TWM643466U - Loan guarantee and affiliated account receivables impairment loss assessment operating system - Google Patents

Abstract

This model mainly provides a loan guarantee and its associated receivable impairment evaluation operation system, which includes a credit classification data import module, a credit classification data collection module, a credit classification data calculation module and a credit calculation data output module. The credit classification data import module is used to import credit asset classification case data from the credit asset database according to scheduling instructions. The credit classification data collection module is used to classify credit asset classification case data according to a credit asset classification logic, and generate a credit asset case classification table. The credit classification data calculation module is used to calculate the allowance for bad debts in the credit asset case classification table according to a loss calculation rule. The credit calculation data output module is used to output the logical classification loss calculation table to the operator's device of the operator.

Description

Loan Guarantee and its affiliated receivables impairment assessment operating system

The present invention relates to an impairment assessment operating system, and in particular to a loan guarantee and its associated receivable impairment assessment operating system that can assist in impairment calculations.

When handling credit granting cases, financial institutions must classify assets on a regular basis (for example, every month) according to the five classifications of competent authorities and the International Financial Reporting Standard 9 (International Financial Reporting Standard 9, abbreviated as IFRS 9) for individual account (sub) numbers according to the presence or absence of collateral, the length of overdue time, and changes in credit risk, so as to perform subsequent impairment calculations and various allowance impairments.

However, the bank has millions of credit asset cases, and it is not only time-consuming and labor-intensive to calculate the impairment after manual individual assessment, but also has the risk of operational errors. Accordingly, in order to improve the bank's operational efficiency in classifying credit asset cases and performing impairment calculations and reduce operational risks, this model provides a loan guarantee and its associated receivable impairment assessment operating system.

In order to improve the bank's operational efficiency of classifying credit asset cases and performing impairment calculations and reduce operational risks, this model mainly provides a loan guarantee and its associated receivable impairment assessment operating system, which is set in a bank host server with a credit asset database, and the credit assets The credit asset database is used to store a plurality of credit asset classification case data. The loan guarantee and its associated receivable impairment assessment operation system are used to provide the contractor to obtain a logical classification impairment calculation table through the operator device, and the loan guarantee and its associated receivable impairment assessment operation system include a plurality of hardware circuits electrically connected to each other, which are configured into multiple modules. The multiple modules include a credit classification data import module, a credit classification data collection module, a credit classification data calculation module and a Credit calculation data output module. The credit classification data import module is connected to the credit asset database with a signal, and is used to import the credit asset classification case data from the credit asset database according to a scheduling instruction. The signal of the credit classification data collection module is connected to the credit classification data import module, which is used to classify the credit asset classification case data according to a credit asset classification logic, and generate a credit asset case classification table. The credit classification data calculation module is signal-connected to the credit classification data collection module, and is used to calculate the credit asset case classification table according to a loss calculation rule, and generate the logical classification impairment calculation table, wherein the logical classification impairment calculation table includes an allowance for bad debts. The credit calculation data output module is signal-connected to the credit classification data calculation module for outputting the logical classification loss calculation table to the operator's device of the operator.

Optionally, the credit asset classification case data: the five classifications include loans, guarantees, letters of credit, receivables, or combinations thereof, and the International Financial Reporting Standard 9 (IFRS 9) classification includes loans, guarantees, letters of credit, receivables, financing commitments, or combinations thereof.

Optionally, the credit asset classification logic is a five-classification method of the competent authority, and the credit asset classification logic further includes a provision for bad debts.

Optionally, the provision for bad debts includes whether there is collateral, an assessment of a sufficient guarantee amount, an assessment of an unrecoverable amount, an annual recoverable amount, or a combination thereof.

