Bank-enterprise account checking method and system, electronic equipment and storage medium

Abstract

The invention discloses a bank-enterprise account checking method and system, electronic equipment and a storage medium, and the method comprises the steps: creating a collection task which comprises an online bank flow collection task and an enterprise diary bookkeeping task; respectively configuring header fields and mapping of the collected data based on the collection task; establishing a reconciliation model table according to key elements of financial subject classes, abstracts, amounts, dates, accounting voucher numbers, online banking accounts, online banking housenames, bank names, time, borrowing and lending directions and amounts based on the acquired data; configuring a reconciliation association relationship between each data item of the online banking flow and the enterprise diary accounting data item; setting weight parameters of the data items in the account checking model table based on the incidence relation; and based on the weight parameters, successfully matching the online banking flow data items with the enterprise diary accounting data items to complete account checking. According to the invention, the timeliness, comprehensiveness, accuracy and integrity of bank and enterprise account checking are greatly improved, the efficiency of financial management is improved, and the cost and risk of financial management are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of account reconciliation, and in particular to a bank-enterprise account reconciliation method, system, electronic equipment and storage medium. Background Art

[0002] With the continuous development of enterprise scale and business, as well as the gradual enrichment of enterprise information and digital capabilities and means, the amount of data such as enterprise online banking flow, receipt, balance and statement, as well as enterprise journal and voucher is increasing. At the same time, enterprise management requirements are becoming more and more refined, and bank-enterprise reconciliation, balance adjustment sheet generation, receipt and voucher linkage, etc. require more timely, wider dimensions, finer granularity, and higher accuracy. Therefore, comprehensive, refined, intelligent, and fully automatic realization of bank-enterprise reconciliation, balance adjustment sheet generation, receipt and voucher linkage has gradually become a difficulty and pain point in enterprise financial management.

[0003] At present, the main implementation methods for corporate bank-enterprise reconciliation, balance adjustment sheet generation, receipt and voucher linkage are: medium and large enterprises generally build a treasury platform, use bank-enterprise direct connection technology to automatically synchronize corporate online banking flow, receipt, balance, statement and other information from different banks, and then associate the data synchronized by bank-enterprise direct connection with the accounting and voucher modules of the financial system, so as to realize automatic bank-enterprise reconciliation, balance adjustment sheet generation, receipt and voucher linkage. Most small and micro enterprises manually collect online banking flow, receipt and statement, as well as balance and other information, manually compile bank-enterprise reconciliation and balance adjustment table, and manually associate receipt and voucher.

[0004] Existing methods of collecting online banking data based on direct bank-enterprise connection or manually collecting online banking data and realizing bank-enterprise reconciliation. Based on the online banking data collection of bank-enterprise direct connection, bank-enterprise reconciliation requires financial system R&D manufacturers to develop interfaces and data docking or parameter configuration with the front-end machines and front-end programs provided by various banks, and then realize bank-enterprise reconciliation according to the actual accounting rules, habits and reconciliation rules of different enterprises. However, since not all banks can provide bank-enterprise direct connection services, a large part of the bank's online banking data cannot be collected through bank-enterprise direct connection, and the remaining online banking data for bank-enterprise reconciliation still needs to be collected manually. Manual collection of online banking data and manual realization of bank-enterprise reconciliation are completely dependent on individuals. Existing bank-enterprise reconciliation in the following scenarios and situations: only some banks provide bank-enterprise direct connection services; fixed reconciliation rules and logic cannot cover most corporate reconciliation needs; corporate journal data comes from multiple systems or platforms; bank-enterprise direct connection services have time and quantity restrictions on collecting online banking data; the timeliness and frequency of bank-enterprise reconciliation do not meet the financial management requirements of enterprises. In view of the above scenarios and situations, the existing bank-enterprise reconciliation solution cannot fully cover the automatic synchronization and collection of all corporate online banking data through the system or platform, and it is necessary to collect data manually in the online banking system or other methods. The bank-enterprise reconciliation rules and reconciliation logic are different for each enterprise, and it is impossible to meet the reconciliation needs of most enterprises through one system or platform. Involving different financial systems or platforms and different bank online banking front-end programs, they all need to be customized and implemented according to the specific needs of different enterprises and the requirements of different systems. At the same time, due to the time and quantity limitations of data synchronization through bank-enterprise direct connection, it is impossible to collect the full amount of online banking data in a more timely, comprehensive and accurate manner. Bank-enterprise reconciliation cannot be fully automated, and a large amount of manual operations are required. The timeliness, frequency, logic and rules of reconciliation are fixed, which cannot meet the financial management requirements of enterprises. Transaction flow, receipt and voucher cannot be effectively associated, and cannot be structured and archived and managed according to financial requirements. It is also impossible to achieve automatic data verification of one-to-many, many-to-one, many-to-many, etc., nor does it support business personnel to initiate real-time reconciliation at any time according to bank accounts, accounting units, reconciliation periods, etc., batch multiple accounts, single account triggering, support triggering the generation of pre-reconciliation at any time. Support account period locking, balance adjustment table export, etc.

