CN113763153A - Bank credit risk analysis method and device - Google Patents
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Abstract
The invention discloses a bank credit risk analysis method and a bank credit risk analysis device, wherein the method comprises the following steps: acquiring first-class index data, second-class index data, third-class index data and fourth-class index data of a bank; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank; calculating credit risk scores of the banks according to the first type index data, the second type index data, the third type index data and the fourth type index data of the banks and the corresponding weight of each type of index data; according to the credit risk score of the bank, the credit risk grade of the bank is determined, so that the accuracy of a bank credit risk analysis result can be improved, the efficiency of bank credit risk analysis can be improved, the credit risk control capability of the bank can be improved, and the user experience can be improved.
Description
Technical Field
The invention relates to the technical field of computer data processing, in particular to a bank credit risk analysis method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
With the rapid development of economy, banks face unprecedented opportunities for development and serious challenges. The current unstable factors of the financial market increase credit risks, the credit business is the most important part of banking business and is also the main profit means of banks, and the risk that the interest cannot be recovered on time is large because the paying is separated from the control of the banks.
Therefore, there is a need for a bank credit risk analysis scheme that can overcome the above-mentioned problems.
Disclosure of Invention
The embodiment of the invention provides a bank credit risk analysis method, which is used for analyzing the credit risk of a bank according to different types of index data, improving the accuracy of the bank credit risk analysis result, improving the efficiency of the bank credit risk analysis, improving the credit risk control capability of the bank and improving the user experience, and comprises the following steps:
acquiring first-class index data, second-class index data, third-class index data and fourth-class index data of a bank; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank;
calculating credit risk scores of the banks according to the first type index data, the second type index data, the third type index data and the fourth type index data of the banks and the corresponding weight of each type of index data;
and determining the credit risk grade of the bank according to the credit risk score of the bank.
The embodiment of the present invention further provides a bank credit risk analysis device, configured to analyze a bank credit risk according to different types of index data, so as to improve accuracy of a bank credit risk analysis result, improve efficiency of bank credit risk analysis, improve a bank credit risk management and control capability, and improve user experience, where the device includes:
the data acquisition module is used for acquiring first-class index data, second-class index data, third-class index data and fourth-class index data of a bank; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank;
the score calculating module is used for calculating credit risk scores of the banks according to the first type index data, the second type index data, the third type index data and the fourth type index data of the banks and the weight corresponding to each type of index data;
and the grade determining module is used for determining the credit risk grade of the bank according to the credit risk score of the bank.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the bank credit risk analysis method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing the bank credit risk analysis method is stored in the computer-readable storage medium.
In the embodiment of the invention, first-class index data, second-class index data, third-class index data and fourth-class index data of a bank are obtained; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank; calculating credit risk scores of the banks according to the first type index data, the second type index data, the third type index data and the fourth type index data of the banks and the corresponding weight of each type of index data; according to the credit risk score of the bank, the credit risk grade of the bank is determined, the credit risk of the bank can be analyzed according to different types of index data, the accuracy of the credit risk analysis result of the bank is improved, the efficiency of the credit risk analysis of the bank is improved, the credit risk control capability of the bank is improved, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a process of a bank credit risk analysis method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a bank credit risk analysis device according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of a bank credit risk analysis device according to the present invention;
FIG. 4 is a schematic structural diagram of an embodiment of a bank credit risk analysis device according to the present invention;
FIG. 5 is a schematic structural diagram of an embodiment of a bank credit risk analysis device according to the present invention;
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
FIG. 1 is a flow chart of a process of a bank credit risk analysis method according to an embodiment of the present invention. As shown in fig. 1, the method for analyzing bank credit risk in the embodiment of the present invention may include:
102, calculating credit risk scores of the bank according to first-class index data, second-class index data, third-class index data and fourth-class index data of the bank and corresponding weights of each-class index data;
and 103, determining the credit risk level of the bank according to the credit risk score of the bank.
As can be known from the flow shown in fig. 1, the bank credit risk analysis method in the embodiment of the present invention may analyze the credit risk of the bank according to different types of index data, so as to improve accuracy of a bank credit risk analysis result, improve efficiency of bank credit risk analysis, improve a bank credit risk management and control capability, and improve user experience.
In specific implementation, before performing bank credit risk analysis, different types of index data of a bank can be obtained, the types of the index data can be classified according to the actual situation of bank credit, and the different types of index data can reflect credit risk levels of different layers of the bank.