Optionally, when the credit asset classification case data belongs to the first category and belongs to the specific assets designated by the competent authority (such as real estate loans and credit in mainland China), the allowance for bad debts is 1.5% of the balance of the specific asset; the credit asset classification case data belongs to the first category of non-specific assets, and the allowance for bad debts is 1% of the credit asset balance minus the credit balance of the Chinese government agency; when the credit asset classification case data belongs to the second category, the allowance for bad debts is 2% of the credit asset balance; If the asset classification case materials belong to the third category, the allowance for bad debts is 10% of the credit asset balance; when the credit assets classification case materials belong to the fourth category, the bad debt allowance amount is 50% of the credit asset balance; and, when the credit asset classification case materials belong to the fifth category, the bad debt allowance amount is 100% of the credit asset balance.

Optionally, when the credit asset classification case information belongs to the first category, and it is a mainland area credit case and a real estate loan case at the same time, the amount of provision for bad debts in this type of case is only calculated based on the mainland area credit case.

Optionally, the credit asset classification logic is an International Financial Reporting Standard No. 9 (IFRS 9) classification.

Optionally, the first stage of the IFRS No. 9 classification method refers to the credit risk of the credit asset classification case data has not increased significantly after the original recognition; the second stage of the IFRS No. 9 classification method refers to the credit asset classification case data has increased significantly after the original recognition. A judgment standard for this second stage is whether the payment is overdue (the loan has not amortized the principal on time, or the interest is more than one month but less than three months), whether the review level is not good or the warning level Whether it is unsatisfactory, whether the rating has deteriorated (the internal credit risk rating method of the Bank, the credit rating will be downgraded by a certain number of levels), and the special regulations on non-recourse purchase accounts receivable; and, the third stage of the IFRS 9 classification method refers to the fact that the credit asset classification case data has been credit-impaired after the original recognition. The judgment standard for this third stage and for major cases is whether to agree to extend the loan, overdue for more than 3 months, or transfer to call collections (including exempt from reporting overdue loans) Or monitoring cases, whether the review level is bad or the warning level is bad, whether the review level is dangerous or whether the warning level is dangerous, whether there is a major change in a company, special regulations on receivable non-recourse purchase accounts, whether it is a debt elimination negotiation case, and whether the rating has deteriorated (that is, according to the bank's credit risk internal rating method, the credit rating is D in default level).

Optionally, when the classified case data of credit assets belong to the first stage, the amount of allowance for bad debts is the probability of default in the next 12 months multiplied by the amount that cannot be recovered due to default; when the case data of classified credit assets belongs to the second stage, the amount of allowance for bad debts is the probability of default during the duration multiplied by the amount that cannot be recovered due to default; 00% for calculation). The aforementioned unrecoverable amount due to breach of contract is estimated based on the calculation formula of different stages based on the loss given default (LGD for short) and the total book amount.

In short, the loan guarantee and its associated receivables impairment assessment system provided by the embodiment of the present invention can classify credit asset cases in advance according to the five classifications of the competent authority or the International Financial Reporting Standard No. 9 (IFRS 9). Improve operating efficiency and reduce operating risks.

In order to make the above and other objectives, features and advantages of the new embodiment of the present invention more comprehensible, a detailed description is given below with reference to the accompanying drawings.

100: Bank host server

110: Credit assets database

200: Loan Guarantee and its Subordinate Receivables Impairment Assessment Operating System

210: Import credit classification data into module

220:Credit classification data collection module

230: Credit classification data calculation module

240:Credit calculation data output module

300: Credit business system

310, 320, 330, 340: pilot device

400: Accounting Systems

500: Central accounting system

S202~S224: steps

S302~S322: steps

The accompanying drawings are provided to enable those having ordinary knowledge in the technical field of the present invention to further understand the present invention, and are incorporated and constitute a part of the description of the present invention. The drawings illustrate exemplary embodiments of the invention and, together with the description of the invention, serve to explain the principle of the invention.

Fig. 1 is a functional block diagram of a loan guarantee and its associated receivable impairment assessment system according to an embodiment of the present invention; Fig. 2 is a flow chart of a five-class classification method of a loan guarantee and its associated receivable impairment assessment system according to an embodiment of the present invention; and Fig. 3 is a flowchart of a loan guarantee and its associated receivable impairment assessment system according to an embodiment of the present invention.

