CN111476660A - Intelligent wind control system and method based on data analysis - Google Patents
Intelligent wind control system and method based on data analysis Download PDFInfo
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Abstract
The invention belongs to the technical field of Internet computers, and particularly relates to an intelligent wind control system and method based on data analysis, wherein the system comprises: the system comprises an ordering management unit, a borrowing management unit and a processing unit, wherein the ordering management unit is used for acquiring multidimensional data related to borrowing enterprises, cleaning and summarizing the data and acquiring enterprise information; the pre-credit evaluation unit is used for analyzing the asset operation repayment capacity of the enterprise according to the enterprise information, evaluating the credit line of the enterprise and generating a pre-credit evaluation report of the enterprise; the system comprises an in-credit monitoring unit, a risk classification unit and a risk rating unit, wherein the in-credit monitoring unit is used for monitoring the classified risk of an enterprise in the financing process, evaluating the financing risk and grading the risk; and the post-loan early warning unit is used for monitoring and early warning the enterprise after loan through the wind control model after the enterprise financing. According to the invention, through the collected multidimensional data of each aspect of the borrowing enterprise, the borrowing enterprise is subjected to pre-loan assessment, mid-loan monitoring and post-loan early warning, the whole credit process is monitored and managed, the credit efficiency is improved, and the credit risk of a fund lender is reduced.
Description
Technical Field
The invention belongs to the technical field of internet computers, and particularly relates to an intelligent wind control system and method based on data analysis.
Background
Loans are a form of credit activity in which a bank or other financial institution borrows monetary funds at a rate and must return. In the loan business, the loan safety is the primary problem in the financial loan process, and in order to control the loan risk, a wind control person or an auditor is required to check the credit granting process of each loan.
In the prior art, loan credit granting information is mainly audited in a manual mode to control risks, and auditing processing in the manual mode is low in efficiency and easy to make mistakes, and the whole enterprise loan process before, during and after loan cannot be monitored and managed, so that the loan risk is high.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent wind control system and method based on data analysis, which are used for carrying out pre-loan assessment, mid-loan monitoring and post-loan early warning on a borrowing enterprise through collected multidimensional data of all aspects of the borrowing enterprise, carrying out monitoring management on the whole credit process, improving the efficiency of the credit process and reducing the credit risk of a fund lender.
In a first aspect, the present invention provides an intelligent wind control system based on data analysis, including:
the system comprises an ordering management unit, a borrowing management unit and a processing unit, wherein the ordering management unit is used for acquiring multidimensional data related to borrowing enterprises, cleaning and summarizing the data and acquiring enterprise information;
the pre-credit evaluation unit is used for analyzing the asset operation repayment capacity of the enterprise according to the enterprise information, evaluating the credit line of the enterprise and generating a pre-credit evaluation report of the enterprise;
the system comprises an in-credit monitoring unit, a risk classification unit and a risk rating unit, wherein the in-credit monitoring unit is used for monitoring the classified risk of an enterprise in the financing process, evaluating the financing risk and grading the risk;
and the post-loan early warning unit is used for monitoring and early warning the enterprise after loan through the wind control model after the enterprise financing.
Preferably, the enterprise information includes enterprise subject information, asset liability information, online business information, financial subject information, and upstream and downstream merchant information.
Preferably, the tuning management unit includes:
the main body information module is used for constructing a basic data portrait of the enterprise according to the enterprise basic data, enterprise shareholder data, judicial data, industrial and commercial data and paper pledge data so as to obtain enterprise main body information of the enterprise;
the system comprises a capital and debt information module, a data processing module and a data processing module, wherein the capital and debt information module is used for constructing an enterprise capital and debt portrait according to personal asset data, personal liability data, enterprise asset data and enterprise liability data so as to obtain enterprise capital and debt information;
the business information module is used for acquiring order data, invoice data and flow data from different online data sources, and summarizing and sorting the order data, the invoice data and the flow data to obtain online business information of an enterprise;
the financial information module is used for performing financial processing on the imported enterprise asset form, the imported enterprise liability form and the imported enterprise profit form to obtain financial subject information of the enterprise;
and the upstream and downstream information module is used for summarizing and sorting the related data of upstream and downstream merchants to obtain the information of the upstream and downstream merchants.
Preferably, the pre-credit evaluation unit includes:
a rule factor module for configuring and storing rule factors;
the strategy module is used for configuring a wind control strategy based on the rule factors and forming various evaluation models;
the financial evaluation module is used for performing financial analysis on the financial subject information through the evaluation model to obtain a financial evaluation result;
the business evaluation module is used for carrying out business analysis on the online business information and the upstream and downstream merchant information through an evaluation model, carrying out cross validation on the three-in-one data of the enterprise and calculating the credit granting amount;
the integral evaluation module is used for comprehensively analyzing and evaluating the main body information of the enterprise, the liability information of the assets, the online business information, the financial subject information and the information of upstream and downstream merchants through an evaluation model to obtain an operation evaluation result of the enterprise;
and the report generation module is used for summarizing various evaluation results and forming an integral pre-credit evaluation report for the risks of the enterprises.
