CN112116402A - Credit risk customer-level default rate determination method and device - Google Patents
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Abstract
The embodiment of the application provides a method and a device for determining a client-level default rate of credit risk, wherein the method comprises the following steps: constructing a risk exposure weighted average default rate model according to a preset transition probability matrix; determining target customer-level default rate prediction data of each grade according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data; the method and the device can accurately refine and conduct the known prediction result of the credit risk downwards, and further obtain client-level default rate data.
Description
Technical Field
The application relates to the field of risk monitoring, in particular to a method and a device for determining a client-level default rate of credit risk.
Background
After financial crisis in 2008, the effect of pressure testing is rapidly increased, and many new severe requirements are proposed by regulatory authorities for bank pressure testing, for example, in the united states, DFAST rules are adopted in 2012, EPS rules are officially released in 2014 (chinese banks and industrial and commercial banks have satisfied the third-level standards and need to regularly develop pressure testing and submit reports), the BASEL committee has updated the pressure testing principle in 2018 in 10 months, and the bank protection and supervision society has proposed requirements on pressure testing in various supervision and guidance. In order to meet the supervision requirements as soon as possible, effectively support management decisions and better provide services for branches at different levels in and out of the country, a pressure test system platform needs to be developed and constructed urgently, corresponding pressure test data and models are deployed, and pressure test scenes are designed and released flexibly and efficiently.
The inventor finds that the pressure test default rate calculation scheme in the prior art can only calculate to the organization level, if the pressure test default rate calculation scheme needs to be subdivided to the customer level, a large amount of detailed level data needs to be used, and the customer level data has the problems of large data volume, difficult statistics, machine density and the like, so that the pressure test default rate calculation scheme is difficult to use in practical application.
Disclosure of Invention
Aiming at the problems in the prior art, the method and the device for determining the client-level default rate of the credit risk can be used for accurately refining and conducting the known prediction result of the credit risk downwards so as to obtain the client-level default rate data.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a method for determining a client-level default rate of credit risk, comprising:
constructing a risk exposure weighted average default rate model according to a preset transition probability matrix;
and determining target customer-level default rate prediction data of each grade according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data.
Further, determining target customer-level default rate prediction data for each rating according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data comprises:
determining an adjustment factor of the transition probability matrix according to the weighted average default rate model of the risk exposure and initial customer-level default rate prediction data;
and determining the client-level default rate of each grade of the transition probability matrix according to the adjusting factor, and setting the client-level default rate of each grade as target client-level default rate prediction data.
Further, the determining the customer-level default rate of each rating of the transition probability matrix according to the adjustment factor includes:
determining default rate cumulative density of the transition probability matrix according to the adjusting factor;
and determining the client-level default rate of each grade according to the default rate accumulated density.
Further, before the constructing the risk exposure weighted average default rate model according to the preset transition probability matrix, the method includes:
calculating the caliber according to the number of default customers and the preset money amount, and determining the weight of each grade;
and determining a transition probability matrix according to the weight of each rating and the migration rate of the preset customer credit rating.
In a second aspect, the present application provides a credit risk customer-level default rate determination apparatus, comprising:
the average default rate model building module is used for building an average default rate model of risk exposure weighting according to the preset transition probability matrix;
and the target client-level default rate prediction module is used for determining target client-level default rate prediction data of each grade according to the average default rate model of the risk exposure weighting and the initial client-level default rate prediction data.
Further, the target customer-level breach rate prediction module comprises:
an adjustment factor determining unit, configured to determine an adjustment factor of the transition probability matrix according to the weighted average default rate model of risk exposure and initial customer-level default rate prediction data;
and the customer-level default rate determining unit is used for determining the customer-level default rate of each grade of the transition probability matrix according to the adjusting factor and setting the customer-level default rate of each grade as target customer-level default rate prediction data.
Further, the customer-level default rate determination unit includes:
a default rate cumulative density determining subunit, configured to determine a default rate cumulative density of the transition probability matrix according to the adjustment factor;
and the client-level default rate determining subunit is used for determining the client-level default rate of each grade according to the default rate cumulative density.
