CN113034046A - Data risk metering method and device, electronic equipment and storage medium - Google Patents

Data risk metering method and device, electronic equipment and storage medium Download PDF

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CN113034046A
CN113034046A CN202110428720.8A CN202110428720A CN113034046A CN 113034046 A CN113034046 A CN 113034046A CN 202110428720 A CN202110428720 A CN 202110428720A CN 113034046 A CN113034046 A CN 113034046A
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data
risk
sensitivity
target analysis
index
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林天成
杨佳
刘丹
何杰斌
刘捷
陈婧
罗智聪
李冠萍
高楚楚
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China Construction Bank Corp
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Abstract

The embodiment of the application discloses a data risk metering method and device, electronic equipment and a storage medium. Wherein, the method comprises the following steps: determining the type of an index to be acquired according to the data to be analyzed; screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data; and performing risk measurement evaluation on the target analysis data. The technical scheme provided by the embodiment of the application can meet the requirements of business personnel on adjusting and flexibly configuring the screening rules at any time, improves the flexibility of data risk assessment, and provides a new idea for the data risk assessment.

Description

Data risk metering method and device, electronic equipment and storage medium
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a data risk metering method and device, electronic equipment and a storage medium.
Background
In recent years, financial institutions implement a new market risk standard method, which requires more comprehensive management and control of financial market business and higher requirements on risk measurement and evaluation.
In the prior art, because the domestic supervision method is not fallen to the ground, the measurement range of the risk measurement evaluation of the current new standard method and the like are not finally determined.
Therefore, a flexible method for measuring data risk is needed to satisfy the requirement of rapid adjustment and trial calculation of business in the measuring process and avoid program change as much as possible.
Disclosure of Invention
The embodiment of the application provides a data risk metering method, a data risk metering device, electronic equipment and a storage medium, which can meet the requirements of business personnel on adjusting and flexibly configuring screening rules at any time, improve the flexibility of data risk assessment and provide a new idea for the data risk assessment.
In a first aspect, an embodiment of the present application provides a data risk metering method, where the method includes:
determining the type of an index to be acquired according to the data to be analyzed;
screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data;
and performing risk measurement evaluation on the target analysis data.
In a second aspect, an embodiment of the present application provides a data risk metering device, including:
the determining module is used for determining the type of the index to be acquired according to the data to be analyzed;
the screening module is used for screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data;
and the evaluation module is used for carrying out risk measurement evaluation on the target analysis data.
In a third aspect, an embodiment of the present application provides an electronic device, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the data risk metering method of any embodiment of the present application.
In a fourth aspect, the embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the data risk metering method according to any embodiment of the present application.
The embodiment of the application provides a data risk metering method, a data risk metering device, electronic equipment and a storage medium, and the data risk metering method and the electronic equipment determine the type of an index to be acquired according to data to be analyzed; screening data to be analyzed according to index data selected by a user for the index type to obtain target analysis data; and performing risk measurement evaluation on the target analysis data. By executing the scheme, the method and the system can meet the requirements of business personnel on adjusting and flexibly configuring the screening rules at any time, improve the flexibility of data risk assessment and provide a new idea for the data risk assessment.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1A is a first flowchart of a data risk measurement method according to an embodiment of the present disclosure;
fig. 1B is a schematic diagram illustrating a table translation of a data risk measurement method according to an embodiment of the present application;
fig. 2 is a second flowchart of a data risk measurement method according to a second embodiment of the present application;
fig. 3 is a third flow chart of a data risk metering method according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a data risk metering device according to a fourth embodiment of the present application;
fig. 5 is a block diagram of an electronic device for implementing a data risk metering method according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 some 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.
Example one
Fig. 1A is a first flowchart of a data risk measurement method according to an embodiment of the present disclosure;
fig. 1B is a schematic diagram illustrating a table translation of a data risk measurement method according to an embodiment of the present application. The embodiment can be applied to the condition of risk measurement and evaluation of data to be analyzed. The data risk metering method provided by the embodiment of the present application may be executed by a data risk metering device provided by the embodiment of the present application, where the device may be implemented in a software and/or hardware manner, and is integrated in an electronic device executing the method and carried by a risk metering system.
Referring to fig. 1A, the method of the present embodiment includes, but is not limited to, the following steps:
and S110, determining the type of the index to be acquired according to the data to be analyzed.
