CN109658217B - Intelligent financial decision big data analysis system - Google Patents
Intelligent financial decision big data analysis system Download PDFInfo
- Publication number
- CN109658217B CN109658217B CN201811564114.3A CN201811564114A CN109658217B CN 109658217 B CN109658217 B CN 109658217B CN 201811564114 A CN201811564114 A CN 201811564114A CN 109658217 B CN109658217 B CN 109658217B
- Authority
- CN
- China
- Prior art keywords
- enterprise
- financial
- invested
- month
- income
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/12—Accounting
- G06Q40/125—Finance or payroll
Abstract
The invention discloses an intelligent financial decision big data analysis system which comprises a financial information acquisition module, an information screening and dividing module, an investment evaluation module, a risk storage database, a financial checking module, a financial analysis module, a local storage database, a decision server and an early warning display terminal, wherein the financial information acquisition module is used for acquiring financial information; the financial information acquisition module is respectively connected with the information screening and dividing module and the financial checking module, the investment evaluation module is respectively connected with the information screening and dividing module and the risk storage database, the financial analysis module is respectively connected with the financial checking module and the local storage database, and the decision server is respectively connected with the investment evaluation module, the financial checking module, the financial analysis module, the local storage database and the early warning display terminal. The invention carries out risk assessment on the enterprise finance, provides reliable preventive measures, realizes the overall planning of the finance, provides reliable decision analysis and guidance for managers on financial management and reduces the risk in the enterprise development process.
Description
Technical Field
The invention belongs to the technical field of enterprise financial analysis, and relates to an intelligent financial decision big data analysis system.
Background
Corporate finance refers to the movement of value in the process of enterprise reproduction, and is often expressed as a series of activities such as acquisition, use, consumption, distribution and the like of enterprise funds. The essence of the method is the economic relationship between enterprises and all aspects embodied by fund movement in the reproduction process, and the relationship is represented as follows: (1) financial relationships between businesses and countries include: payment and fund-shifting relation between enterprises and national finance. And the fund transfer relationship between the enterprise and the administrative department. And thirdly, the deposit and loan relationship between the enterprise and the national bank. (2) The financial relationship between the enterprise and other entities refers to the monetary funds settlement relationship between the enterprise and the enterprise, and between the enterprise and other non-enterprise entities for providing products or labor to each other. Which essentially embodies the commodity equivalence between units. (3) The financial relationship among all departments in an enterprise refers to the accounting relationship between the factory department of the enterprise and all management departments as well as the supply, production and sale links of the enterprise on the aspect of capital occupation and consumption. The division and cooperation relationship between each department and each level of units of the enterprise under the leader of the plant is embodied. The responsibility, authority and interest of the enterprise are reflected among all links. (4) Financial relationships between enterprises and employees. Enterprises pay wages, bonus, benefits and the like to employees according to the social principle of fully allocating the sales of the enterprises per se and the fully allocated social principle, so that the fund settlement relationship among the same employees of the enterprises is formed, and the enterprise is substantially a distribution relationship.
With the diversification of enterprises, enterprises invest in redundant funds, but effectively assess the risk of enterprises to be invested in the investment process, once the investment is lost, the financial loss of the enterprises is caused, in addition, errors are easy to occur in the income and expenditure processes of the enterprises, the income and expenditure of the enterprises are not in accordance with the actual situation, in order to improve the accuracy of financial data in the enterprise transaction process, the income and expenditure finances of the enterprises and the income and expenditure on the list are compared, whether the income and expenditure of the enterprises have errors or not is determined, the problems of poor financial accuracy and low safety exist, in order to solve the problems, a large intelligent financial decision data analysis coefficient is designed, and once the finance is in danger, reliable measures and suggestions are provided.
Disclosure of Invention
The invention aims to provide an intelligent financial decision big data analysis system which carries out enterprise financial risk assessment through a financial checking module, a financial analysis module, an investment assessment module and a decision server, solves the problems of poor accuracy and poor safety of enterprise finance, provides reliable decision analysis and guidance for managers, and greatly reduces financial risk coefficients in the enterprise development process.
The purpose of the invention can be realized by the following technical scheme:
an intelligent financial decision big data analysis system comprises a financial information acquisition module, an information screening and dividing module, an investment evaluation module, a risk storage database, a financial checking module, a financial analysis module, a local storage database, a decision server and an early warning display terminal;
the financial information acquisition module is respectively connected with the information screening and dividing module and the financial checking module, the investment evaluation module is respectively connected with the information screening and dividing module and the risk storage database, the financial analysis module is respectively connected with the financial checking module and the local storage database, and the decision server is respectively connected with the investment evaluation module, the financial checking module, the financial analysis module, the local storage database and the early warning display terminal;
the financial information acquisition module is used for acquiring the basic operation information of the enterprise and respectively sending the acquired basic operation information of the enterprise to the information screening and dividing module and the financial checking module;
the information screening and dividing module is used for receiving the basic business information of the enterprise sent by the financial information receiving module, extracting a plurality of items to be invested in the basic business information of the enterprise, dividing the registration places where the names of the items to be invested in the contents of the items to be invested are located according to the set investment areas, meanwhile, sending the enterprise types, the registered funds, the enterprise credit levels, the total asset amount and the loan amount of the items to be invested to the investment evaluation module by the information screening and dividing module, and numbering the items to be invested according to the time sequence of company registration, wherein the numbers are respectively 1,2, a.