Preferably, the service evaluation module is specifically configured to:
analyzing the order data, the invoice data and the flow data to obtain a purchase amount and an upstream risk coefficient related to an upstream merchant and a sales amount and a downstream risk coefficient related to a downstream merchant;
analyzing the information of upstream and downstream merchants to obtain an upstream evaluation coefficient and a downstream evaluation coefficient which are related to the upstream merchants;
calculating the credit line of the enterprise through an amount calculation formula, wherein the amount calculation formula is as follows: the credit limit is the purchase amount and the upstream risk coefficient is 10% + the sale amount and the downstream risk coefficient is 10%.
Preferably, the analyzing the order data, the invoice data and the flow data to obtain a purchase amount and an upstream risk coefficient related to an upstream merchant, and a sales amount and a downstream risk coefficient related to a downstream merchant specifically includes:
order data, invoice data and running data within a set time range are extracted from a database, and order amount, invoice amount and running amount related to an upstream merchant and order amount, invoice amount and running amount related to a downstream merchant are obtained from the three types of data;
taking the minimum effective amount from the order amount, the invoice amount and the running amount related to the upstream merchant as the purchase amount;
judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the upstream merchant, if so, the upstream risk coefficient is 1, and if not, the upstream risk coefficient is N smaller than 1;
taking the minimum effective amount from the order amount, the invoice amount and the running amount related to the downstream merchant as the sales amount;
and judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the downstream merchant, if so, the downstream risk coefficient is 1, and if not, the downstream risk coefficient is M smaller than 1.
Preferably, the analyzing the information of the upstream and downstream merchants to obtain an upstream evaluation coefficient and a downstream evaluation coefficient related to the upstream merchant specifically includes:
respectively grading the upstream merchant and the downstream merchant to obtain the grade of the upstream merchant and the grade of the downstream merchant;
and respectively comparing the upstream merchant grade and the downstream merchant grade with the grade coefficient comparison table to obtain an upstream merchant evaluation coefficient and a downstream merchant evaluation coefficient.
Preferably, the post-credit warning unit is specifically configured to:
analyzing the enterprise according to the fuzzy query to obtain the number of the tolerant turnover days of the enterprise;
calculating daily required transaction amount according to the credit line;
comparing the daily required transaction limit with the daily actual transaction limit to obtain the number of abnormal transaction days;
and if the transaction abnormal days are larger than the tolerant turnover days, sending out early warning information of high-risk risks of the enterprises.
Preferably, the enterprise is analyzed according to the fuzzy query to obtain the number of turnover-tolerant days of the enterprise, which specifically comprises:
analyzing according to online business information of an enterprise to obtain an upstream market end ratio index and a downstream client end ratio index;
calculating the upstream market end proportion index and the downstream client end proportion index to obtain a comprehensive index;
and inquiring the early warning grading table, and obtaining the early warning level and the turnover tolerant days corresponding to the enterprise according to the comprehensive index.
In a second aspect, the present invention provides an intelligent wind control method based on data analysis, which is suitable for the intelligent wind control system based on data analysis in the first aspect, and includes the following steps:
acquiring multidimensional data related to borrowing enterprises, cleaning and summarizing the data to obtain enterprise information;
analyzing the asset operation repayment capacity of the enterprise according to the enterprise information, evaluating the credit line of the enterprise, and generating a pre-credit evaluation report of the enterprise;
monitoring the classified risk of the enterprise in the financing process, evaluating the financing risk and grading the risk;
after financing of the enterprise, monitoring and early warning are carried out on the enterprise after loan through the wind control model.
According to the technical scheme, the borrowing enterprise is subjected to pre-loan assessment, mid-loan monitoring and post-loan early warning through the collected multi-dimensional data of all aspects of the borrowing enterprise, the whole credit process is monitored and managed, the efficiency of the credit process is improved, and the credit risk of a fund lender is reduced.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a structural diagram of an intelligent wind control system based on data analysis in the embodiment;
fig. 2 is a flowchart of the intelligent wind control method based on data analysis in this embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The first embodiment is as follows:
the embodiment provides an intelligent wind control system based on data analysis, which comprises an exhaustive management unit, a pre-loan assessment unit, an in-loan monitoring unit, a post-loan early warning unit and the like, as shown in fig. 1.
The system comprises an ordering management unit, a borrowing management unit and a processing unit, wherein the ordering management unit is used for acquiring multidimensional data related to borrowing enterprises, cleaning and summarizing the data and acquiring enterprise information;
the pre-credit evaluation unit is used for analyzing the asset operation repayment capacity of the enterprise according to the enterprise information, evaluating the credit line of the enterprise and generating a pre-credit evaluation report of the enterprise;
the system comprises an in-credit monitoring unit, a risk classification unit and a risk rating unit, wherein the in-credit monitoring unit is used for monitoring the classified risk of an enterprise in the financing process, evaluating the financing risk and grading the risk;
and the post-loan early warning unit is used for monitoring and early warning the enterprise after loan through the wind control model after the enterprise financing.
According to the technical scheme, risk quantification services related to borrowing enterprises are provided for core enterprises, fund parties and industrial internet platforms in a data wind control mode, the problem of whole-process risk management before, during and after loan is solved, the core enterprises, the fund parties and the industrial internet platforms can borrow and loan to the borrowing enterprises with confidence, and the risk that the loan cannot be recovered is reduced.