In order to enable those skilled in the art to further understand the technical features, content and advantages of the invention and the effects it can achieve, the content of the invention will be described in detail below in the form of appropriate embodiments in conjunction with drawings. The embodiments are only for illustration and assistance in explaining the invention of the invention, and do not limit the scope of rights of the invention in actual embodiments.

In order to improve the bank's operational efficiency in classifying credit asset cases and performing impairment calculations and reduce operational risks, this model mainly provides a loan guarantee and its associated receivables impairment assessment. For the business system, please refer to FIG. 1. FIG. 1 is a functional block diagram of a loan guarantee and its associated receivable impairment assessment system according to an embodiment of the present invention.

The loan guarantee and its affiliated receivables impairment assessment operating system 200 are used to provide a contractor to obtain a logical classification impairment calculation table through the operator device 340, and can assist the contractor to compare the loan guarantees and receivable balance schedules from various credit business units.

It is additionally mentioned that the multiple credit asset cases mentioned above are entered into the credit business system 300 through the operator devices 310 , 320 , and 330 by the undertaking staff of each credit business unit. In addition, there are multiple staff devices 310, 320, and 330 in the credit business system 300 in FIG. 1, mainly to show that there are different types of credit business, and different credit business units are in charge, and FIG. 1 is only a schematic diagram, and the present invention is not limited thereto.

The credit classification data import module 210 is signally connected to the credit asset database 110 for importing the credit asset classification case data from the credit asset database 110 according to a scheduling instruction. Wherein, the credit asset classification case data includes a loan type, a guarantee type, a letter of credit type, a receivable type to which it belongs, a financing commitment type or a combination thereof. .

In addition, the scheduling instruction means that the contractor enters the loan guarantee and its affiliated receivables impairment assessment operation system 200 to set a specified period in advance. When the specified period is reached, the credit classification data import module 210 automatically imports the credit asset classification case data from the credit asset database 110.

The credit classification data collection module 220 is signally connected to the credit classification data import module 210 for classifying the credit asset classification case data according to a credit asset classification logic and generating a credit asset case classification table. Wherein, the said credit asset classification logic is a five-classification method of the competent authority or an International Financial Reporting Standard No. 9 (IFRS 9) classification method.

First, the technical features of the five classifications of competent authorities will be explained.

In this embodiment, the first category of the five classifications by the competent authority refers to non-defective products; the second category refers to those that should be paid attention to; the third category refers to those that are expected to be recovered; the fourth category refers to those that are difficult to recover; and the fifth category refers to those that are hopeless to recover.

Further explain the method of making allowance for bad debts. When the credit asset classification case data belongs to the first category of the five-category method of the competent authority and belongs to the specific assets designated by the competent authority (such as credit in the mainland area and real estate loans), the allowance for bad debts is 1.5% of the balance of the specific asset; when the credit asset classification case data does not belong to the first category of specific assets, the allowance for bad debts is 1% of the credit asset balance minus the credit balance of Chinese government agencies; When the classified credit assets case materials belong to the third category, the allowance for bad debts is 10% of the credit assets balance; when the credit assets classification case materials belong to the fourth category, the allowance for bad debts is 50% of the credit assets balance; and, when the credit assets classification case materials belong to the fifth category, the bad debts allowance amount is 100% of the credit assets balance.

In addition, it is worth mentioning that when the credit asset classification case information belongs to the first category, and it is a mainland area credit case and a real estate loan case at the same time, in order to avoid repeated addition operations, the amount of provision for bad debts in this type of case is only calculated based on the mainland area credit case.

Next, technical features of the International Financial Reporting Standards No. 9 (IFRS 9) taxonomy will be described.