[0005] That is, the existing bank-enterprise reconciliation does not collect data in a comprehensive and timely manner, and a large part of it still requires manual ferrying and manual collection. When the enterprise journals come from multiple heterogeneous systems, they cannot be effectively integrated and synchronized. The reconciliation rules and logic are fixed, which cannot meet the differentiated and diversified needs of the enterprise's financial field. The reconciliation frequency and flexibility are low, and the reconciliation accuracy is not high, which does not meet the requirements of enterprise refined management. Transaction records, receipts and vouchers cannot be effectively structured and archived and managed. Summary of the invention

[0006] In view of the shortcomings existing in the above problems, the present invention provides a bank-enterprise reconciliation method, system, electronic device and storage medium.

[0007] To achieve the above object, the present invention provides a bank-enterprise reconciliation method, comprising: Creating collection tasks, including online banking transaction collection tasks and corporate diary accounting tasks; Based on the collection task, respectively configure the header fields and mapping of the collected data; Based on the collected data, a reconciliation model table is established according to the key elements of financial subject type, summary, amount, date, accounting voucher number, online banking account, online banking account name, bank name, time, debit and credit direction, and amount; Configure the reconciliation relationship between each data item of online banking flow and the data item of enterprise journal accounting, and the said relationship includes including, greater than, less than, equal to, greater than or equal to, less than or equal to, and combined equal to; The weight parameters of the data items in the reconciliation model table are set based on the association relationship, the weight parameters of equal and combined equal are s, the weight parameters of inclusion are p, and the weight parameters of greater than or equal to and less than or equal to are e; Based on the weight parameters, the online banking transaction data items and the enterprise journal accounting data items are matched to successfully complete the reconciliation.

[0008] Preferably, the data source for collecting online banking transactions is an online banking system or a treasury system, and the data source for corporate diary accounting is a financial system or an ERP system.

[0009] Preferably, creating a collection task includes collection time, collection cycle, collection account and collection of running virtual machine resource information, and the running virtual machine resource information includes IP address, number of CPU cores, memory size, operating system and storage size.

[0010] Preferably, the mapping relationship between the entity table corresponding to the collected data and the reconciliation model table is stored in the corresponding flow management module.

[0011] Preferably, based on the weight parameter, successfully matching the online banking transaction data item with the enterprise journal account data item to complete the reconciliation includes: Calculate the total score of the comparison using the formula: t=g+q+(s+p+e)*j; In the formula: g is the result value of semantic comparison data; q is the weight score of the amount constraint condition; j is the number of auxiliary comparison fields; Calculate the matching result, the formula is: o=x+t; In the formula: x is the correlation relationship, if there is a correlation relationship, x=1, if there is no correlation relationship, x=0; If t / o>0.9, the weight of the corresponding data item in the reconciliation model table is not increased and the match is judged to be successful. When t / o<0.9, the weight of the corresponding data item in the reconciliation model table is increased and recalculated until the match is successful.

[0012] Preferably, g is the formula for the result value of the semantic comparison data: ɡ=θ+n*θ; Where: θ is the similarity; n is the character text distance.

[0013] Preferably, n is the character text distance formula: n = α - β / k; Where: α is 1, β is the number of edited characters in the text, and k is the maximum number of characters.

[0014] This application also provides a bank-enterprise reconciliation system, including: A creation module is used to create collection tasks, including online banking flow collection tasks and corporate diary accounting tasks; A configuration module, used to configure the header fields and mapping of the collected data based on the collection tasks; A reconciliation module is used to establish a reconciliation model table based on the collected data according to the key elements of financial subject type, summary, amount, date, accounting voucher number, online banking account, online banking account name, bank name, time, debit and credit direction, and amount; An association module is used to configure the reconciliation association relationship between each data item of the online banking flow and the data item of the enterprise diary account, and the association relationship includes including, greater than, less than, equal to, greater than or equal to, less than or equal to, and combined equal to; A weight module, used to set weight parameters of the data items in the reconciliation model table based on the association relationship, the weight parameter of equal and combined equal is s, the weight parameter of inclusion is p, and the weight parameter of greater than or equal to and less than or equal to is e; A matching module is used to match the online banking transaction data item with the enterprise diary accounting data item based on the weight parameter to successfully complete the reconciliation.