The scheduling management unit of this embodiment includes:
the main body information module is used for constructing a basic data portrait of the enterprise according to the enterprise basic data, enterprise shareholder data, judicial data, industrial and commercial data and paper pledge data so as to obtain enterprise main body information of the enterprise;
the system comprises a capital and debt information module, a data processing module and a data processing module, wherein the capital and debt information module is used for constructing an enterprise capital and debt portrait according to personal asset data, personal liability data, enterprise asset data and enterprise liability data so as to obtain enterprise capital and debt information;
the business information module is used for acquiring order data, invoice data and flow data from different online data sources, and summarizing and sorting the order data, the invoice data and the flow data to obtain online business information of an enterprise;
the financial information module is used for performing financial processing on the imported enterprise asset form, the imported enterprise liability form and the imported enterprise profit form to obtain financial subject information of the enterprise;
and the upstream and downstream information module is used for summarizing and sorting the related data of upstream and downstream merchants to obtain the information of the upstream and downstream merchants.
The data of the borrowing enterprise collected in the embodiment is multidimensional, and after the data of all aspects are obtained from different channels and different docking platforms, the data are classified, so that enterprise main body information, asset and debt information, online business information, financial subject information and upstream and downstream merchant information are obtained, and the five types of enterprise information are obtained. After the five types of enterprise information are obtained, performing pre-credit evaluation according to the enterprise information, wherein the pre-credit evaluation unit comprises:
a rule factor module for configuring and storing rule factors;
the strategy module is used for configuring a wind control strategy based on the rule factors and forming various evaluation models;
the financial evaluation module is used for performing financial analysis on the financial subject information through the evaluation model to obtain a financial evaluation result;
the business evaluation module is used for carrying out business analysis on the online business information and the upstream and downstream merchant information through an evaluation model, carrying out cross validation on the three-in-one data of the enterprise and calculating the credit granting amount;
the integral evaluation module is used for comprehensively analyzing and evaluating the main body information of the enterprise, the liability information of the assets, the online business information, the financial subject information and the information of upstream and downstream merchants through an evaluation model to obtain an operation evaluation result of the enterprise;
and the report generation module is used for summarizing various evaluation results and forming an integral pre-credit evaluation report for the risks of the enterprises.
In this embodiment, the rule factors are basic variables of the entire system, the wind control strategy is a connection framework of the entire system, and is an analysis basis for implementing data analysis, data summarization and data association. Such as an evaluation model about finance, an evaluation model about business, and an evaluation model about overall evaluation. The financial assessment of the present embodiment is used to analyze profitability, such as sales profit margin, net profit margin, etc., of an enterprise, operational capability, such as total asset turnover days, stock year turnover days, accounts receivable turnover days, operational fund amount, etc. The overall evaluation of the embodiment is used for comprehensive analysis and evaluation of all aspects of an enterprise, such as tax analysis, upstream and downstream analysis of the enterprise, platform financing analysis and the like. The business evaluation of the embodiment is used for evaluating borrowing enterprises and granting credit based on the evaluation. The pre-credit assessment report of the present embodiment includes profitability, operational capability, tax analysis, enterprise upstream and downstream analysis, and the like.
The service evaluation module is specifically configured to:
analyzing the order data, the invoice data and the flow data to obtain a purchase amount and an upstream risk coefficient related to an upstream merchant and a sales amount and a downstream risk coefficient related to a downstream merchant;
analyzing the information of upstream and downstream merchants to obtain an upstream evaluation coefficient and a downstream evaluation coefficient which are related to the upstream merchants;
calculating the credit line of the enterprise through an amount calculation formula, wherein the amount calculation formula is as follows: the credit limit is the purchase amount and the upstream risk coefficient is 10% + the sale amount and the downstream risk coefficient is 10%.
In this embodiment, the order data, the invoice data, and the running data within the set time range are extracted from the database, and the order amount, the invoice amount, and the running amount related to the upstream merchant, and the order amount, the invoice amount, and the running amount related to the downstream merchant are obtained from the three types of data. For example, the order data, invoice data and order data of borrowing enterprises 12.01-12.07 in seven days are extracted from the database, and the order amount 253200, the invoice amount 262000 and the running amount 255300 related to upstream merchants are obtained from the data, and the order amount 312000, the invoice amount 315000 and the running amount 0 related to downstream merchants are obtained from the data.
The minimum effective amount is taken as the purchase amount from the order amount, invoice amount and running amount associated with the upstream merchant. The effective amount is an amount greater than zero, and for the upstream merchant, the minimum effective amount 253200 is selected from 253200, 262000 and 255300, so that the purchase amount of the upstream merchant is 253200.
And judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the upstream merchant, if so, the upstream risk coefficient is 1, and if not, the upstream risk coefficient is N smaller than 1. In this embodiment, the three types of money amounts of the upstream merchant are all greater than zero and are all valid, which indicates that the three types of data are all complete, so the risk is low, the risk coefficient is high, and the upstream risk coefficient is 1 (the lower the risk coefficient is, the lower the credit amount calculated later is).