The first stage (Stage 1) of the IFRS No. 9 classification method refers to the fact that the credit risk of the credit asset classification case data of the credit asset case has not increased significantly after the original recognition; the second stage (Stage 2) of the IFRS No. 9 classification method refers to the credit risk of the credit asset classification case data has increased significantly after the original recognition. The first judgment standard of this second stage is whether the payment is overdue (the loan has not amortized the principal on time, or the interest is more than one month but less than three months), review Whether the rating is poor or the warning level is poor, whether the rating is deteriorating (according to the Bank’s credit risk internal rating method, the credit rating is lowered by a certain number of levels), and the special regulations on receivable non-recourse purchase accounts; and, the third stage (Stage 3) of the IFRS 9 taxonomy refers to the credit asset classification case data that has been credit-impaired after the original recognition. Long-term loan) or monitoring cases, whether the review level is bad or the early warning level is bad, whether the review level is dangerous or the early warning level is dangerous, whether there is a major change in a company, special regulations on receivable non-recourse purchase accounts, whether it is a debt elimination negotiation case, and whether the rating has deteriorated (that is, according to the internal credit risk rating method of the Bank, the credit rating is D in default).

The manner in which impairment calculations are performed is further explained.

First, explain the "credit asset impairment calculation formula", "credit asset impairment calculation formula is Expected Credit Loss (Expected Credit Loss, abbreviated as ECL) = PD * LGD * EAD, where PD is the default probability of credit assets (Probability of Default, abbreviated as PD), LGD is default loss Rate (Loss Given Default, abbreviated as LGD) and EAD is Exposure at Default (abbreviated as EAD).

Further, when the credit asset classification case data is classified into the third stage (Stage 3) of IFRS 9, the probability of default (PD) is 100%, that is, ECL=100% * LGD * EAD, and the loss given default (LGD) = 1-(annual recovery rate), the exposure at default (EAD) is the total book amount (including interest receivable), off-balance sheet amount, that is, the amount of allowance for bad debts is the amount that cannot be recovered due to default during the duration .

獲得備抵呆帳金額，其中，PD _t 為累積t年違 約率減去累積t-1年違約率，違約損失率(LGD)為已折現的違約損失率，違約曝險額(EAD)為預估第t年期初總帳面金額(含應收利息)、表外承諾、表外保證及信用狀，即備抵呆帳金額為存續期間違約機率乘以因違約而無法回收的金額。 When the credit asset classification case data is classified into the second stage (Stage 2) of IFRS 9, through

Obtain the amount of allowance for bad debts, among which, PD _t is the accumulated default rate in t years minus the accumulated default rate in t-1 years, the loss given default (LGD) is the discounted loss given default, and the exposure at default (EAD) is the estimated total book value (including interest receivable) at the beginning of the t year, off-balance sheet commitments, off-balance sheet guarantees and letters of credit.

When the credit asset classification case data belongs to the first stage (Stage 1) of IFRS 9, the amount of provision for bad debts is obtained through ECL=PD * LGD * EAD, where the probability of default (PD) is the probability of default in the next 12 months, the loss given default (LGD) is the discounted loss given default, and the exposure at default (EAD) is the total book amount (including interest receivable), off-balance sheet commitments, off-balance sheet guarantees and letters of credit, that is, the amount of provision for bad debts is The probability of default in the next 12 months is multiplied by the amount not recoverable due to default.

It should be added that the probability of default refers to the probability of default of credit assets (Probability of Default, abbreviated as PD), and its evaluation method is:

Pattern 1: If the borrower has credit rating information, the credit risk internal rating method (Internal Ratings-Based Approach, abbreviated as IRB) model IRB rating, the Bank's corporate credit rating method BOT rating and external ratings are used to determine the rating. After the category rating or risk classification of the IRB model, the actual rating transfer ratio is calculated, and the average one-year transposition matrix is used. The Markov Chain method is used to derive the multi-year accumulation Default rate, to estimate the probability of default.

Pattern 2: If there is no rating information on the account or the borrower, the credit asset portfolio is used to evaluate the impairment, and the actual marginal default rate of each year is estimated based on internal historical data, and the multi-year historical cumulative default rate is established to estimate the probability of default. Jingheng considers the credit business and data type, and sets appropriate and reasonable default probability group setting standards. For example, the corporate finance category is divided into government agencies, public enterprises, large private enterprises, small and medium private enterprises, individuals, sole proprietorships or partnerships, and other institutions and groups according to the credit objects. The consumer finance category is divided into groups such as housing loans, consumer loans, deposit pledges, credit loans, and student loans.

