Data processing method, apparatus, device, medium, and product

Abstract

The application discloses a data processing method and device, equipment, medium and product, and relates to the technical field of data processing. The data processing method comprises the following steps: for the i-th service in N services of a target user, determining a risk conversion coefficient corresponding to the i-th service according to historical default data corresponding to the i-th service; determining a default probability corresponding to the i-th service according to the number of historical default users corresponding to the i-th service and the total number of historical users; determining a default loss corresponding to the i-th service according to the total number of resources of the target user for the i-th service, the number of used resources of the target user for the i-th service when the target user defaults and the risk conversion coefficient corresponding to the i-th service; and determining a default loss corresponding to the target user according to the default probability and the default loss corresponding to the N services. According to the embodiment of the application, the accuracy of the default loss can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to a data processing method and device, equipment, medium and product. BACKGROUND

[0002] Default is the behavior of a party to a contract who does not perform the contract or does not perform the contract obligation in accordance with the agreement. When one party to the contract defaults, it usually causes losses to the other party.

[0003] In related technologies, when determining the losses caused to a bank by off-balance sheet business default, the used resource quantity of the defaulting party in the off-balance sheet business is counted as the loss, and the unused resource quantity of the defaulting party in the off-balance sheet business is usually measured by a risk conversion coefficient according to the risk level of the defaulting party, and then the total loss is calculated according to the risk conversion coefficient, the used resource quantity and the unused resource quantity.

[0004] However, the risk conversion coefficient of the unused resource quantity according to the risk level of the defaulting party is not accurate, and thus the losses caused to the bank by the default of the defaulting party calculated according to the risk conversion coefficient are not accurate. SUMMARY

[0005] The embodiments of the application provide a data processing method, device, equipment, medium and product, which can solve the problem of inaccurate default loss calculation.

[0006] In a first aspect, the embodiments of the application provide a data processing method, comprising:

[0007] For the i-th business of the N businesses of the target user, a risk conversion coefficient corresponding to the i-th business is determined according to historical default data corresponding to the i-th business, wherein the historical default data comprises: a default quantity in a preset statistical time period, a default time point corresponding to each default, a default time loss corresponding to each default, and a total resource quantity corresponding to each default;

[0008] A default probability corresponding to the i-th business is determined according to a historical default user quantity corresponding to the i-th business and a total historical user quantity;

[0009] A default loss corresponding to the i-th business is determined according to a total resource quantity of the target user for the i-th business, a used resource quantity of the target user for the i-th business when the target user defaults, and the risk conversion coefficient corresponding to the i-th business;

[0010] A default loss corresponding to the target user is determined according to the default probabilities and the default losses corresponding to the N businesses.

[0011] In a second aspect, the embodiments of the application provide a data processing device, comprising:

[0012] The first determining module is configured to determine, for an i-th service of N services of a target user, a risk conversion coefficient corresponding to the i-th service according to historical default data corresponding to the i-th service, wherein the historical default data comprises: a default quantity in a preset statistical time period, a default time point corresponding to each default, a default loss corresponding to each default, and a total quantity of resources corresponding to each default;

[0013] The second determining module is configured to determine, for the i-th service, a default probability corresponding to the i-th service according to a historical default user quantity corresponding to the i-th service and a historical total user quantity.

[0014] The third determining module is configured to determine, for the i-th service, a default loss corresponding to the i-th service according to a total quantity of resources of the target user for the i-th service, a used resource quantity of the target user for the i-th service when defaulting, and the risk conversion coefficient corresponding to the i-th service.

[0015] The fourth determining module is configured to determine, for the target user, a default loss according to the default probability and the default loss corresponding to the N services.

[0016] In a third aspect, an electronic device is provided, and the electronic device comprises a processor and a memory storing computer program instructions; and the processor implements the data processing method of the first aspect when executing the computer program instructions.

[0017] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer program instructions; and the computer program instructions are executed by a processor to implement the data processing method of the first aspect.

[0018] In a fifth aspect, a computer program product is provided, and instructions in the computer program product are executed by a processor of an electronic device to enable the electronic device to perform the data processing method of the first aspect.

[0019] In the embodiment of the present application, the risk conversion coefficient corresponding to the i th service is determined according to the historical default data corresponding to the i th service of the N services of the target user; the default loss corresponding to the i th service is determined according to the total number of resources of the target user for the i th service, the number of used resources of the target user for the i th service when the target user defaults, and the risk conversion coefficient corresponding to the i th service; the default probability corresponding to the i th service is determined according to the number of historical default users of the i th service and the total number of historical users; and then the default loss corresponding to the target user is determined according to the default probability and the default loss corresponding to the N services. In this way, the default loss corresponding to the target user can be determined. Since the risk conversion coefficient corresponding to the service of the target user is determined according to the historical default data of the service, rather than according to the risk level of the target user, the accuracy of the default loss corresponding to the target user can be improved when the risk conversion coefficient is used to determine the default loss corresponding to the target user. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments of the present application will be briefly introduced. Those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0021] Figure 1 is a flow diagram of the data processing method provided by the embodiments of the present application;

[0022] Figure 2 is a schematic diagram of the historical default of the i th service provided by the embodiments of the present application;

[0023] Figure 3 is a structural diagram of the data processing device provided by the embodiments of the present application;

[0024] Figure 4 is a structural diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0025] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purposes, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. The following description of the embodiments is only intended to provide a better understanding of the present application by showing examples of the present application.