The data to be analyzed refers to data that needs risk measurement and evaluation, for example, in the financial institution industry, the data to be analyzed may be position data; the index type is attribute information capable of distinguishing different types of data to be analyzed.
In the embodiment of the application, the index type corresponding to the data to be analyzed is obtained by extracting the common attribute and the derivative attribute of the data to be analyzed. The common attributes of the data to be analyzed are attributes which are most common for distinguishing the data to be analyzed, such as account types, industry and institutions and the like; the derived attribute refers to attribute information calculated on a statistical basis, such as whether the attribute is expired or not. And obtaining the index type corresponding to the data to be analyzed by summarizing the contents specifically contained in the common attributes and the derived attributes, for example, if the account type contains a transaction account and a bank account, the index type corresponding to the data to be analyzed is the transaction account or the bank account.
For example, in the financial institution industry, if the data to be analyzed is transaction detail data, since one piece of transaction detail data includes a plurality of attributes, such as a common attribute including an account type, an industry institution, whether an attribute of derivative processing is due, and the like, an index type of the transaction detail data is obtained by summarizing the transaction detail data from different dimensions of the account type, whether the transaction detail data is due, the industry institution, and the like, where the index type includes a transaction account or a bank account, expired or unexpired, a security company or a bank, and the like.
And S120, screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data.
In the embodiment of the application, after determining the index types to be acquired, the electronic device may provide a front-end interactive interface to a user based on the index types, where the interactive interface includes selectable index data corresponding to each index type to be acquired, which can be selected by a service person, and the service person may select the index data corresponding to the index type on the front-end interactive interface according to a requirement of the service person, that is, a screening rule for screening data to be analyzed. And screening target analysis data from the data to be analyzed by the risk metering system according to the index data selected by the user for the index type.
Optionally, in this step, the specific process of screening the data to be analyzed according to the index data selected by the user for the index type to obtain the target analysis data may be implemented through the following three substeps:
and S1201, determining a screening rule information table according to the index data selected by the user for the index type.
The index data is a screening rule for screening data to be analyzed, and comprises an index type, a screening condition of the index type and an association relation among the index types. The screening rule information table is used for storing screening rules of the data to be analyzed, and comprises an index type information table, a screening condition information table of the index type and an incidence relation information table among the index types. Wherein, the screening condition of the index type is information such as the value range of the index type; the association relationship between the index types is an association relationship between a plurality of index types, and the association relationship includes a sum relationship or a relationship.
For example: when the index type is the account type, the filtering condition of the index type may be "the account type is a trading account", when the index type is due, the filtering condition of the index type may be "whether due is due", when the index type is an industry institution, the filtering condition of the index type may be "the industry institution is a stock company", and the like. For example: the association between the index types may be "the account type is a transaction account" and "whether it is due or not", "the account type is a transaction account", or "whether it is due or not".
In the embodiment of the application, a service person selects index data corresponding to an index type on a front-end interface, and the risk metering system stores the index data in an index type information table, a screening condition information table of the index type and an association relation information table among the index types respectively.
And S1202, constructing a rule engine component.
The rule engine component is an executable code translated by the screening rule information table, and can screen the data to be analyzed according to screening conditions set by business personnel.
In the embodiment of the present application, specifically, the process of constructing the rule engine component is as follows: firstly, translating a screening rule information table into a machine language, and solidifying the machine language into a related database table; the machine language is then translated into database SQL code, i.e., executable code. As shown in fig. 1B, the filtering rule information table is translated into executable code. The method has the advantages that the problems that codes need to be redeveloped and a large number of tests need to be carried out when the screening rules are changed in the prior art can be solved, the requirements of business personnel on adjusting and flexibly configuring the screening rules at any time can be met, the time cost is saved, and the redevelopment of the technology is avoided.
S1203, according to the screening rule information table, calling a rule engine component to screen data to be analyzed to obtain target analysis data.
In the embodiment of the application, the screening rule information table is added to the statement for screening the data to be analyzed, and then the rule engine component is called to screen the data to be analyzed according to the screening rule information table, so that the target analysis data is obtained.
And S130, performing risk measurement evaluation on the target analysis data.