The parameters of probability of default in Pattern 1 and Pattern 2 should be adjusted forward-looking, and the adjustment should be reflected in the estimation of expected credit loss.

The evaluation method of LGD of credit assets is as follows: First, the Bank’s historical recovery rate or individual assessment recovery rate calculation method is used to estimate the default loss rate. The default loss rate is equal to 1 minus the recovery rate discounted by the effective interest rate.

Then, after considering the credit business and data type, an appropriate and reasonable historical recovery rate group setting standard is set. Corporate finance is divided into groups such as guaranteed and real estate guaranteed, guaranteed and credit guaranteed, guaranteed and deposit pledge, guaranteed and movable property guaranteed, unsecured and unsecured, and unsecured and secondary collateral. .

Calculation principles for credit asset recovery rate:

(1) Those who assess derogation in combination:

1. For non-major cases in the first, second and third stages, all cases are based on historical loss experience, and the recovery amount data of loss reduction cases that occurred in an appropriate period in the past are used as samples as the basis for calculating the historical recovery rate.

2. At present, the historical recovery rate is calculated based on the sampling principle of the processing completion period in the past 3 to 10 years, and may be adjusted appropriately depending on the actual situation and considering changes in the economic situation. After reviewing the past practical operations and the state of derogation data, set the data sampling period (processing completion period) as the latest 7 years with collateral, and the latest 3 years without collateral.

3. Calculation of each recovery rate The recovery period is based on 10 years from the date of impairment, and the part recovered after 10 years is included in the calculation of the recovery amount in the 10th year.

(2) Individually assessing the impairment: it is the third stage and is a major case, for example, the balance of the enterprise's loan amount to the account reaches NT$100 million, credits granted by government agencies, credits granted by public enterprises, foreign credits, credits granted by financial institutions, guarantees, and letters of credit.

(3) Estimate the default loss rate based on the combined or individual assessed annual recovery amount on a case-by-account basis. The recovery amount in the combined assessment method adopts the historical combined annual recovery rate. The recovery amount in the individual assessment method is calculated according to the actual individual assessment, including the recovery amount that may continue to be paid, the possible recovery amount of collateral disposal, and other possible recovery amounts.

The credit calculation data output module 240 is signally connected to the credit classification data calculation module 230 for outputting the logical classification impairment calculation table to the operator device 340 of the operator.

In order to make people with ordinary knowledge in the field understand this model better, please refer to Fig. 2 and Fig. 3 respectively. Fig. 2 is a main unit of a credit asset evaluation and classification operation system according to an embodiment of the present invention Flow chart of the five classifications; FIG. 3 is a flow chart of the International Financial Reporting Standard No. 9 classification of a loan guarantee and its associated receivables impairment assessment operating system according to an embodiment of the present invention.

First, in FIG. 2 , in step S202 , classify according to the provision for bad debts and the five-category method of the competent authority.

Next, in step S204, when the credit asset classification case data is classified into the first category, proceed to step S205, and then determine whether the credit asset classification case data of the first type belongs to specific assets. If yes, go to step S206, and the amount of allowance for bad debts is 1.5% of the balance of the specific asset; if the credit asset classification case data belongs to the first category of non-specific assets, then go to step S208, and the amount of allowance for bad debts is 1% of the credit balance of the credit assets minus the credit balance for government agencies in my country.

In step S210, when the credit asset classification case data is classified into the second category, then enter step S212, the amount of provision for bad debts=credit asset balance*2%.

In step S214, when the credit asset classification case data is classified into the third category, then enter step S216, the amount of provision for bad debts = credit asset balance * 10%.

In step S218, when the credit asset classification case data is classified into the fourth category, then enter step S220, the amount of allowance for bad debts=credit asset balance*50%.

In step S222, when the credit asset classification case data is classified into the fifth category, then enter step S224, the amount of provision for bad debts = credit asset balance * 100%.

