Method and device for calculating loan default probability and default loss rate of commercial bank

Abstract

The invention discloses a commercial bank loan default probability and default loss rate calculation method and device, and belongs to the technical field of big data analysis, and the method comprises the steps: S1, obtaining historical loan data of a commercial bank, the historical loan data comprising loan detail data and verification data; s2, performing cleaning and deformation processing on the historical loan data, and establishing a related rule based on a set anchor point for processing; s3, based on the processed historical loan data, calculating migration rates of different data dates and different periods; and S4, calculating a default probability and a default loss rate based on the migration rate. According to the method, the commercial bank is assisted to complete the estimation of the default probability and the default loss rate by defining the default and the loss, sorting the loan data, calculating the migration rate in each period, and calculating the default probability and the default loss rate, so that the overall risk measurement capability is improved, and the accurate and steady loan value reduction preparation of the commercial bank is promoted.

Description

Technical Field

[0001] The present invention relates to a method and device for calculating the probability of default and loss rate of default of commercial bank loans, and belongs to the technical field of big data analysis. Background Art

[0002] Banks and other financial institutions need to consider the credit risk of borrowers when granting loans in order to assess their repayment ability and the possibility of loan default. The probability of default and the loss given default are important indicators for assessing credit risk.

[0003] With the implementation of the expected credit loss method, the method of commercial banks to make provision for loan impairment has changed from the original provisioning ratio according to the five-level classification to the current provisioning according to the formula of default probability × default loss rate × risk exposure. Among them, the estimation of default probability and default loss rate is a difficult problem in the accounting estimation of commercial banks.

[0004] How to make good use of internal data to accurately estimate the probability of loan default and the loss rate given default will affect the accuracy of commercial banks' provision for loan impairment, which is of great practical significance. Therefore, the present invention proposes a method and device for calculating the probability of loan default and the loss rate given default of commercial banks. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method and device for calculating the probability of default and the loss rate of default of commercial bank loans, which can complete the estimation of the probability of default and the loss rate of default, and promote commercial banks to accurately and prudently make loan impairment provisions.

[0006] The technical solution adopted by the present invention to solve the technical problem is: In a first aspect, an embodiment of the present invention provides a method for calculating the probability of default and the loss given default of a commercial bank loan, comprising the following steps: Step S1, obtaining historical loan data of a commercial bank, wherein the historical loan data includes loan details data and write-off data; Step S2, cleaning and transforming the historical loan data, and establishing relevant rules based on the set anchor points for processing; Step S3, based on the processed historical loan data, calculating the migration rates of different periods on different data dates; Step S4, calculating the probability of default and the loss given default based on the migration rate.

[0007] As a possible implementation of this embodiment, the loan details data are all transaction details under the bank's accounting subjects of loans and advances, covering corporate loans, retail loans, commercial acceptance bill discounts, bank acceptance bill advances and trade financing; the loan details data include data date, customer number, overdue days, five-level classification, and balance; The write-off data includes the write-off date, customer number and balance.

[0008] As a possible implementation of this embodiment, step S2, cleaning and transforming the historical loan data, and establishing relevant rules based on the set anchor point for processing, includes: Step S21, setting the default time point of the default loan and the loss time point of the loss loan, and formulating the default and loss rules; Step S22, based on the default and loss rules, adding observation points, determining specific default and loss rules, and marking the defaulted loans with overdue period labels; Step S23, classify and summarize the data marked with overdue issue labels.

[0009] As a possible implementation of this embodiment, the defaulted loan is a loan that is overdue for more than 90 days and falls into the last three of the five-level classifications (substandard, doubtful, and loss); The loss loans are loans that have been overdue for more than 180 days and have been written off.

[0010] As a possible implementation method of this embodiment, the specific default and loss rules are: the overdue period label of non-overdue loans is M0, the overdue period label of 1-30 days overdue is M1, the overdue period label of 31-60 days overdue is M2, the overdue period label of 61-90 days overdue is M3, the overdue period label of 91-120 days or the last three categories of the five-level classification is M4, the overdue period label of 121-150 days is M5, the overdue period label of 150-180 days is M6, and the overdue period label of more than 180 days and written-off loans is M6+.