[0026] It should be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0027] The data processing method, device, equipment, medium and product provided by the embodiments of the present application will be described in detail below in combination with the drawings, specific embodiments and application scenarios.

[0028] Figure 1 is a flowchart of the data processing method provided by the embodiments of the present application. As shown in Figure 1 , the data processing method can include:

[0029] S101: For the i-th service of the N services of the target user, according to the historical default data corresponding to the i-th service, determine the risk conversion coefficient corresponding to the i-th service, wherein the historical default data includes: the number of defaults in a preset statistical time period, the default time point corresponding to each default, the default loss corresponding to each default, and the total number of resources corresponding to each default;

[0030] S102: According to the historical default user number corresponding to the i-th service and the historical total user number, determine the default probability corresponding to the i-th service;

[0031] S103: According to the total number of resources of the target user for the i-th service, the number of resources used by the target user for the i-th service when defaulting, and the risk conversion coefficient corresponding to the i-th service, determine the default loss corresponding to the i-th service;

[0032] S104: According to the default probability and the default loss corresponding to the N services, determine the default loss corresponding to the target user.

[0033] The specific implementation mode of each step will be described in detail below.

[0034] In this embodiment, the risk conversion coefficient corresponding to the i-th service among the target user's N services is determined; the default loss corresponding to the i-th service is determined based on the target user's total resources for the i-th service, the amount of resources used for the i-th service when the target user defaults, and the risk conversion coefficient; the default probability corresponding to the i-th service is determined based on the number of historical defaulting users and the total number of historical users; and finally, the default loss corresponding to the target user is determined based on the default probability and default loss corresponding to the N services. In this way, the default loss corresponding to the target user can be determined. Since the risk conversion coefficient corresponding to the target user's service is determined based on the historical default data of that service, rather than based on the target user's risk level, the accuracy of determining the default loss corresponding to the target user using this risk conversion coefficient can be improved.

[0035] In some possible implementations of the embodiments of this application, the preset statistical time period can be the time period corresponding to the T years (e.g., 5 years) closest to the current time. For example, if the current time is September 8, 2022, the preset statistical time period is from September 9, 2017 to September 8, 2022, a total of 5 years.

[0036] In some possible implementations of the embodiments of this application, S101 may include: dividing a preset statistical time period into X time windows; for the kth default among M defaults occurring in the jth time window of the X time windows, determining a first risk conversion coefficient corresponding to the kth default based on the default time point, the loss at the time of default, and the total amount of resources corresponding to the kth default; determining a second risk conversion coefficient corresponding to the jth time window based on the first risk conversion coefficient corresponding to the M defaults; determining a default weight corresponding to the jth time window based on the number of defaults M in the jth time window and the total number of defaults in the preset statistical time period; determining a third risk conversion coefficient corresponding to the preset statistical time period based on the second risk conversion coefficient corresponding to the X time windows and the default weight corresponding to the X time windows; and using the third risk conversion coefficient as the risk conversion coefficient corresponding to the i-th business.

[0037] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the historical defaults corresponding to the i-th service provided in this application embodiment. Figure 2In the embodiment, the preset statistical time period is divided into five time windows, and the five time windows are time window 1, time window 2, time window 3, time window 4 and time window 5. For the i-th service, the preset statistical time period has 10 default numbers of defaults for the i-th service, and the 10 defaults are default 1 to default 10. The default time points of default 1, 3 and 5 are in the time window 1, the default time points of default 2 and 7 are in the time window 2, the default time point of default 4 is in the time window 3, the default time points of default 8 and 10 are in the time window 4, and the default time points of default 6 and 9 are in the time window 5.

[0038] In some possible implementations of the embodiment, the first risk conversion coefficient corresponding to the k-th default can be determined according to the default time point corresponding to the k-th default, the default time loss and the total number of resources, which can include:

[0039] The first risk conversion coefficient TW(k) corresponding to the k-th default can be determined according to the following formula (1):

[0040]

[0041] In formula (1), E(Td k ) is the default time loss of the k-th default, E(T0 k ) is the used resource quantity of the k-th default at the estimated time point T0 k corresponding to the k-th default, C(k) is the total number of resources corresponding to the k-th default, and the estimated time point T0 k is a time point determined according to the default time point Td k of the k-th default.

[0042] In some possible implementations of the embodiment, E(Td k ) is the unrepaid principal and interest and fees of the k-th default at the default time point Td k .