[0015] The present invention also provides an electronic device, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit executes the above method.

[0016] The present invention also provides a storage medium storing a computer program executable by an electronic device, and when the program runs on the electronic device, the electronic device executes the above method.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention greatly improves the timeliness, comprehensiveness, accuracy and completeness of bank-enterprise reconciliation, improves the efficiency of financial management, and reduces the cost and risk of financial management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural diagram of the bank-enterprise reconciliation method of the present invention; Figure 2 It is a flow chart of the bank-enterprise reconciliation method of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 The bank-enterprise reconciliation method is mainly implemented through the combination of software and hardware. The software part mainly adopts B / S architecture, including: intelligent reconciliation platform, automatic reconciliation service of intelligent reconciliation platform, automatic data collection service, external online banking system, treasury system, financial system or ERP system, etc., container service of public basic services, etc. The hardware part mainly includes servers running VMs and containers, USBServer centrally managed by online banking U shield, and online banking click-press matrix. The automatic data collection service requires users to configure VM scheduling strategy, basic data collection information, data collection rules and collection task arrangement, and the relationship mapping between online banking U shield and clicker. The intelligent reconciliation platform mainly completes account period management, receipt management, data checking, model management, data mapping management, flow management, and uncollected account management. The automatic reconciliation service of the intelligent reconciliation platform automatically analyzes, structures, and automatically checks the data according to the rules and models configured by the intelligent reconciliation platform, as well as the online banking data and enterprise journal data collected by the automatic data collection service. The online banking system or treasury system is the data source for online banking data collection, and the financial system or ERP system is the database source for enterprise journal data collection. The container service mainly provides services such as load, data storage, log service and file for all applications. The server of the hardware service realizes the management of VM through virtualization software. The USB Server and the online banking U shield click button must correspond to the physical online banking U shield one by one.

[0021] The core of the present invention is the data modeling related to bank-enterprise reconciliation. According to the knowledge in the field of enterprise finance, the online banking data and the enterprise journal data are automatically logically associated and mapped to model, and the system differences and data differences are shielded. Through the automatic matching of bank-enterprise data and manual correction of uncollected items, the enterprise accounting rules are automatically learned, and finally the intelligent automatic reconciliation reaches or approaches 100% accuracy.

[0022] Reference Figure 2 The present invention provides a bank-enterprise reconciliation method, comprising: Create collection tasks, including online banking transaction collection tasks and corporate diary accounting tasks; Configure the header fields and mapping of collected data based on the collection tasks; Based on the collected data, establish a reconciliation model table according to the key elements of financial account type, summary, amount, date, accounting voucher number, online banking account, online banking account name, bank name, time, debit and credit direction, and amount; Configure the reconciliation relationship between each data item in the online banking statement and the data item in the enterprise journal. The relationship includes including, greater than, less than, equal to, greater than or equal to, less than or equal to, and combined equal to; Set the weight parameters of the data items in the reconciliation model table based on the association relationship. The weight parameter for equality, equals and combined equals is s, the weight parameter for inclusion is p, and the weight parameter for greater than or equal to and less than or equal to is e; Based on the weight parameters, the online banking transaction data items and the enterprise journal accounting data items are matched to successfully complete the reconciliation.

[0023] In this embodiment, the data source for collecting online banking transactions is the online banking system or the treasury system, and the data source for corporate diary accounting is the financial system or the ERP system.

[0024] Specifically, the creation of a collection task includes the collection time, collection cycle, collection account, and collection of virtual machine resource information. The virtual machine resource information includes IP address, number of CPU cores, memory size, operating system, and storage size. The mapping relationship between the entity table corresponding to the collected data and the reconciliation model table is stored in the corresponding flow management module.

