TWM581252U - Rating and ranking simulation system for credit evaluation before loan - Google Patents

Abstract

The present invention provides a simulation system for credit rating evaluation, including a credit rating database, a data synchronization module, a data import module, a data construction module, an external serial module, a data exploration module, Data prediction module, data analysis module and data display module.

Description

Credit rating evaluation

A simulation system for credit rating evaluation, especially a system including credit evaluation, data synchronization, data import, data construction, and PERT analysis.

Borrowing refers to a kind of financial financing behavior in which a creditor or a lender transfers funds to a debtor or a borrower. With the development of the social economy, some enterprises or individuals use bank loans to solve the existing insufficient funds in order to make the operating projects operate normally. The problem has thus contributed to the development of the bank loan business.

At present, the bank's loan review system is based on the analysis of the company's financial statements, or through the company's visits and communication, or through the evaluation of credit officers or credit officers, to decide whether it is possible to issue loans to the company, not only the cost. High, long time, and unable to obtain accurate and comprehensive information about the company in real time, there is a great risk, and it is not conducive to large-scale, fast, low-cost loan business. Especially in the evaluation of small and medium-sized enterprises, micro-enterprises and risk management, the most important business operations and information are lacking.

In view of this, the creation provides a credit rating system such as credit rating evaluation, which includes: a credit rating database; a data synchronization module for concatenating a credit correlation The system and the credit rating database are based on a historical credit granting information typed into the credit rating database; a data importing module for using a credit granting data to evaluate the credit rating system Typing, and importing the data entered by the credit center host or a credit authorization system into the analog system of the rating of the credit grant evaluation; a data construction module for setting the credit grant data to the credit The rating of the loan evaluation is in the simulation system; an external serial module for introducing a third party system fair evaluation data; a data exploration module serially connecting the data import module and the data construction module And the external serial connection module, and the credit rating database is connected in series to extract the historical credit grant data and the credit grant data; a data prediction module is connected in series with the data exploration module, Through the data exploration module to predict the overdue probability of the aforementioned loan; and a data analysis module connected in series with the data prediction module for calculating the expectation through the PERT analysis sub-module And a data display module, the data connection module and the data prediction module, wherein the data analysis module operation is displayed on the analog system of the rating of the credit grant evaluation to provide Relevant personnel review.

Preferably, the foregoing rating system is a credit evaluation through a credit 5p principle, which includes a borrower, a fund use, a repayment source, a credit guarantee, and a credit prospect.

Preferably, the above-mentioned scoring and so on simulation system performs the calculation of the majority or the average number according to the credit amount of each item of the above-mentioned credit 5p principle, and obtains the value of the most likely loan release number; The data of a certain proportion is taken as the most optimistic loan, and the data of the lower proportion of the loan is taken as the most pessimistic loan.

Preferably, the foregoing scoring simulation system calculates the most likely overdue period, the most optimistic overdue period, the most pessimistic overdue period according to each item of the aforementioned credit 5p principle, and individually inputs the aforementioned PERT analysis sub-module. Through the PERT analysis sub-module, the number of overdue periods and the probability are calculated.

Preferably, in the foregoing simulation system, the credit granting data includes a customer age, gender, service unit, work experience, and financial status.

Preferably, in the foregoing scoring simulation system, the foregoing third-party system fair evaluation data source includes a reconciliation center or a related external system.

1000‧‧ ‧ Credit rating evaluation, etc.

101‧‧‧credit rating database

102‧‧‧Data Synchronization Module

103‧‧‧credit related systems

104‧‧‧ Data Import Module

105‧‧‧Information Construction Module

106‧‧‧External serial module

107‧‧‧ Third party system

108‧‧‧Data Exploration Module

109‧‧‧Data Prediction Module

110‧‧‧Data Analysis Module

111‧‧‧Data Display Module

The first picture is a flow chart of the simulation system such as the rating of the credit evaluation of the case.

In order to make the technical content, creative purpose and the effect achieved by this creation more complete and clear, please elaborate below, and please refer to the drawings and drawings.

