CN110941652A - Analysis method of bank flow data - Google Patents
Analysis method of bank flow data Download PDFInfo
- Publication number
- CN110941652A CN110941652A CN201911231575.3A CN201911231575A CN110941652A CN 110941652 A CN110941652 A CN 110941652A CN 201911231575 A CN201911231575 A CN 201911231575A CN 110941652 A CN110941652 A CN 110941652A
- Authority
- CN
- China
- Prior art keywords
- bank
- data
- enterprise
- transaction
- flow data
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/08—Payment architectures
- G06Q20/10—Payment architectures specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
- G06Q20/102—Bill distribution or payments
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Probability & Statistics with Applications (AREA)
- Marketing (AREA)
- Technology Law (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
本发明提供了一种银行流水数据的分析方法。所述方法包括：获取企业经营过程中产生的银行流水数据；从银行流水数据中，分别识别经营性流水、筹资性流水、投资性流水、往来款、异常交易、关联交易；对往来款、异常交易及关联交易从企业经营的银行流水数据中清除；根据清除往来款、异常交易、关联交易后的银行流水数据，全程自动分析企业的经营状况。本发明所提供的银行流水数据的分析方法消除银行流水数据中出现的与往来款、异常交易、关联交易相关的流水数据对最终分析结果的不利影响，更加真实科学的反应企业的经营情况。
The invention provides a method for analyzing bank flow data. The method includes: acquiring bank flow data generated in the course of business operation; from the bank flow data, respectively identifying operating flow, financing flow, investment flow, current account, abnormal transaction, and associated transaction; Transactions and related transactions are cleared from the bank flow data operated by the enterprise; according to the bank flow data after clearing current accounts, abnormal transactions, and related transactions, the entire operation status of the enterprise is automatically analyzed. The analysis method of bank flow data provided by the present invention eliminates the adverse effects of flow data related to current accounts, abnormal transactions and related transactions in the bank flow data on the final analysis result, and more truly and scientifically reflects the business situation of the enterprise.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a bank flow data analysis method.
Background
The bank flow automatic analysis system classifies the flow through multidimensional data analysis, screens out real and effective enterprise operation data, and provides powerful data support for enterprise credit lines.
At present, the bank pipelining analysis system on the market only performs perspective analysis on the bank pipelining data and only simply classifies the collected bank pipelining data. Tests show that the type of data adopted in the analysis system is single, and the accuracy of the analysis result is poor, so that the operation condition of an enterprise cannot be truly and scientifically reflected.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an analysis method of bank flow data, which can comprehensively and accurately analyze the operation condition of an enterprise.
In order to solve the technical problem, the invention provides a method for analyzing bank pipelining data, which comprises the following steps: acquiring bank flow data generated in the enterprise operation process; identifying operational flow, financing flow, investing flow, money, abnormal transaction and associated transaction from bank flow data respectively; eliminating the forward money, abnormal transactions and related transactions from bank running data managed by enterprises; and automatically analyzing the operation condition of the enterprise in the whole process according to the bank running data after the removal of the incoming money, the abnormal transaction and the associated transaction.
In some embodiments, acquiring bank flow data generated during the business operation process includes: and acquiring bank running data managed by the enterprise through a plurality of different accounts of the enterprise.
In some embodiments, acquiring bank flow data generated in the enterprise operation process further comprises: after acquiring bank flow data managed by an enterprise from a plurality of different accounts of the enterprise, splicing the bank flow data of the same bank account at different times; the same six key fields are extracted through a key word matching technology, wherein the key fields comprise: transaction time, opposite account name, credit generation amount, debit generation amount, balance and abstract.
In some embodiments, identifying from the bank flow data, an operational flow, a financing flow, a round trip, an anomalous transaction, a correlation transaction, respectively, comprises: screening out the associated transactions by taking the name of the opposite party as an index according to the enterprise associated list; according to the characteristics of transaction amount, transaction frequency, transaction time, refund and re-payment and the like, screening abnormal transactions, wherein the abnormal transactions comprise: high-volume transfer, public transfer in non-working time and refund repayment; and screening out the money to be paid according to the transaction characteristics and the matching result of the abstract information and the keywords.
In some embodiments, the whole-course automatic analysis of the business condition of the enterprise according to the bank running data after clearing the money, the abnormal transaction and the associated transaction comprises the following steps: and in the transaction time dimension, the business operation condition is counted.