Further, still include:
each rating weight determining unit is used for calculating the caliber according to the number of default customers and the preset money amount and determining the weight of each rating;
and the transition probability matrix construction unit is used for determining a transition probability matrix according to the weight of each grade and the migration rate of the preset customer credit grade.
In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for determining a credit risk client-level default rate when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for determining a credit risk client-level default rate.
According to the technical scheme, the transfer probability matrix is built based on initial customer-level default rate prediction data, the average default rate model of risk exposure weighting is built according to the transfer probability matrix, target customer-level default rate prediction data of each grade is determined according to the average default rate model of risk exposure weighting and the initial customer-level default rate prediction data, the known prediction result of the credit risk is accurately refined and conducted downwards, and further the customer-level default rate data are obtained.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a method for determining a default rate at a credit risk client level according to an embodiment of the present application;
FIG. 2 is a second flowchart illustrating a method for determining a default rate at a credit risk client level according to an embodiment of the present application;
FIG. 3 is a third flowchart illustrating a method for determining a default rate at a credit risk client level according to an embodiment of the present application;
FIG. 4 is a fourth flowchart illustrating a method for determining a default rate at a credit risk client level according to an embodiment of the present application;
FIG. 5 is a block diagram of one embodiment of a credit risk client-level default rate determination apparatus according to the present application;
FIG. 6 is a second block diagram of the apparatus for determining a default rate at a credit risk client level according to an embodiment of the present application;
FIG. 7 is a third block diagram of a credit risk client-level default rate determination apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device in 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.
Considering that the pressure test default rate calculation scheme in the prior art can only calculate to the organization level, if the pressure test default rate calculation scheme needs to be subdivided to the customer level, a large amount of detail level data needs to be used, the application provides a credit risk customer-level default rate determination method and device because the customer-level data has the problems of large data volume, difficult statistics, machine density and the like and difficult use in practical application, by constructing a transition probability matrix based on the initial customer-level default rate prediction data, and constructing a risk exposure weighted average default rate model from the transition probability matrix, and meanwhile, determining target customer-level default rate prediction data of each grade according to the average default rate model of the risk exposure weighting and the initial customer-level default rate prediction data, and accurately refining and conducting the known prediction result of the credit risk downwards to obtain the customer-level default rate data.
In order to refine and conduct the known prediction result of the credit risk accurately and further obtain the client-level default rate data, the present application provides an embodiment of a method for determining the client-level default rate of the credit risk, and referring to fig. 1, the method for determining the client-level default rate of the credit risk specifically includes the following contents:
step S101: and constructing a risk exposure weighted average default rate model according to the preset transition probability matrix.
Optionally, the preset transition probability matrix is a Z-shift transition probability matrix including an adjustment factor Z.
Optionally, the Z-shift transition probability matrix is used for refining and conducting downward with an existing pressure prediction result, where the existing pressure prediction result is default rate prediction data of an organization level obtained according to an existing pressure test model (e.g., a Wilson pressure test model), and the default rate prediction data is high in dimensionality and incomplete in result.
Optionally, an average breach rate model of risk exposure weighting, that is, a calculation formula of the average breach probability WADR of risk exposure weighting, may be constructed based on the Z-shift transition probability matrix of the present application, specifically:
wherein m isiNumber of default customers, T, rated as iidA customer-level default rate of i for transitions through the Z-shift transition probability matrix.
Step S102: and determining target customer-level default rate prediction data of each grade according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data.
Optionally, the initial customer-level default rate refers to default rate prediction data of the customer level at the institution level obtained according to an existing stress test model (e.g., Wilson stress test model), such as a customer default rate and an amount default rate, and in other embodiments of the present application, the customer-level default rate also includes default rate indicators of other customer dimensions.
Optionally, the present application may set the calculation result of the risk exposure weighted average default probability WADR obtained by the above construction as default rate prediction data PD at the customer level of the organization level obtained from the existing stress test model (for example, Wilson stress test model)stessedEquality, i.e. let WADR equal to PDStessed。
Optional, due to PDStessedFor known data, the weighted average breach probability WADR of risk exposure at this time is T in the formulaidCan thus find (m)iAlso known data) and then the adjustment factor Z for constructing the Z-shift transition probability matrix for the WADR can be derived back-calculated, i.e., from this the complete Z-shift transition probability matrix can be determined (the adjustment factor Z was not known data before).