In the embodiment of the application, after target analysis data are screened from the data to be analyzed, risk measurement evaluation is performed on the target analysis data to obtain a risk evaluation value. Optionally, the dimension for risk measurement evaluation on the target analysis data may be value evaluation, sensitivity analysis, risk evaluation, etc., and evaluation may be performed from one dimension or multiple dimensions. When risk measurement evaluation is performed on target analysis data, the target analysis data can be evaluated through a pre-trained neural network model, and a preset evaluation algorithm can also be called for evaluation. And taking the target analysis data as input, and analyzing and processing the target analysis data through a pre-trained neural network model or a preset evaluation algorithm to obtain a risk measurement evaluation value.
According to the technical scheme provided by the embodiment, the index type to be acquired is determined according to the data to be analyzed; screening data to be analyzed according to index data selected by a user for the index type to obtain target analysis data; and performing risk measurement evaluation on the target analysis data. According to the method and the device, the to-be-analyzed data are screened by using the established rule engine component, the target analysis data are obtained, the problem that codes are required to be redeveloped when the screening rule is changed in the prior art can be solved, the requirements of business personnel on adjusting and flexibly configuring the screening rule at any time can be met, the flexibility of data risk assessment is improved, and a new thought is provided for the data risk assessment.
Example two
Fig. 2 is a second flowchart of a data risk metering method according to the second embodiment of the present application. The embodiment of the application is optimized on the basis of the embodiment, and specifically optimized as follows: a detailed explanation of the process of risk metric assessment of the target analysis data is added.
Referring to fig. 2, the method of the present embodiment includes, but is not limited to, the following steps:
and S210, determining the type of the index to be acquired according to the data to be analyzed.
And S220, screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data.
And S230, evaluating the value of the target analysis data.
In the embodiment of the application, after target analysis data are screened from the data to be analyzed, the corresponding estimation engine component is called according to different types of the target analysis data, and estimation operation is performed on the target analysis data to obtain a value evaluation result. The valuation engine component is not limited in the present application and may be a valuation engine component in the prior art. For example, if the target analysis data is a fixed interest rate bond, the valuation engine component can be a mid-debt model; the valuation engine component can be a cash flow discount method if the target analysis data increments a coupon by a bond.
S240, carrying out sensitivity analysis on the value evaluation result of the target analysis data according to the influence factor of at least one dimension to obtain a sensitivity curve.
In the embodiment of the application, the sensitivity analysis is to analyze the uncertainty of the value evaluation result from the influence factors of at least one dimension and measure the influence degree and the sensitivity degree of the value evaluation result on the project index. After the value evaluation result of the target analysis data is subjected to sensitivity analysis, a sensitivity curve can be drawn. And judging the risk bearing capacity of the project through the sensitivity curve. Taking the financial institution industry as an example, the dimensions of the influence factors can be risk factor points, positions, products, overseas and overseas, and the like, and the influence factors at least comprise the risk factor points; wherein the risk factor points are asset value, liability value and other financial data published periodically by a regulatory authority; position refers to the number of particular goods, securities, currency, etc. that an individual or entity holds or possesses; the product refers to various carriers of the fund financing process, including currency, gold, foreign exchange, securities and the like. The foreign and foreign means whether the transaction data corresponding to the target analysis data is a foreign or a foreign remittance. The sensitivity curve is a relation curve between response influence factors and sensitivity values, the influence factors corresponding to the sensitivity curve are represented by an x-axis, and the sensitivity values corresponding to the value evaluation results under a certain influence factor are represented by a y-axis.
Optionally, when the number of the influencing factors is at least two, performing sensitivity analysis on the value evaluation result of the target analysis data according to the influencing factors of at least two dimensions, and obtaining a sensitivity curve can be realized through the following two substeps:
s2401, determining a dimension information table according to the influence factors of at least two dimensions and the logical relationship between the influence factors of at least two dimensions.
In the embodiment of the application, at least two dimensions of influence factors are extracted according to target analysis data, and a hierarchical relationship between the influence factors is constructed, for example, the hierarchical relationship may be a compatible relationship between a summary level, a detail level and a dimension. And then, according to each influence factor and the hierarchical relation among the influence factors, a dimension information table is made. Specifically, the influence factors of the target analysis data are listed first, then the incidence relation is established for each influence factor according to the hierarchical relation of the influence factors, for example, when the influence factors are overseas, the overseas and overseas are refined, namely the product and the position, and finally the risk factor point is refined.
S2402, determining a sensitivity calculation range according to the dimension information table.