In some embodiments, the method for automatically analyzing the operation condition of the enterprise in the whole course according to the bank running data after clearing the money from the incoming and the abnormal transactions and the associated transactions further comprises the following steps: performing fluctuation analysis of the operating cost according to the bank running data; and carrying out wage analysis on the enterprise staff according to the bank flow data.
In some embodiments, payroll analysis of enterprise employees is performed, including: and comparing the salary analysis result of the enterprise staff with salary statistical data published by each province statistical bureau.
In some embodiments, the statistics of business operations include: the average daily balance, and the first ten counter-parties.
In some embodiments, the statistics of the top ten counterparties include: statistics of the outflow of funds, and statistics of the inflow of funds.
After adopting such design, the invention has at least the following advantages:
the invention can eliminate the adverse effect of the running data related to the current money, the abnormal transaction and the associated transaction in the running data of the bank on the final analysis result, and more truly and scientifically reflect the operation condition of the enterprise.
Drawings
The foregoing is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description.
FIG. 1 is a schematic diagram of a data item structure of bank pipelining data provided by the present invention;
fig. 2 is a flowchart of an analysis method of bank flow data provided by the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
FIG. 1 illustrates various data items in the banking pipeline data provided by the present invention. Referring to fig. 1, the bank pipelining data includes: banking data relating to the business is collected from four banks, each commercial bank, and each different city merchant bank.
It should be noted that the system is compatible with bank flow data in different formats. Therefore, the system can be compatible no matter from which bank the data source of the pipeline data comes.
By screening and summarizing the bank running data from different data sources, key fields in the bank running data are identified from the running data in different formats. Referring to fig. 1, these key fields may have: time of transaction, name of the opposite party, amount of credit, amount of debit, balance, summary, etc.
Through further analysis of the data characteristics in the various key fields described above, bank flow data can be distinguished into six categories. These six categories are: management flow, financing flow, investment flow, associated transaction, money to go, and abnormal transaction.
Through the analysis of the characteristics, six types of flow data are identified, and the most typical case is that the forward and backward money is identified through the characteristics of the summary data. Because, the running data of the current money is explicitly stated in the abstract.
The analysis method of the bank flow book provided by the invention classifies the six types of flow data respectively in the analysis process, so that the analysis result of the operation condition obtained by analyzing the flow data classified according to the classification is more comprehensive and accurate.
In addition, among the six types of flow data, some types of flow data have adverse effects on the final analysis result if no special processing is performed. For example, anomalous transactions, due to their own data characteristics, should not be taken into account during the analysis process. Similar categories also include: to and from and associated transactions.
The abnormal transactions, the money to be sent and the related transactions are cleared in the analysis process, and the final analysis result is not adversely affected, so that the analysis result of the operation condition is more accurate.
The system has higher compatibility. An enterprise has multiple bank accounts, each with different functions. If an account is analyzed, the data is too single. The system supports multiple accounts of one enterprise, the flow of different banks can be analyzed simultaneously and uniformly, the flow formats of different banks can be automatically adjusted, the data of each account in the same time period are collected and analyzed, the system is automatic in the whole process, and a complete analysis report can be automatically generated once the report is submitted.
The system has comprehensiveness and accuracy in data analysis. The bank running data is divided into six categories and two statistics, wherein the six categories comprise management running, financing running, investment running, money to go, abnormal transactions and related transactions. Both statistics are daily balance statistics, and the top ten counterparties (including inflow/outflow). Here, the counterparty refers to the counterparty in the transaction.
The detail of the commercial flowing water is sales return, income outside the business and others. The operating running water flow details are purchase money, salary, water, electricity, gas, rent, tax and others.
The salary part is analyzed and compared in combination with the wage level of the region where the enterprise is located, the hydropower gas is analyzed and whether large fluctuation exists or not in the time dimension, and the salary part and the water gas part can greatly reflect whether the enterprise normally operates or not.
The financing pipelining module counts the proportion of the liabilities of the enterprises and can fully reflect the liabilities of the enterprises. And adding lists of various large-scale financial mechanisms for matching, and ensuring the accuracy of data.
The associated transactions comprise transactions of shareholders, enterprises with invested out-of-shareholders, enterprises invested out-of-business, director of invested out-of-business and the like. The proportion of the associated transaction flow occupying the flow is large, and the probability of false transactions is also large, so the system independently divides the transactions into one block.