[0043] In some possible implementations of the embodiment, the time point Td k of the k-th default can be used as the estimated time point T0 k , and when the k-th default corresponds to a contract signed for less than X years (for example, 1 year), the signing time of the contract corresponding to the k-th default can be used as the estimated time point T0 k .

[0044] For example, for default 8, it is assumed that the total number of resources C(k) corresponding to default 8 is 10, the default time loss E(Td k ) is 8 at the default time point Td k , and the estimated time point T0k When the number of used resources corresponding to the kth default is 5, the first risk conversion coefficient corresponding to the default 8 is: (8-5) / (10-5)=0.6.

[0045] In some possible implementations of the embodiments of the present application, determining the second risk conversion coefficient corresponding to the jth time window according to the first risk conversion coefficients corresponding to the M defaults can include: determining the average value of the first risk conversion coefficients corresponding to the M defaults as the second risk conversion coefficient corresponding to the jth time window.

[0046] For example, for the above-mentioned time window 4, the first risk conversion coefficient corresponding to the default 8 is 0.6, and the first risk conversion coefficient corresponding to the default 10 is 0.8, and the second risk conversion coefficient corresponding to the above-mentioned time window 4 is: (0.6+0.8) / 2=0.7.

[0047] In some possible implementations of the embodiments of the present application, determining the default weight corresponding to the jth time window according to the number of defaults M in the jth time window and the total number of defaults in the preset statistical time period can include: determining the quotient of the number of defaults M in the jth time window and the total number of defaults in the preset statistical time period as the default weight corresponding to the jth time window.

[0048] For example, for the above-mentioned time window 4, the number of defaults in the above-mentioned time window 4 is 2, and the total number of defaults in the preset statistical time period is 10, and the default weight corresponding to the above-mentioned time window 4 is 2 / 10=0.2.

[0049] In some possible implementations of the embodiments of the present application, determining the third risk conversion coefficient corresponding to the preset statistical time period according to the second risk conversion coefficients corresponding to the X time windows and the default weights corresponding to the X time windows can include:

[0050] The third risk conversion coefficient WG_TW corresponding to the preset statistical time period is determined according to the following formula (2):

[0051]

[0052] In formula (2), TW j is the second risk conversion coefficient corresponding to the jth time window, and w j is the default weight corresponding to the jth time window.

[0053] For example, the second risk conversion coefficients corresponding to the above-mentioned time windows 1 to 5 are respectively: 0.8, 0.6, 0.75, 0.7, 0.85; and the second risk conversion coefficients corresponding to the above-mentioned time windows 1 to 5 are respectively: 0.3, 0.2, 0.1, 0.2, 0.2.

[0054] Then, the third risk conversion coefficient corresponding to the preset statistical time period is 0.745, which is taken as the risk conversion coefficient corresponding to the ith service, that is, the risk conversion coefficient corresponding to the ith service is 0.745.

[0055] The third risk conversion coefficient corresponding to the preset statistical time period is taken as the risk conversion coefficient corresponding to the ith service, that is, the risk conversion coefficient corresponding to the ith service is 0.745.

[0056] In some possible implementations of the embodiments of the present application, S102 can include: determining, as the default probability corresponding to the ith service, the quotient of the historical default user quantity corresponding to the ith service and the historical total user quantity corresponding to the ith service.

[0057] For example, the historical default user quantity corresponding to the ith service is 10, and the historical total user quantity corresponding to the ith service is 20, then the default probability corresponding to the ith service is 10 / 20=0.2.

[0058] In some possible implementations of the embodiments of the present application, S103 can include:

[0059] The default loss LOSS corresponding to the ith service is determined according to the following formula (3) i :

[0060] LOSS i =P i +WG_TW i *(Z i -P i ) (3)

[0061] Wherein, in formula (3), P i is the used resource quantity of the ith service when the target user defaults, WG_TW i is the risk conversion coefficient corresponding to the ith service, and Z i is the total resource quantity of the target user for the ith service.

[0062] For example, the total resource quantity of the target user for the ith service is 200, the used resource quantity of the ith service when the target user defaults is 150, and the risk conversion coefficient corresponding to the ith service is 0.745, then the default loss LOSS i corresponding to the ith service is 150+0.745*(200-150)=187.25.

[0063] In some possible implementations of the embodiments of the present application, S104 can include:

[0064] The default loss R_LOSS corresponding to the target user is determined according to formula (4) as follows:

[0065]

[0066] In formula (4), LOSS i is the default loss corresponding to the ith business, Q i is the default probability corresponding to the ith business.

[0067] For example, the target user corresponds to five businesses, and the default losses of the five businesses are 150, 200, 210, 240, and 350 respectively, and the default probabilities of the five businesses are 0.3, 0.5, 0.4, 0.4, and 0.3 respectively.

[0068] The default loss R_LOSS corresponding to the target user is 150*0.3+200*0.5+210*0.4+240*0.4+350*0.3=430.