The risk storage database is used for storing basic parameter standards of the enterprise investment project company, wherein the basic parameter standards comprise information of enterprise types, registered funds, enterprise credit, credit-fund ratio and the like of the investment project company, the enterprise types comprise personal sole resource enterprises, partnership enterprises, limited responsibility enterprises and share limited companies, and the enterprise type proportion coefficients of the personal sole resource enterprises, the partnership enterprises, the limited responsibility enterprises and the share limited companies are x1, x2, x3 and x4, x1 is more than x2 is more than x3 is more than x4, and x4 is 1; the method comprises the steps that registered funds are divided according to set registered fund levels, wherein the registered funds are divided into k1, k2, k, ki, k, kn, ki are represented as the ith registered fund level, k1, k2, k, ki, k, kn, and the registered fund range corresponding to the kn is 0-50 ten, 51-100 ten, k, 50(i-1) + 1-50 i ten thousand, 50(n-1) + 1-50 n, and different registered fund levels correspond to different standard fund level coefficients gk1, gk2, k, gki, g, gkn, and gk1 < gk2 < gki > < gkn; the method comprises the following steps that enterprises are divided into a plurality of enterprise credit levels according to enterprise credit levels, and the divided enterprise level credits form a standard enterprise credit level set B (B1, B2, B3, B4, B5), B1, B2, B3, B4 and B5 which are respectively corresponding to a first enterprise credit level, a second enterprise credit level, a third enterprise credit level, a fourth enterprise credit level and a fifth enterprise credit level; the method comprises the steps that a loan fund ratio is expressed as a ratio of total assets amount to loan amount of an enterprise to be invested, the loan fund ratio is divided into a plurality of loan fund ratios according to a set ratio range, a standard loan fund ratio set D (D1, D2, dj, dm) is formed, dj is expressed as a jth loan fund ratio, and if the ratio of the total assets amount to the loan amount of the enterprise is smaller than dj and larger than D (j-1), the loan fund ratio of the enterprise is dj;
the investment evaluation module receives the enterprise types, the registered funds, the enterprise credit levels, the total asset amount and the loan amount of each to-be-invested project company sent by the information screening and dividing module, compares the obtained enterprise types of each to-be-invested project company with the enterprise types stored in the risk storage database one by one to obtain the proportion occupied by each enterprise type, and forms a to-be-invested enterprise type proportion set C (C1, C2,. so, cs,. so, cw), wherein cs represents the enterprise type proportion coefficient corresponding to the s to-be-invested project enterprise, and belongs to x1, x2, x3, x4, s is 1,2,. so, w; respectively comparing the registered funds with registered fund ranges corresponding to different registered fund grades stored in a risk storage database, and obtaining registered fund grades of all project companies to be invested and fund grade coefficients corresponding to the registered fund grades, wherein the fund grade coefficients corresponding to the registered fund grades of all project companies to be invested form an enterprise fund grade coefficient set D (D1, D2, D, ds, D, dw) to be tested, ds is expressed as a fund grade coefficient corresponding to an s-th project enterprise to be invested, and belongs to an element of gk1, gk2, g. gki, g. gkn in standard fund grade coefficients; the enterprise credit levels of all to-be-invested project companies are matched with enterprise credit levels stored in a risk storage database one by one to obtain enterprise credit levels corresponding to all to-be-invested project companies, and the obtained enterprise credit levels form an enterprise credit level set V (V1, V2.,. ts.,. vw) to be detected, wherein vs is expressed as enterprise credit levels corresponding to the s-th to-be-invested project company; comparing the total amount of funds of all to-be-invested project companies with the amount of loans to obtain a ratio of the loans to funds, comparing the obtained ratio of the loans to funds with each ratio of the loans to funds in a standard ratio set of the loans to funds stored in a risk storage database one by one to obtain a ratio of the loans to funds corresponding to each to-be-invested project company, and forming a ratio set U (U1, U2,..,. us,..,. U.,. uw.) of the to-be-invested project companies and a ratio set R (R1, R2.,. rs,. R.,. rw) of the to-be-invested project companies, wherein us represents the ratio of the to-be-invested project companies and rs represents the ratio of the to-be-invested project companies;
the investment evaluation module counts the evaluation risk coefficient of each to-be-invested project company of enterprise investment according to the to-be-invested enterprise type specific gravity set C, the to-be-invested enterprise fund grade coefficient set D, the to-be-invested enterprise credit grade set V, the to-be-invested enterprise credit fund ratio set U and the to-be-invested enterprise credit fund specific gravity set R
Sending the statistical investment evaluation risk coefficient of each to-be-invested project company to a decision server;
the financial checking module is used for receiving the basic business information of the enterprise sent by the financial information acquisition module, extracting the financial income, financial expenditure, inventory quantity, inventory type and unit price corresponding to the inventory type in the basic business information of the enterprise, dividing the extracted financial income and financial expenditure into month income and month expenditure according to the month of income and expenditure, respectively dividing the month income and expense into month income and month expense, sending the divided month income and expense to the financial analysis module, and sending the inventory quantity, inventory type and unit price corresponding to the inventory type to the decision server;
the financial analysis module is used for receiving income and expenditure of each month of the enterprise sent by the financial information acquisition module, acquiring financial income and financial expenditure list contents of each month recorded in a local storage database, extracting financial information in the financial income and financial expenditure list contents, accumulating to obtain a financial income sum and a financial expenditure sum recorded by each month, comparing the accumulated financial income sum recorded by each month with income of a corresponding month of the enterprise to obtain a month income comparison set H (H1, H2., hq.,. H12), comparing the accumulated financial expenditure sum with the expenditure of the corresponding month of the enterprise to obtain a month expenditure comparison set Z (Z1, Z2.,.., zq..,. Z12), sending the statistical month income comparison set and the month expenditure comparison set to the decision server by the financial analysis module, wherein hq is expressed as a difference value between income of the enterprise in the qth month and the financial income accumulated in the list recorded by the qth and the financial expenditure sum of the month in the corresponding month of the enterprise, zq is expressed as the difference between the business expenditure in the qth month and the financial expenditure accumulated in the entry list;
the decision server receives the monthly income comparison set and the monthly payment comparison set sent by the financial analysis module, and calculates the monthly income statistical error coefficient delta and the monthly payment statistical error coefficient delta according to the monthly income comparison set and the monthly payment comparison set, and the decision server judges the monthly income statistical error coefficient delta and the monthly payment statistical error coefficient delta
Respectively comparing the monthly income statistical error coefficient with a set standard monthly payment error coefficient and the standard monthly payment error coefficient, sending the monthly income statistical error coefficient and the monthly payment statistical error coefficient to an early warning display terminal, and if the monthly income statistical error coefficient is larger than the set standard monthly income error coefficient or the monthly payment statistical error coefficient is larger than the set standard monthly payment error coefficient, extracting preventive measures and suggestions stored in a local storage database to the early warning display terminal by the decision server;
meanwhile, the decision server receives the investment evaluation risk coefficients of all to-be-invested project companies sent by the investment evaluation module, screens out the to-be-invested project company with the minimum investment evaluation risk coefficient from the received investment evaluation risk coefficients of all to-be-invested project companies, and screens out the to-be-invested project company with the minimum investment evaluation risk coefficient and the corresponding investment evaluation risk coefficientThe risk coefficients are sent to an early warning display terminal, and the screened investment assessment risk coefficients are combined with a monthly income statistical error coefficient delta and a monthly payment statistical error coefficient of an enterprise
The enterprise comprehensive financial risk coefficient is counted, and the counted enterprise comprehensive risk coefficient is sent to an early warning display terminal, wherein the calculation formula of the enterprise comprehensive financial risk coefficient is
Eta is expressed as an enterprise comprehensive financial risk coefficient,
the investment evaluation risk coefficient corresponding to the project company to be invested with the minimum investment evaluation risk coefficient is expressed, delta is the monthly income statistical error coefficient accumulated by each month of the enterprise,
the statistical error coefficient is expressed for the accumulated month count of each month of the enterprise,
expressed as a scale factor, take 0.52;
the local storage database is used for storing financial income and financial expenditure list contents which are input by personnel in advance, storing the goods types sold by the enterprises in each month and the quantity sold by each goods type, and simultaneously storing preventive measures and suggestions corresponding to different enterprise operation risks;
the early warning display terminal is used for displaying and displaying the monthly income statistical error coefficient, the monthly payment statistical error coefficient, the investment evaluation risk coefficient, the comprehensive financial risk coefficient and the project company to be invested with the minimum screened investment evaluation risk coefficient sent by the decision server, and displaying preventive measures and suggestions corresponding to the monthly income statistical error coefficient, the monthly payment statistical error coefficient, the investment evaluation risk coefficient and the comprehensive financial risk coefficient.