The sensitivity calculation range refers to the sensitivity analysis performed from which dimension and which hierarchical relationship are used to analyze the value evaluation result of the target analysis data.
In the embodiment of the application, the dimension information table is displayed on an interface of the risk measurement system, and the risk measurement system selects the sensitivity calculation range for carrying out sensitivity analysis on the value evaluation result according to the dimension information table. Illustratively, taking transaction detail data in the financial institution industry as an example, if the dimension of the influencing factor is selected from the interior and the exterior, and the hierarchical relationship is selected from a summary hierarchy, the hierarchy sequentially descending in the sensitivity calculation range is firstly the interior and the exterior, the product and then the position, and finally the sensitivity calculation range is refined to a risk factor point.
Optionally, a sensitivity calculation range for performing sensitivity analysis on the value evaluation result may be selected by a service person according to the dimension information table.
In the prior art, processing of each dimension is generally performed according to the requirement of a data table sample and according to the required dimension of each level. The various dimensional calculations are relatively fixed in the correlation logic operations and the result output. If the sensitivity calculation range is adjusted by the service personnel, the system also needs to be adjusted, and the prior art cannot meet the requirement of the service personnel on the flexible analysis of the market risk. By making the dimension information table on the risk measurement system, business personnel can flexibly select or change the sensitivity calculation range without adjusting the system.
S2403, according to the sensitivity calculation range, carrying out sensitivity analysis on the value evaluation result of the target analysis data to obtain a sensitivity curve.
In the embodiment of the application, after the sensitivity calculation range is determined according to the dimension information table, the value evaluation result of the target analysis data is subjected to sensitivity analysis to obtain a sensitivity curve. Since the influence factors include at least the risk factor points, the sensitivity curve includes at least the relationship between the risk factor points and the sensitivity values.
And S250, determining a risk assessment value of the target analysis data according to the sensitivity curve.
The method for risk assessment of the target analysis data in the embodiment of the present application is not particularly limited, and a risk assessment method in the prior art may be used. Optionally, the sensitivity curve may be calculated according to the basel official standard, so as to obtain the risk assessment value of the target analysis data.
Specifically, the sensitivity curve is divided into at least two sub-curve segments according to the incidence relation between the risk factor points and the currency types; and carrying out sensitivity value weighting calculation on at least two sub-curve segments to obtain a risk assessment value of the target analysis data.
In the embodiment of the present application, the sensitivity curve is first divided into at least two sub-curve segments by block division according to the coin type. Secondly, for each sub-curve segment, weighting and summing the risk factor points on each sub-curve segment according to the sensitive values corresponding to the risk factor points and the sub-weight coefficients of the sub-curve segment to obtain the sub-risk values of the sub-curve segment. And then, carrying out weighted summation on the sub-risk values of each sub-curve segment according to the sub-risk values of the sub-curve segments and the total weight coefficient of the sensitivity curve to obtain the total risk value of the sensitivity curve. And finally, taking the total risk value of the sensitivity curve as a risk assessment value of the target analysis data.
According to the technical scheme provided by the embodiment, the index type to be acquired is determined according to the data to be analyzed; screening data to be analyzed according to index data selected by a user for the index type to obtain target analysis data; evaluating the value of the target analysis data; carrying out sensitivity analysis on the value evaluation result of the target analysis data according to the influence factor of at least one dimension to obtain a sensitivity curve; and determining a risk assessment value of the target analysis data according to the sensitivity curve. According to the method and the device, value evaluation, sensitivity analysis and risk evaluation are sequentially carried out on the target analysis data, so that the risk evaluation value of the target analysis data is obtained. In the sensitivity analysis process, the dimension information table is manufactured on the risk measurement system, so that the requirement of business personnel on rapid sensitivity analysis can be met, and rapid analysis response to risk analysis is realized.
EXAMPLE III
Fig. 3 is a third flow chart of a data risk metering method according to the third embodiment of the present application. The embodiment of the application is optimized on the basis of the embodiment, and specifically optimized as follows: the determination process of the data to be analyzed and the generation process of the risk measurement statistical report are added for detailed explanation.
Referring to fig. 3, the method of the present embodiment includes, but is not limited to, the following steps:
and S310, processing the acquired transaction data to obtain data to be analyzed.
The transaction data refers to information on transactions between individuals or entities for specific commodities, securities, and currencies held or owned by the individuals or entities.