[0069] In some possible implementations of the embodiments of the present application, the data processing method provided by the embodiments of the present application can further include: obtaining a Gross Domestic Product (GDP) growth rate of a region to which the target user belongs and an industry price-earnings ratio of an industry to which the target user belongs; and adjusting the default loss corresponding to the target user according to the GDP growth rate and the industry price-earnings ratio.

[0070] For example, the obtained GDP growth rate of the ith region to which the target user belongs is shown in Table 1.

[0071] Table 1

[0072]

[0073] The obtained industry price-earnings ratio of the jth industry to which the target user belongs is shown in Table 2.

[0074] Table 2

[0075]

[0076] In some possible implementations of the embodiments of the present application, adjusting the default loss corresponding to the target user according to the GDP growth rate and the industry price-earnings ratio can include: calculating an average value and a standard deviation of the GDP growth rate of a region to which the target user belongs; determining a first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs according to the average value of the GDP growth rate of the region to which the target user belongs and a current GDP growth rate of the region to which the target user belongs; calculating an average value and a standard deviation of an industry price-earnings ratio of an industry to which the target user belongs; determining a second adjustment factor corresponding to the industry price-earnings ratio of the industry to which the target user belongs according to the average value of the industry price-earnings ratio of the industry to which the target user belongs and a current industry price-earnings ratio of the industry to which the target user belongs; determining a correlation coefficient of the GDP growth rate of the region to which the target user belongs and the industry price-earnings ratio of the industry to which the target user belongs according to the average value and the standard deviation of the GDP growth rate of the region to which the target user belongs and the average value and the standard deviation of the industry price-earnings ratio of the industry to which the target user belongs; and adjusting the default loss corresponding to the target user according to the first adjustment factor, the second adjustment factor, and the correlation coefficient.

[0077] In some possible implementations of the embodiments of the present application, the average value and the standard deviation of the GDP growth rate of the region to which the target user belongs are respectively an average value and a standard deviation of GDP growth rates of the previous 1 year, the previous 2 years,..., and the previous T years at the current time. The average value and the standard deviation of the industry price-earnings ratio of the industry to which the target user belongs are respectively an average value and a standard deviation of industry price-earnings ratios of the previous 1 year, the previous 2 years,..., and the previous T years at the current time.

[0078] In some possible implementations of the embodiments of the present application, determining the first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs according to the average value of the GDP growth rate of the region to which the target user belongs and the current GDP growth rate of the region to which the target user belongs can include: determining, as the first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs, a difference between the average value of the GDP growth rate of the region to which the target user belongs and the current GDP growth rate of the region to which the target user belongs.

[0079] For example, assuming that the average value of the GDP growth rate of the region to which the target user belongs is AVG_GR i , and the current GDP growth rate of the region to which the target user belongs is GR i , then the first adjustment factor A i corresponding to the GDP growth rate of the region to which the target user belongs is A i0 .

[0080] In some possible implementation of the embodiments of the present application, the second adjustment factor corresponding to the industry P / E ratio of the industry to which the target user belongs can be determined according to the average value of the industry P / E ratio of the industry to which the target user belongs and the current industry P / E ratio of the industry to which the target user belongs, which can include: determining the difference between the average value of the industry P / E ratio of the industry to which the target user belongs and the current industry P / E ratio of the industry to which the target user belongs as the second adjustment factor corresponding to the industry P / E ratio of the industry to which the target user belongs.

[0081] For example, it is assumed that the average value of the industry P / E ratio of the industry to which the target user belongs is AVG_IR j , and the current industry P / E ratio of the industry to which the target user belongs is IR j . j , then the first adjustment factor B j0 corresponding to the GDP growth rate of the region to which the target user belongs is

[0082] In some possible implementation of the embodiments of the present application, the correlation coefficient of the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs can be determined according to the average value and the standard deviation of the GDP growth rate of the region to which the target user belongs and the average value and the standard deviation of the industry P / E ratio of the industry to which the target user belongs, which can include:

[0083] The correlation coefficient R i,j of the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs is determined according to the following formula (5):

[0084]

[0085] In formula (5), T is the statistical time, GR it is the GDP growth rate of the region i to which the target user belongs at time t, AVG_GR i and STD_GR i are the average value and the standard deviation of the GDP growth rate of the region i to which the target user belongs, IR jt is the industry P / E ratio of the industry j to which the target user belongs at time t, AVG_IR j and STD_IR j are the average value and the standard deviation of the industry P / E ratio of the industry j to which the target user belongs.

[0086] In some possible implementation of the embodiments of the present application, the default loss corresponding to the target user can be adjusted according to the first adjustment factor, the second adjustment factor and the correlation coefficient, which can include:

[0087] The default loss corresponding to the target user is adjusted according to the following formula (6):

[0088] S_LOSS = (1 + R i,j )*(1 + Ai )*(1+B j )*R_LOSS (6)

[0089] Wherein, in formula (6), S_LOSS is the adjusted target user corresponding default loss, R_LOSS is the target user corresponding default loss before adjustment, R i,j is the correlation coefficient, A i is the first adjustment factor, B j is the second adjustment factor.