[0011] As a possible implementation of this embodiment, the step S23, classifying and aggregating the data marked with the overdue period label, includes: Add a data date to the write-off data, where the data date is the last day of the month of the write-off date; Keep the three fields of data date, overdue period, and balance to combine all credit details and write-off details into one large table; The balances are categorized and summarized with data dates as rows and overdue periods as columns.

[0012] As a possible implementation of this embodiment, the calculation formula of the migration rate is: P=Si+1j+1 / S ij , Among them, S ij is the balance of the jth period on the i-th data date; i is the data date sequence number, i=1, 2, 3…11; j is the number of overdue times, j=1, 2, 3…7.

[0013] As a possible implementation of this embodiment, the step S4, calculating the probability of default and the loss given default based on the migration rate, includes: Step S41, according to the definition of default, calculate the default probability of each data date, and calculate the annual default probability; the default probability of the data date includes: the loan default probability of M0 is obtained by multiplying the migration rate of M0-M1, the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the loan default probability of M1 is obtained by multiplying the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the default probability of M2 is obtained by multiplying the migration rate of M2-M3 and the migration rate of M3-M4; the default probability of M3 is the migration rate of M3-M4; the default probabilities of M4 to M6+ are all 1; the annual default probability is the average value of the default probabilities corresponding to each data date; Step S42, according to the definition of loss, calculate the default probability of each data date and calculate the annual default loss rate; the default probability of the data date includes: the loss rate of M4 is obtained by multiplying the migration rate of M4-M5, the migration rate of M5-M6, and the migration rate of M6-M6+; the loss rate of M5 is obtained by multiplying the migration rate of M5-M6 and the migration rate of M6-M6+; the loss rate of M6 is the migration rate of M6-M6+; the loss rate of M6+ is 1; the annual default loss rate LGD is the average value of the LGD corresponding to each data date.

[0014] In a second aspect, an embodiment of the present invention provides a device for calculating the probability of default and the loss given default of a commercial bank loan, comprising: A data collection module is used to obtain the historical loan data of commercial banks, wherein the historical loan data includes loan details data and write-off data; The data processing module is used to clean and transform historical loan data and establish relevant rules based on the set anchor points for processing; The migration rate calculation module is used to calculate the migration rates of different periods on different data dates based on the processed historical loan data; The default calculation module is used to calculate the default probability and default loss rate based on the migration rate.

[0015] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for calculating the probability of default and loss given default of any commercial bank loan as described above.

[0016] In a fourth aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for calculating the probability of default and the loss given default of any commercial bank loan as described above are executed.

[0017] The beneficial effects of the technical solution of the embodiment of the present invention are as follows: The present invention will assist commercial banks in estimating the probability of default and the loss rate of default by defining default and loss, collating loan data, calculating migration rates for each period, and calculating the probability of default and the loss rate of default, thereby improving the overall risk measurement capabilities and promoting commercial banks to accurately and prudently make loan impairment provisions.

[0018] The present invention is applied to the daily management of commercial bank provisioning, which will enable commercial banks to more accurately calculate loan default probability and default loss rate, and provide strong support for the calculation of impairment provision.

[0019] In the process of calculating the migration rate, the present invention does not only consider the number of overdue days, nor the five-level classification, but processes the overdue period field based on the two to observe the migration status, which is convenient for observing the impact of multiple indicators; the present invention gives priority to statistics on the balance summary data of each stage of each month, and then compares the changes in the balance of each stage of each month, which is convenient for calculating the default probability of businesses with large data volumes, and greatly reduces the workload for customer changes; the observation period of the present invention is set more precisely, which can be used not only to calculate the default probability but also the default loss rate; the present invention reduces the impact of the loan amount on the measurement results.