In FIG. 3 , in step S302, the credit classification data collection module 220 classifies according to IFRS No. 9 taxonomy, and then enters step S304, where the credit classification data collection module 220 judges whether the credit asset case is a case of the third stage (Stage 3).

If yes, go to step S308 to further judge whether it meets the major threshold. If so, that is, the credit asset case belongs to the third-stage case and meets the major threshold. Then go to step S309, then evaluate the recoverable amount in each case one by one in an individual assessment method, calculate the loss at default (LGD), and go to step S310, and calculate the expected credit loss (ECL) with the formula ECL=PD * LGD * EAD, where the probability of default is calculated as 100%, that is, ECL=100 % *LGD *EAD.

Continuing with step S308, when the credit asset case does not meet the major threshold, proceed to step S306, and calculate by combination evaluation, wherein LGD is directly calculated with the parameters in the system, and then proceed to step S310, and calculate the expected credit loss (ECL) with the formula ECL=PD * LGD * EAD, wherein the probability of default is calculated as 100%, that is, ECL=100% *LGD * EAD.

計 算預期信用損失(ECL)，即備抵呆帳金額為存續期間違約機率乘以因違約而無法回收的金額。 Continuing with step S304, if the credit asset case is not a third-stage case, proceed to step S312 to further determine whether the credit asset case is a second-stage (Stage 2) case. If yes, proceed to step S314, where the credit asset case is evaluated in the form of combined evaluation. Among them, PD is the cumulative default rate for t years minus the cumulative t-1 year default rate, and LGD is equal to 1 minus the recovery rate discounted by the effective interest rate. Then, go to step S316, with the formula

Calculate the expected credit loss (ECL), that is, the amount of allowance for doubtful debts is the probability of default multiplied by the amount that cannot be recovered due to default.

If not, it will enter step S318, and the credit classification data collection module 220 will classify the credit asset case as a first-stage (Stage 1) case, and then enter step S320, where the credit asset case will be evaluated in a combined evaluation method, where PD is the probability of default in the next 12 months, and enter step S322, where the expected credit loss is calculated by the formula ECL=PD * LGD * EAD, that is, the amount of allowance for bad debts is the probability of default in the next 12 months multiplied by In amounts not recoverable due to default.

Based on the above, through the new type of loan guarantee and its associated receivable impairment assessment system, credit asset cases can be classified in advance, for example, according to the five classifications of competent authorities or the International Financial Reporting Standard No. 9 classification. Moreover, after classification, the loan guarantee and its associated receivable impairment assessment operating system can perform impairment calculations based on classification or assessment results. In this way, through the new type of loan guarantee and its related receivables impairment assessment operation system, the operating time of the contractor can be reduced, the operation efficiency can be improved, and the operation risk can be reduced.

In addition, the new loan guarantee and its affiliated receivables impairment assessment operation system can automatically transmit the impairment calculation table of logical classification and impairment calculation to the accounting system for accounting, which can also save colleagues' work time and improve efficiency.

The present invention has been disclosed above with preferred embodiments, but those skilled in the art should understand that the above embodiments are only used to describe the present invention, and should not be construed as limiting the scope of the present invention. It should be noted that all changes and replacements equivalent to those of the foregoing embodiments should be included in the scope of the present invention. Therefore, the scope of protection of the present invention shall prevail as defined by the scope of the patent applied for.

100: Bank host server

110: Credit assets database

200: Loan Guarantee and its Subordinate Receivables Impairment Assessment Operating System

210: Import credit classification data into module

220:Credit classification data collection module

230: Credit classification data calculation module

240:Credit calculation data output module

300: Credit business system

310, 320, 330, 340: pilot device

400: Accounting Systems

500: Central accounting system

Claims (9)