Optionally, the last column T of the probability matrix is transferred according to Z-shift in the present applicationidThe customer-level default rates for each rating are updated to achieve the goal of subdividing the overall default probability under stress to the debtor level (i.e., the customer level).
As can be seen from the above description, the method for determining a client-level default rate of credit risk provided in the embodiment of the present application can determine target client-level default rate prediction data of each rating according to the average default rate model of risk exposure weighting and the initial client-level default rate prediction data by constructing a transition probability matrix based on the initial client-level default rate prediction data, and constructing an average default rate model of risk exposure weighting according to the transition probability matrix, and accurately refine and conduct downward the known prediction result of credit risk, thereby obtaining client-level default rate data.
In order to obtain the adjustment factor in the transition probability matrix by backward-deducing according to the initial customer-level default rate prediction data to obtain the customer-level default rate of each rating in the transition probability matrix, in an embodiment of the credit risk customer-level default rate determination method of the present application, referring to fig. 2, the following may be further specifically included:
step S201: and determining an adjustment factor of the transition probability matrix according to the weighted average default rate model of the risk exposure and the initial customer-level default rate prediction data.
Step S202: and determining the client-level default rate of each grade of the transition probability matrix according to the adjusting factor, and setting the client-level default rate of each grade as target client-level default rate prediction data.
Optionally, the present application may set the calculation result of the risk exposure weighted average default probability WADR obtained by the above construction as default rate prediction data PD at the customer level of the organization level obtained from the existing stress test model (for example, Wilson stress test model)StessedEquality, i.e. let WADR equal to PDStessed。
Optional, due to PDstessedFor known data, the weighted average breach probability WADR of risk exposure at this time is T in the formulaidCan thus find (m)iAlso known data) and then the adjustment factor Z for constructing the Z-shift transition probability matrix for the WADR can be derived back-calculated, i.e., from this the complete Z-shift transition probability matrix can be determined (the adjustment factor Z was not known data before).
Optionally, the last column T of the probability matrix is transferred according to Z-shift in the present applicationidThe customer-level default rates for each rating are updated to achieve the goal of subdividing the overall default probability under stress to the debtor level (i.e., the customer level).
In order to obtain the default rate cumulative density in the transition probability matrix by reverse-deducing according to the adjustment factor in the transition probability matrix to obtain the client-level default rate of each rating in the transition probability matrix, in an embodiment of the credit risk client-level default rate determination method of the present application, referring to fig. 3, the following may be further specifically included:
step S301: and determining the default rate cumulative density of the transition probability matrix according to the adjusting factor.
Step S302: and determining the client-level default rate of each grade according to the default rate accumulated density.
Optionally, after the adjustment factor Z in the Z-shift transition probability matrix is obtained through the above steps by reverse extrapolation, other key parameters in the Z-shift transition probability matrix, such as a standard normal distribution cumulative density function (i.e., default rate cumulative density), may be further determined, specifically:
where N is a standard normal distribution cumulative density function (i.e., the default rate cumulative density).
Optionally, after each row of the Z-shift transition probability matrix of the present application is mapped to a normal distribution, there are:
wherein N is-1P is an inverse function of the standard normal distribution cumulative density function, i.e., the default rate prediction data at the customer level at the institution level obtained from an existing stress testing model (e.g., Wilson stress testing model).
In order to conduct the default rate prediction to the deeper-dimensional client-level default rate without adding detail data, in an embodiment of the credit risk client-level default rate determination method of the present application, referring to fig. 4, the following may be specifically included:
step S401: and calculating the caliber according to the number of default customers and the preset money amount, and determining the weight of each grade.
Step S402: and determining a transition probability matrix according to the weight of each rating and the migration rate of the preset customer credit rating.
Optionally, the number of the default customers, the calculation aperture of the preset money amount, and the migration rate of the preset customer credit rating may be manually set, or may be obtained through calculation in a third-party system.