In one embodiment, the method may further include: receiving an initialization configuration request of the weight corresponding to each type of index data; and initializing and configuring the weight corresponding to each type of index data according to the initialization configuration request.
In one embodiment, the method may further include: receiving a modification request for the weight corresponding to the index data of the specified category; and modifying the weight corresponding to the index data of the corresponding category according to the modification request.
In specific implementation, before bank credit risk analysis, initialization configuration can be performed on the weight corresponding to each type of index data according to an initialization configuration request; after the initial configuration, in the process of calculating the credit risk score of the bank, the weight corresponding to the index data of the corresponding category can be modified according to the modification request of the weight corresponding to the index data of the specified category, and the credit risk score of the bank is continuously calculated according to the weight corresponding to the index data of the corresponding category after the modification is completed.
In one embodiment, the method may further include: and displaying credit risk scores of a plurality of banks in a panoramic view mode.
In one embodiment, presenting credit risk scores for a plurality of banks in the form of a panoramic view may include: and displaying the credit risk scores and the credit risk score ranks of the banks in a specified time period according to the geographic positions of the banks in the panoramic view.
As shown in table 1, taking the credit risk score of each branch of a bank in the third quarter of 2019 as an example, the credit risk score and the credit risk score ranking of each branch in the third quarter of 2019 can be shown according to the geographic location of the bank, such as the places a, B, C, D, and the like. In specific implementation, credit risk scoring and ranking can be performed on each branch of a certain bank in a panoramic view mode, the total score and the ranking condition within a target range of each branch of the bank in the third quarter of 2019 can be clearly shown, meanwhile, the score condition of each data index of each branch can be shown, symptomatic medicine administration according to actual problems of each branch is facilitated, the work arrangement of each branch is optimized in a targeted mode, and therefore the credit risk of each branch is effectively reduced.
TABLE 1
In one embodiment, the first type of metric data may include: credit risk level data, credit risk prevention and control data and bad loan disposition data of the bank; wherein the credit risk level data comprises: bad loan data, overdue loan data, underwriting loan data; the credit risk prevention and control data includes: risk classification data, risk processing data and key risk reporting data; the bad loan disposition data includes: bad loan disposition proportion data and bad loan cash recycling proportion data.
In one embodiment, the second type of metric data may include: credit structure data, credit cost data of the bank; wherein the credit structure data includes: credit structure adjustment data, fine credit data; the credit cost data includes: credit risk cost proportion data and fund return proportion data after credit risk adjustment.
In one embodiment, the third type of metric data may include: credit initiation and termination data, credit intermediate flow data and credit guarantee data of the bank; wherein the credit initiation and end data includes: credit customer level data, credit tracking early warning verification data; the credit intermediate flow data includes: credit approval data, credit loan data; the credit collateral data includes: credit collateral audit data, credit collateral value data, credit collateral disposition data.
In one embodiment, the fourth type of metric data may include: the credit business checking personnel data, the credit business checking task data and the credit business checking result data of the bank; wherein the credit business checker data comprises: credit business checker assignment data; the credit business check task data includes: credit business handling quality data; the credit business check result data comprises: credit service responsibility identifies the data.
In one embodiment, calculating the credit risk score of the bank according to the first type index data, the second type index data, the third type index data and the fourth type index data of the bank and the corresponding weight of each type index data may include: determining a plurality of value intervals of each type of index data and a critical value of each value interval; calculating an index score of each index data according to the real value of each index data, the numerical interval corresponding to the real value, the critical value of the numerical interval corresponding to the real value, and the incidence relation between the real value of each index data and the numerical interval corresponding to the real value and the critical value of the numerical interval corresponding to the real value; and calculating the credit risk score of the bank according to the index score of each index data and the weight corresponding to each type of index data.
In specific implementation, the true value of the index data may represent a numerical value corresponding to the actual index data obtained in the currently and actually performed bank credit risk analysis process. The process of calculating the index score is explained by taking the bad loan data in the first quarter of the year of the bank A as sample index data, and assuming that the weight corresponding to the initially configured bad loan data is 21%, for convenience of statistics, the bad loan data is expressed by the percentage of the sum of the bad loan data in the bank A to the sum of the loan data, namely the bad loan rate. The characteristics of the bad loan data, the historical bad loan data of the bank a and the like can be combined to determine the value interval corresponding to the bad loan rate and the critical value of each value interval, wherein the critical value of the value interval may represent the credit risk classification standard of the bad loan rate, for example, the critical value corresponding to the bad loan rate in the bank a may be sequentially set as: 0. 0.8%, 1.6%, 3.0%, that is, the bad loan rate in bank a can be divided into four value intervals: 0, 0.8 percent, 1.6 percent, 3.0 percent and more than 3.0 percent.