Further, the basic operation information of the enterprise comprises financial income, financial expenditure, total profit amount, accounts receivable amount, bank borrowing amount, inventory quantity, inventory type, unit price corresponding to the cargo type and a plurality of contents of the items to be invested, wherein the contents of the items to be invested comprise company names of the items to be invested, enterprise types, registered places, registered funds, enterprise credit levels, total amount of assets, loan amount and amount to be invested.
Furthermore, different specific values of the credits and funds correspond to different specific weight coefficients of the credits and funds, namely gd1, gd2,. gdj,. gdm, and gd1 < gd2 <. gdj. < gdm, gd1+ gd2+. + gdj +. + gdm ═ 1, and gdj represents a specific weight coefficient occupied by the jth credit and funds ratio.
Further, the calculation formula of the investment assessment risk coefficient is
Wherein f is the time period for the establishment of the s-th project company to be invested, cs is the enterprise type specific gravity coefficient corresponding to the s-th project company to be invested, vs is the enterprise credit level corresponding to the s-th project company to be invested, ds is the fund level coefficient corresponding to the s-th project company to be invested, us is the ratio of the funds to be invested corresponding to the s-th project company to be invested, and rs is the specific gravity coefficient occupied by the credit fund ratio corresponding to the s-th project company to be invested.
Further, the monthly income statistical error coefficient
Lambda is expressed as an error influence factor, 0.162 is taken, hq is expressed as the difference between the income of the enterprise in the qth month and the financial income accumulated in the entry list, hqFruit of Chinese wolfberryExpressed as actual revenue, hq, for the business in month qRecording deviceExpressed as the cumulative sum of the income entered by the business at month q.
Further, the monthly expenditure statistical error coefficient
Lambda denotesFor the error-influencing factor, 0.162 is taken, zq is expressed as the difference between the business expenditure in the qth month and the financial expenditure accumulated in the entry list, zqFruit of Chinese wolfberryExpressed as the actual expenditure, zq, of the business in month qRecording deviceExpressed as the cumulative sum of the entered expenses for the business at month q.
Further, the decision server receives the inventory quantity, the inventory type and the unit price corresponding to the inventory type sent by the financial checking module, counts the current inventory type and the stored quantity corresponding to each inventory type, compares the current inventory type and the stored quantity corresponding to each inventory type with the commodity type sold in the last month and the quantity of each commodity type sold in the local storage database, and extracts the corresponding preventive decision measures and suggestions from the local storage database to the early warning display terminal if the stored commodity type is insufficient or the stored quantity corresponding to each commodity type is less than the quantity of each commodity type sold in the last month;
and if the stored goods types are the same as the goods types sold in the last month in quantity, and the ratio of the quantity of each stored goods type to the quantity of the goods sold in the last month is greater than the set quantity ratio of the goods types, extracting corresponding preventive measures and suggestions stored in the local storage database by the decision server to the early warning display terminal.
The invention has the beneficial effects that:
according to the intelligent financial decision big data analysis system, the basic operation information of an enterprise is obtained through the financial information obtaining module, the risk of each to-be-invested project company is effectively evaluated by combining the risk evaluation module, accurate screening is conveniently provided for the enterprise, the risk degree of enterprise investment is reduced, and the benefit of the enterprise is improved to the maximum extent;
the financial accounting module is used for counting the monthly expenditure and income of an enterprise, comparing and analyzing the monthly expenditure and income with the input expenditure and income, and combining the monthly income statistical error coefficient and the monthly expenditure statistical error coefficient with the decision server, the accuracy of enterprise financial statistics can be effectively judged, the error rate in the financial statistics process is reduced, the statistical error is reduced, the decision server calculates the enterprise comprehensive risk coefficient according to the statistical enterprise investment risk and income error, corresponding preventive measures and suggestions are screened according to risk types, the enterprise financial overall planning is realized, reliable decision analysis and guidance are provided for managers on financial management, and the risk in the enterprise development process is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of an intelligent financial decision big data analysis system according to the present invention.
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, an intelligent financial decision big data analysis system includes a financial information acquisition module, an information screening and dividing module, an investment evaluation module, a risk storage database, a financial checking module, a financial analysis module, a local storage database, a decision server and an early warning display terminal;
the financial information acquisition module is respectively connected with the information screening and dividing module and the financial checking module, the investment evaluation module is respectively connected with the information screening and dividing module and the risk storage database, the financial analysis module is respectively connected with the financial checking module and the local storage database, and the decision server is respectively connected with the investment evaluation module, the financial checking module, the financial analysis module, the local storage database and the early warning display terminal;
the financial information acquisition module is used for acquiring basic operation information of an enterprise and respectively sending the acquired basic operation information of the enterprise to the information screening and dividing module and the financial checking module, wherein the basic operation information of the enterprise comprises financial income, financial expenditure, total profit amount, accounts receivable amount, bank borrowing amount, inventory quantity, inventory type, unit price corresponding to the inventory type, a plurality of contents of items to be invested and the like, and the contents of the items to be invested comprise the company name of the items to be invested, enterprise type, registration place, registered fund, enterprise credit level, total asset amount, loan amount, investment amount and the like;
the information screening and dividing module is used for receiving the basic business information of the enterprise sent by the financial information receiving module, extracting a plurality of items to be invested in the basic business information of the enterprise, dividing the registration places where the names of the items to be invested in the contents of the items to be invested are located according to the set investment areas, meanwhile, sending the enterprise types, the registered funds, the enterprise credit levels, the total asset amount and the loan amount of the items to be invested to the investment evaluation module by the information screening and dividing module, and numbering the items to be invested according to the time sequence of company registration, wherein the numbers are respectively 1,2, a.