The association transaction mainly involves the corporate legal person and the company associated with the board of director. Aiming at each enterprise, the system can automatically generate an associated enterprise list, and the user name is compared with the associated enterprise list.
Abnormal transactions are screened in dimensions such as wrong refund, public transactions in non-working time, high-frequency and large-amount (non-top ten suppliers) transactions, and account which does not have transactions for a long time suddenly enters large-amount funds. High frequency: more than 30% of the daily average number of strokes. Large amount: the monthly income is more than 15 percent.
The system can provide data such as inflow/outflow proportion, management water flow detail and proportion, liability rate, net inflow, daily average balance and the like, and can comprehensively and truly reflect the enterprise management condition.
After an enterprise uploads a bank flow to the system, the system starts to read bank flow documents (xls or xlsx formats), data splicing is carried out on flow of the same account at different times, the difference of the flow document formats among different banks is large, and the system extracts the same six fields through a keyword matching technology: 1. transaction time 2, opposite account name 3, credit generation amount 4, debit generation amount 5, balance 6 and summary. The transaction time field is extracted from fields such as transaction time, transaction date or date and the like into a field containing date and time; the opposite account name field is named uniformly by the opposite account name, the opposite name and the like; the credit generation amount field is named uniformly by the fields of credit generation amount, income and the like; the debit generation amount field is named uniformly by the debit generation amount field, the debit expenditure field and other fields; the balance field is named uniformly by account balance, transaction balance and the like; the abstract fields are named uniformly by abstract, epilogue, transaction information, transaction purpose, remarks and the like.
And analyzing the business operation condition on the transaction time dimension, wherein the business transaction and the daily average balance/minimum balance are counted from the relation of money and time. The opposite account name is used for transaction detail statistics of the first ten counter parties and each module, and similar financial institutions/banks in the related transaction and the financing transaction are matched from the opposite account name. The credit generation amount, the debit generation amount and the balance can be counted from the inflow/outflow of each module and the proportion of the amount. Most transaction classifications are matched through keywords in the summary information, and mainly comprise wages, rent, payment, water, electricity, gas, tax, commission fees, interest and the like.
The modules are overlapped, for example, one transaction may belong to related transaction, abnormal transaction, and transaction money, so that the analysis sequence of the data and the data screening logic have a large influence on the final analysis result and the operation speed, and the system performs key design on the data analysis logic. The system preferentially extracts data with high extraction definition by using data which is easy to scatter. Firstly, a data pivot table is adopted, the name of the opposite user is used as an index, and the related transaction is screened out, so that the data accuracy is high. Secondly, the refund in the abnormal transaction is paid again, if part of the data is classified into other types, the data cannot be screened subsequently, and if the data is forged, the amount of the refund is larger, so that the data is screened preferentially. Thirdly, transferring accounts in non-working time, and sorting data with high accuracy through time checking; fourthly, the money to be exchanged has clear remarks in the abstract and the transaction characteristics are obvious. Fifthly, financing the running water. And sixthly, the outflow part of the business transaction comprises compensation, house renting, water, electricity, gas and tax. The abstract characteristics are also obvious. Seventh, the rest of the commercial effluent, which mainly includes money (low value consumables, office supplies, deposit, etc.). And eighthly, dividing the rest transaction flow into a sales return and a purchase of an operational flow according to income and expenditure.
Fig. 2 shows an execution flow chart of the analysis method of the bank flow data provided by the invention. Referring to fig. 2, the method for analyzing the bank flow data may include the following operation steps:
s201, the user logs in.
S202, the user uploads the bank flow data.
The system is compatible with bank flow data in various formats, so that the source bank of the uploaded bank flow data is not limited to one bank.
And S203, uploading the bank flow data to a background database.
And uploading the data to a background database, wherein the data consistency of the data in the analysis process is mainly required to be ensured.
And S204, triggering the background code to calculate.
The calculation process may be keyword matching, category identification, and the like.
And S205, unifying and splicing the format of the pipeline data.
Here, splicing refers to splicing of the pipeline data of the same account in different time periods.
S206, a plurality of data tables are generated according to the pipeline data.
For the generation of the data table, the original pipeline data has been processed considerably. Thus, these data tables may already be manually analyzed for some assessment of the business' operational status.
And S207, drawing a chart according to the data table.
The purpose of graphing is to make the presentation of the final generated report more intuitive.
And S208, generating an excel report according to the set format.
S209, converting the excel report into a PDF format.
The conversion here only involves the conversion of the data format and does not further process the report content.