The minimum effective amount is taken as the sales amount from the order amount, invoice amount, and running amount associated with the downstream merchant. For downstream merchants, the minimum valid amount 312000(0 being the invalid amount) is taken from 312000, 315000, and 0, thus resulting in a downstream merchant sales amount of 312000.
And judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the downstream merchant, if so, the downstream risk coefficient is 1, and if not, the downstream risk coefficient is M smaller than 1. In this embodiment, the running amount of the three types of amounts of the downstream merchant is 0, which indicates that there is no running amount, and the three types of amounts are incomplete, so that the risk is higher, the risk coefficient is lower, and the risk coefficient of the downstream is M (in this embodiment, both N and M are natural numbers less than 1, such as 0.6, 0.7, 0.8, and the like).
In this embodiment, the upstream merchant and the downstream merchant are respectively rated to obtain a level of the upstream merchant and a level of the downstream merchant; and respectively comparing the upstream merchant grade and the downstream merchant grade with the grade coefficient comparison table to obtain an upstream merchant evaluation coefficient and a downstream merchant evaluation coefficient. In this embodiment, the upstream merchants include four levels of AAA, AA, a, and BBB, and the downstream merchants include four levels of a, BBB, BB, and B, for example, upstream central enterprises, national enterprises, and listed companies are classified into AAA levels, upstream private enterprises are classified into AA levels, upstream agents are classified into a levels, upstream distributors are classified into B levels, downstream central enterprises, national enterprises, and listed companies are classified into a levels, downstream private enterprises are classified into BBB levels, downstream secondary terminal enterprises are classified into BB levels, and downstream prefecture and county entrance cities are classified into B levels. In the ranking coefficient comparison table, the upstream AAA, AA, a, and BBB respectively correspond to 35%, 34%, 25%, and 18% of the evaluation coefficients, and the downstream AAA, BBB, BB, and B respectively correspond to 19%, 21%, 24%, and 29% of the evaluation coefficients. Because the enterprise nature and the enterprise scale of the upstream and downstream merchants of the borrowing enterprise are different, the repayment capacity is different, and therefore the evaluation coefficients are different.
After the related data are calculated, the credit line of the borrowing enterprise is calculated through an amount calculation formula, wherein the credit line is the purchase amount and the upstream risk coefficient is 10% and the sale amount and the downstream risk coefficient is 10% respectively. Through the formula, the credit line is related to the purchase amount, the sales amount, the risk coefficients of upstream and downstream merchants and the evaluation coefficients of the upstream and downstream merchants of the borrowing enterprise. If the purchase amount and the sales amount of the borrowing enterprise are higher, the business capability of the enterprise is better, and therefore the credit amount is high. If the three-stream data (orders, invoices and running water) of the borrowing enterprise are complete and not lack of items, the data are normal, the risk is low, the risk coefficient is high, and the credit line is high; if the three-stream data are incomplete, the data are possibly abnormal, the risk coefficient is low, and the credit line is low. And if the refund ability of the upstream and downstream merchants of the borrowing enterprise is better, the evaluation coefficient is higher. According to the embodiment, the borrowing enterprise is analyzed through the upstream and downstream merchants and the three-stream data, so that the credit line is determined, and the subsequent loan risk is reduced.
In this embodiment, the core enterprise, the fund party, or the industry internet platform borrows the borrowing enterprise according to the credit line calculated by this embodiment. In the financing process of the enterprise, the sales volume and the merchant group of the enterprise are constantly changing, so by the technical scheme of the embodiment, risk monitoring in loan of the borrowing enterprise is also carried out. After the core enterprise or the fund side or the industry internet platform borrows money for the borrowing enterprise, the technical scheme of this embodiment still carries out the post-credit monitoring early warning to the borrowing enterprise, wherein, post-credit early warning unit specifically is used for:
analyzing the enterprise according to the fuzzy query to obtain the number of the tolerant turnover days of the enterprise;
calculating daily required transaction amount according to the credit line;
comparing the daily required transaction limit with the daily actual transaction limit to obtain the number of abnormal transaction days;
and if the transaction abnormal days are larger than the tolerant turnover days, sending out early warning information of high-risk risks of the enterprises.
The enterprise is analyzed according to the fuzzy query, and the number of tolerant turnover days of the enterprise is obtained, specifically:
analyzing according to online business information of an enterprise to obtain an upstream market end ratio index and a downstream client end ratio index;
calculating according to the upstream market end proportion index and the downstream client end proportion index to obtain a comprehensive index;
and inquiring the early warning grading table, and obtaining the early warning level and the turnover tolerant days corresponding to the enterprise according to the comprehensive index.
In this embodiment, for example, if the transaction amount of the upstream market is 430200 and the transaction amount of the downstream client is 572300, the upstream market percentage index is about 43% and the downstream client percentage index is 57% of all the transaction amounts. Then, the two proportion indexes are subjected to a human-representative weighting algorithm, and a comprehensive index is calculated to be 0.56. The early warning level in the early warning grading table of the embodiment is divided into six early warning levels, and each early warning level has a corresponding comprehensive index range and a tolerance turnover number of days. The early warning grade corresponding to the comprehensive index of 0.56 is the third grade, and the number of the tolerant turnover days of the third grade is seven days.