In the embodiment of the application, firstly, transaction data is obtained from an external market or a financial transaction platform; and summarizing the transaction data from different dimensions, marking the category to which the transaction data belongs, taking the transaction data as data to be analyzed, and storing the data in a database. For example, transactional data is divided into forex data and non-forex data from whether it is a dimension of forex data.
And S320, determining the type of the index to be acquired according to the data to be analyzed.
S330, screening the data to be analyzed according to the index data selected by the user according to the index type to obtain target analysis data.
And S340, performing risk measurement evaluation on the target analysis data.
And S350, generating a risk measurement statistical report according to the value evaluation result, the sensitive curve and the risk evaluation result of the target analysis data.
In the embodiment of the present application, according to the requirement of the service, report data statistics needs to be performed on the intermediate result and the final result in the calculation process, so as to perform subsequent checking and confirmation. For example, in the value evaluation process, after the value evaluation result of the target analysis data is obtained, the value evaluation result is represented in the form of a General Interest Rate Risk (GIRR) weighted sensitive factor table by a report generation module; in the sensitivity analysis process, after a sensitivity curve is obtained, information in the sensitivity curve is embodied in a GIRR bucket summary table form through a report generation module; in the risk assessment process, after the risk assessment value is obtained, the risk assessment value is embodied in the form of a general interest rate risk scenario table and a general interest rate risk summary table through a report generation module.
It should be noted that, in the sensitivity analysis process, the risk measurement system performs the basic sensitivity calculation on the value evaluation result according to different dimensions. Therefore, in the generation stage of the statistical report, according to the table sample of the report set by the user, the risk metering system can perform sensitivity analysis according to the hierarchical relationship among the influence factors selected by the business personnel. Where the reported table contains information from which influencing factors and what hierarchical relationships sensitivity was analyzed. Because the basic sensitivity is calculated in different dimensions according to the value evaluation result, when business personnel adjust the reported form sample, the risk measurement system does not need to perform the basic sensitivity calculation and perform the steps before the calculation again, such as the value evaluation and the like. And only the report generation stage needs to be operated again, the report is reprocessed according to the table sample of the newly set report, the new table sample can be regenerated in a short time, the requirement of quick analysis sensitivity of business personnel is met, and quick analysis response to risk analysis is realized. The method can save about two hours of time in a value evaluation link, and meanwhile, a time window with more than two hours can be provided, so that more calculation power is released for other operations.
According to the technical scheme provided by the embodiment, the data to be analyzed is obtained by processing the acquired transaction data; screening data to be analyzed according to index data selected by a user for the index type to obtain target analysis data; performing risk measurement evaluation on the target analysis data; and generating a risk measurement statistical report according to the value evaluation result, the sensitivity curve and the risk evaluation result of the target analysis data. According to the method and the device, the transaction data are processed to obtain the data to be analyzed, the target analysis data are screened out from the data to be analyzed, then the target data are evaluated in risk measurement, and finally a risk measurement statistical report is generated. The method and the device can meet the requirements of business personnel on adjusting and flexibly configuring the screening rules at any time and can also meet the requirements of business personnel on quickly analyzing the sensitivity.
Example four
Fig. 4 is a schematic structural diagram of a data risk metering device according to an embodiment of the present application, and as shown in fig. 4, the device 400 may include:
the determining module 410 is configured to determine the type of the index to be acquired according to the data to be analyzed.
And the screening module 420 is configured to screen the data to be analyzed according to the index data selected by the user for the index type, so as to obtain target analysis data.
An evaluation module 430, configured to perform risk measurement evaluation on the target analysis data.
Further, the screening module 420 includes: the device comprises a rule determining unit, a component constructing unit and a data screening unit;
the rule determining unit is used for determining a screening rule information table according to the index data selected by the user for the index type;
the component construction unit is used for constructing a rule engine component;
and the data screening unit is used for calling the rule engine component to screen the data to be analyzed according to the screening rule information table to obtain target analysis data.
Optionally, the index data includes: the method comprises the steps of obtaining an index type, screening conditions of the index type and an incidence relation among the index types.
Further, the evaluation module 430 includes: the system comprises a value evaluation unit, a sensitivity evaluation unit and a risk evaluation unit;
the value evaluation unit is used for evaluating the value of the target analysis data;
the sensitivity evaluation unit is used for carrying out sensitivity analysis on the value evaluation result of the target analysis data according to the influence factors of at least one dimension to obtain a sensitivity curve;
and the risk assessment unit is used for determining a risk assessment value of the target analysis data according to the sensitivity curve.