And S210, uploading the report in the PDF format to a background database.
The purpose of uploading the database is to do data persistence work.
And S211, downloading the running water analysis report by the user.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the present invention in any way, and it will be apparent to those skilled in the art that the above description of the present invention can be applied to various modifications, equivalent variations or modifications without departing from the spirit and scope of the present invention.
Claims (9)
1. A bank flow data analysis method is characterized by comprising the following steps:
acquiring bank flow data generated in the enterprise operation process, wherein the bank flow data comprises bank flow data in various formats;
identifying operational flow, financing flow, investing flow, money, abnormal transaction and associated transaction from bank flow data respectively;
clearing the current money, abnormal transactions and related transactions from bank running data of enterprise operation;
and automatically analyzing the operation condition of the enterprise in the whole process according to the bank running data after the removal of the money from the coming and going, the abnormal transaction and the associated transaction.
2. The method for analyzing bank flow data according to claim 1, wherein the acquiring bank flow data generated in the enterprise operation process comprises:
and acquiring bank running data managed by the enterprise through a plurality of different accounts of the enterprise.
3. The method for analyzing bank flow data according to claim 2, wherein acquiring bank flow data generated in an enterprise operation process further comprises:
after acquiring bank flow data managed by an enterprise from a plurality of different accounts of the enterprise, splicing the bank flow data of the same bank account at different times;
the same six key fields are extracted through a key word matching technology, wherein the key fields comprise: transaction time, opposite account name, credit generation amount, debit generation amount, balance and abstract.
4. The method for analyzing bank flow data according to claim 1, wherein identifying operational flow, financing flow, investing flow, money to go, abnormal transaction, and related transaction from the bank flow data comprises:
screening out the associated transactions by taking the name of the opposite party as an index according to the associated enterprise list;
screening abnormal transactions according to characteristics such as transaction amount and frequency, transaction time, refund and reimbursement and the like;
and screening out the money to be paid according to the transaction characteristics and the matching result of the abstract information and the keywords.
5. The method for analyzing the bank running data according to claim 1, wherein the method for automatically analyzing the operation condition of the enterprise in the whole process according to the bank running data after clearing the money, the abnormal transaction and the associated transaction comprises the following steps:
and in the transaction time dimension, the business operation condition is counted.
6. The method for analyzing the bank flow data according to claim 5, wherein the operation status of the enterprise is automatically analyzed in the whole course according to the bank flow data after the removal of the money, the abnormal transaction and the associated transaction, and further comprising:
performing fluctuation analysis of the operating cost according to the bank running data;
and carrying out wage analysis on the enterprise staff according to the bank flow data.
7. The method for analyzing bank pipelining data according to claim 6, wherein performing payroll analysis of employees of a business comprises:
and comparing the salary analysis result of the enterprise staff with salary statistical data published by each province statistical bureau.
8. The method for analyzing bank pipelining data according to claim 5, wherein the statistics of business operations include: the average daily balance, and the first ten counter-parties.
9. The method of analyzing bank pipelining data according to claim 8, wherein the statistics of the top ten counterparties include: statistics of the outflow of funds, and statistics of the inflow of funds.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911231575.3A CN110941652A (en) | 2019-12-05 | 2019-12-05 | Analysis method of bank flow data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911231575.3A CN110941652A (en) | 2019-12-05 | 2019-12-05 | Analysis method of bank flow data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110941652A true CN110941652A (en) | 2020-03-31 |
Family
ID=69909851
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911231575.3A Pending CN110941652A (en) | 2019-12-05 | 2019-12-05 | Analysis method of bank flow data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110941652A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111738847A (en) * | 2020-08-03 | 2020-10-02 | 成都寻道数财科技有限公司 | System and method for matching lenders of same economic service in certificate sheet |