For example, the credit line is 200 ten thousand, the required transaction line is 200 × 3 to 600 ten thousand per week, and the required transaction line is 600/7 to 85.7 ten thousand per day. Comparing the daily actual transaction amount with the daily required transaction amount to obtain that the actual transaction amount of continuous eight days is less than the daily required transaction amount, so that the number of transaction abnormal days is eight days, and the eight days are more than seven days of the tolerant turnover days, which indicates that the borrowing enterprise possibly has high risk, and sends out early warning to the fund lender borrowing the enterprise so that the fund lender can take precautionary measures in time.
In summary, in this embodiment, the borrowing enterprise is assessed and evaluated in a pre-loan measuring manner through the collected multidimensional data of all aspects of the borrowing enterprise, the fund lender borrows the borrowing enterprise according to the assessed and evaluated credit line, the borrowing enterprise is monitored in real time in the enterprise financing process, the risk level is evaluated, and the borrowing enterprise is monitored and early-warned after loan after the enterprise financing, so that the whole credit process is monitored and managed, the fund lender can master the operating state of the borrowing enterprise in real time, the credit risk of the fund lender is reduced, and early warning can be timely performed when the risk appears, so that the fund lender can timely take effective measures.
Example two:
the embodiment provides an intelligent wind control method based on data analysis, which is suitable for the intelligent wind control system based on data analysis in the first embodiment, and as shown in fig. 2, the method includes the following steps:
s1, acquiring multi-dimensional data related to the borrowing enterprise, and cleaning and summarizing the data to obtain enterprise information;
s2, analyzing the asset operation repayment capacity of the enterprise according to the enterprise information, evaluating the credit line of the enterprise, and generating a pre-credit evaluation report of the enterprise;
s3, monitoring the classified risk of the enterprise in the financing process, evaluating the financing risk and grading the risk;
and S4, after financing of the enterprise, monitoring and early warning the enterprise after loan through the wind control model.
According to the technical scheme, risk quantification services related to borrowing enterprises are provided for core enterprises, fund parties and industrial internet platforms in a data wind control mode, the problem of whole-process risk management before, during and after loan is solved, the core enterprises, the fund parties and the industrial internet platforms can borrow and loan to the borrowing enterprises with confidence, and the risk that the loan cannot be recovered is reduced.
Step S1 of the schedule management of the present embodiment includes the following:
constructing a basic data portrait of the enterprise according to the enterprise basic data, the enterprise shareholder data, the judicial data, the industrial and commercial data and the paper mortgage data, thereby obtaining enterprise main body information of the enterprise;
constructing an asset and liability portrait of the enterprise according to the personal asset data, the personal liability data, the enterprise asset data and the enterprise liability data, so as to obtain asset and liability information of the enterprise;
acquiring order data, invoice data and pipelining data from different online data sources, and summarizing and sorting according to the order data, the invoice data and the pipelining data to obtain online business information of an enterprise;
performing financial processing on the imported enterprise asset table, the imported enterprise liability table and the imported enterprise profit table to obtain financial subject information of the enterprise;
and collecting and sorting the related data of the upstream and downstream merchants to obtain the information of the upstream and downstream merchants.
The data of the borrowing enterprise collected in the embodiment is multidimensional, and after the data of all aspects are obtained from different channels and different docking platforms, the data are classified, so that enterprise main body information, asset and debt information, online business information, financial subject information and upstream and downstream merchant information are obtained, and the five types of enterprise information are obtained. After the five types of enterprise information are obtained, pre-credit evaluation is performed according to the enterprise information, wherein the step S2 of pre-credit evaluation in this embodiment includes the following steps:
configuring and storing a rule factor;
configuring a wind control strategy based on the rule factors, and forming various evaluation models;
performing financial analysis on the financial subject information through the evaluation model to obtain a financial evaluation result;
performing service analysis on the online service information and the upstream and downstream merchant information through an evaluation model, performing cross validation on the three-in-one data of the enterprise, and calculating a credit line;
comprehensively analyzing and evaluating the main body information of the enterprise, the liability information, the online business information, the financial subject information and the upstream and downstream merchant information through an evaluation model to obtain an operation evaluation result of the enterprise;
and summarizing the various evaluation results, and forming an integral pre-credit evaluation report for the risks of the enterprises.
In this embodiment, the rule factors are basic variables of the entire system, the wind control strategy is a connection framework of the entire system, and is an analysis basis for implementing data analysis, data summarization and data association. Such as an evaluation model about finance, an evaluation model about business, and an evaluation model about overall evaluation. The financial assessment of the present embodiment is used to analyze profitability, such as sales profit margin, net profit margin, etc., of an enterprise, operational capability, such as total asset turnover days, stock year turnover days, accounts receivable turnover days, operational fund amount, etc. The overall evaluation of the embodiment is used for comprehensive analysis and evaluation of all aspects of an enterprise, such as tax analysis, upstream and downstream analysis of the enterprise, platform financing analysis and the like. The business evaluation of the embodiment is used for evaluating borrowing enterprises and granting credit based on the evaluation. The pre-credit assessment report of the present embodiment includes profitability, operational capability, tax analysis, enterprise upstream and downstream analysis, and the like.