Since the lower the bad loan rate is, the smaller the corresponding bank credit risk is, the higher the index score of the bad loan rate is, different basic scores may be set in calculating the index score of the bad loan rate for different numerical intervals, for example, the reference scores corresponding to the above four intervals in sequence may be set as: 130. 110, 90, 70;
in this example, the relationship between the bad loan fidelity value and the threshold value of the numerical interval corresponding to the bad loan fidelity value and the numerical interval corresponding to the bad loan fidelity value may be set as follows: the standard score is +20 x (numerical interval critical maximum value-bad loan rate true value)/(numerical interval critical maximum value-numerical interval critical minimum value), and the interval score of the bad loan rate can be calculated according to the bad loan rate true value, the numerical interval corresponding to the bad loan rate true value, the critical value of the numerical interval corresponding to the bad loan rate true value and the incidence relation among the three values.
In one embodiment, calculating the credit risk score of the bank according to the index score of each index data and the corresponding weight of each index data may include: comparing the true value of each index data with the corresponding historical value of each index data; adjusting the index score of each index data according to the comparison result; and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
In specific implementation, the historical value of the index data can represent a numerical value corresponding to the acquired historical index data which can be referred to in bank credit risk analysis, when the index score corresponding to the real value of each index data is calculated, the change of the corresponding bank credit risk can be represented due to the change of the real value of the index data relative to the historical value of the index data, and the index score is adjusted according to the relative change, so that more accurate bank credit risk score can be acquired; more specifically, when the index score is adjusted, different adjustments may be made according to different value intervals corresponding to the true value of each index data. Still taking the index score corresponding to the bad loan rate in the first quarter of the year of the bank a as an example, if the real value corresponding interval of the bad loan rate in this example is (0.8%, 1.6%), the index score can be increased by 1 score every time the index score is reduced by 0.1 percentage point compared with the historical value of the bad loan data in the first quarter of the last year; the score can not be reduced when the increase does not exceed 0.3 percentage point; when the percentage is increased to exceed 0.3 percentage, the index score can be reduced by 1 point every time the index score exceeds 0.1 percentage, and the variation score of the bad loan rate can be obtained according to the index score adjusting method, namely the variation adjusting score of the corresponding bad loan rate interval.
After the analysis, the section scores and the variation scores of the bad loan rate can be combined to obtain the index score of the bad loan rate, namely: the bad loan rate index score is the bad loan rate interval score plus the bad loan rate variation score, wherein the bad loan rate variation score can be a negative number. When calculating the bad loan rate index scores in different value intervals, the following specific analysis process in table 2 can be referred to:
TABLE 2
In one embodiment, calculating the credit risk score of the bank according to the index score of each index data and the corresponding weight of each type of index data may include: calculating the difference value between the real value of each index data and the target value corresponding to each index data; adjusting the index score of each index data according to the difference value; and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
In specific implementation, before the index score of each index data is calculated, a target value of each index data can be preset as an expected value reference according to the actual credit condition of a bank, and when the index score corresponding to the real value of each index data is calculated, if the real value of the index data is different from the target value, the difference can be used as an additional item to adjust the index score, so that more accurate credit risk score of the bank can be obtained;
taking the index score corresponding to the bad loan rate of the first quarter of the year of the bank A as an example, on the basis of calculating the section score of the bad loan rate and the variation score of the bad loan rate according to the method, when the true value of the bad loan rate is smaller than the preset target value of the bad loan rate in the example, the index score of the bad loan rate is improved through additional points; when the true value of the rate of bad loan in this example is larger than the target value set in advance for the rate of bad loan, the index score may be reduced by an additional score. The bad loan rate may be determined, for example, by determining a specific bad loan rate bonus score as follows: the additional value of the bad loan rate is [ (target value of bad loan rate-true value of bad loan rate)/total loan amount ] × 10000, and in this example, the value range of the additional value is [ -2.5, 2.5 ].
According to the content, the index score corresponding to the bad loan rate in the first quarter of the year of the bank A can be calculated, other index scores can be calculated according to the method, and finally the credit risk score in the first quarter of the year of the bank A can be calculated according to the index score of each index data and the weight corresponding to each type of index data.