Referring to the first figure, in order to achieve the above-mentioned creative purpose, the present invention provides a credit rating evaluation simulation system 1000, which includes a credit rating database 101; a data synchronization module 102, which is connected in series The credit rating database 101 and the credit related system 103 (the ELOAN system or the credit center accounting system) are connected in series with a credit related system and the credit rating database, and the credit rating information is entered according to a historical credit granting data. The library will use the credit information module to enter the credit rating and other credits of each loan account. The data importing module 104: the data of the credit granting (including the crediting 5p data) can be input into the simulation system of the credit rating evaluation of the author in a credit center host or a credit system, and then Through the data import module, the relationship between the credit center host/recruitment system and the like The information entered into the system is used to import the credit rating of the credit evaluation system; a data construction module 105: the credit information (including credit information 5p), and the credit grant evaluation through this creation The data building module of the simulation system, the credit card data will be built into the credit evaluation system, and the evaluation system will be credited; the external serial module 106, the rating of the credit grant evaluation, etc. The third simulation system can be used to extract the historical credit grant data and the credit grant data through the external serial connection module to the third party system 107 such as the association center or the related external system. It is connected to the data import module, the data building module and the credit card data (such as credit 5p) typed by the external serial module, and the credit rating evaluation by the author The data exploration module of the first simulation system is connected with the credit rating database to capture the credit amount and the number of credits of the relevant industries and related conditions, and remove the extreme values; a data prediction module 109 It is mainly used to predict the probability of the overdue days of the loan. The data exploration module is connected in series with the data mining module of the credit evaluation system. Data of the overdue period of the previous relevant industries and related conditions, and the extreme values are removed; a data analysis module 110: the data prediction module is connected in series, and the sub-module is calculated and operated by the project evaluation method (PERT) The number of the release, the number of overdue periods or the expected value, and the relevant conditions are all filtered, and the data analysis module can be used for analysis. According to all the items of the credit information, etc., the historical credits are taken according to each item, and the data values are transmitted. The sub-module, taking out the credits of each item and calculating the benchmarks such as the majority or the average, can get the value of the most likely loan-to-deposit ratio; and the loan is put into a higher proportion of the specific ratio, which is taken as the most optimistic Loan is put into a number; the other is to put the loan into a lower specific proportion of the data, which is taken as the most pessimistic loan. Each project calculates the most likely loan amount, the most optimistic loan, and the most pessimistic loan. The PERT analysis sub-module is used to calculate the expected value and the loan amount through the PERT analysis sub-module. And through the expected value sub-module, the company's established expectation key Into the desired value sub-module, it is expected that the value sub-module will calculate the most suitable loan amount, and the overall expected value and loan amount. The higher authority or the credit reviewer may also set the weight condition for each of the credit 5p items; a data display module 111: display the result of the data analysis module operation through the data display module, and display the score of the credit grant evaluation of the present creation. The simulation system is provided for the creditors and reviewers to conduct audits, and relevant statistical charts are also provided.

The credit 5p principle described in this creation contains the following project borrower factors: such as business history (creation time, business life cycle, business project, etc.), business capability (profit), honesty and credit, relationship business, corporate organization History, enterprise equipment scale overview, business overview, financial overview, industry profile, repayment ability analysis, operation and capital planning.

Use of funds: purchase of assets (current assets, fixed assets, etc.), repayment of existing debts, replacement of equity.

Repayments: operating income, retained earnings, or external funds.

Protection factor: The good financial structure of the borrower, the terms of the loan contract, the assets of the borrower, the guarantor, the endorsement guarantee, and the guarantee of the third party's assets.

Prospective factor (perspective): Bank interest, handling fees, guarantee fees and other income; liquidity risk, financial risk, exchange rate risk and other risk exposure.

Example 1: Using the PERT analysis to evaluate the loan households to evaluate the number of credits

PERT formula description

1. Weighted average => (1 * optimistic + 4 * most likely +1 * pessimistic) / 6

2. Standard deviation => (pessimistic - optimistic) / 6

Table 1 is an illustrative description, in which the weighted average to be brought in: 305.5 is There is a total weighted average phase of the project. Please note that when evaluating the loan amount, the sum is required here; the standard deviation to be brought in: 6.8 is the sum of the squares of each item, and then the square root is opened.

The expected value spacing to be brought in: 1.8 is 6.8/4 (actually it should be 6.8/4 for more than 1.7, taking unconditional carry).

In Table 2, the first number of the expected value table is 293, which is the weighted average of the above table - twice the standard deviation of the above table, the second number of the expected value table is 293 + 1.8 = 294.8 and so on, the value of the expected value table can be As a desired score for company evaluation.