| CN114187097A (en) * | 2021-12-14 | 2022-03-15 | 中国建设银行股份有限公司 | Data analysis methods, devices, equipment, media and products |
| CN114841694A (en) * | 2022-04-06 | 2022-08-02 | 北京蓝海在线科技有限公司 | A method of receiving and disbursing funds |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102982421A (en) * | 2012-12-18 | 2013-03-20 | 远光软件股份有限公司 | Preparation method and system for merging cash flow statements of group based on bank reconciliations |
| CN104966172A (en) * | 2015-07-21 | 2015-10-07 | 上海融甸信息科技有限公司 | Large data visualization analysis and processing system for enterprise operation data analysis |
| CN106127522A (en) * | 2016-06-29 | 2016-11-16 | 苏州迈科网络安全技术股份有限公司 | Dig based on network data and analyze method and system according to the enterprise management condition of technology |
| CN109658478A (en) * | 2017-10-10 | 2019-04-19 | 爱信诺征信有限公司 | It is a kind of that the method and system of enterprise's portrait are provided |
-
2019
- 2019-12-05 CN CN201911231575.3A patent/CN110941652A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102982421A (en) * | 2012-12-18 | 2013-03-20 | 远光软件股份有限公司 | Preparation method and system for merging cash flow statements of group based on bank reconciliations |
| CN104966172A (en) * | 2015-07-21 | 2015-10-07 | 上海融甸信息科技有限公司 | Large data visualization analysis and processing system for enterprise operation data analysis |
| CN106127522A (en) * | 2016-06-29 | 2016-11-16 | 苏州迈科网络安全技术股份有限公司 | Dig based on network data and analyze method and system according to the enterprise management condition of technology |
| CN109658478A (en) * | 2017-10-10 | 2019-04-19 | 爱信诺征信有限公司 | It is a kind of that the method and system of enterprise's portrait are provided |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111738847A (en) * | 2020-08-03 | 2020-10-02 | 成都寻道数财科技有限公司 | System and method for matching lenders of same economic service in certificate sheet |
| CN111738847B (en) * | 2020-08-03 | 2020-11-20 | 成都寻道数财科技有限公司 | System and method for matching lenders of same economic service in certificate sheet |
| CN114187097A (en) * | 2021-12-14 | 2022-03-15 | 中国建设银行股份有限公司 | Data analysis methods, devices, equipment, media and products |
| CN114187097B (en) * | 2021-12-14 | 2024-12-24 | 中国建设银行股份有限公司 | Data analysis methods, devices, equipment, media and products |
| CN114841694A (en) * | 2022-04-06 | 2022-08-02 | 北京蓝海在线科技有限公司 | A method of receiving and disbursing funds |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20040064404A1 (en) | Computer-based method for automatic remote coding of debtor credit databases with bankruptcy filing information | |
| US20220398583A1 (en) | Transaction reconciliation and deduplication | |
| Dabbicco | A comparison of debt measures in fiscal statistics and public sector financial statements | |
| US10984446B1 (en) | Method and system for predicting relevant offerings for users of data management systems using machine learning processes | |
| CN110941652A (en) | Analysis method of bank flow data | |
| US11798100B2 (en) | Transaction counterpart identification | |
| US11935073B1 (en) | System and methods for general ledger flagging | |
| KR20200090721A (en) | A system of providing consulting optimization financial statement | |
| US20070255651A1 (en) | Batch processing of financial transactions | |
| RU2474872C2 (en) | Electronic accounting device and method of recording data into financial account base used therein | |
| Jermakowicz et al. | Framework-based teaching of IFRS: the case of Deutsche Bank | |
| US8745073B2 (en) | Nationwide send process | |
| US20160125554A1 (en) | System of managing and analyzing irs account data | |
| CN117009529A (en) | SWIFT message intelligent classification method, SWIFT message intelligent classification device, SWIFT message intelligent classification equipment and storage medium | |
| KR20200027842A (en) | A system of providing consulting optimization financial statement | |
| Bwacha et al. | The impact of liquidity on profitability: An explanatory study of the banking sector between 2008 and 2017 | |
| CN114463107A (en) | Intelligent financial working system based on artificial intelligence and method thereof | |
| Huang | General ledger computer auditing practice: Implementing a smart auditing approach | |
| Hosono et al. | Bank lending and firm activities: Overcoming identification problems | |
| Simanjuntak et al. | Explain How a Ledger and Posting help in The Recording Process. | |
| Reinamah et al. | Design of Accounting Information Systems of Cash Receiving and Expenditure in Improving Internal Control of Income of Weaving Tie MSME in the District South Central Timor | |
| Meliawati et al. | Accounting Treatment of Accounts Receivable and its Effect on Financial Statements and Account Receivables Collectability at The Royal Santrian Luxury Beach Villas | |
| CN120297257B (en) | Automatic financial statement generation method for ERP system | |
| Yesseleva-Plonka | " Blockchain Network System for Assessing SMEs' Creditworthiness | |
| Zalozhnev | Big banks' management control systems' software: Architecture. General requirements and functional components |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200331 |