The method comprises the following steps of performing business analysis on online business information and upstream and downstream merchant information through an evaluation model, performing cross validation on three-in-one data of an enterprise, and calculating a credit line, and specifically comprises the following steps:
a1, analyzing the order data, the invoice data and the running data to obtain a purchase amount and an upstream risk coefficient related to an upstream merchant and a sale amount and a downstream risk coefficient related to a downstream merchant;
a2, analyzing the information of upstream and downstream merchants to obtain upstream evaluation coefficients and downstream evaluation coefficients related to the upstream merchants;
a3, calculating the credit line of the enterprise through an line calculation formula, wherein the line calculation formula is as follows: the credit limit is the purchase amount and the upstream risk coefficient is 10% + the sale amount and the downstream risk coefficient is 10%.
In this embodiment, the order data, the invoice data, and the running data within the set time range are extracted from the database, and the order amount, the invoice amount, and the running amount related to the upstream merchant, and the order amount, the invoice amount, and the running amount related to the downstream merchant are obtained from the three types of data. For example, the order data, invoice data and order data of borrowing enterprises 12.01-12.07 in seven days are extracted from the database, and the order amount 253200, the invoice amount 262000 and the running amount 255300 related to upstream merchants are obtained from the data, and the order amount 312000, the invoice amount 315000 and the running amount 0 related to downstream merchants are obtained from the data.
The minimum effective amount is taken as the purchase amount from the order amount, invoice amount and running amount associated with the upstream merchant. The effective amount is an amount greater than zero, and for the upstream merchant, the minimum effective amount 253200 is selected from 253200, 262000 and 255300, so that the purchase amount of the upstream merchant is 253200.
And judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the upstream merchant, if so, the upstream risk coefficient is 1, and if not, the upstream risk coefficient is N smaller than 1. In this embodiment, the three types of money amounts of the upstream merchant are all greater than zero and are all valid, which indicates that the three types of data are all complete, so the risk is low, the risk coefficient is high, and the upstream risk coefficient is 1 (the lower the risk coefficient is, the lower the credit amount calculated later is).
The minimum effective amount is taken as the sales amount from the order amount, invoice amount, and running amount associated with the downstream merchant. For downstream merchants, the minimum valid amount 312000(0 being the invalid amount) is taken from 312000, 315000, and 0, thus resulting in a downstream merchant sales amount of 312000.
And judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the downstream merchant, if so, the downstream risk coefficient is 1, and if not, the downstream risk coefficient is M smaller than 1. In this embodiment, the running amount of the three types of amounts of the downstream merchant is 0, which indicates that there is no running amount, and the three types of amounts are incomplete, so that the risk is higher, the risk coefficient is lower, and the risk coefficient of the downstream is M (in this embodiment, both N and M are natural numbers less than 1, such as 0.6, 0.7, 0.8, and the like).
In this embodiment, the upstream merchant and the downstream merchant are respectively rated to obtain a level of the upstream merchant and a level of the downstream merchant; and respectively comparing the upstream merchant grade and the downstream merchant grade with the grade coefficient comparison table to obtain an upstream merchant evaluation coefficient and a downstream merchant evaluation coefficient. In this embodiment, the upstream merchants include four levels of AAA, AA, a, and BBB, and the downstream merchants include four levels of a, BBB, BB, and B, for example, upstream central enterprises, national enterprises, and listed companies are classified into AAA levels, upstream private enterprises are classified into AA levels, upstream agents are classified into a levels, upstream distributors are classified into B levels, downstream central enterprises, national enterprises, and listed companies are classified into a levels, downstream private enterprises are classified into BBB levels, downstream secondary terminal enterprises are classified into BB levels, and downstream prefecture and county entrance cities are classified into B levels. In the ranking coefficient comparison table, the upstream AAA, AA, a, and BBB respectively correspond to 35%, 34%, 25%, and 18% of the evaluation coefficients, and the downstream AAA, BBB, BB, and B respectively correspond to 19%, 21%, 24%, and 29% of the evaluation coefficients. Because the enterprise nature and the enterprise scale of the upstream and downstream merchants of the borrowing enterprise are different, the repayment capacity is different, and therefore the evaluation coefficients are different.
After the related data are calculated, the credit line of the borrowing enterprise is calculated through an amount calculation formula, wherein the credit line is the purchase amount and the upstream risk coefficient is 10% and the sale amount and the downstream risk coefficient is 10% respectively. Through the formula, the credit line is related to the purchase amount, the sales amount, the risk coefficients of upstream and downstream merchants and the evaluation coefficients of the upstream and downstream merchants of the borrowing enterprise. If the purchase amount and the sales amount of the borrowing enterprise are higher, the business capability of the enterprise is better, and therefore the credit amount is high. If the three-stream data (orders, invoices and running water) of the borrowing enterprise are complete and not lack of items, the data are normal, the risk is low, the risk coefficient is high, and the credit line is high; if the three-stream data are incomplete, the data are possibly abnormal, the risk coefficient is low, and the credit line is low. And if the refund ability of the upstream and downstream merchants of the borrowing enterprise is better, the evaluation coefficient is higher. According to the embodiment, the borrowing enterprise is analyzed through the upstream and downstream merchants and the three-stream data, so that the credit line is determined, and the subsequent loan risk is reduced.
