For a specific manner of determining the data security domain corresponding to each data running track, in one embodiment, as shown in fig. 3, the step S150 specifically includes:
step S310, according to the target business scale and the matching result, the development base number of the target business and the monitoring grade of the target business are obtained.
In the step, the target service scale refers to service related information which can be used for determining a development base and a monitoring level corresponding to the target service and represents the scale of the target service; the matching result refers to a matching result between various numerical type associated data and corresponding industry reference data, and the matching result and the target service scale affect the magnitude of the development base of the target service and the monitoring level of the target service together; the development base of the target service is determined according to the target service scale and the matching result between various numerical value type associated data and corresponding industry reference data; the monitoring level of the target service refers to the monitoring level of the target service determined according to the target service scale and the matching result between various numerical association data and corresponding industry reference data, and the monitoring level can be associated with the development base, i.e. the higher the monitoring level of the target service is, the higher the development base of the target service is.
In practical applications, the monitoring level of the target service may include a high-level monitoring level, a medium-level monitoring level, a low-level monitoring level, and the like. Assuming that the evaluation criteria of the monitoring level are more than 1 million of service demand amount-high level, more than 5000 ten thousand of service demand amount-medium level, and other demand amounts-low level, however, the matching degree between various numerical value type associated data of the current target service and corresponding industry reference data is higher, and the service demand amount of the current target service is only 3000 ten thousand, the monitoring level of the target service may be set to medium level based on comprehensive consideration of the foregoing matching degree and the target service scale.
Step S320, based on the development base and the monitoring level, determining the data security domain corresponding to each data running track.
In the step, the development base, namely the development base of the target service, is determined according to the scale of the target service and the matching result between various numerical value type associated data and corresponding industry reference data; the monitoring level, namely the monitoring level of the target service, is determined according to the target service scale and the matching result between various numerical value type associated data and corresponding industry reference data; the data security domain corresponding to each data running track, namely the data security domain corresponding to each data running track, is used for determining the data security upper limit and the data security lower limit corresponding to each data running track, and on the basis of the fact that the matching result between various numerical associated data and corresponding industry reference data, which are one of the influencing factors of the development base and the monitoring level of the target service, can change dynamically along with the time, the specific form of the data security domain corresponding to each data running track can be wavy.
According to the embodiment, the data security domain corresponding to each data running track is determined based on the target service scale and the matching result between the dynamic associated data and the industry reference data, so that the real-time monitoring and evaluation of the dynamic associated factors of each service influence factor of the target service are realized, and the timeliness and accuracy of the risk evaluation result of the target service are further effectively ensured.
For a specific way of generating the data movement track, in one embodiment, as shown in fig. 4, the step S140 specifically includes:
and step S410, carrying out quantization assignment on the corresponding dynamic associated data by adopting each quantization assignment parameter, and generating a data running track of each business influence factor based on the dynamic associated data after quantization assignment.
In the step, each quantization assignment parameter refers to an assignment parameter used for carrying out quantization assignment on corresponding dynamic associated data, and the quantization assignment parameter of the corresponding business influence factor can be deduced based on the assignment parameter of each dynamic associated data; the data running track of each business influence factor, namely the data running track of each business influence factor, can be the generated data running track of each business influence factor based on the quantized and assigned dynamic associated data corresponding to each business influence factor, or can be the data running track of each business influence factor after the quantized and assigned parameters of the business influence factor corresponding to each dynamic associated data are obtained based on the assigned parameters of each dynamic associated data, and the quantized and assigned data of each business influence factor is regenerated.
Step S420, based on the development base and the overall assignment parameters, generating the overall running track of the target service.
In the step, the development base, namely the development base of the target service, is determined according to the scale of the target service and the matching result between various numerical value type associated data and corresponding industry reference data; the overall assignment parameters, namely the overall assignment parameters of the target service, refer to the overall assignment parameters of the target service obtained based on the respective corresponding influence weights of all service influence factors of the target service and the quantized assignment parameters of all service influence factors; the overall running track of the target service refers to a data running track which is generated based on the development base of the target service and the overall assignment parameters of the target service and used for representing the overall running trend of the target service.