The probability is obtained from the expected value (X) by the look-up table method, which needs to be standardized to its standard score (Z), and then corresponds to the normal distribution table; if X is the normal distribution with the mean μ standard deviation σ, then its standard score; Z=( X-μ)/σ has a standard normal distribution. For example: the standard normal distribution probability value (Z value), Z = (X-μ) / σ has a standard normal distribution; X = 293; μ = 305.5 (in this case, the value after the original operation must be substituted, can not arbitrarily take an integer) ; σ = 6.85 (in this case, the value after the original operation must be substituted, and the integer cannot be arbitrarily selected); then Z = (293-305.5) / 6.85 = - 1.825. Therefore, the 1.825 corresponds to between -1.82 and -1.83 through the look-up table, and the probability is between 0.0336 and 0.0344. The exact value is obtained by interpolation.

Example 2: Using PERT analysis to assess the probability of overdue periods

PERT formula description

1. Weighted average => (1 * optimistic + 4 * most likely +1 * pessimistic) / 6

2. Standard deviation => (pessimistic - optimistic) / 6

Table 3 is an illustrative description, in which the weighted average to be brought in: 6.8 is the weighted average of all items. Please note that when evaluating the number of overdue periods, the average (/number of items) is required here; the standard deviation to be brought in: 3.3 is per The sum of the squares of the items, and then the square root; the expected value spacing to be brought in: 0.9 is 3.3/4 (actually 0.825, taking 0.9).

In Table 4, the first number of the overdue period table is 0, which is the weight of the above table. Average - twice the standard deviation of the above table; the second number of the overdue period table is 0 + 0.9 = 0.9 and so on.

The probability is obtained from the expected value (X) by the look-up table method, which needs to be standardized to its standard score (Z), and then corresponds to the normal distribution table; if X is the normal distribution with the mean μ standard deviation σ, then its standard score; Z=( X-μ)/σ has a standard normal distribution.

Claims (8)

A credit simulation evaluation system, including: a credit rating database; a data synchronization module, which is connected in series with a credit related system and the credit rating database, based on a historical credit granting data The credit rating database is input; a data importing module is used to input a credit granting data into the simulation system by the rating of the credit grant evaluation, and input a credit center host or a crediting system. The data is imported into the simulation system of the rating of the credit grant assessment; a data construction module is used to construct the credit grant data in the simulation system of the rating of the credit grant evaluation; an external serial connection a module for importing a third party system fair evaluation data; a data exploration module serially connecting the data import module, the data construction module and the external serial connection module, and serially connecting the credit rating And a database for extracting the historical credit granting information and the credit granting data; a data forecasting module serially connecting the data mining module to the data mining module to predict the overdue of the loan Rate; a data analysis module serially connected to the data prediction module for calculating a loan release number, a period of overdue or an expected value through a PERT analysis sub-module; and a data display The module is configured to display the data analysis module operation on the simulation system of the rating of the credit grant evaluation to provide relevant personnel for review. For example, the simulation system described in the first paragraph of the patent application scope is a credit evaluation through a credit 5p principle, which includes the borrower, the use of funds, the source of repayment, the creditor's rights guarantee or the credit prospect. For example, in the simulation system described in the second paragraph of the patent application scope, the calculation result is based on the number of credits of each item of the credit 5p principle, and the value of the most likely loan amount is obtained. In addition, the loan will be put into a higher proportion of the specific ratio, which is taken as the most optimistic loan release; the other will be credited to a lower proportion of the specific ratio, which is taken as the most pessimistic loan. For example, the simulation system according to the third paragraph of the patent application scope is the most optimistic loan release number, the most likely loan release number, the most pessimistic loan release number, and the weighted average calculated according to each item of the credit 5p principle. , standard deviation or expected value, and individually type the PERT analysis sub-module, calculate the overdue period and probability through the PERT analysis sub-module. A scoring simulation system as described in claim 4, wherein the expected value is provided as a desired score. The simulation system, such as the rating system described in item 3 of the patent application, is the most likely number of overdue periods, the most optimistic overdue period, the most pessimistic overdue period, and the weighted average calculated according to each item of the credit 5p principle. , standard deviation or expected value, and individually type the aforementioned PERT analysis sub-module, calculate the overdue period and probability through the PERT analysis sub-module. For example, the first simulation system of the rating system described in claim 1 wherein the historical credit granting information and the credit granting information include a customer's age, gender, service unit, work experience or financial status. For example, the rating system described in claim 1 of the patent scope, wherein the third party system fair evaluation data source includes a joint center.