The risk storage database is used for storing basic parameter standards of the enterprise investment project company, wherein the basic parameter standards comprise information of enterprise types, registered funds, enterprise credit, credit-fund ratio and the like of the investment project company, the enterprise types comprise personal sole resource enterprises, partnership enterprises, limited responsibility enterprises and share limited companies, and the enterprise type proportion coefficients of the personal sole resource enterprises, the partnership enterprises, the limited responsibility enterprises and the share limited companies are x1, x2, x3 and x4, x1 is more than x2 is more than x3 is more than x4, and x4 is 1; the method comprises the steps that registered funds are divided according to set registered fund levels, wherein the registered funds are divided into k1, k2, k, ki, k, kn, ki are represented as the ith registered fund level, k1, k2, k, ki, k, kn, and the registered fund range corresponding to the kn is 0-50 ten, 51-100 ten, k, 50(i-1) + 1-50 i ten thousand, 50(n-1) + 1-50 n, and different registered fund levels correspond to different standard fund level coefficients gk1, gk2, k, gki, g, gkn, and gk1 < gk2 < gki > < gkn; the method comprises the following steps that enterprises are divided into a plurality of enterprise credit levels according to enterprise credit levels, and the divided enterprise level credits form a standard enterprise credit level set B (B1, B2, B3, B4, B5), B1, B2, B3, B4 and B5 which are respectively corresponding to a first enterprise credit level, a second enterprise credit level, a third enterprise credit level, a fourth enterprise credit level and a fifth enterprise credit level; the method comprises the steps that a loan fund ratio is expressed as a ratio of total assets and loan amounts of an enterprise to be invested, the loan fund ratio is divided according to a set ratio range and divided into a plurality of loan fund ratios to form a standard loan fund ratio set D (D1, D2, dj, dm), dj is expressed as a j-th loan fund ratio, if the ratio of the total assets and the loan amounts of the enterprise is smaller than dj and larger than D (j-1), the loan fund ratio of the enterprise is selected as dj, and different loan fund ratios respectively correspond to different loan fund ratio weighting coefficients, namely gd1, gd2, gdj, gdm, and gd1 < gd2 > < gdj > < gdm, gd2+ gd2+. gdj +. 9. + gdm equals 1, and gd 3684 is expressed as a specific weighting coefficient of the j-th fund ratio;
the investment evaluation module receives the enterprise types, the registered funds, the enterprise credit levels, the total asset amount and the loan amount of each to-be-invested project company sent by the information screening and dividing module, compares the obtained enterprise types of each to-be-invested project company with the enterprise types stored in the risk storage database one by one to obtain the proportion occupied by each enterprise type, and forms a to-be-invested enterprise type proportion set C (C1, C2,. so, cs,. so, cw), wherein cs represents the enterprise type proportion coefficient corresponding to the s to-be-invested project enterprise, and belongs to x1, x2, x3, x4, s is 1,2,. so, w; respectively comparing the registered funds with registered fund ranges corresponding to different registered fund grades stored in a risk storage database, and obtaining registered fund grades of all project companies to be invested and fund grade coefficients corresponding to the registered fund grades, wherein the fund grade coefficients corresponding to the registered fund grades of all project companies to be invested form an enterprise fund grade coefficient set D (D1, D2, D, ds, D, dw) to be tested, ds is expressed as a fund grade coefficient corresponding to an s-th project enterprise to be invested, and belongs to an element of gk1, gk2, g. gki, g. gkn in standard fund grade coefficients; the enterprise credit levels of all to-be-invested project companies are matched with enterprise credit levels stored in a risk storage database one by one to obtain enterprise credit levels corresponding to all to-be-invested project companies, and the obtained enterprise credit levels form an enterprise credit level set V (V1, V2.,. ts.,. vw) to be detected, wherein vs is expressed as enterprise credit levels corresponding to the s-th to-be-invested project company; comparing the total amount of funds of all to-be-invested project companies with the amount of loans to obtain a ratio of the loans to funds, comparing the obtained ratio of the loans to funds with each ratio of the loans to funds in a standard ratio set of the loans to funds stored in a risk storage database one by one to obtain a ratio of the loans to funds corresponding to each to-be-invested project company, and forming a ratio set U (U1, U2,..,. us,..,. U.,. uw.) of the to-be-invested project companies and a ratio set R (R1, R2.,. rs,. R.,. rw) of the to-be-invested project companies, wherein us represents the ratio of the to-be-invested project companies and rs represents the ratio of the to-be-invested project companies;
the investment evaluation module counts the evaluation risk coefficient of each to-be-invested project company of enterprise investment according to the to-be-invested enterprise type specific gravity set C, the to-be-invested enterprise fund grade coefficient set D, the to-be-invested enterprise credit grade set V, the to-be-invested enterprise credit fund ratio set U and the to-be-invested enterprise credit fund specific gravity set R
And sending the statistical investment evaluation risk coefficient of each to-be-invested project company to a decision server, wherein the calculation formula of the investment evaluation risk coefficient is
Wherein f is the time period for the establishment of the s-th to-be-invested project company, cs is the enterprise type specific gravity coefficient corresponding to the s-th to-be-invested project enterprise, vs is the enterprise credit level corresponding to the s-th to-be-invested project company, ds is the fund level coefficient corresponding to the s-th to-be-invested project enterprise, us is the to-be-invested fund ratio corresponding to the s-th to-be-invested project company, and rs is the credit fund ratio corresponding to the s-th to-be-invested project companyThe larger the investment evaluation risk coefficient is, the larger the risk of investing the to-be-invested project enterprise is, and the larger the loss brought to the enterprise is;
the financial checking module is used for receiving the basic business information of the enterprise sent by the financial information acquisition module, extracting the financial income, financial expenditure, inventory quantity, inventory type and unit price corresponding to the inventory type in the basic business information of the enterprise, dividing the extracted financial income and financial expenditure into month income and month expenditure according to the month of income and expenditure, respectively dividing the month income and expense into month income and month expense, sending the divided month income and expense to the financial analysis module, and sending the inventory quantity, inventory type and unit price corresponding to the inventory type to the decision server;
the financial analysis module is used for receiving income and expenditure of each month of the enterprise sent by the financial information acquisition module, acquiring financial income and financial expenditure list contents of each month recorded in a local storage database, extracting financial information in the financial income and financial expenditure list contents, accumulating to obtain a financial income sum and a financial expenditure sum recorded by each month, comparing the accumulated financial income sum recorded by each month with income of a corresponding month of the enterprise to obtain a month income comparison set H (H1, H2., hq.,. H12), comparing the accumulated financial expenditure sum with the expenditure of the corresponding month of the enterprise to obtain a month expenditure comparison set Z (Z1, Z2.,.., zq..,. Z12), sending the statistical month income comparison set and the month expenditure comparison set to the decision server by the financial analysis module, wherein hq is expressed as a difference value between income of the enterprise in the qth month and the financial income accumulated in the list recorded by the qth and the financial expenditure sum of the month in the corresponding month of the enterprise, zq is expressed as the difference between the business expenditure in the qth month and the financial expenditure accumulated in the entry list;
the decision server receives the month income comparison set and the month payment comparison set sent by the financial analysis module, and calculates the month income statistical error coefficient and the month payment statistical error coefficient according to the month income comparison set and the month payment comparison set, and the month income statistical error coefficient