According to the embodiment, the comprehensive evaluation of the development trend of the target service from multiple dimensions is realized by generating the data running track of each service influence factor and the overall running track of the target service based on each quantitative assignment parameter, the dynamic associated data and the development base of the target service, so that the risk evaluation result of the target service can timely reflect the risk change condition of the target service.
For a specific way of extracting the abnormal data operation trend in each data operation track, in one embodiment, the step S160 specifically includes:
extracting abnormal data operation trends in each data operation track according to the fluctuation condition of each data operation track in the corresponding data security domain and the comparison result between each data operation track and the corresponding reference data operation track; the reference data running track is generated based on industry reference data.
Wherein, each data running track, namely each data running track, can comprise the data running track of each business influencing factor and the whole running track of the target business; industry reference data refers to industry reference data which is obtained by sampling a large number of similar events in the same area and can be used as a matching reference of each dynamic associated data; the comparison result between each data running track and the corresponding reference data running track can be based on the coincidence degree between the fluctuation condition of each data running track and the corresponding reference data running track, extracting a part of data running tracks inconsistent with the fluctuation condition of the corresponding reference data running track from each data running track, and taking the part of data running tracks as abnormal data running trends in each data running track; the reference data running track can be generated directly based on the corresponding industry reference data, or generated according to the quantized and assigned industry reference data after the corresponding industry reference data is subjected to quantization and assignment by adopting the quantization and assignment parameters of the corresponding dynamic associated data.
According to the method, the device and the system, according to the fluctuation condition of each data running track in the corresponding data safety domain and the comparison result between each data running track and the corresponding reference data running track, the abnormal data running trend in each data running track is extracted, a reliable data basis is provided for decision making related to the target service, the comprehensive evaluation efficiency of the development trend of the target service is improved, and the comprehensive evaluation of the development trend of the target service is further effectively improved.
For the specific manner of acquiring the event portrayal of the target service, in one embodiment, as shown in fig. 5, the step S160 specifically includes:
step S510, extracting abnormal data in each data running track in the abnormal data running trend, and generating corresponding abnormal alarm information and an abnormal event treatment scheme based on the abnormal data.
In the step, the abnormal data operation trend refers to the extracted corresponding abnormal data operation trend based on abnormal fluctuation generated by each data operation track in a corresponding data security domain; the abnormal data in each data running track refers to the positioned abnormal data which possibly generates serious adverse results in the abnormal data running trend extracted from each data running track; the generation of corresponding abnormal alarm information and abnormal event treatment schemes refers to the generation of corresponding abnormal alarm information for alarming abnormal data and corresponding abnormal event treatment schemes for serious adverse effects possibly generated by the abnormal data based on the severity of the serious adverse effects possibly generated by the abnormal data in each data running track.
Step S520, integrating the abnormal alarm information and the abnormal event handling scheme to obtain the event portrait of the target service.
In this step, the abnormal alarm information and the abnormal event handling scheme refer to the generated corresponding abnormal alarm information for alarming the abnormal data and the corresponding abnormal event handling scheme for the serious adverse effect possibly generated by the abnormal data based on the severity of the serious adverse effect possibly generated by the abnormal data in each data running track; event portrayal of the target business is based on the integration result of the abnormal alarm information and the abnormal event handling scheme.
In practical application, the event portrayal of the target service may include, in addition to the integration result of the abnormal alarm information and the abnormal event handling scheme, information related to the event portrayal of the target service, such as an overall operation trend evaluation level of the target service, an external risk evaluation level of the target service, an internal risk evaluation level of the target service, an industry reference data matching degree, an asset-event matching degree evaluation, an abnormal operation trend evaluation result of the target service, a development potential evaluation result of the target service, a development prediction result of the target service, and the like; when performing risk assessment for a business transaction request issued by the same event subject, a stored historical event profile of the event subject (i.e., an event profile generated based on a business transaction request issued by the event subject in the past) may be used as one of the bases for performing risk assessment of a target business.