[0020] A device for calculating the default probability and default loss rate of a commercial bank loan according to the technical solution of an embodiment of the present invention has the same beneficial effects as a method for calculating the default probability and default loss rate of a commercial bank loan according to the technical solution of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a method for calculating the probability of default and loss given default of a commercial bank loan according to an exemplary embodiment; Figure 2 It is a schematic diagram of a structure of a device for calculating the probability of default and loss rate of default of a commercial bank loan according to an exemplary embodiment; Figure 3 is a data acquisition flow chart according to an exemplary embodiment; Figure 4 The present invention is a data sorting flow chart shown according to an exemplary embodiment. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical features of the solution of the present invention, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, a method for calculating the probability of default and the loss given default of a commercial bank loan provided by an embodiment of the present invention comprises the following steps: Step S1, obtaining historical loan data of a commercial bank, wherein the historical loan data includes loan details data and write-off data; Step S2, cleaning and transforming the historical loan data, and establishing relevant rules based on the set anchor points for processing; Step S3, based on the processed historical loan data, calculating the migration rates of different periods on different data dates; Step S4, calculating the probability of default and the loss given default based on the migration rate.

[0024] As a possible implementation of this embodiment, the loan details data are all transaction details under the bank's accounting subjects of loans and advances, covering corporate loans, retail loans, commercial acceptance bill discounts, bank acceptance bill advances and trade financing; the loan details data include data date, customer number, overdue days, five-level classification, and balance; The write-off data includes the write-off date, customer number and balance.

[0025] As a possible implementation of this embodiment, step S2, cleaning and transforming the historical loan data, and establishing relevant rules based on the set anchor point for processing, includes: Step S21, setting the default time point of the default loan and the loss time point of the loss loan, and formulating the default and loss rules; Step S22, based on the default and loss rules, adding observation points, determining specific default and loss rules, and marking the defaulted loans with overdue period labels; Step S23, classify and summarize the data marked with overdue issue labels.

[0026] As a possible implementation of this embodiment, the defaulted loan is a loan that is overdue for more than 90 days and falls into the last three of the five-level classifications (substandard, doubtful, and loss); The loss loans are loans that have been overdue for more than 180 days and have been written off.

[0027] As a possible implementation method of this embodiment, the specific default and loss rules are: the overdue period label of non-overdue loans is M0, the overdue period label of 1-30 days overdue is M1, the overdue period label of 31-60 days overdue is M2, the overdue period label of 61-90 days overdue is M3, the overdue period label of 91-120 days or the last three categories of the five-level classification is M4, the overdue period label of 121-150 days is M5, the overdue period label of 150-180 days is M6, and the overdue period label of more than 180 days and written-off loans is M6+.

[0028] As a possible implementation of this embodiment, the step S23, classifying and aggregating the data marked with the overdue period label, includes: Add a data date to the write-off data, where the data date is the last day of the month of the write-off date; Keep the three fields of data date, overdue period, and balance to combine all credit details and write-off details into one large table; The balances are categorized and summarized with data dates as rows and overdue periods as columns.

[0029] As a possible implementation of this embodiment, the calculation formula of the migration rate is: P=S i+1j+1 / S ij , Among them, S ij is the balance of the jth period on the i-th data date; i is the data date sequence number, i=1, 2, 3…11; j is the number of overdue times, j=1, 2, 3…7.

[0030] As a possible implementation of this embodiment, the step S4, calculating the probability of default and the loss given default based on the migration rate, includes: Step S41, according to the definition of default, calculate the default probability of each data date, and calculate the annual default probability; the default probability of the data date includes: the loan default probability of M0 is obtained by multiplying the migration rate of M0-M1, the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the loan default probability of M1 is obtained by multiplying the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the default probability of M2 is obtained by multiplying the migration rate of M2-M3 and the migration rate of M3-M4; the default probability of M3 is the migration rate of M3-M4; the default probabilities of M4 to M6+ are all 1; the annual default probability is the average value of the default probabilities corresponding to each data date; Step S42, according to the definition of loss, calculate the default probability of each data date and calculate the annual default loss rate; the default probability of the data date includes: the loss rate of M4 is obtained by multiplying the migration rate of M4-M5, the migration rate of M5-M6, and the migration rate of M6-M6+; the loss rate of M5 is obtained by multiplying the migration rate of M5-M6 and the migration rate of M6-M6+; the loss rate of M6 is the migration rate of M6-M6+; the loss rate of M6+ is 1; the annual default loss rate LGD is the average value of the LGD corresponding to each data date.