Monthly expenditure statistical error coefficient
Lambda is expressed as an error influence factor, 0.162 is taken, hq is expressed as the difference between the income of the enterprise in the qth month and the financial income accumulated in the entry list, zq is expressed as the difference between the expense of the enterprise in the qth month and the financial expense accumulated in the entry list, hqFruit of Chinese wolfberryExpressed as the actual revenue of the business in month q, zqFruit of Chinese wolfberryExpressed as actual expenses for the business in month q, hqRecording deviceExpressed as the cumulative sum of the income entered for the business in month q, zqRecording deviceExpressed as the cumulative sum of the input expenses of the enterprise of the qth month, the decision server judges a monthly income statistical error coefficient delta and a monthly expenditure statistical error coefficient
Respectively comparing the monthly income statistical error coefficient with a set standard monthly payment error coefficient and the standard monthly payment error coefficient, sending the monthly income statistical error coefficient and the monthly payment statistical error coefficient to an early warning display terminal, and if the monthly income statistical error coefficient is larger than the set standard monthly income error coefficient or the monthly payment statistical error coefficient is larger than the set standard monthly payment error coefficient, extracting preventive measures and suggestions stored in a local storage database to the early warning display terminal by the decision server;
meanwhile, the decision server receives the investment evaluation risk coefficients of all to-be-invested project companies sent by the investment evaluation module, screens out the to-be-invested project company with the minimum investment evaluation risk coefficient from the received investment evaluation risk coefficients of all to-be-invested project companies, sends the screened to-be-invested project company with the minimum investment evaluation risk coefficient and the corresponding investment evaluation risk coefficient to the early warning display terminal, and combines the screened investment evaluation risk coefficients with the monthly income statistical error coefficient delta and the monthly payment statistical error coefficient delta of the enterprise
Counting the enterprise comprehensive financial risk coefficient, and sending the counted enterprise comprehensive risk coefficient to an early warning display terminal, wherein the enterprise is integratedThe calculation formula of the financial risk coefficient is
Eta is expressed as an enterprise comprehensive financial risk coefficient,
the investment evaluation risk coefficient corresponding to the project company to be invested with the minimum investment evaluation risk coefficient is expressed, delta is the monthly income statistical error coefficient accumulated by each month of the enterprise,
the statistical error coefficient is expressed for the accumulated month count of each month of the enterprise,
expressed as a scale factor, take 0.52;
in addition, the decision server receives the inventory quantity, the inventory type and the unit price corresponding to the cargo type sent by the financial checking module, counts the current inventory type and the storage quantity corresponding to each cargo type, compares the current inventory type and the storage quantity corresponding to each cargo type with the cargo type sold in the last month and the quantity of each cargo type sold in the local storage database, if the stored cargo type is insufficient or the storage quantity corresponding to each cargo type is less than the quantity of each cargo type sold in the last month, the decision server extracts corresponding preventive decision measures and suggestions in the local storage database to the early warning display terminal, if the types of the stored goods are the same as the types and the quantity of the goods sold in the last month, and the ratio of the quantity of each stored goods category to the quantity of goods categories sold in the last month is larger than the set quantity ratio of the goods categories, the decision server extracts corresponding preventive measures and suggestions stored in the local storage database to the early warning display terminal;
the local storage database is used for storing financial income and financial expenditure list contents which are input by personnel in advance, storing the goods types sold by the enterprises in each month and the quantity sold by each goods type, and simultaneously storing preventive measures and suggestions corresponding to different enterprise operation risks;
the early warning display terminal is used for displaying and displaying the monthly income statistical error coefficient, the monthly payment statistical error coefficient, the investment evaluation risk coefficient, the comprehensive financial risk coefficient and the project company to be invested with the minimum screened investment evaluation risk coefficient, and displaying corresponding preventive measures and suggestions of the monthly income statistical error coefficient, the monthly payment statistical error coefficient, the investment evaluation risk coefficient and the comprehensive financial risk coefficient, so that reliable decision analysis is provided for managers.
According to the intelligent financial decision big data analysis system, the basic operation information of an enterprise is obtained through the financial information obtaining module, the risk of each to-be-invested project company is effectively evaluated by combining the risk evaluation module, accurate screening is conveniently provided for the enterprise, the risk degree of enterprise investment is reduced, and the benefit of the enterprise is improved to the maximum extent;
the financial accounting module is used for counting the monthly expenditure and income of an enterprise, comparing and analyzing the monthly expenditure and income with the input expenditure and income, and combining the monthly income statistical error coefficient and the monthly expenditure statistical error coefficient with the decision server, the accuracy of enterprise financial statistics can be effectively judged, the error rate in the financial statistics process is reduced, the statistical error is reduced, the decision server calculates the enterprise comprehensive risk coefficient according to the statistical enterprise investment risk and income error, corresponding preventive measures and suggestions are screened according to risk types, the enterprise financial overall planning is realized, reliable decision analysis and guidance are provided for managers on financial management, and the risk in the enterprise development process is reduced.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (7)
1. An intelligent financial decision big data analysis system is characterized in that: the system comprises a financial information acquisition module, an information screening and dividing module, an investment evaluation module, a risk storage database, a financial checking module, a financial analysis module, a local storage database, a decision server and an early warning display terminal;
the financial information acquisition module is respectively connected with the information screening and dividing module and the financial checking module, the investment evaluation module is respectively connected with the information screening and dividing module and the risk storage database, the financial analysis module is respectively connected with the financial checking module and the local storage database, and the decision server is respectively connected with the investment evaluation module, the financial checking module, the financial analysis module, the local storage database and the early warning display terminal;
the financial information acquisition module is used for acquiring the basic operation information of the enterprise and respectively sending the acquired basic operation information of the enterprise to the information screening and dividing module and the financial checking module;
the information screening and dividing module is used for receiving the basic business information of the enterprise sent by the financial information acquisition module, extracting a plurality of items to be invested in the basic business information of the enterprise, dividing a registration place where the name of each item to be invested in the plurality of items to be invested is located according to a set investment area, meanwhile, sending the enterprise type, the registered fund, the enterprise credit level, the total asset amount and the loan amount of the item to be invested to the investment evaluation module by the information screening and dividing module, and numbering the items to be invested according to the time sequence of company registration, wherein the numbers are respectively 1,2, a.