In order to refine and conduct the known prediction result of the credit risk accurately and further obtain the default rate data of the client level, the present application provides an embodiment of a credit risk client level default rate determination apparatus for implementing all or part of the content of the credit risk client level default rate determination method, and referring to fig. 5, the credit risk client level default rate determination apparatus specifically includes the following contents:
and an average default rate model construction module 10, configured to construct an average default rate model of risk exposure weighting according to the preset transition probability matrix.
And the target client-level default rate prediction module 20 is configured to determine target client-level default rate prediction data of each rating according to the weighted average default rate model of risk exposure and the initial client-level default rate prediction data.
As can be seen from the above description, the device for determining a client-level default rate of credit risk provided in the embodiment of the present application can determine target client-level default rate prediction data of each rating according to the average default rate model of risk exposure weighting and the initial client-level default rate prediction data by constructing a transition probability matrix based on the initial client-level default rate prediction data, and constructing an average default rate model of risk exposure weighting according to the transition probability matrix, and accurately refine and conduct downward the known prediction result of credit risk, thereby obtaining client-level default rate data.
In order to obtain the adjustment factor in the transition probability matrix by backward-deriving according to the initial customer-level default rate prediction data to obtain the customer-level default rate of each rating in the transition probability matrix, in an embodiment of the credit risk customer-level default rate determination apparatus of the present application, referring to fig. 6, the target customer-level default rate prediction module 20 includes:
an adjustment factor determining unit 21, configured to determine an adjustment factor of the transition probability matrix according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data.
And the customer-level default rate determining unit 22 is configured to determine a customer-level default rate of each rating of the transition probability matrix according to the adjustment factor, and set the customer-level default rate of each rating as target customer-level default rate prediction data.
In order to obtain the default rate cumulative density in the transition probability matrix by reverse-deducing according to the adjustment factor in the transition probability matrix to obtain the client-level default rate of each rating in the transition probability matrix, in an embodiment of the credit risk client-level default rate determination apparatus of the present application, referring to fig. 7, the client-level default rate determination unit 22 includes:
a default rate cumulative density determining subunit 221, configured to determine a default rate cumulative density of the transition probability matrix according to the adjustment factor.
And a customer-level default rate determining subunit 222, configured to determine a customer-level default rate of each rating according to the default rate cumulative density.
In order to conduct the default rate prediction to the deeper-dimension client-level default rate without adding detail data, an embodiment of the credit risk client-level default rate determination apparatus of the present application further includes the following components:
and each rating weight determining unit is used for calculating the caliber according to the number of default customers and the preset money amount and determining the weight of each rating.
And the transition probability matrix construction unit is used for determining a transition probability matrix according to the weight of each grade and the migration rate of the preset customer credit grade.
To further illustrate the present solution, the present application further provides a specific application example of implementing the method for determining a default rate of a credit risk client level by using the apparatus for determining a default rate of a credit risk client level, which specifically includes the following contents:
in step 1, since when the change situation of the default rate is actually predicted, we consider that the customer grade is moved to level 19 as the default, and in the following example, for the sake of concise and intuitive description, we assume that the customer grade is moved to level 6 as the default. In this example, it is assumed that the data are shown in table 1:
TABLE 1 hypothesis data
| Raw rating | Customer migration to rating 6 probability | Weight of | Weighted breach probability |
| 1 | 0.1 | 0.2 | 0.02 |
| 2 | 0.1 | 0.2 | 0.02 |
| 3 | 0.1 | 0.2 | 0.02 |
| 4 | 0.3 | 0.2 | 0.06 |
| 5 | 0.4 | 0.2 | 0.08 |
| Total of | 1 | 0.2 |
It will be appreciated that the sum of the probability weights for migrating to default ratings under each rating needs to be 1.