A loan guarantee and its associated receivable impairment assessment operating system are set in a bank host server with a credit asset database, and the credit asset database is used to store a plurality of credit asset classification case data. The loan guarantee and its associated receivable impairment assessment operating system are used to provide a contractor to obtain a logical classification impairment calculation table through a staff device. , and these modules include: a credit classification data import module, the signal is connected to the credit asset database, and is used to import the credit asset classification case data from the credit asset database according to a scheduling instruction; a credit classification data collection module, the signal is connected to the credit classification data import module, and is used to collect the credit asset classification case data according to a credit asset classification logic, and generate a credit asset case classification table; a credit classification data calculation module, the signal is connected to the credit classification data collection The whole module is used to calculate the credit asset case classification table according to an impairment calculation rule, and generate the logical classification impairment calculation table, wherein the logical classification impairment calculation table includes an allowance for bad debts; and a credit calculation data output module, which is signal-connected to the credit classification data calculation module, and is used to output the logical classification impairment calculation table to the operator's device of the operator. The loan guarantee and its associated receivables impairment assessment operating system as described in claim 1, wherein The credit asset classification case data belonging to a competent authority’s five-classification method includes a loan category, a guarantee category, a letter of credit category, a receivable category or a combination thereof; or the credit asset classification case information belonging to IFRS No. 9 includes the loan category, the guarantee category, the letter of credit category, the receivable category, a financing commitment category, or a combination thereof. As stated in Claim 2, the loan guarantee and its affiliated receivables impairment assessment operating system, wherein the credit asset classification logic is the five-category method of the competent authority, and the credit asset classification logic further includes a provision for bad debts. The loan guarantee and its associated receivables impairment assessment operating system as described in claim 3, wherein the provision for bad debts includes whether there is a collateral, an assessment of a sufficient guarantee amount, an assessment of an unrecoverable amount, a sub-annual recoverable amount, or a combination thereof. Loan guarantee and its associated receivables impairment assessment operating system as described in claim item 4, wherein, when the classified case data of credit assets belong to the first category and belong to a specific asset designated by a competent authority, the amount of bad debt allowance is 1.5% of the balance of credit assets; If the materials belong to the second category, the allowance for bad debts is 2% of the balance of the credit asset; when the case materials of the classified credit assets belong to the third category, the allowance for bad debts is 10% of the balance of the credit asset; When the classified case materials of the credit assets belong to the fourth category, the amount of the provision for bad debts is 50% of the balance of the credit assets; and when the case materials of the classified credit assets belong to the fifth category, the amount of the allowance for bad debts is 100% of the balance of the credit assets. As for the loan guarantee and its affiliated receivables impairment assessment operating system as described in claim item 5, when the data of the credit asset classification cases belong to the first category, and it is a credit case in the mainland area and a real estate loan case at the same time, the amount of provision for bad debts in this type of case is only calculated based on the credit case in the mainland area. The loan guarantee and its related receivables impairment assessment operating system as described in claim 2, wherein the credit asset classification logic is the International Financial Reporting Standard No. 9 classification. The loan guarantee and its associated receivables impairment assessment operating system as described in claim item 7, wherein the first stage of the IFRS No. 9 classification means that the credit risk of the credit asset classification case data has not increased significantly after the original recognition; the second stage of the IFRS No. 9 taxonomy refers to the credit asset classification of the credit asset classification case data after the original recognition. and the special provisions on non-recourse purchase accounts receivable; and the third stage of the International Financial Reporting Standard No. 9 classification means that the credit asset classification case data has been credit-impaired after the original recognition. The judgment criteria of the third stage are whether the loan is extended by agreement, overdue for more than 3 months or transferred to collection or monitoring cases, whether the review level is bad or the warning level is bad, whether the review level is dangerous or the warning level is dangerous, and whether a company has a major incident Changes, special regulations on non-recourse purchase accounts receivable, whether it is a debt elimination negotiation case, and whether the rating has deteriorated. The loan guarantee and its associated receivables impairment assessment operating system as described in claim item 8, wherein, when the credit asset classification case data belongs to the first stage, the allowance for bad debts is the probability of default in the next 12 months multiplied by an unrecoverable amount due to default; when the credit asset classification case data belongs to the second stage, the allowance for bad debts is a duration of default probability multiplied by the default due to the amount that cannot be recovered; and when the credit asset classification case data belongs to the third stage, the allowance for bad debts The amount is the 100% probability of default during the duration multiplied by the amount that cannot be recovered due to default.