The risk storage database is used for storing basic parameter standards of the enterprise investment project company, the basic parameter standards comprise enterprise types, registered funds, enterprise credit and credit-fund ratio information of the investment project company, the enterprise types comprise personal sole resource enterprises, partnership enterprises, limited responsibility enterprises and share limited companies, and the enterprise type proportion coefficients of the personal sole resource enterprises, the partnership enterprises, the limited responsibility enterprises and the share limited companies are x1, x2, x3 and x4 respectively, x1 < x2 < x3 < x4, and x4 is 1; the method comprises the steps that registered funds are divided according to set registered fund levels, wherein the registered funds are divided into k1, k2, k, ki, k, kn, ki are represented as the ith registered fund level, k1, k2, k, ki, k, kn correspond to registered fund ranges of 0-50 ten thousand, 51-100 ten thousand, 50(i-1) + 1-50 i ten thousand, 50(n-1) + 1-50 n ten thousand, different registered fund levels correspond to different standard fund level coefficients gk1, gk2, k, gki, k, gkn, and gk1 < gk2 < gki < ny < gkn; the method comprises the following steps that enterprises are divided into a plurality of enterprise credit levels according to enterprise credit levels, and the divided enterprise level credits form a standard enterprise credit level set B (B1, B2, B3, B4, B5), B1, B2, B3, B4 and B5 which are respectively corresponding to a first enterprise credit level, a second enterprise credit level, a third enterprise credit level, a fourth enterprise credit level and a fifth enterprise credit level; the method comprises the steps that a loan fund ratio is expressed as a ratio of total assets amount to loan amount of an enterprise to be invested, the loan fund ratio is divided into a plurality of loan fund ratios according to a set ratio range, a standard loan fund ratio set D (D1, D2, dj, dm) is formed, dj is expressed as a jth loan fund ratio, and if the ratio of the total assets amount to the loan amount of the enterprise is smaller than dj and larger than D (j-1), the loan fund ratio of the enterprise is dj;
the investment evaluation module receives the enterprise types, the registered funds, the enterprise credit levels, the total asset amount and the loan amount of each to-be-invested project company sent by the information screening and dividing module, compares the obtained enterprise types of each to-be-invested project company with the enterprise types stored in the risk storage database one by one to obtain the proportion occupied by each enterprise type, and forms a to-be-invested enterprise type proportion set C (C1, C2,. so, cs,. so, cw), wherein cs represents the enterprise type proportion coefficient corresponding to the s to-be-invested project enterprise, and belongs to x1, x2, x3, x4, s is 1,2,. so, w; respectively comparing the registered funds with registered fund ranges corresponding to different registered fund grades stored in a risk storage database, and obtaining registered fund grades of all project companies to be invested and fund grade coefficients corresponding to the registered fund grades, wherein the fund grade coefficients corresponding to the registered fund grades of all project companies to be invested form an enterprise fund grade coefficient set D (D1, D2, D, ds, D, dw) to be tested, ds is expressed as a fund grade coefficient corresponding to an s-th project enterprise to be invested, and belongs to an element of gk1, gk2, g. gki, g. gkn in standard fund grade coefficients; the enterprise credit levels of all to-be-invested project companies are matched with enterprise credit levels stored in a risk storage database one by one to obtain enterprise credit levels corresponding to all to-be-invested project companies, and the obtained enterprise credit levels form an enterprise credit level set V (V1, V2.,. ts.,. vw) to be detected, wherein vs is expressed as enterprise credit levels corresponding to the s-th to-be-invested project company; comparing the total amount of funds of all to-be-invested project companies with the amount of loans to obtain a ratio of the loans to funds, comparing the obtained ratio of the loans to funds with each ratio of the loans to funds in a standard ratio set of the loans to funds stored in a risk storage database one by one to obtain a ratio of the loans to funds corresponding to each to-be-invested project company, and forming a ratio set U (U1, U2,..,. us,..,. U.,. uw.) of the to-be-invested project companies and a ratio set R (R1, R2.,. rs,. R.,. rw) of the to-be-invested project companies, wherein us represents the ratio of the to-be-invested project companies and rs represents the ratio of the to-be-invested project companies;
the investment evaluation module counts evaluation risk coefficients of each to-be-invested item company of enterprise investment according to the to-be-invested enterprise type specific gravity set C, the to-be-invested enterprise fund grade coefficient set D, the to-be-invested enterprise credit grade set V, the to-be-invested enterprise credit fund ratio set U and the to-be-invested enterprise credit fund specific gravity coefficient set R
Sending the counted investment evaluation risk coefficients of the companies for investing the items to be invested to the decision server;
the financial checking module is used for receiving the basic business information of the enterprise sent by the financial information acquisition module, extracting the financial income, financial expenditure, inventory quantity, inventory type and unit price corresponding to the inventory type in the basic business information of the enterprise, dividing the extracted financial income and financial expenditure into month income and month expenditure according to the month of income and expenditure, respectively dividing the month income and expense into month income and month expense, sending the divided month income and expense to the financial analysis module, and sending the inventory quantity, inventory type and unit price corresponding to the inventory type to the decision server;
the financial analysis module is used for receiving income and expenditure of each month of the enterprise sent by the financial checking module, meanwhile, acquiring financial income and financial expenditure list contents of each month recorded in a local storage database, extracting financial information in the financial income and financial expenditure list contents, accumulating to obtain a financial income sum and a financial expenditure sum recorded by each month, comparing the accumulated financial income sum recorded by each month with income of a corresponding month of the enterprise to obtain a month income comparison set H (H1, H2.., hq.,. H12), comparing the accumulated financial expenditure sum with the expenditure of the corresponding month of the enterprise to obtain a month expenditure comparison set Z (Z1, Z2.,. the.,. zq.., Z12), sending the statistical month income comparison set and the month expenditure comparison set to the decision server, wherein hq is expressed as a difference value between income of the enterprise in the qth month and financial income accumulated in the monthly expenditure list, zq is expressed as the difference between the business expenditure in the qth month and the financial expenditure accumulated in the entry list;