And 2, calculating the accumulated default rate of the previous rating moved to the default rating under each rating. It will be appreciated that the sum of the probabilities of migrating to the respective ratings under the respective ratings is required to satisfy 1, and thus the 1-P6, i.e., the total probability minus the probability of migrating to the 6 th level, in the example is the cumulative sum of the probabilities of migrating to the rating 5, as shown in table 2:
table 2 transition to rating 5 probability cumulative sum
Step 3, as can be understood, the standard normal distribution cumulative function is:
the inverse function of the sum is shown in table 3:
TABLE 3 cumulative and inverse function values
| Raw rating | Standard normal cumulative inverse function value |
| 1 | 1.28155 |
| 2 | 1.28155 |
| 3 | 1.28155 |
| 4 | 0.52440 |
| 5 | 0.25335 |
Step 4, adding a Z value to each inverse function value obtained in step 3, wherein the total value of the Z value is [ -3,3], and taking-0.3 as an initial Z value, as shown in table 4:
TABLE 4 inverse function values after Z addition
| Raw rating | Inverse function value after adding Z |
| 1 | 0.98155 |
| 2 | 0.98155 |
| 3 | 0.98155 |
| 4 | 0.22440 |
| 5 | -0.04665 |
And 5, adding Z to each rating to obtain an accumulative distribution inverse function value, and processing the accumulative distribution inverse function value by using a standard normal accumulative distribution function. It will be appreciated that, depending on the nature of the function and the inverse function, this step actually means solving the cumulative sum of probabilities of migrating to rating 5 after each rating plus Z, as shown in table 5:
TABLE 5 cumulative probability sum after Z addition
| Raw rating | Sum of probabilities after adding Z |
| 1 | 0.83684 |
| 2 | 0.83684 |
| 3 | 0.83684 |
| 4 | 0.58878 |
| 5 | 0.48139 |
Step 7, as can be understood, the default rate after each rating is added with Z can be obtained by subtracting the cumulative sum of the probabilities of each rating from the total probability 1, and the default rate is multiplied by the corresponding weight to obtain the weighted probability, as shown in table 6:
TABLE 6 weighted violation probability
| Raw rating | Rate of breach after Z addition | Weight of | Weighted breach probability |
| 1 | 0.16316 | 0.2 | 0.032632 |
| 2 | 0.16316 | 0.2 | 0.032632 |
| 3 | 0.16316 | 0.2 | 0.032632 |
| 4 | 0.41122 | 0.2 | 0.082245 |
| 5 | 0.51861 | 0.2 | 0.103722 |
| Total of | 1 | 0.283863 |
Step 8, it can be understood that assuming that we have previously obtained a total default rate of 0.3 under pressure, obviously 0.283863 is less than 0.3, then we use binary to reduce Z, whereas if the total default rate is less than that obtained, then Z is increased. And (4) iterating the steps 4-7 until the difference between the obtained default probability and the total default rate under the pressure is within 0.00001, and considering the Z value as the idealized Z value.
In order to refine and conduct the known prediction result of the credit risk accurately and further obtain the default rate data of the client level from a hardware level, the application provides an embodiment of an electronic device for implementing all or part of the content in the method for determining the default rate of the credit risk client level, where the electronic device specifically includes the following content:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the credit risk client-level default rate determination device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may refer to the embodiment of the method for determining a default rate of a credit risk client level and the embodiment of the device for determining a default rate of a credit risk client level in the embodiments, which are incorporated herein, and repeated details are omitted.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the method for determining the default rate at the credit risk client level may be performed at the electronic device side as described above, or all operations may be performed at the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
Fig. 8 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 8, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 8 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the credit risk client-level default rate determination method functions may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
step S101: and constructing a risk exposure weighted average default rate model according to the preset transition probability matrix.
Step S102: and determining target customer-level default rate prediction data of each grade according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data.
As can be seen from the above description, in the electronic device provided in the embodiment of the present application, a transition probability matrix is constructed based on the initial customer-level default rate prediction data, an average default rate model weighted by risk exposure is constructed according to the transition probability matrix, and meanwhile, target customer-level default rate prediction data of each rating is determined according to the average default rate model weighted by risk exposure and the initial customer-level default rate prediction data, so that known prediction results of credit risks are refined and conducted downwards, and further, customer-level default rate data are obtained.
In another embodiment, the credit risk client level default rate determination means may be configured separately from the central processor 9100, for example, the credit risk client level default rate determination means may be configured as a chip connected to the central processor 9100, and the function of the credit risk client level default rate determination method may be implemented by the control of the central processor.