[0025] Furthermore, based on the weight parameters, the online banking transaction data items and the enterprise journal account data items are matched and the reconciliation is successfully completed, including: Calculate the total score of the comparison using the formula: t=g+q+(s+p+e)*j; In the formula: g is the result value of semantic comparison data; q is the weight score of the amount constraint condition; j is the number of auxiliary comparison fields; Calculate the matching result, the formula is: o=x+t; In the formula: x is the correlation relationship, if there is a correlation relationship, x=1, if there is no correlation relationship, x=0; If t / o≥0.9, the weight of the corresponding data item in the reconciliation model table is not increased and the match is judged to be successful. When t / o<0.9, the weight of the corresponding data item in the reconciliation model table is increased and recalculated until the match is successful.

[0026] Among them, g is the formula for the result value of semantic comparison data: ɡ=θ+n*θ; Where: θ is the similarity; n is the character text distance, n=α-β / k; Where: α is 1, β is the number of edited characters in the text, and k is the maximum number of characters.

[0027] In this embodiment, the main process of the collected data analysis and cleaning stage is as follows: The first step is to establish a bank-enterprise reconciliation model table in the financial field according to key elements such as financial subject type, summary, amount, date, accounting voucher number, online banking account, online banking account name, bank name, time and debit and credit direction, amount, etc. in the model management of the intelligent bank-enterprise reconciliation platform.

[0028] The second step is to set the header fields and mappings for collecting online banking transaction data according to the bank type (corporate treasury is considered a Class I bank) and account type on the intelligent bank-enterprise reconciliation platform.

[0029] The third step is to set the header fields and mappings for collecting corporate journal data according to bank type (corporate treasurers are considered a type 1 bank) and account type on the intelligent bank-enterprise reconciliation platform.

[0030] The fourth step is to set the header fields and mappings for the receipt data to be collected according to the bank type (corporate treasury is considered a Class I bank) and account type on the intelligent bank-enterprise reconciliation platform.

[0031] The fifth step is to configure the online banking or treasury transaction data, receipt data, enterprise journal data collection information, collection rules, collection tasks, etc. on the intelligent bank-enterprise reconciliation platform.

[0032] Step 6. Configure the online banking USB server information, U-shield location, click and press information, online banking address, account password and other information, enterprise journal address, account password and other information in the online banking U-shield management of the intelligent bank-enterprise reconciliation platform.

[0033] Step 7. Configure the VM virtual machine resource information required for the collection task in the online banking V management of the intelligent bank-enterprise reconciliation platform, including IP address, number of CPU cores, memory size, operating system, storage size and other information.

[0034] Step 8. Configure the collection tasks (collection time, bank / ERP / financial system, collection cycle, account, specific VM, etc.) on the intelligent bank-enterprise reconciliation platform, and automatically collect online banking transaction data, receipt data, and enterprise journal data. According to the mapping relationship between the corresponding entity table and the model table of the intelligent bank-enterprise reconciliation, save them to the corresponding transaction management, receipt management and other modules.

[0035] The main process of data verification and model learning phase is as follows: The first step is to configure the reconciliation relationship between each data item of the online banking flow and the data item of the enterprise journal in the rule management module of the intelligent bank-enterprise reconciliation platform. The relationship includes "includes, greater than, less than, equal to, greater than or equal to, less than or equal to, similar, merge equal to, not equal to", etc., where merge equal to is represented by multiple data on both sides, or multiple data on one side and single data or multiple data on the other side. The multiple data are combined and calculated to be equal to the value on the other side.

[0036] The second step is to automatically normalize specific field data types, such as amount types, in the back-end service of the model management module of the intelligent bank-enterprise reconciliation platform. Since the amount of the original data may be a pure number, a number with a thousand separator, or a string with a currency prefix, it is converted into unified data. For example, the original data with multiple date formats is converted into data in a unified date format.

[0037] The third step is to calculate the text distance n of the characters to be checked in the back-end service of the model management module of the intelligent bank-enterprise reconciliation platform. For plain text content such as summaries, backups, notes, and transaction instructions, the formula for text distance is: n= α - β / k, α is set to 1, β is the number of characters to be edited in the text, and k is the maximum number of characters to be compared. For example, for the comparison of 123 and 1234 characters, since the first character only needs to add a 4 to be exactly the same as the second character, the result of n is 0.75 when it is substituted into the formula, and the NLP similarity is θ. The final semantic comparison data result value ɡ=θ+n*θ. The higher the g value, the more similar the comparison data is. During the entire comparison process, modal particles, auxiliary words, punctuation marks and other words in the vocabulary of the comparison character content will also be removed.