[0031] like Figure 2 As shown, an embodiment of the present invention provides a device for calculating the probability of default and the loss rate of default of a commercial bank loan, comprising: A data collection module is used to obtain the historical loan data of commercial banks, wherein the historical loan data includes loan details data and write-off data; The data processing module is used to clean and transform historical loan data and establish relevant rules based on the set anchor points for processing; The migration rate calculation module is used to calculate the migration rates of different periods on different data dates based on the processed historical loan data; The default calculation module is used to calculate the default probability and default loss rate based on the migration rate.

[0032] The specific process of using the calculation device of the present invention to calculate the probability of default and the loss rate of default of commercial bank loans is as follows.

[0033] 1. Data acquisition.

[0034] It is used to calculate the default probability and default loss rate of the loan, so the data required by the present invention mainly include loan detail data and write-off data. The loan detail data mainly includes all transaction details under the bank's accounting subjects of loans and advances, mainly covering corporate loans, retail loans, commercial acceptance bill discounts, bank acceptance bill advances, trade financing, etc. In order to obtain the default probability and default loss rate for one year, the loan detail data at the end of 12 consecutive months is required. Among them, each data must include the data date, customer number, overdue days, five-level classification, and balance. At the same time, in order to restore the impact of the bank's write-off on the migration situation, it is also necessary to obtain the write-off ledger for each month, which should include the write-off date, customer number, and balance. Figure 3 As shown, the present invention will extract the above-mentioned credit and write-off data from the bank's credit management system and core system through information technology means to form a basic database for subsequent related rule applications.

[0035] 2. Data collation.

[0036] like Figure 4As shown in the figure, data collation mainly involves cleaning and transforming the basic data so that it can be directly used to observe the migration of the amount. In this process, the key nodes of observation must be set first, and then relevant rules must be established based on the set anchor points to complete the processing of the basic data.

[0037] ① Set up rules for breach of contract and loss.

[0038] Since it is necessary to observe when default and loss occur when applying migration rate of future data, setting the default time point and loss time point becomes two important anchor points in rule setting. The present invention sets loans that are overdue for more than 90 days and are classified into the last three categories (substandard, doubtful, and loss) of the five-level classification as default loans. Set loans that are overdue for more than 180 days and written-off loans as loss loans. In the application, the settings of default and loss can be adjusted according to different product types, different regions, and different customers.

[0039] ②Configure the parameters of data sorting rules.

[0040] Since the basic data used in the method is monthly data, we need to further refine the default and loss rules set above to meet the needs of monthly observation. The refinement idea here is based on the default and loss rules, and then add an observation point every 30 days, so that we can clearly see the migration path of the data every month. The specific rules are as follows: the overdue period label of non-overdue loans is M0, the overdue period label of 1-30 days overdue is M1, the overdue period label of 31-60 days overdue is M2, the overdue period label of 61-90 days overdue is M3, the overdue period label of 91-120 days or the last three categories of the five-level classification is M4, the overdue period label of 121-150 days is M5, the overdue period label of 150-180 days is M6, and the overdue period label of more than 180 days and written-off loans is M6+. The specific parameters are shown in Table 1.

[0041] Table 1 Specific parameters

[0042] According to the rules set in the parameter table, processing rules are formed using information technology, applied to the basic data, and the base data is labeled with overdue issues.

[0043] ③Merge and organize the processed data.

[0044] Here, the labeled data needs to be classified and summarized, that is, summarized according to the data date and the number of overdue periods, so the data still needs to be further sorted. In order to splice all the detailed data into one table, first of all, it is necessary to add the data date to the write-off data. The data date is a virtual date, defined as the last day of the month of the write-off date. For example, if the write-off date is January 15, 2024, then its data date is January 31, 2024. Secondly, retain the three fields of data date, overdue period, and balance to splice all credit detail tables and write-off detail tables into one large table. Finally, the balances are classified and summarized with the data date as the row and the overdue period as the column, as shown in Table 2.

[0045] Table 2 Classification summary table

[0046] After completing this step, you can observe the migration of balances every month according to the level of overdue payments.

[0047] 3. Calculation of migration rate for each period.