According to the embodiment, the mode of generating the event portraits of the target service containing the abnormal alarm information and the abnormal event handling scheme based on the abnormal data in each data running track extracted from the abnormal data running trend not only enriches the information dimension provided by the risk assessment result of the target service, but also effectively improves the risk assessment efficiency of the target service.
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 business risk assessment device based on dynamic information quantification, which is used for realizing the business risk assessment method based on dynamic information quantification. 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 service risk assessment device based on dynamic information quantization provided below may be referred to the limitation of the service risk assessment method based on dynamic information quantization hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 6, there is provided a business risk assessment apparatus based on dynamic information quantization, the apparatus 600 comprising:
a service information obtaining module 610, configured to determine service related information of a target service in response to a service handling request; the service related information comprises a target service attribute and a target service scale;
a dynamic data obtaining module 620, configured to obtain a plurality of service influencing factors of the target service based on the target service attribute, and obtain dynamic associated data of each service influencing factor respectively;
The assignment parameter obtaining module 630 is configured to match the dynamic association data with corresponding industry reference data to obtain a matching result, and obtain quantized assignment parameters of the business influence factors according to the matching result;
the running track generating module 640 is configured to generate a plurality of data running tracks by using the quantization assignment parameters and the dynamic association data;
the data security domain determining module 650 is configured to determine, according to the target service scale and the matching result, a data security domain corresponding to each data running track;
the event portrait generation module 660 is configured to analyze the fluctuation situation of each data running track in the corresponding data security domain, extract an abnormal data running trend in each data running track, and generate an event portrait of the target service based on the abnormal data running trend;
and the evaluation result output module 670 is configured to perform risk evaluation on the target service based on the event portrait, so as to obtain a risk evaluation result of the target service.
In one embodiment, the assignment parameter obtaining module 630 is specifically configured to obtain, based on a preset quantization assignment criterion, a first assignment parameter corresponding to each of the non-numerical association data; matching each numerical type associated data with corresponding industry reference data, and determining a second assignment parameter corresponding to each numerical type associated data according to a matching result; and obtaining the quantized assignment parameters of the business influence factors and the overall assignment parameters of the target business according to the first assignment parameters and the second assignment parameters.
In one embodiment, the data security domain determining module 650 is specifically configured to obtain, according to the target service scale and the matching result, a development base of the target service and a monitoring level of the target service; and determining a data security domain corresponding to each data running track based on the development base and the monitoring level.
In one embodiment, the running track generating module 640 is specifically configured to perform quantization assignment on the corresponding dynamic association data by using each quantization assignment parameter, and generate a data running track of each service influencing factor based on the dynamic association data after quantization assignment; and generating the overall running track of the target service based on the development base and the overall assignment parameters.
In one embodiment, the event portrait generation module 660 is specifically configured to extract an abnormal data running trend in each data running track according to a fluctuation condition of each data running track in a corresponding data security domain and a comparison result between each data running track and a corresponding reference data running track; the reference data running track is generated based on the industry reference data.
In one embodiment, the event portrait generating module 660 is further configured to extract, in the abnormal data running trend, abnormal data in each data running track, and generate corresponding abnormal alarm information and an abnormal event handling scheme based on the abnormal data; integrating the abnormal alarm information and the abnormal event handling scheme to obtain the event portrait of the target service.
The modules in the business risk assessment device based on dynamic information quantification can be all or partially realized 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.
It should be noted that, the business risk assessment method and the device of the present application may be used in business risk assessment in the financial field, and may also be used in any field other than the financial field, and the application field of the business risk assessment method and the device of the present application is not limited.
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 equipment is used for storing data such as business risk assessment related 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 a processor, implements a business risk assessment method based on dynamic information quantification.
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 one embodiment, a computer device is provided, 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, implements 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 comply with the related laws and regulations and standards of the related countries and regions.