As shown in fig. 8, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 8; further, the electronic device 9600 may further include components not shown in fig. 8, which may be referred to in the art.
As shown in fig. 8, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps of the method for determining a client-level default rate of credit risk with an execution subject being a server or a client in the above embodiments, where the computer-readable storage medium stores thereon a computer program, and when being executed by a processor, the computer program implements all steps of the method for determining a client-level default rate of credit risk with an execution subject being a server or a client in the above embodiments, for example, the processor implements the following steps when executing the computer program:
step S101: and constructing a risk exposure weighted average default rate model according to the preset transition probability matrix.
Step S102: and determining target customer-level default rate prediction data of each grade according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data.
As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application constructs a transition probability matrix based on the initial customer-level default rate prediction data, constructs an average default rate model of risk exposure weighting according to the transition probability matrix, determines target customer-level default rate prediction data of each rating according to the average default rate model of risk exposure weighting and the initial customer-level default rate prediction data, and accurately refines and conducts the known prediction result of credit risk downward, thereby obtaining the customer-level default rate data.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for determining a rate of default at a credit risk client level, the method comprising:
constructing a risk exposure weighted average default rate model according to a preset transition probability matrix;
and determining target customer-level default rate prediction data of each grade according to the weighted average default rate model of risk exposure and the initial customer-level default rate prediction data.
2. The method of claim 1, wherein determining target customer-level default rate forecast data for each rating based on the weighted average default rate model of risk exposure and initial customer-level default rate forecast data comprises:
determining an adjustment factor of the transition probability matrix according to the weighted average default rate model of the risk exposure and initial customer-level default rate prediction data;
and determining the client-level default rate of each grade of the transition probability matrix according to the adjusting factor, and setting the client-level default rate of each grade as target client-level default rate prediction data.
3. The method of claim 2, wherein determining the client-level default rate for each rating of the transition probability matrix according to the adjustment factor comprises:
determining default rate cumulative density of the transition probability matrix according to the adjusting factor;
and determining the client-level default rate of each grade according to the default rate accumulated density.
4. The method of claim 1, wherein before the constructing the weighted average default rate model of risk exposure according to the predetermined transition probability matrix, the method comprises:
calculating the caliber according to the number of default customers and the preset money amount, and determining the weight of each grade;
and determining a transition probability matrix according to the weight of each rating and the migration rate of the preset customer credit rating.
5. A credit risk customer-level default rate determination apparatus, comprising:
the average default rate model building module is used for building an average default rate model of risk exposure weighting according to the preset transition probability matrix;
and the target client-level default rate prediction module is used for determining target client-level default rate prediction data of each grade according to the average default rate model of the risk exposure weighting and the initial client-level default rate prediction data.
6. The credit risk customer-level default rate determination apparatus of claim 5, wherein the target customer-level default rate prediction module comprises:
an adjustment factor determining unit, configured to determine an adjustment factor of the transition probability matrix according to the weighted average default rate model of risk exposure and initial customer-level default rate prediction data;
and the customer-level default rate determining unit is used for determining the customer-level default rate of each grade of the transition probability matrix according to the adjusting factor and setting the customer-level default rate of each grade as target customer-level default rate prediction data.
7. The credit risk customer-level breach rate determination device of claim 6, wherein said customer-level breach rate determination unit comprises:
a default rate cumulative density determining subunit, configured to determine a default rate cumulative density of the transition probability matrix according to the adjustment factor;
and the client-level default rate determining subunit is used for determining the client-level default rate of each grade according to the default rate cumulative density.
8. The credit risk client level default rate determination apparatus of claim 5, further comprising:
each rating weight determining unit is used for calculating the caliber according to the number of default customers and the preset money amount and determining the weight of each rating;
and the transition probability matrix construction unit is used for determining a transition probability matrix according to the weight of each grade and the migration rate of the preset customer credit grade.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method for determining a credit risk client level default rate as claimed in any one of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for determining a credit risk client-level default rate according to any one of claims 1 to 4.
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