The embodiment of the invention also provides a bank credit risk analysis device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to the bank credit risk analysis method, the implementation of the device can refer to the implementation of the bank credit risk analysis method, and repeated parts are not described again.
Fig. 2 is a schematic structural diagram of a bank credit risk analysis device according to an embodiment of the present invention. As shown in fig. 2, the apparatus for analyzing bank credit risk in the embodiment of the present invention may include:
the data acquisition module 201 is configured to acquire first-type index data, second-type index data, third-type index data, and fourth-type index data of a bank; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank;
the score calculating module 202 is configured to calculate a credit risk score of the bank according to the first type of index data, the second type of index data, the third type of index data, and the fourth type of index data of the bank, and a weight corresponding to each type of index data;
and the grade determining module 203 is used for determining the credit risk grade of the bank according to the credit risk score of the bank.
Fig. 3 is a schematic structural diagram of an embodiment of a bank credit risk analysis device according to the present invention. As shown in fig. 3, in an embodiment, the apparatus for analyzing bank credit risk shown in fig. 2 further includes a configuration request receiving module 301, configured to:
receiving an initialization configuration request of the weight corresponding to each type of index data;
and initializing and configuring the weight corresponding to each type of index data according to the initialization configuration request.
Fig. 4 is a schematic structural diagram of an embodiment of a bank credit risk analysis device according to the present invention. As shown in fig. 4, in an embodiment, the bank credit risk analysis apparatus shown in fig. 3 further includes a modification request receiving module 401 for:
receiving a modification request for the weight corresponding to the index data of the specified category;
and modifying the weight corresponding to the index data of the corresponding category according to the modification request.
Fig. 5 is a schematic structural diagram of an embodiment of a bank credit risk analysis device according to the present invention. As shown in fig. 5, in an embodiment, the bank credit risk analysis apparatus shown in fig. 2 further includes a display module 501, configured to:
and displaying credit risk scores of a plurality of banks in a panoramic view mode.
The apparatus of fig. 5 may include the configuration request receiving module 301 shown in fig. 3;
the apparatus of fig. 5 may include the modification request receiving module 401 shown in fig. 4.
In one embodiment, the display module 501 is specifically configured to:
and displaying the credit risk scores and the credit risk score ranks of the banks in a specified time period according to the geographic positions of the banks in the panoramic view.
In one embodiment, the first type of metric data may include: credit risk level data, credit risk prevention and control data and bad loan disposition data of the bank; wherein the credit risk level data comprises: bad loan data, overdue loan data, underwriting loan data; the credit risk prevention and control data includes: risk classification data, risk processing data and key risk reporting data; the bad loan disposition data includes: bad loan proportion data and bad loan cash recycling data.
In one embodiment, the second type of metric data may include: credit structure data, credit cost data of the bank; wherein the credit structure data includes: credit structure adjustment data, fine credit data; the credit cost data includes: credit risk cost proportion data and credit risk adjusted fund return data.
In one embodiment, the third type of metric data may include: credit initiation and termination data, credit intermediate flow data and credit guarantee data of the bank; wherein the credit initiation and end data includes: credit customer level data, credit tracking early warning verification data; the credit intermediate flow data includes: credit approval data, credit loan data; the credit collateral data includes: credit collateral audit data, credit collateral value data, credit collateral disposition data.
In one embodiment, the fourth type of metric data may include: the credit business checking personnel data, the credit business checking task data and the credit business checking result data of the bank; wherein the credit business checker data comprises: credit business checker assignment data; the credit business check task data includes: credit business handling quality data; the credit business check result data comprises: credit service responsibility identifies the data.
In one embodiment, the score calculation module 202 is specifically configured to:
determining a plurality of value intervals of each type of index data and a critical value of each value interval;
calculating an index score of each index data according to the real value of each index data, the numerical interval corresponding to the real value, the critical value of the numerical interval corresponding to the real value, and the incidence relation between the real value of each index data and the numerical interval corresponding to the real value and the critical value of the numerical interval corresponding to the real value;
and calculating the credit risk score of the bank according to the index score of each index data and the weight corresponding to each type of index data.