In this embodiment, the core enterprise, the fund party, or the industry internet platform borrows the borrowing enterprise according to the credit line calculated by this embodiment. In the financing process of the enterprise, the sales volume and the merchant group of the enterprise are constantly changing, so by the technical scheme of the embodiment, risk monitoring in loan of the borrowing enterprise is also carried out. After the core enterprise, the fund provider or the industrial internet platform borrows money for the borrowing enterprise, the technical solution of this embodiment further performs post-credit monitoring and early warning on the borrowing enterprise, wherein step S4 of the post-credit early warning in the embodiment includes the following contents:
analyzing the enterprise according to the fuzzy query to obtain the number of the tolerant turnover days of the enterprise;
calculating daily required transaction amount according to the credit line;
comparing the daily required transaction limit with the daily actual transaction limit to obtain the number of abnormal transaction days;
and if the transaction abnormal days are larger than the tolerant turnover days, sending out early warning information of high-risk risks of the enterprises.
The enterprise is analyzed according to the fuzzy query, and the number of tolerant turnover days of the enterprise is obtained, specifically:
analyzing according to online business information of an enterprise to obtain an upstream market end ratio index and a downstream client end ratio index;
calculating according to the upstream market end proportion index and the downstream client end proportion index to obtain a comprehensive index;
and inquiring the early warning grading table, and obtaining the early warning level and the turnover tolerant days corresponding to the enterprise according to the comprehensive index.
In this embodiment, for example, if the transaction amount of the upstream market is 430200 and the transaction amount of the downstream client is 572300, the upstream market percentage index is about 43% and the downstream client percentage index is 57% of all the transaction amounts. Then, the two proportion indexes are subjected to a human-representative weighting algorithm, and a comprehensive index is calculated to be 0.56. The early warning level in the early warning grading table of the embodiment is divided into six early warning levels, and each early warning level has a corresponding comprehensive index range and a tolerance turnover number of days. The early warning grade corresponding to the comprehensive index of 0.56 is the third grade, and the number of the tolerant turnover days of the third grade is seven days.
For example, the credit line is 200 ten thousand, the required transaction line is 200 × 3 to 600 ten thousand per week, and the required transaction line is 600/7 to 85.7 ten thousand per day. Comparing the daily actual transaction amount with the daily required transaction amount to obtain that the actual transaction amount of continuous eight days is less than the daily required transaction amount, so that the number of transaction abnormal days is eight days, and the eight days are more than seven days of the tolerant turnover days, which indicates that the borrowing enterprise possibly has high risk, and sends out early warning to the fund lender borrowing the enterprise so that the fund lender can take precautionary measures in time.
To sum up, in this embodiment, through the collected multidimensional data of each aspect of the borrowing enterprise, the borrowing enterprise is subjected to pre-loan assessment to evaluate the credit line of the borrowing enterprise, the fund lender borrows for the borrowing enterprise according to the credit line, the borrowing enterprise is monitored in real time in the enterprise financing process, the risk level is evaluated, the borrowing enterprise is subjected to post-loan monitoring and early warning after the enterprise financing, so that the whole credit flow is monitored and managed, the fund lender can master the operating state of the borrowing enterprise in real time, the credit risk of the fund lender is reduced, and early warning can be timely performed when the risk occurs, so that the fund lender can timely take effective measures.
Those of ordinary skill in the art will appreciate that the various illustrative steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and have been described generally in terms of their functionality in the foregoing description for clarity of interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present application, it should be understood that the division of the steps is only one logical functional division, and there may be other division ways in actual implementation, for example, multiple steps may be combined into one step, one step may be split into multiple steps, or some features may be omitted.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. An intelligent wind control system based on data analysis, comprising:
the system comprises an ordering management unit, a borrowing management unit and a processing unit, wherein the ordering management unit is used for acquiring multidimensional data related to borrowing enterprises, cleaning and summarizing the data and acquiring enterprise information;
the pre-credit evaluation unit is used for analyzing the asset operation repayment capacity of the enterprise according to the enterprise information, evaluating the credit line of the enterprise and generating a pre-credit evaluation report of the enterprise;
the system comprises an in-credit monitoring unit, a risk classification unit and a risk rating unit, wherein the in-credit monitoring unit is used for monitoring the classified risk of an enterprise in the financing process, evaluating the financing risk and grading the risk;
and the post-loan early warning unit is used for monitoring and early warning the enterprise after loan through the wind control model after the enterprise financing.
2. The intelligent wind control system based on data analysis according to claim 1, wherein the enterprise information comprises enterprise subject information, asset liability information, online business information, financial subject information and upstream and downstream merchant information.
3. The intelligent wind control system based on data analysis according to claim 2, wherein the transfer management unit comprises:
the main body information module is used for constructing a basic data portrait of the enterprise according to the enterprise basic data, enterprise shareholder data, judicial data, industrial and commercial data and paper pledge data so as to obtain enterprise main body information of the enterprise;
the system comprises a capital and debt information module, a data processing module and a data processing module, wherein the capital and debt information module is used for constructing an enterprise capital and debt portrait according to personal asset data, personal liability data, enterprise asset data and enterprise liability data so as to obtain enterprise capital and debt information;
the business information module is used for acquiring order data, invoice data and flow data from different online data sources, and summarizing and sorting the order data, the invoice data and the flow data to obtain online business information of an enterprise;
the financial information module is used for performing financial processing on the imported enterprise asset form, the imported enterprise liability form and the imported enterprise profit form to obtain financial subject information of the enterprise;
and the upstream and downstream information module is used for summarizing and sorting the related data of upstream and downstream merchants to obtain the information of the upstream and downstream merchants.