[0048] The balances on each data date except M0 are migrated from the previous data date, such as S 22 By S 11 Migration, S 33 By S 22 According to this downward migration law, the formula for calculating the migration rate is: P=S i+1j+1 / S ij , Among them, i is 1, 2, 3, …, 11, and j is 1, 2, 3, …, 7.

[0049] Through the above formula, computer information technology is used to calculate the data in the table to obtain the migration rate of different data dates and different periods, as shown in Table 3.

[0050] Table 3 Migration rate

[0051] As above, we obtain the migration rate of the balance in different periods on a monthly basis.

[0052] 4. Calculation of default probability and default loss rate.

[0053] ① Calculation of default probability. According to the definition of default, a loan default occurs when it is overdue for more than 90 days and is classified as the last three of the five-level classifications. Therefore, it can be concluded that when a loan migrates to M4, the loan is in default. The loan default of M0 needs to go through four stages: M1, M2, M3, and M4. Therefore, the loan default probability of M0 is obtained by multiplying the migration rate of M0-M1, the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the loan default probability of M1 is obtained by multiplying the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the default probability of M2 is obtained by multiplying the migration rate of M2-M3 and the migration rate of M3-M4; the default probability of M3 is obtained by multiplying the migration rate of M3-M4; the default probability of M4 and beyond is 1 because default has already occurred. The default probability of each data date is shown in Table 4 below.

[0054] Table 4 Default probability by data date

[0055] The final annual default probability PD is the average of the PD corresponding to each data date, as shown in Table 5 below.

[0056] Table 5 Annual Default Probability

[0057] ②Calculation of default loss rate. According to the definition of loss, an overdue period of more than 180 days is considered a loss. The premise of default loss rate is default first, so the migration rate of M4-M6+ is calculated here. The loss rate of M4 is obtained by multiplying the migration rate of M4-M5, the migration rate of M5-M6, and the migration rate of M6-M6+; the loss rate of M5 is obtained by multiplying the migration rate of M5-M6 and the migration rate of M6-M6+; the loss rate of M6 is the migration rate of M6-M6+; since M6+ has already suffered losses, its loss rate is 1. The corresponding loss rates of M0, M1, M2, and M3 should be the same as those of M4, so the default probability of each data date is shown in Table 6 below.

[0058] Table 6 Default probability by data date

[0059] The final annual default loss rate LGD is the average value of the LGD corresponding to each data date, as shown in Table 7 below.

[0060] Table 7 Annual LGD

[0061] At this point, the estimation of the probability of default PD and the loss given default LGD under this method is completed. When applying it, a parameter table is established according to the overdue periods, as shown in Table 8, so that the business balance can be mapped one by one according to the overdue periods.

[0062] Table 8 Overdue Period Parameters

[0063] 5. Case description based on the above method.

[0064] ① Loan data, as shown in Table 9: Table 9 Loan data

[0065] ②Write-off data, as shown in Table 10: Table 10 Write-off data

[0066] ③ Perform data sorting and generate the overdue period field according to the overdue period generation rule in the method of the present invention. The loan data changes are shown in Table 11: Table 11 Loan data after data sorting

[0067] The changes in write-off data are shown in Table 12: Table 12 Write-off data after data sorting

[0068] Integrate loan data and write-off data, as shown in Table 13: Table 13 Consolidated loan data and write-off data

[0069] ④ Classify and summarize the balances according to the data date and the number of overdue periods, as shown in Table 14: Table 14 Balance Classification Summary

[0070] ⑤ Calculation of migration rate for each period: Use Mn+1 of next month / Mn of this month to calculate the migration rate as shown in Table 15: Table 15 Migration rate values ​​for various data dates

[0071] ⑥ Calculate the probability of default and loss given default by multiplying the corresponding periods, as shown in Table 16 and Table 17: Table 16 Default probability values

[0072] Table 17 LGD Values

[0073] ⑦ Calculate the average of the probability of default and the loss rate given default, which is the final probability of default and loss rate given default, as shown in Table 18 and Table 19.