In one embodiment, the score calculation module 202 is specifically configured to:
comparing the true value of each index data with the corresponding historical value of each index data;
adjusting the index score of each index data according to the comparison result;
and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
In one embodiment, the score calculation module 202 is specifically configured to:
calculating the difference value between the real value of each index data and the target value corresponding to each index data;
adjusting the index score of each index data according to the difference value;
and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
Based on the aforementioned inventive concept, as shown in fig. 6, the present invention further provides a computer device 600, which includes a memory 610, a processor 620 and a computer program 630 stored on the memory 610 and operable on the processor 620, wherein the processor 620 implements a bank credit risk analysis method when executing the computer program 630.
Based on the foregoing inventive concept, the present invention proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the foregoing bank credit risk analysis method.
In summary, in the embodiment of the present invention, first-type index data, second-type index data, third-type index data, and fourth-type index data of a bank are obtained; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank; calculating credit risk scores of the banks according to the first type index data, the second type index data, the third type index data and the fourth type index data of the banks and the corresponding weight of each type of index data; according to the credit risk score of the bank, the credit risk grade of the bank is determined, the credit risk of the bank can be analyzed according to different types of index data, the accuracy of the credit risk analysis result of the bank is improved, the efficiency of the credit risk analysis of the bank is improved, the credit risk control capability of the bank is improved, and the user experience is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, 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 (systems), 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 above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (26)
1. A bank credit risk analysis method is characterized by comprising the following steps:
acquiring first-class index data, second-class index data, third-class index data and fourth-class index data of a bank; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank;
calculating credit risk scores of the banks according to the first type index data, the second type index data, the third type index data and the fourth type index data of the banks and the corresponding weight of each type of index data;
and determining the credit risk grade of the bank according to the credit risk score of the bank.
2. The method of claim 1, further comprising:
receiving an initialization configuration request of the weight corresponding to each type of index data;
and initializing and configuring the weight corresponding to each type of index data according to the initialization configuration request.
3. The method of claim 2, further comprising:
receiving a modification request for the weight corresponding to the index data of the specified category;
and modifying the weight corresponding to the index data of the corresponding category according to the modification request.
4. The method of claim 1, further comprising:
and displaying credit risk scores of a plurality of banks in a panoramic view mode.
5. The method of claim 4, wherein presenting credit risk scores for a plurality of banks in a panoramic view comprises:
and displaying the credit risk scores and the credit risk score ranks of the banks in a specified time period according to the geographic positions of the banks in the panoramic view.
6. The method of claim 1, wherein the first type of metric data comprises: credit risk level data, credit risk prevention and control data and bad loan disposition data of the bank; wherein the credit risk level data comprises: bad loan data, overdue loan data, underwriting loan data; the credit risk prevention and control data includes: risk classification data, risk processing data and key risk reporting data; the bad loan disposition data includes: bad loan proportion data and bad loan cash recycling data.
7. The method of claim 1, wherein the second type of metric data comprises: credit structure data, credit cost data of the bank; wherein the credit structure data includes: credit structure adjustment data, fine credit data; the credit cost data includes: credit risk cost proportion data and credit risk adjusted fund return data.
8. The method of claim 1, wherein the third type of metric data comprises: credit initiation and termination data, credit intermediate flow data and credit guarantee data of the bank; wherein the credit initiation and end data includes: credit customer level data, credit tracking early warning verification data; the credit intermediate flow data includes: credit approval data, credit loan data; the credit collateral data includes: credit collateral audit data, credit collateral value data, credit collateral disposition data.
9. The method of claim 1, wherein the fourth type of metric data comprises: the credit business checking personnel data, the credit business checking task data and the credit business checking result data of the bank; wherein the credit business checker data comprises: credit business checker assignment data; the credit business check task data includes: credit business handling quality data; the credit business check result data comprises: credit service responsibility identifies the data.
10. The method of claim 1, wherein calculating the credit risk score of the bank according to the first type index data, the second type index data, the third type index data and the fourth type index data of the bank and the corresponding weight of each type index data comprises:
determining a plurality of value intervals of each type of index data and a critical value of each value interval;
calculating an index score of each index data according to the real value of each index data, the numerical interval corresponding to the real value, the critical value of the numerical interval corresponding to the real value, and the incidence relation between the real value of each index data and the numerical interval corresponding to the real value and the critical value of the numerical interval corresponding to the real value;
and calculating the credit risk score of the bank according to the index score of each index data and the weight corresponding to each type of index data.