the decision server receives the monthly income comparison set and the monthly payment comparison set sent by the financial analysis module, and calculates a monthly income statistical error coefficient delta and a monthly payment statistical error coefficient according to the monthly income comparison set and the monthly payment comparison set
The decision server judges the monthly income statistical error coefficient delta and the monthly payment statistical error coefficient
Respectively comparing the monthly income statistical error coefficient with a set standard monthly payment error coefficient and the standard monthly payment error coefficient, sending the monthly income statistical error coefficient and the monthly payment statistical error coefficient to an early warning display terminal, and if the monthly income statistical error coefficient is larger than the set standard monthly income error coefficient or the monthly payment statistical error coefficient is larger than the set standard monthly payment error coefficient, extracting preventive measures and suggestions stored in a local storage database to the early warning display terminal by the decision server;
meanwhile, the decision server receives all items to be invested sent by the investment evaluation moduleThe investment evaluation risk coefficients of the companies are screened out from the received investment evaluation risk coefficients of all the to-be-invested project companies, the to-be-invested project company with the smallest investment evaluation risk coefficient is screened out, the screened to-be-invested project company with the smallest investment evaluation risk coefficient and the corresponding investment evaluation risk coefficient are sent to an early warning display terminal, and the screened investment evaluation risk coefficients are combined with the monthly income statistical error coefficient delta and the monthly payment statistical error coefficient delta of the enterprise
The enterprise comprehensive financial risk coefficient is counted, and the counted enterprise comprehensive risk coefficient is sent to an early warning display terminal, wherein the calculation formula of the enterprise comprehensive financial risk coefficient is
Eta is expressed as an enterprise comprehensive financial risk coefficient,
the investment evaluation risk coefficient corresponding to the project company to be invested with the minimum investment evaluation risk coefficient is expressed, delta is the monthly income statistical error coefficient accumulated by each month of the enterprise,
the statistical error coefficient is expressed for the accumulated month count of each month of the enterprise,
expressed as a scale factor, take 0.52;
the local storage database is used for storing financial income and financial expenditure list contents which are input by personnel in advance, storing the goods types sold by the enterprises in each month and the quantity sold by each goods type, and simultaneously storing preventive measures and suggestions corresponding to different enterprise operation risks;
the early warning display terminal is used for displaying and displaying the monthly income statistical error coefficient, the monthly payment statistical error coefficient, the investment evaluation risk coefficient, the enterprise comprehensive financial risk coefficient and the project company to be invested with the minimum screened investment evaluation risk coefficient, and displaying corresponding preventive measures and suggestions of the monthly income statistical error coefficient, the monthly payment statistical error coefficient, the investment evaluation risk coefficient and the enterprise comprehensive financial risk coefficient.
2. An intelligent financial decision big data analysis system according to claim 1, wherein: the basic operation information of the enterprise comprises financial income, financial expenditure, total profit amount, accounts receivable amount, bank debit amount, inventory type, unit price corresponding to the cargo type and a plurality of contents of the items to be invested, wherein the contents of the items to be invested comprise names of the companies to be invested, enterprise types, registration places, registration funds, enterprise credit levels, total amount of assets, loan amount and amount to be invested.
3. An intelligent financial decision big data analysis system according to claim 1, wherein: different ratio of the credits and funds correspond to different ratio of the credits and the funds, namely gd1, gd 2., gdj., gdm, and gd1 < gd 2. < gdj. < gdm, gd1+ gd 2. + gdj. + gdm. +1, gdj represents the ratio of the credits and the funds of the jth ratio.
4. An intelligent financial decision big data analysis system according to claim 1, wherein: the calculation formula of the investment evaluation risk coefficient is
Wherein f issExpressed as the time period for the establishment of the s-th to-be-invested project company, cs is expressed as the enterprise type specific gravity coefficient corresponding to the s-th to-be-invested project enterprise, vs is expressed as the enterprise credit level corresponding to the s-th to-be-invested project company, ds is expressed as the fund level coefficient corresponding to the s-th to-be-invested project enterprise, us is expressed as the ratio of the to-be-invested funds corresponding to the s-th to-be-invested project company, and rs is expressed as the ratio of the to-be-invested funds corresponding to the s-The proportion coefficient of the corresponding credit fund ratio of the company.
5. An intelligent financial decision big data analysis system according to claim 1, wherein: the monthly income statistical error coefficient
Lambda is expressed as an error influence factor, 0.162 is taken, hq is expressed as the difference between the income of the enterprise in the qth month and the financial income accumulated in the entry list, hqFruit of Chinese wolfberryExpressed as actual revenue, hq, for the business in month qRecording deviceExpressed as the cumulative sum of the income entered by the business at month q.
6. An intelligent financial decision big data analysis system according to claim 1, wherein: the monthly expenditure statistical error coefficient
Lambda is expressed as the error-influencing factor, 0.162 is taken, zq is expressed as the difference between the business expenditure in the qth month and the financial expenditure accumulated in the entry list, zqFruit of Chinese wolfberryExpressed as the actual expenditure, zq, of the business in month qRecording deviceExpressed as the cumulative sum of the entered expenses for the business at month q.