[0074] Table 18 Final Default Probability

[0075] Table 19 Final LGD

[0076] The present invention will assist commercial banks in completing the estimation of default probability and default loss rate by defining default and loss, collating loan data, calculating migration rate of each period, and calculating default probability and default loss rate, thereby improving the overall risk measurement capability and promoting commercial banks to accurately and robustly make provision for loan impairment. The present invention is applied to the daily management of provisioning of commercial banks, which will enable commercial banks to more accurately calculate loan default probability and default loss rate, and provide strong support for the calculation of impairment provision.

[0077] Compared with the traditional method of calculating migration rate, the present invention has the following advantages: 1) In the process of calculating the migration rate, we do not only consider the number of overdue days or the five-level classification, but we process the overdue period field based on the two to observe the migration status. This method facilitates the observation of the impact of multiple indicators.

[0078] 2) It is convenient to calculate the default probability for businesses with large amounts of data. For some personal loans, the number of customers may reach hundreds of thousands or even millions per month. If the changes in customers are observed every month within 12 months, it will bring certain pressure to both manual and machine. The present invention takes "yuan" as the unit, gives priority to the summary data of the balance of each stage of each month, and then compares the changes in the balance of each stage of each month, which greatly reduces the workload of customer changes.

[0079] 3) The observation period is set more precisely, which can calculate not only the probability of default but also the loss rate of default. The traditional approach only observes the situation from normal loan to default, but does not observe the situation after default, so it can only calculate the probability of default. In addition to observing the situation from normal to default, the present invention also observes the situation from default to loss, which can be used to calculate not only the probability of default but also the loss rate of default.

[0080] 4) Reduce the impact of the loan amount on the measurement results. The traditional method observes the migration changes of customers without considering the size of the customer's loan amount. If only customers with small loan amounts migrate down, the impairment measurement results will be increased, and the default probability of large customers will be overestimated. The present invention uses "yuan" as the unit and observes the migration changes of loan balances. The results will not be deviated due to the difference in loan amounts between large and small customers.

[0081] An electronic device provided by an embodiment of the present invention includes a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for calculating the probability of default and the loss given default of any commercial bank loan as described above.

[0082] Specifically, the above-mentioned memory and processor can be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer program stored in the memory, it can execute the above-mentioned method for calculating the commercial bank loan default probability and default loss rate.

[0083] Those skilled in the art will appreciate that the structure of the computer device does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently.

[0084] In some embodiments, the computer device may also include a touch screen that can be used to display a graphical user interface (e.g., a startup interface of an application) and receive user operations on the graphical user interface (e.g., startup operations on an application). The specific touch screen may include a display panel and a touch panel. The display panel may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. The touch panel may collect the user's contact or non-contact operations on or near it, and generate pre-set operation instructions, for example, the user uses any suitable object such as a finger, a stylus, or an accessory on or near the touch panel. In addition, the touch panel may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and posture, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into information that the processor can process, and then sends it to the processor, and can receive and execute commands from the processor. In addition, the touch panel can be implemented by various types such as resistive, capacitive, infrared and surface acoustic wave, and any technology developed in the future can also be used to implement the touch panel. Further, the touch panel can cover the display panel, and the user can operate on or near the touch panel covered on the display panel according to the graphical user interface displayed on the display panel. After the touch panel detects the operation on or near it, it is transmitted to the processor to determine the user input, and then the processor provides corresponding visual output on the display panel in response to the user input. In addition, the touch panel and the display panel can be implemented as two independent components or integrated.

[0085] Corresponding to the method for starting the above-mentioned application, an embodiment of the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for calculating the probability of default and the loss rate of default of any commercial bank loan are executed.

[0086] The application startup device provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0088] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0089] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional module in the embodiments provided in the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the probability of default and loss given default of commercial bank loans, characterized in that: The steps include: Step S1, obtaining historical loan data of a commercial bank, wherein the historical loan data includes loan details data and write-off data; Step S2, cleaning and transforming the historical loan data, and establishing relevant rules based on the set anchor points for processing; Step S3, based on the processed historical loan data, calculating the migration rates of different periods on different data dates; Step S4, calculating the probability of default and the loss given default based on the migration rate.