[0090] Exemplarily, assuming that the target user corresponding default loss before adjustment is 500, the target user belongs to the region's GDP growth rate corresponding adjustment factor is 0.2, the target user belongs to the industry's industry P / E ratio corresponding adjustment factor is 0.1, the target user belongs to the region's GDP growth rate and the target user belongs to the industry's industry P / E ratio corresponding correlation coefficient is 0.5, then the target user corresponding default loss after adjustment is: 500*(1+0.5)*(1+0.2)*(1+0.1)=990.

[0091] It should be noted that the acquisition, storage, use, processing of data in all embodiments of the present application comply with the relevant provisions of national laws and regulations.

[0092] The present application also provides a data processing device, as shown in Figure 3 . Figure 3 It is the structure schematic diagram of the data processing device provided by the present application, the data processing device 300 can include:

[0093] The first determination module 301 is used for determining the risk conversion coefficient corresponding to the i th service of the target user according to the historical default data corresponding to the i th service for the i th service of the target user, wherein the historical default data includes: the number of defaults in a preset statistical time period, the default time point corresponding to each default, the default loss corresponding to each default and the total number of resources corresponding to each default;

[0094] The second determination module 302 is used for determining the default probability corresponding to the i th service according to the historical default user number corresponding to the i th service and the total number of historical users;

[0095] The third determination module 303 is used for determining the default loss corresponding to the i th service according to the total number of resources of the target user for the i th service, the used resource number of the target user for the i th service when defaulting and the risk conversion coefficient corresponding to the i th service;

[0096] The fourth determination module 304 is used for determining the default loss corresponding to the target user according to the default probability and the default loss corresponding to the N services.

[0097] In the embodiment of the present application, the risk conversion coefficient corresponding to the i-th service is determined according to the historical default data corresponding to the i-th service of the N services of the target user; the default loss corresponding to the i-th service is determined according to the total resource quantity of the target user for the i-th service, the used resource quantity of the target user for the i-th service when the target user defaults, and the risk conversion coefficient corresponding to the i-th service; the default probability corresponding to the i-th service is determined according to the historical default user quantity of the i-th service and the historical total user quantity; and then the default loss corresponding to the target user is determined according to the default probability and the default loss corresponding to the N services. In this way, the default loss corresponding to the target user can be determined. Since the risk conversion coefficient corresponding to the service of the target user is determined according to the historical default data of the service, rather than according to the risk level of the target user, the accuracy of the default loss corresponding to the target user can be improved when the risk conversion coefficient is used to determine the default loss corresponding to the target user.

[0098] In some possible implementations of the embodiment of the present application, the first determining module 301 can include:

[0099] The dividing module is configured to divide the preset statistical time period into X time windows;

[0100] The first determining sub-module is configured to determine, for the k-th default of the M defaults occurring in the j-th time window of the X time windows, a first risk conversion coefficient corresponding to the k-th default according to the default time point, the loss at the time of default, and the total resource quantity of the k-th default;

[0101] The second determining sub-module is configured to determine a second risk conversion coefficient corresponding to the j-th time window according to the first risk conversion coefficients corresponding to the M defaults;

[0102] The third determining sub-module is configured to determine a default weight corresponding to the j-th time window according to the default quantity M in the j-th time window and the total default quantity of the preset statistical time period;

[0103] The fourth determining sub-module is configured to determine a third risk conversion coefficient corresponding to the preset statistical time period according to the second risk conversion coefficients corresponding to the X time windows and the default weights corresponding to the X time windows;

[0104] The fifth determining sub-module is configured to take the third risk conversion coefficient as the risk conversion coefficient corresponding to the i-th service.

[0105] In some possible implementations of the embodiment of the present application, the first determining sub-module can be specifically configured to:

[0106] The first risk conversion coefficient corresponding to the k-th default is determined according to the above formula (1).

[0107] In some possible implementation of the embodiments of the present application, the second determining sub-module can be specifically configured to:

[0108] determine the average value of the M default corresponding first risk conversion coefficients as the second risk conversion coefficient corresponding to the jth time window.

[0109] In some possible implementation of the embodiments of the present application, the fourth determining sub-module can be specifically configured to:

[0110] determine the third risk conversion coefficient corresponding to the preset statistical time period according to the above formula (2).

[0111] In some possible implementation of the embodiments of the present application, the second determining module 302 can be specifically configured to:

[0112] determine the default probability corresponding to the ith service as the quotient of the historical default user quantity corresponding to the ith service and the historical total user quantity corresponding to the ith service.

[0113] In some possible implementation of the embodiments of the present application, the third determining module 303 can be specifically configured to:

[0114] determine the default loss corresponding to the ith service according to the above formula (3).

[0115] In some possible implementation of the embodiments of the present application, the fourth determining module 304 can be specifically configured to:

[0116] determine the default loss corresponding to the target user according to the above formula (4).