7. An intelligent financial decision big data analysis system according to claim 1, wherein: the decision server receives the inventory quantity, the inventory type and the unit price corresponding to the inventory type sent by the financial checking module, counts the current inventory type and the storage quantity corresponding to each inventory type, compares the current inventory type and the storage quantity corresponding to each inventory type with the commodity type sold in the last month and the quantity of each commodity type sold in the local storage database, and extracts the corresponding preventive decision measures and suggestions from the local storage database to the early warning display terminal if the stored commodity type is insufficient or the storage quantity corresponding to each commodity type is less than the quantity of each commodity type sold in the last month;
and if the stored goods types are the same as the goods types sold in the last month in quantity, and the ratio of the quantity of each stored goods type to the quantity of the goods sold in the last month is greater than the set quantity ratio of the goods types, extracting corresponding preventive measures and suggestions stored in the local storage database by the decision server to the early warning display terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811564114.3A CN109658217B (en) | 2018-12-20 | 2018-12-20 | Intelligent financial decision big data analysis system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811564114.3A CN109658217B (en) | 2018-12-20 | 2018-12-20 | Intelligent financial decision big data analysis system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109658217A CN109658217A (en) | 2019-04-19 |
| CN109658217B true CN109658217B (en) | 2021-01-12 |
Family
ID=66115974
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811564114.3A Active CN109658217B (en) | 2018-12-20 | 2018-12-20 | Intelligent financial decision big data analysis system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109658217B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111402021A (en) * | 2020-03-09 | 2020-07-10 | 济源职业技术学院 | Financial risk early warning management system |
| CN111369177A (en) * | 2020-03-30 | 2020-07-03 | 深圳市云智融科技有限公司 | Financial data analysis method and device and computer readable storage medium |
| CN111652705A (en) * | 2020-06-11 | 2020-09-11 | 南阳理工学院 | Comprehensive financial management platform |
| CN111724266A (en) * | 2020-06-29 | 2020-09-29 | 信阳农林学院 | Financial decision big data analysis system |
| CN112767120A (en) * | 2020-12-31 | 2021-05-07 | 山东数字能源交易中心有限公司 | Enterprise evaluation data processing method and device |
| CN112767114A (en) * | 2021-02-26 | 2021-05-07 | 科大讯飞股份有限公司 | Enterprise diversified decision method and device, electronic equipment and storage medium |
| CN115564545A (en) * | 2022-10-13 | 2023-01-03 | 连云港锐腾信息科技有限公司 | Big data intelligent safety system based on financial management |
| CN116341917B (en) * | 2023-04-27 | 2023-10-13 | 山东柏源技术有限公司 | Engineering financial management risk assessment system based on data analysis |
| CN116703130B (en) * | 2023-08-08 | 2023-10-13 | 威海市城市规划技术服务中心有限公司 | Engineering measurement wisdom planning design management system |
| CN117687764A (en) * | 2024-02-04 | 2024-03-12 | 南京九洲会计咨询有限公司 | Financial data intelligent accounting method and system based on SaaS platform |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7962396B1 (en) * | 2006-02-03 | 2011-06-14 | Jpmorgan Chase Bank, N.A. | System and method for managing risk |
| CN105389732A (en) * | 2015-11-30 | 2016-03-09 | 安徽融信金模信息技术有限公司 | Enterprise risk assessment method |
| CN106156968A (en) * | 2015-04-10 | 2016-11-23 | 上海道拓文化传播有限公司 | A kind of many storehouses checking method and system |
| CN108446970A (en) * | 2018-03-30 | 2018-08-24 | 广州市华泓鞋业有限公司 | A kind of company information big data analysis system |
| CN108596432A (en) * | 2018-03-21 | 2018-09-28 | 安徽天勤盛创信息科技股份有限公司 | A kind of management of investment assessment system based on Cloud Server |
| CN108765116A (en) * | 2018-05-18 | 2018-11-06 | 北京大账房网络科技股份有限公司 | Financial intelligence air control method for early warning |
| CN108932577A (en) * | 2018-04-25 | 2018-12-04 | 广州广电研究院有限公司 | A kind of assessment of business risk and early warning system |
| CN109034991A (en) * | 2018-09-07 | 2018-12-18 | 重庆满助智能科技研究院有限公司 | A kind of control of financial risk method for early warning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8843410B2 (en) * | 2008-01-22 | 2014-09-23 | United Parcel Services Of America, Inc. | Systems, methods, and computer program products for supply chain finance |
-
2018
- 2018-12-20 CN CN201811564114.3A patent/CN109658217B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7962396B1 (en) * | 2006-02-03 | 2011-06-14 | Jpmorgan Chase Bank, N.A. | System and method for managing risk |
| CN106156968A (en) * | 2015-04-10 | 2016-11-23 | 上海道拓文化传播有限公司 | A kind of many storehouses checking method and system |
| CN105389732A (en) * | 2015-11-30 | 2016-03-09 | 安徽融信金模信息技术有限公司 | Enterprise risk assessment method |
| CN108596432A (en) * | 2018-03-21 | 2018-09-28 | 安徽天勤盛创信息科技股份有限公司 | A kind of management of investment assessment system based on Cloud Server |
| CN108446970A (en) * | 2018-03-30 | 2018-08-24 | 广州市华泓鞋业有限公司 | A kind of company information big data analysis system |
| CN108932577A (en) * | 2018-04-25 | 2018-12-04 | 广州广电研究院有限公司 | A kind of assessment of business risk and early warning system |
| CN108765116A (en) * | 2018-05-18 | 2018-11-06 | 北京大账房网络科技股份有限公司 | Financial intelligence air control method for early warning |
| CN109034991A (en) * | 2018-09-07 | 2018-12-18 | 重庆满助智能科技研究院有限公司 | A kind of control of financial risk method for early warning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109658217A (en) | 2019-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109658217B (en) | Intelligent financial decision big data analysis system | |
| Cumby et al. | On the definition and magnitude of recent capital flight | |
| Tamirisa | Exchange and capital controls as barriers to trade | |
| US7668768B2 (en) | Computer-implemented method for evaluating an investment | |
| Ongena et al. | Creditor concentration: An empirical investigation | |
| CN112508674A (en) | Financial risk intelligent analysis method and system based on big data | |
| CN109118094A (en) | A kind of enterprises service application system based on credit system | |
| CN111402021A (en) | Financial risk early warning management system | |
| Legeida et al. | Modeling value added tax (VAT) revenues in a transition economy: Case of Ukraine | |
| Bala et al. | Corporate liquidity and profitability of listed food and beverages firms in Nigeria | |
| CN111695721B (en) | Accounting information comparability determination method, application method and device | |
| ÖZkul et al. | Financial condition analysis in municipalities: A case of Turkey | |
| Shillinglaw | Profit analysis for abandonment decisions | |
| Ambunya | The relationship between exchange rate movement and stock market returns volatility at the nairobi securities exchange | |
| Rutherford | The interpretation of cash flow reports and the other allocation problem | |
| Fenyves | Problems with the application of conventional financial ratios in corporate risk measurement | |
| Fixler et al. | Computing Real Bank Services | |
| Das et al. | A Study on cash management and determinants of cash holding | |
| Iren et al. | The Applicability Of The Prudence Principle To Bank Companies In The Vision Of Ias 37 | |
| Harberger | Notes on some issues in social project evaluation | |
| Cramer | The work money does: The transaction velocity of circulation of money in the Netherlands, 1950–1978 | |
| Heningtyas et al. | Comparative financial risk of conventional and Islamic bank | |
| Putra et al. | The Exchange Rate and Exporting: Evidence from the Indonesian Manufacturing Sector | |
| Markowicz et al. | Mirror data asymmetry in international trade by commodity group: the case of intra-Community trade | |
| Gregory | Excessive prices and the role of profitability analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Li Yongming Inventor after: Chen Xulong Inventor after: Huang Hongliang Inventor after: Gong Jiaxin Inventor after: Zhong Hu Inventor before: Chen Xulong Inventor before: Huang Hongliang Inventor before: Gong Jiaxin Inventor before: Zhong Hu |