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 business risk assessment method based on dynamic information quantification, the method comprising:
determining service related information of a target service in response to a service handling request; the service related information comprises a target service attribute and a target service scale;
based on the target service attribute, obtaining a plurality of service influence factors of the target service, and respectively obtaining dynamic associated data of each service influence factor;
Matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result;
generating a plurality of data running tracks by adopting the quantization assignment parameters and the dynamic associated data;
determining a data security domain corresponding to each data running track according to the target service scale and the matching result;
analyzing fluctuation conditions of the data running tracks in corresponding data safety domains, extracting abnormal data running trends in the data running tracks, and generating event portraits of the target service based on the abnormal data running trends;
and carrying out risk assessment on the target service based on the event portrait to obtain a risk assessment result of the target service.
2. The method of claim 1, wherein the dynamic association data comprises numeric association data and non-numeric association data;
matching the dynamic association data with corresponding industry reference data to obtain a matching result, and obtaining quantitative assignment parameters of each business influence factor according to the matching result, wherein the quantitative assignment parameters comprise:
Acquiring a first assignment parameter corresponding to each non-numerical value type associated data based on a preset quantization assignment standard;
matching each numerical type associated data with corresponding industry reference data, and determining a second assignment parameter corresponding to each numerical type associated data according to a matching result;
and obtaining the quantized assignment parameters of the business influence factors and the overall assignment parameters of the target business according to the first assignment parameters and the second assignment parameters.
3. The method according to claim 2, wherein the determining the data security domain corresponding to each data running track according to the target traffic scale and the matching result includes:
obtaining the development base number of the target service and the monitoring grade of the target service according to the target service scale and the matching result;
and determining a data security domain corresponding to each data running track based on the development base and the monitoring level.
4. The method of claim 3, wherein said generating a plurality of data tracks using said quantized assigned parameters and said dynamically associated data comprises:
Performing quantization assignment on the corresponding dynamic associated data by adopting each quantization assignment parameter, and generating a data running track of each business influence factor based on the quantized and assigned dynamic associated data;
and generating the overall running track of the target service based on the development base and the overall assignment parameters.
5. The method according to any one of claims 1 to 4, wherein analyzing the fluctuation condition of each of the data tracks in the corresponding data security domain, and extracting the abnormal data operation trend in each of the data tracks, comprises:
extracting abnormal data operation trends in the data operation tracks according to fluctuation conditions of the data operation tracks in corresponding data safety domains and comparison results between the data operation tracks and corresponding reference data operation tracks; the reference data running track is generated based on the industry reference data.
6. The method of claim 1, wherein generating the event portrayal of the target business based on the anomalous data run trend comprises:
extracting abnormal data in each data running track from the abnormal data running trend, and generating corresponding abnormal alarm information and an abnormal event disposal scheme based on the abnormal data;
Integrating the abnormal alarm information and the abnormal event handling scheme to obtain the event portrait of the target service.
7. A business risk assessment device based on dynamic information quantification, the device comprising:
the service information acquisition module is used for responding to the service handling request and determining service related information of the target service; the service related information comprises a target service attribute and a target service scale;
the dynamic data acquisition module is used for acquiring a plurality of service influence factors of the target service based on the target service attribute and respectively acquiring dynamic associated data of each service influence factor;
the assignment parameter acquisition module is used for matching the dynamic associated data with corresponding industry reference data to obtain a matching result, and obtaining quantized assignment parameters of the business influence factors according to the matching result;
the running track generation module is used for generating a plurality of data running tracks by adopting the quantitative assignment parameters and the dynamic associated data;
the data security domain determining module is used for determining the data security domain corresponding to each data running track according to the target service scale and the matching result;
The event portrait generation module is used for analyzing the fluctuation condition of each data running track in the corresponding data security domain, extracting the abnormal data running trend in each data running track and generating the event portrait of the target service based on the abnormal data running trend;
and the evaluation result output module is used for carrying out risk evaluation on the target service based on the event portrait to obtain a risk evaluation result of the target service.
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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