[0117] In some possible implementation of the embodiments of the present application, the data processing apparatus 300 can further include:

[0118] an acquisition module configured to acquire the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs;

[0119] an adjustment module configured to adjust the default loss corresponding to the target user according to the GDP growth rate and the industry P / E ratio.

[0120] In some possible implementation of the embodiments of the present application, the adjustment module can include:

[0121] a first calculating sub-module configured to calculate the average value and the standard deviation of the GDP growth rate of the region to which the target user belongs;

[0122] a sixth determining sub-module configured to determine the first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs according to the average value of the GDP growth rate of the region to which the target user belongs and the current GDP growth rate of the region to which the target user belongs.

[0123] a second calculation sub-module, configured to calculate an average value and a standard deviation of an industry P / E ratio of an industry to which the target user belongs;

[0124] a seventh determination sub-module, configured to determine a second adjustment factor corresponding to the industry P / E ratio of the industry to which the target user belongs according to the average value of the industry P / E ratio of the industry to which the target user belongs and a current industry P / E ratio of the industry to which the target user belongs;

[0125] an eighth determination sub-module, configured to determine a correlation coefficient of the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs according to the average value and the standard deviation of the GDP growth rate of the region to which the target user belongs and the average value and the standard deviation of the industry P / E ratio of the industry to which the target user belongs;

[0126] an adjustment sub-module, configured to adjust the default loss corresponding to the target user according to the first adjustment factor, the second adjustment factor and the correlation coefficient.

[0127] In some possible implementations of the embodiments of the present application, the sixth determination sub-module can be specifically configured to:

[0128] determine a difference between the average value of the GDP growth rate of the region to which the target user belongs and a current GDP growth rate of the region to which the target user belongs as the first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs.

[0129] In some possible implementations of the embodiments of the present application, the seventh determination sub-module can be specifically configured to:

[0130] determine a difference between the average value of the industry P / E ratio of the industry to which the target user belongs and a current industry P / E ratio of the industry to which the target user belongs as the second adjustment factor corresponding to the industry P / E ratio of the industry to which the target user belongs.

[0131] In some possible implementations of the embodiments of the present application, the eighth determination sub-module can be specifically configured to:

[0132] determine the correlation coefficient of the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs according to the above formula (5).

[0133] In some possible implementations of the embodiments of the present application, the adjustment sub-module can be specifically configured to:

[0134] adjust the default loss corresponding to the target user according to the above formula (6).

[0135] Figure 4 FIG. 1 is a structural schematic diagram of an electronic device provided in the embodiments of the present application.

[0136] The electronic device can include a processor 401 and a memory 402 having computer program instructions stored therein.

[0137] Specifically, the processor 401 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or can be configured to implement one or more integrated circuits that implement the embodiments of the present application.

[0138] The memory 402 can include a mass storage for data or instructions. By way of example and not limitation, the memory 402 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. The memory 402 can include removable or non-removable (or fixed) media, where appropriate. The memory 402 can be internal or external to the electronic device, where appropriate. In some particular embodiments, the memory 402 is a non-volatile solid-state memory.

[0139] In some particular embodiments, the memory can include read-only memory (ROM), random access memory (RAM), a magnetic disk storage medium, an optical storage medium, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software that, when executed (e.g., by one or more processors), is operable to perform operations described with reference to the data processing method according to the present application.

[0140] The processor 401 implements the data processing method provided by the embodiments of the present application by reading and executing computer program instructions stored in the memory 402.

[0141] In one example, the electronic device can also include a communication interface 403 and a bus 410. As shown, the processor 401, the memory 402, the communication interface 403 are connected through the bus 410 and complete communication among each other. Figure 4

[0142] The communication interface 403 is mainly used to realize the communication between the modules, devices, units and / or equipment in the embodiments of the present application.

[0143] ​Bus 410 includes a hardware, software, or both that couples components of electronic device to each other. As an example and not by way of limitation, bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand™ interconnect, a Low Pin Count (LPC) bus, a storage bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or a combination of two or more of these. Where appropriate, bus 410 can include one or more buses. Although this application describes and shows a particular bus, this application contemplates any suitable bus or interconnect.

[0144] The electronic device can execute the data processing method provided by the embodiments of the present application, so as to achieve the corresponding technical effects of the data processing method provided by the embodiments of the present application.

[0145] In addition, in combination with the data processing method in the above embodiments, the embodiments of the present application also provide a computer readable storage medium for implementation. The computer readable storage medium stores computer program instructions; the computer program instructions are executed by the processor to implement the data processing method provided by the embodiments of the present application. Examples of computer readable storage medium include non-transitory computer readable medium, such as ROM, RAM, magnetic disk or optical disk, etc.

[0146] The embodiments of the present application provide a computer program product, the instructions in the computer program product are executed by the processor of the electronic device, so that the electronic device executes the data processing method provided by the embodiments of the present application, and can achieve the same technical effects. To avoid repetition, it will not be repeated here.