4. The intelligent wind control system based on data analysis according to claim 3, wherein the pre-credit evaluation unit comprises:
a rule factor module for configuring and storing rule factors;
the strategy module is used for configuring a wind control strategy based on the rule factors and forming various evaluation models;
the financial evaluation module is used for performing financial analysis on the financial subject information through the evaluation model to obtain a financial evaluation result;
the business evaluation module is used for carrying out business analysis on the online business information and the upstream and downstream merchant information through an evaluation model, carrying out cross validation on the three-in-one data of the enterprise and calculating the credit granting amount;
the integral evaluation module is used for comprehensively analyzing and evaluating the main body information of the enterprise, the liability information of the assets, the online business information, the financial subject information and the information of upstream and downstream merchants through an evaluation model to obtain an operation evaluation result of the enterprise;
and the report generation module is used for summarizing various evaluation results and forming an integral pre-credit evaluation report for the risks of the enterprises.
5. The intelligent wind control system based on data analysis according to claim 4, wherein the service evaluation module is specifically configured to:
analyzing the order data, the invoice data and the flow data to obtain a purchase amount and an upstream risk coefficient related to an upstream merchant and a sales amount and a downstream risk coefficient related to a downstream merchant;
analyzing the information of upstream and downstream merchants to obtain an upstream evaluation coefficient and a downstream evaluation coefficient which are related to the upstream merchants;
calculating the credit line of the enterprise through an amount calculation formula, wherein the amount calculation formula is as follows: the credit limit is the purchase amount and the upstream risk coefficient is 10% + the sale amount and the downstream risk coefficient is 10%.
6. The intelligent wind control system based on data analysis according to claim 5, wherein the analysis of the order data, the invoice data and the running data is performed to obtain a purchase amount and an upstream risk coefficient associated with an upstream merchant and a sale amount and a downstream risk coefficient associated with a downstream merchant, specifically:
order data, invoice data and running data within a set time range are extracted from a database, and order amount, invoice amount and running amount related to an upstream merchant and order amount, invoice amount and running amount related to a downstream merchant are obtained from the three types of data;
taking the minimum effective amount from the order amount, the invoice amount and the running amount related to the upstream merchant as the purchase amount;
judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the upstream merchant, if so, the upstream risk coefficient is 1, and if not, the upstream risk coefficient is N smaller than 1;
taking the minimum effective amount from the order amount, the invoice amount and the running amount related to the downstream merchant as the sales amount;
and judging whether the three types of money are valid or not from the order amount, the invoice amount and the running amount related to the downstream merchant, if so, the downstream risk coefficient is 1, and if not, the downstream risk coefficient is M smaller than 1.
7. The intelligent wind control system based on data analysis according to claim 5, wherein the analysis of the information of the upstream and downstream merchants is performed to obtain an upstream evaluation coefficient and a downstream evaluation coefficient related to the upstream merchants, and specifically:
respectively grading the upstream merchant and the downstream merchant to obtain the grade of the upstream merchant and the grade of the downstream merchant;
and respectively comparing the upstream merchant grade and the downstream merchant grade with the grade coefficient comparison table to obtain an upstream merchant evaluation coefficient and a downstream merchant evaluation coefficient.
8. The intelligent wind control system based on data analysis according to claim 7, wherein the post-credit warning unit is specifically configured to:
analyzing the enterprise according to the fuzzy query to obtain the number of the tolerant turnover days of the enterprise;
calculating daily required transaction amount according to the credit line;
comparing the daily required transaction limit with the daily actual transaction limit to obtain the number of abnormal transaction days;
and if the transaction abnormal days are larger than the tolerant turnover days, sending out early warning information of high-risk risks of the enterprises.
9. The intelligent wind control system based on data analysis according to claim 8, wherein the enterprise is analyzed according to the fuzzy query to obtain the number of turnover tolerant days of the enterprise, specifically:
analyzing according to online business information of an enterprise to obtain an upstream market end ratio index and a downstream client end ratio index;
calculating the upstream market end proportion index and the downstream client end proportion index to obtain a comprehensive index;
and inquiring the early warning grading table, and obtaining the early warning level and the turnover tolerant days corresponding to the enterprise according to the comprehensive index.
10. An intelligent wind control method based on data analysis, which is suitable for the intelligent wind control system based on data analysis in any one of claims 1-9, and is characterized by comprising the following steps:
acquiring multidimensional data related to borrowing enterprises, cleaning and summarizing the data to obtain enterprise information;
analyzing the asset operation repayment capacity of the enterprise according to the enterprise information, evaluating the credit line of the enterprise, and generating a pre-credit evaluation report of the enterprise;
monitoring the classified risk of the enterprise in the financing process, evaluating the financing risk and grading the risk;
after financing of the enterprise, monitoring and early warning are carried out on the enterprise after loan through the wind control model.
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