11. The method of claim 10, wherein calculating the credit risk score of the bank based on the index score of each index data and the corresponding weight of each index data type comprises:
comparing the true value of each index data with the corresponding historical value of each index data;
adjusting the index score of each index data according to the comparison result;
and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
12. The method of claim 10, wherein calculating the credit risk score of the bank based on the index score of each index data and the corresponding weight of each index data type comprises:
calculating the difference value between the real value of each index data and the target value corresponding to each index data;
adjusting the index score of each index data according to the difference value;
and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
13. A bank credit risk analysis device, comprising:
the data acquisition module is used for acquiring first-class index data, second-class index data, third-class index data and fourth-class index data of a bank; the first type of index data reflects the credit risk control capability of the bank, the second type of index data reflects the credit structure optimization capability of the bank, the third type of index data reflects the credit process control capability of the bank, and the fourth type of index data reflects the credit business check capability of the bank;
the score calculating module is used for calculating credit risk scores of the banks according to the first type index data, the second type index data, the third type index data and the fourth type index data of the banks and the weight corresponding to each type of index data;
and the grade determining module is used for determining the credit risk grade of the bank according to the credit risk score of the bank.
14. The apparatus of claim 13, further comprising a configuration request receiving module to:
receiving an initialization configuration request of the weight corresponding to each type of index data;
and initializing and configuring the weight corresponding to each type of index data according to the initialization configuration request.
15. The apparatus of claim 14, further comprising a modification request receiving module to:
receiving a modification request for the weight corresponding to the index data of the specified category;
and modifying the weight corresponding to the index data of the corresponding category according to the modification request.
16. The apparatus of claim 13, further comprising a display module to:
and displaying credit risk scores of a plurality of banks in a panoramic view mode.
17. The apparatus of claim 16, wherein the display module is specifically configured to:
and displaying the credit risk scores and the credit risk score ranks of the banks in a specified time period according to the geographic positions of the banks in the panoramic view.
18. The apparatus of claim 13, wherein the first type of metric data comprises: credit risk level data, credit risk prevention and control data and bad loan disposition data of the bank; wherein the credit risk level data comprises: bad loan data, overdue loan data, underwriting loan data; the credit risk prevention and control data includes: risk classification data, risk processing data and key risk reporting data; the bad loan disposition data includes: bad loan proportion data and bad loan cash recycling data.
19. The apparatus of claim 13, wherein the second type of metric data comprises: credit structure data, credit cost data of the bank; wherein the credit structure data includes: credit structure adjustment data, fine credit data; the credit cost data includes: credit risk cost proportion data and credit risk adjusted fund return data.
20. The apparatus of claim 13, wherein the third type of metric data comprises: credit initiation and termination data, credit intermediate flow data and credit guarantee data of the bank; wherein the credit initiation and end data includes: credit customer level data, credit tracking early warning verification data; the credit intermediate flow data includes: credit approval data, credit loan data; the credit collateral data includes: credit collateral audit data, credit collateral value data, credit collateral disposition data.
21. The apparatus of claim 13, wherein the fourth type of metric data comprises: the credit business checking personnel data, the credit business checking task data and the credit business checking result data of the bank; wherein the credit business checker data comprises: credit business checker assignment data; the credit business check task data includes: credit business handling quality data; the credit business check result data comprises: credit service responsibility identifies the data.
22. The apparatus of claim 13, wherein the score calculation module is specifically configured to:
determining a plurality of value intervals of each type of index data and a critical value of each value interval;
calculating an index score of each index data according to the real value of each index data, the numerical interval corresponding to the real value, the critical value of the numerical interval corresponding to the real value, and the incidence relation between the real value of each index data and the numerical interval corresponding to the real value and the critical value of the numerical interval corresponding to the real value;
and calculating the credit risk score of the bank according to the index score of each index data and the weight corresponding to each type of index data.
23. The apparatus of claim 22, wherein the score calculation module is specifically configured to:
comparing the true value of each index data with the corresponding historical value of each index data;
adjusting the index score of each index data according to the comparison result;
and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
24. The apparatus of claim 22, wherein the score calculation module is specifically configured to:
calculating the difference value between the real value of each index data and the target value corresponding to each index data;
adjusting the index score of each index data according to the difference value;
and calculating the credit risk score of the bank according to the adjusted index score of each index data and the weight corresponding to each type of index data.
25. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 12 when executing the computer program.
26. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 12.
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| CN115619517A (en) * | 2022-09-02 | 2023-01-17 | 睿智合创(北京)科技有限公司 | Organization evaluation method and system applied to credit field |
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| CN115619517A (en) * | 2022-09-02 | 2023-01-17 | 睿智合创(北京)科技有限公司 | Organization evaluation method and system applied to credit field |
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