Further, the sensitivity evaluation unit is specifically configured to, when the number of the influencing factors is at least two, determine a dimension information table according to the influencing factors of at least two dimensions and a logical relationship between the influencing factors of at least two dimensions; determining a sensitivity calculation range according to the dimension information table; and carrying out sensitivity analysis on the value evaluation result of the target analysis data according to the sensitivity calculation range to obtain a sensitivity curve.
Optionally, the influence factors at least include risk factor points; the sensitivity curve is a curve representing the relation between the risk factor points and the sensitivity values.
Further, the risk assessment unit is further specifically configured to divide the sensitivity curve into at least two sub-curve segments according to an association relationship between a risk factor point and a currency; and carrying out sensitivity value weighting calculation on the at least two sub-curve segments to obtain a risk assessment value of the target analysis data.
Further, the risk evaluation unit is specifically configured to calculate, for each sub-curve segment, a sub-risk value of the sub-curve segment according to the sensitive value corresponding to each risk factor point and the sub-weight coefficient of the sub-curve segment; and calculating the total risk value of the sensitivity curve as the risk assessment value of the target analysis data according to the sub risk values of the sub curve segments and the total weight coefficient of the sensitivity curve.
Further, the data risk measurement device may further include: a report generation module;
and the report generation module is used for generating a risk measurement statistical report according to the value evaluation result, the sensitivity curve and the risk evaluation result of the target analysis data.
Further, the data risk measurement device may further include: a data processing module;
and the data processing module is used for processing the acquired transaction data to obtain data to be analyzed.
The data risk metering device provided by the embodiment can be applied to the data risk metering method provided by any embodiment, and has corresponding functions and beneficial effects.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention, and fig. 5 shows a block diagram of an exemplary electronic device suitable for implementing the embodiment of the present invention. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention. The electronic device can be a smart phone, a tablet computer, a notebook computer, a vehicle-mounted terminal, a wearable device and the like.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: one or more processors or processing units 516, a memory 528, and a bus 518 that couples the various system components including the memory 528 and the processing unit 516.
Bus 518 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 500 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 500 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 528 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)530 and/or cache memory 532. The electronic device 500 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 534 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 518 through one or more data media interfaces. Memory 528 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 540 having a set (at least one) of program modules 542, including but not limited to an operating system, one or more application programs, other program modules, and program data, may be stored in, for example, the memory 528, each of which examples or some combination may include an implementation of a network environment. The program modules 542 generally perform the functions and/or methods described in connection with the embodiments of the invention.
The electronic device 500 may also communicate with one or more external devices 514 (e.g., keyboard, pointing device, display 524, etc.), with one or more devices that enable a user to interact with the electronic device 500, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 522. Also, the electronic device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 520. As shown in FIG. 5, the network adapter 520 communicates with the other modules of the electronic device 500 via the bus 518. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 516 executes programs stored in the memory 528 to perform various functional applications and data processing, such as implementing the data risk metering method provided by any embodiment of the present invention.
EXAMPLE six
A sixth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program (or referred to as computer-executable instructions) is stored, where the computer program, when executed by a processor, can be used to execute the data risk metering method provided in any of the above embodiments of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the embodiments of the present invention have been described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and many other equivalent embodiments may be included without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (15)

1. A method for data risk metering, the method comprising:
determining the type of an index to be acquired according to the data to be analyzed;
screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data;
and performing risk measurement evaluation on the target analysis data.
2. The method of claim 1, wherein the step of screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data comprises:
determining a screening rule information table according to the index data selected by the user for the index type;
constructing a rule engine component;
and calling the rule engine component to screen the data to be analyzed according to the screening rule information table to obtain target analysis data.
3. The method according to claim 1 or 2, wherein the metric data comprises: the method comprises the steps of obtaining an index type, screening conditions of the index type and an incidence relation among the index types.
4. The method of claim 1, wherein performing a risk metric assessment on the target analysis data comprises:
evaluating the value of the target analysis data;
carrying out sensitivity analysis on the value evaluation result of the target analysis data according to the influence factor of at least one dimension to obtain a sensitivity curve;
and determining a risk assessment value of the target analysis data according to the sensitivity curve.