[0038] The fourth step is that in the back-end service of the model management module of the intelligent bank-enterprise reconciliation platform, the amount is used as a constraint condition for comparing data. If they are consistent, the amounts must be completely equal after normalization, and the weight score of the amount constraint condition is recorded as q=1.

[0039] In the fifth step, the automatic reconciliation service of the intelligent reconciliation platform matches the configured mapping and association of the data items on both sides. If the auxiliary fields other than the amount are completely consistent (the data item content strings are equal or the values ​​are equal), if the single data item is matched successfully, the weight of this data item in the reconciliation model is increased. The weight of equal, equal or combined equal is s=1, the weight of inclusion is p=0.7, and the weight of greater than or equal to and less than or equal to is e=0.5. Under the same conditions, the data items with the largest weight are checked first, and the total score of the comparison is finally calculated as t=g+q+(s+p+e)*j, where j is the number of auxiliary comparison fields.

[0040] In the sixth step, the automatic reconciliation service of the smart reconciliation platform matches the configured mapping and association relationship of the data items on both sides. The matching calculation result is o=x+t, where x is the mapping or association relationship. If the mapping and association relationship exist, x=1, and if not, x=0. If t / o≥0.9, the uncollected item is recorded and the weight of this data item in the reconciliation model is not increased. When t / o<0.9, the weight of this data item is increased and it participates in the next round of reconciliation cycle.

[0041] In the seventh step, the corporate finance staff will check the bilateral data of the unreconciled items based on the actual online banking transactions and corporate journals. If they manually determine that both sides of the unreconciled items (the data on both sides may be one-to-one, one-to-many, many-to-many, or many-to-two) can be matched, they will manually check the data on both sides, and the checked data will be added to the checked list. At the same time, the weight of this data item in the reconciliation model will be increased during the verification, and the weight value is the same as that described in the fifth and sixth steps.

[0042] In the eighth step, repeat the second, third, fourth, fifth, sixth and seventh steps, and perform bank-enterprise reconciliation based on the weight value of the data item content in the reconciliation model. The higher the weight and score of the data item content, the higher the consistency of the reconciliation match on both sides. When the matching consistency reaches a threshold of 90% or above, it can be judged that the data on both sides are successfully matched.

[0043] Step 9. Repeat step 8 until the finance department verifies that all online banking transaction data and corporate journal data are completely consistent, completing a round of bank-enterprise reconciliation and model learning iteration.

[0044] The tenth step is to automatically trigger the automatic collection task of corporate online banking data and corporate journal data on the intelligent bank-enterprise reconciliation platform every day.

[0045] Step 11. Repeat the above steps until the automatic intelligent reconciliation of the enterprise's bank-enterprise reconciliation data is successfully matched to 100% or close to 100%, and the balance adjustment sheet is automatically generated. Based on the set association relationship, the online banking flow, accounting voucher and receipt are linked and matched.

[0046] To fully achieve or approach 100% automatic intelligent bank-enterprise reconciliation, two to three months of online banking data and corporate journal data are needed as the data set of the reconciliation model. During these two to three months, the company's financial personnel are required to manually check the unreconciled items. The accuracy of the one-time automatic intelligent reconciliation will gradually increase with the increase of data volume on both sides and the number of manual checks on unreconciled items, ultimately achieving full or approaching 100% success and accuracy in bank-enterprise reconciliation.

[0047] The present invention adopts supervised learning, based on the basic financial field knowledge model, combined with the actual business data and reconciliation rules of the enterprise, through supervised learning, manual checking and compensation, the accuracy of bank-enterprise reconciliation is continuously improved, and finally a fully automated and intelligent bank-enterprise reconciliation is realized. The fully automated technology combining software and hardware is adopted, without opening the bank-enterprise direct connection service, without interface development, without system integration development, without manual data collection and ferrying, etc., to realize cross-platform, cross-system, unlimited data source, data format, etc., full amount of enterprise online banking flow and receipt and other data automatic collection, analysis and cleaning, unlimited specific financial system or ERP system, etc., enterprise journal automatic collection, analysis, cleaning and cleaning, and generate data reconciliation data model according to defined rules and mapping relationships. It solves the problem of enterprises being unable to obtain full online banking data for bank-to-enterprise reconciliation, realizes fully automated and intelligent bank-to-enterprise reconciliation, and eliminates the need for manual bank-to-enterprise reconciliation. It also solves the automatic conversion and cleaning of data in different systems or platforms and different data formats, as well as the bank-to-enterprise reconciliation needs of different enterprises and the archiving management of data such as online banking transaction receipts, greatly improving the timeliness, comprehensiveness, accuracy and completeness of bank-to-enterprise reconciliation, improving the efficiency of financial management, and reducing the cost and risk of financial management. Example