2. The method for calculating the probability of default and loss given default of commercial bank loans according to claim 1, characterized in that: The loan details data are all transaction details under the bank's accounting subjects of loans and advances, covering corporate loans, retail loans, commercial acceptance bill discounts, bank acceptance bill advances and trade financing; the loan details data include data date, customer number, overdue days, five-level classification, and balance; The write-off data includes the write-off date, customer number and balance.

3. The method for calculating the probability of default and loss given default of commercial bank loans according to claim 1, characterized in that: The step S2 is to clean and transform the historical loan data, and establish relevant rules based on the set anchor points for processing, including: Step S21, setting the default time point of the default loan and the loss time point of the loss loan, and formulating the default and loss rules; Step S22, based on the default and loss rules, adding observation points, determining specific default and loss rules, and marking the defaulted loans with overdue period labels; Step S23, classify and summarize the data marked with overdue issue labels.

4. The method for calculating the probability of default and loss given default of commercial bank loans according to claim 3, characterized in that: The specific default and loss rules are as follows: the overdue period label for non-overdue loans is M0, the overdue period label for 1-30 days overdue is M1, the overdue period label for 31-60 days overdue is M2, the overdue period label for 61-90 days overdue is M3, the overdue period label for 91-120 days or the last three categories of the five-level classification is M4, the overdue period label for 121-150 days is M5, the overdue period label for 150-180 days is M6, and the overdue period label for more than 180 days and written-off loans is M6+.

5. The method for calculating the probability of default and loss given default of commercial bank loans according to claim 4, characterized in that: The step S23, classifying and aggregating the data marked with the overdue period label, includes: Add a data date to the write-off data, where the data date is the last day of the month of the write-off date; Keep the three fields of data date, overdue period, and balance to combine all credit details and write-off details into one large table; The balances are categorized and summarized with data dates as rows and overdue periods as columns.

6. The method for calculating the probability of default and loss given default of commercial bank loans according to claim 5, characterized in that: The calculation formula of the migration rate is: P=S i+1j+1 / S ij , Among them, S ij is the balance of the jth period on the i-th data date; i is the data date sequence number, i=1, 2, 3…11; j is the number of overdue times, j=1, 2, 3…7.

7. The method for calculating the probability of default and loss given default of commercial bank loans according to claim 6, characterized in that: The step S4, calculating the probability of default and the loss given default based on the migration rate, includes: Step S41, according to the definition of default, calculate the default probability of each data date, and calculate the annual default probability; the default probability of the data date includes: the loan default probability of M0 is obtained by multiplying the migration rate of M0-M1, the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the loan default probability of M1 is obtained by multiplying the migration rate of M1-M2, the migration rate of M2-M3, and the migration rate of M3-M4; the default probability of M2 is obtained by multiplying the migration rate of M2-M3 and the migration rate of M3-M4; the default probability of M3 is the migration rate of M3-M4; the default probabilities of M4 to M6+ are all 1; the annual default probability is the average value of the default probabilities corresponding to each data date; Step S42, according to the definition of loss, calculate the default probability of each data date and calculate the annual default loss rate; the default probability of the data date includes: the loss rate of M4 is obtained by multiplying the migration rate of M4-M5, the migration rate of M5-M6, and the migration rate of M6-M6+; the loss rate of M5 is obtained by multiplying the migration rate of M5-M6 and the migration rate of M6-M6+; the loss rate of M6 is the migration rate of M6-M6+; the loss rate of M6+ is 1; the annual default loss rate LGD is the average value of the LGD corresponding to each data date.

8. A device for calculating the probability of default and loss given default of commercial bank loans, characterized in that: include: A data collection module is used to obtain the historical loan data of commercial banks, wherein the historical loan data includes loan details data and write-off data; The data processing module is used to clean and transform historical loan data and establish relevant rules based on the set anchor points for processing; The migration rate calculation module is used to calculate the migration rates of different periods on different data dates based on the processed historical loan data; The default calculation module is used to calculate the default probability and default loss rate based on the migration rate.

9. An electronic device, characterized in that: The electronic device comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to execute the steps of the method for calculating the probability of default and loss given default of commercial bank loans as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, executes the steps of the method for calculating the probability of default and the loss given default of a commercial bank loan as described in any one of claims 1 to 7.

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