[0147] It is to be understood that the application is not limited to the particular configurations and processes described hereinabove and shown in the figures. For the sake of brevity, detailed descriptions of known methods and processes are omitted. In the above embodiments, several specific steps are described and illustrated as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and various changes, modifications and additions can be made thereto by one skilled in the art without departing from the spirit of the present application, and the order of the steps can be changed.

[0148] The functional blocks shown in the above described block diagrams of the structure can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, functional cards, etc. When implemented in software, the elements of the present application are program or code segments used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The "machine-readable medium" can include any medium that can store or transfer information. Examples of the machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable read only memory (EROM), floppy disks, compact discs read-only memory (CD-ROM), optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranet, etc.

[0149] It is also to be understood that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0150] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0151] The above only specifically describes the embodiments of the present application. For the convenience and brevity of description, the specific working processes of the above-described system, module and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein. It should be understood that the protection scope of the present application is not limited in this way. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed in the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A data processing method, characterized by, The method comprises: For the i-th service of N services of a target user, according to historical default data corresponding to the i-th service, a risk conversion coefficient corresponding to the i-th service is determined, wherein the historical default data comprises: a default quantity in a preset statistical time period, a default time point corresponding to each default, a default loss corresponding to each default, and a total quantity of resources corresponding to each default; According to the historical default user quantity and the historical user total quantity corresponding to the i-th service, a default probability corresponding to the i-th service is determined; According to the total quantity of resources of the target user for the i-th service, the used resource quantity of the target user for the i-th service when defaulting, and the risk conversion coefficient corresponding to the i-th service, a default loss corresponding to the i-th service is determined; According to the default probability and the default loss corresponding to the N services, a default loss corresponding to the target user is determined; The method comprises: The preset statistical time period is divided into X time windows; For the k-th default of M defaults occurring in the j-th time window of the X time windows, a first risk conversion coefficient TW(k) corresponding to the k-th default is determined according to the following formula: E(Td k ) is the loss at the time of default corresponding to the kth default, E(T0 k ) is the used resource quantity corresponding to the kth default at the estimated time point T0 k corresponding to the kth default, C(k) is the total resource quantity corresponding to the kth default, and the estimated time point T0 k is a time point determined according to the default time point Td k of the kth default. The average value of the first risk conversion coefficients corresponding to the M defaults is determined as a second risk conversion coefficient corresponding to the j-th time window; According to the default quantity M in the j-th time window and the total default quantity of the preset statistical time period, a default weight corresponding to the j-th time window is determined; According to the second risk conversion coefficients corresponding to the X time windows and the default weights corresponding to the X time windows, a third risk conversion coefficient corresponding to the preset statistical time period is determined; The third risk conversion coefficient is taken as the risk conversion coefficient corresponding to the i-th service.

2. The method of claim 1, wherein, The method comprises: According to the following formula, a third risk conversion coefficient WG_TW corresponding to the preset statistical time period is determined: wherein, TW j is a second risk conversion factor corresponding to the jth time window, w j is a default weight corresponding to the jth time window.

3. The method of claim 1, wherein, The method comprises: The quotient of the historical default user quantity corresponding to the i-th service and the historical user total quantity corresponding to the i-th service is determined as the default probability corresponding to the i-th service.

4. The method of claim 1, wherein, The method comprises: The default loss LOSS corresponding to the i-th service is determined according to the following formula i : LOSS i = P i + WG_TW i * (Z i - P i ) Wherein, P i is the number of resources used by the i-th service when the target user violates the contract, WG_TW i is the risk conversion coefficient corresponding to the i-th service, Z i is the total number of resources of the i-th service for the target user.

5. The method of claim 1, wherein, The total quantity of resources of the target user for the i-th service, the used resource quantity of the target user for the i-th service when defaulting, and the risk conversion coefficient corresponding to the i-th service are determined. The method comprises: The default probability and the default loss corresponding to the N services are determined. The default loss R_LOSS corresponding to the target user is determined according to the following formula: where LOSS i is the default loss corresponding to the ith business, Q i is the default probability corresponding to the ith business.

6. The method of claim 1, wherein, The method further comprises: obtaining a GDP growth rate of a region to which the target user belongs and an industry P / E ratio of an industry to which the target user belongs; adjusting the default loss corresponding to the target user according to the GDP growth rate and the industry P / E ratio.

7. The method of claim 6, wherein, The adjusting the default loss corresponding to the target user according to the GDP growth rate and the industry P / E ratio comprises: calculating an average value and a standard deviation of the GDP growth rate of the region to which the target user belongs; determining a first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs according to the average value of the GDP growth rate of the region to which the target user belongs and a current GDP growth rate of the region to which the target user belongs; calculating an average value and a standard deviation of the industry P / E ratio of the industry to which the target user belongs; determining a second adjustment factor corresponding to the industry P / E ratio of the industry to which the target user belongs according to the average value of the industry P / E ratio of the industry to which the target user belongs and a current industry P / E ratio of the industry to which the target user belongs; determining a correlation coefficient of the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs according to the average value and the standard deviation of the GDP growth rate of the region to which the target user belongs and the average value and the standard deviation of the industry P / E ratio of the industry to which the target user belongs; adjusting the default loss corresponding to the target user according to the first adjustment factor, the second adjustment factor and the correlation coefficient.