5. The method of claim 4, wherein when the influencing factors are at least two, performing sensitivity analysis on the value evaluation result of the target analysis data according to the influencing factors of at least two dimensions to obtain a sensitivity curve, comprises:
determining a dimension information table according to the influence factors of at least two dimensions and the logical relationship between the influence factors of the at least two dimensions;
determining a sensitivity calculation range according to the dimension information table;
and carrying out sensitivity analysis on the value evaluation result of the target analysis data according to the sensitivity calculation range to obtain a sensitivity curve.
6. The method according to claim 4 or 5, wherein the influencing factors comprise at least risk factor points; the sensitivity curve is a curve representing the relation between the risk factor points and the sensitivity values.
7. The method of claim 6, wherein determining a risk assessment value for the target analysis data from the sensitivity profile comprises:
dividing the sensitivity curve into at least two sub-curve segments according to the incidence relation between the risk factor points and the currency types;
and carrying out sensitivity value weighting calculation on the at least two sub-curve segments to obtain a risk assessment value of the target analysis data.
8. The method of claim 7, wherein performing a sensitivity value weighting calculation on the at least two sub-curve segments to obtain a risk assessment value of the target analysis data comprises:
for each sub-curve segment, calculating a sub-risk value of the sub-curve segment according to the sensitive value corresponding to each risk factor point and the sub-weight coefficient of the sub-curve segment;
and calculating the total risk value of the sensitivity curve as the risk assessment value of the target analysis data according to the sub risk values of the sub curve segments and the total weight coefficient of the sensitivity curve.
9. The method of claim 4, further comprising:
and generating a risk measurement statistical report according to the value evaluation result, the sensitivity curve and the risk evaluation result of the target analysis data.
10. The method of claim 1, further comprising:
and processing the acquired transaction data to obtain data to be analyzed.
11. A data risk metering device, the device comprising:
the determining module is used for determining the type of the index to be acquired according to the data to be analyzed;
the screening module is used for screening the data to be analyzed according to the index data selected by the user for the index type to obtain target analysis data;
and the evaluation module is used for carrying out risk measurement evaluation on the target analysis data.
12. The apparatus of claim 11, wherein the screening module comprises: the device comprises a rule determining unit, a component constructing unit and a data screening unit;
the rule determining unit is used for determining a screening rule information table according to the index data selected by the user for the index type;
the component construction unit is used for constructing a rule engine component;
and the data screening unit is used for calling the rule engine component to screen the data to be analyzed according to the screening rule information table to obtain target analysis data.
13. The apparatus of claim 11, wherein the evaluation module comprises: the system comprises a value evaluation unit, a sensitivity evaluation unit and a risk evaluation unit;
the value evaluation unit is used for evaluating the value of the target analysis data;
the sensitivity evaluation unit is used for carrying out sensitivity analysis on the value evaluation result of the target analysis data according to the influence factors of at least one dimension to obtain a sensitivity curve;
and the risk assessment unit is used for determining a risk assessment value of the target analysis data according to the sensitivity curve.
14. An electronic device, characterized in that the electronic device comprises:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the data risk metering method of any of claims 1-10.
15. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the data risk metering method according to any one of claims 1 to 10.
CN202110428720.8A 2021-04-21 2021-04-21 Data risk metering method and device, electronic equipment and storage medium Pending CN113034046A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113627535A (en) * 2021-08-12 2021-11-09 福建中信网安信息科技有限公司 Data grading classification system and method based on data security and privacy protection
CN113691592A (en) * 2021-08-05 2021-11-23 北京淇瑀信息科技有限公司 Method, apparatus, device and medium for providing network service to device
CN116595554A (en) * 2023-05-18 2023-08-15 北京长河数智科技有限责任公司 Method and device for realizing government affair data security analysis based on multiple dimensions

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113691592A (en) * 2021-08-05 2021-11-23 北京淇瑀信息科技有限公司 Method, apparatus, device and medium for providing network service to device
CN113627535A (en) * 2021-08-12 2021-11-09 福建中信网安信息科技有限公司 Data grading classification system and method based on data security and privacy protection
CN116595554A (en) * 2023-05-18 2023-08-15 北京长河数智科技有限责任公司 Method and device for realizing government affair data security analysis based on multiple dimensions
CN116595554B (en) * 2023-05-18 2024-01-19 北京长河数智科技有限责任公司 Method and device for realizing government affair data security analysis based on multiple dimensions

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