8. The method of claim 7, wherein, The determining the first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs according to the average value of the GDP growth rate of the region to which the target user belongs and the current GDP growth rate of the region to which the target user belongs comprises: determining the difference between the average value of the GDP growth rate of the region to which the target user belongs and the current GDP growth rate of the region to which the target user belongs as the first adjustment factor corresponding to the GDP growth rate of the region to which the target user belongs.

9. The method of claim 7, wherein, The determining the second adjustment factor corresponding to the industry P / E ratio of the industry to which the target user belongs according to the average value of the industry P / E ratio of the industry to which the target user belongs and the current industry P / E ratio of the industry to which the target user belongs comprises: determining the difference between the average value of the industry P / E ratio of the industry to which the target user belongs and the current industry P / E ratio of the industry to which the target user belongs as the second adjustment factor corresponding to the industry P / E ratio of the industry to which the target user belongs.

10. The method of claim 7, wherein, The determining the correlation coefficient of the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs according to the average value and the standard deviation of the GDP growth rate of the region to which the target user belongs and the average value and the standard deviation of the industry P / E ratio of the industry to which the target user belongs comprises: The correlation coefficient R of the GDP growth rate of the region to which the target user belongs and the industry P / E ratio of the industry to which the target user belongs is determined according to the following formula i,j : where T is the statistical time, GR it is the GDP growth rate of the region i where the target user belongs at time t, AVG_GR i and STD_GR i are the average and standard deviation of the GDP growth rate of the region i where the target user belongs, IR jt is the industry P / E ratio of the industry j where the target user belongs at time t, AVG_IR j and STD_IR j are the average and standard deviation of the industry P / E ratio of the industry j where the target user belongs.

11. The method of claim 7, wherein, The adjusting the default loss corresponding to the target user according to the first adjustment factor, the second adjustment factor and the correlation coefficient comprises: adjusting the default loss corresponding to the target user according to the following formula: S_LOSS = (1 + R i,j )*(1 + A i )*(1 + B j )*R_LOSS Wherein, S_LOSS is the adjusted breach loss corresponding to the target user, R_LOSS is the breach loss corresponding to the target user before adjustment, R i,j is the correlation coefficient, A i is the first adjustment factor, B j is the second adjustment factor.

12. A data processing apparatus, characterized by The device comprises: The first determining module is configured to determine, for an i-th service of N services of a target user, a risk conversion coefficient corresponding to the i-th service according to historical default data corresponding to the i-th service, wherein the historical default data comprises a default quantity in a preset statistical time period, a default time point corresponding to each default, a default loss corresponding to each default, and a total quantity of resources corresponding to each default. The second determining module is configured to determine a default probability corresponding to the i-th service according to a historical default user quantity corresponding to the i-th service and a historical total user quantity. The third determining module is configured to determine a default loss corresponding to the i-th service according to a total quantity of resources of the target user for the i-th service, a used resource quantity of the target user for the i-th service when the target user defaults, and the risk conversion coefficient corresponding to the i-th service. The fourth determining module is configured to determine a default loss corresponding to the target user according to the default probability and the default loss corresponding to the N services. The first determining module comprises: The division module is configured to divide the preset statistical time period into X time windows. The first determining submodule is configured to determine, for a k-th default of M defaults occurring in a j-th time window of the X time windows, a first risk conversion coefficient TW(k) corresponding to the k-th default according to the following formula: Among them, E(Td) k E(T0) represents the default loss corresponding to the k-th default. k ) represents the estimated time point T0 corresponding to the k-th default. k The estimated time point T0 represents the number of resources used corresponding to the k-th default, where C(k) is the total number of resources corresponding to the k-th default. k It is based on the default time point Td of the kth default. k A specific point in time; The second determining submodule is configured to determine an average value of the first risk conversion coefficients corresponding to the M defaults as a second risk conversion coefficient corresponding to the j-th time window. The third determining submodule is configured to determine a default weight corresponding to the j-th time window according to a default quantity M in the j-th time window and a total default quantity of the preset statistical time period. The fourth determining submodule is configured to determine a third risk conversion coefficient corresponding to the preset statistical time period according to the second risk conversion coefficients corresponding to the X time windows and default weights corresponding to the X time windows. The fifth determining submodule is configured to take the third risk conversion coefficient as the risk conversion coefficient corresponding to the i-th service.

13. An electronic device, comprising: The electronic device comprises a processor and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the data processing method of any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer program instructions, and the computer program instructions are executed by the processor to implement the data processing method of any one of claims 1-11.

15. A computer program product, characterised in that, The instructions in the computer program product are executed by the processor of the electronic device, so that the electronic device executes the data processing method of any one of claims 1-11.

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