[0048] After Xiao Wang, the developer of the intelligent bank-enterprise reconciliation platform, completed the development of the online banking supported by the platform, Xiao Mei, a financial staff member of a certain group, found Xiao Li, a project implementer, to purchase and use the intelligent bank-enterprise reconciliation platform to perform bank-enterprise reconciliation for 150 corporate accounts of 30 types of banks from September 1, 2024 to September 30, 2024. The financial system used was the Yuanguang Financial Sharing System, the corporate journal was in the account management module, 10 types of bank transaction data were in the corporate treasury platform, and the remaining 20 types of online banking and 100 U-shiels did not have the bank-enterprise direct connection service activated.

[0049] Main steps: Xiao Wang, a developer of the intelligent bank-enterprise reconciliation platform, analyzed and created standard fields for online banking transactions based on different online banking transaction sample tables, and configured the mapping relationship between the standard fields and the header fields of the online banking transaction data downloaded from the actual online banking system, which mainly include the other party's account name, other party's account number, amount, debit and credit direction, transaction time, balance, summary, etc. Xiao Wang, a developer of the intelligent bank-enterprise reconciliation platform, analyzed and created standard fields for electronic receipts based on different online banking electronic receipt sample forms, and configured the mapping relationship between the standard fields and the electronic receipt information fields downloaded from the actual online banking system, which mainly include the other party's account name, other party's account number, amount, debit or credit direction, transaction time, balance, summary and other contents.

[0050] Xiao Wang, a staff member of the intelligent bank-enterprise reconciliation platform, analyzed and created standard fields for the enterprise journal based on the sample enterprise journal in this case, and configured the mapping relationship between the standard fields and the enterprise journal header fields downloaded from the actual financial system, which mainly includes information such as the other party's account name, other party account number, amount, debit and credit direction, summary, voucher number, etc.

[0051] Xiaomei, the corporate finance worker, entered information about 100 U-Shields related to 20 types of bank accounts that have not activated the bank-enterprise direct connection service into the online banking U-Shield management module of the intelligent bank-enterprise reconciliation platform, including the U-Shield location, click and press information, online banking address, account name, bank account number, password and other information. It supports automatic login to the online banking system to obtain relevant online banking transaction data and electronic receipt data.

[0052] Project implementer Xiao Li configured corresponding reconciliation rules in the intelligent bank-enterprise reconciliation platform according to Xiaomei Enterprise's reconciliation rules and requirements in the system, and associated the data items on both sides according to the relationships of "includes, greater than, less than, equal to, not less than, not greater than, greater than or equal to, less than or equal to, similar, merge equals, not equal to" according to the other party's account name, account number, amount, debit or credit direction, transaction time, balance, summary and the weight of each field.

[0053] Xiaomei creates the basic information of 150 corporate accounts that need to participate in the platform's automatic reconciliation on the intelligent bank-enterprise reconciliation platform, including account name, bank account number, bank, corporate journal beginning data, online banking transaction beginning data, etc.

[0054] Xiaomei creates 150 corporate accounts in the intelligent bank-enterprise reconciliation platform with an account period from September 1, 2024 to September 30, 2024.

[0055] Xiaomei configured three types of data collection tasks on the intelligent bank-enterprise reconciliation platform, including online banking statements, electronic receipts, and corporate journals from September 1, 2024 to September 30, 2024, to automatically collect data.

[0056] After the task of collecting online banking statements and electronic receipts begins, for the 20 types of bank accounts that have not enabled the bank-enterprise direct connection service, the corresponding online banking U-Shields will be automatically logged into the corresponding online banking system one by one according to the account arrangement order, and the online banking statements or electronic receipts from September 1, 2024 to September 30, 2024 will be queried and downloaded, and the downloaded files will be parsed, and the data will be standardized according to the created mapping relationship, and then the standardized data will be stored in the online banking statement or electronic receipt management module.

[0057] For bank accounts whose 10 types of online banking data exist in the treasury system, there is no need to mount a U-shield, and you can automatically log in and enter the treasury system, query and download the online banking statements or electronic receipts from September 1, 2024 to September 30, 2024, and then parse the downloaded files, standardize the data according to the mapping relationship created in steps 1 and 2, and then store the standardized data in the online banking statement or electronic receipt management module.

[0058] After the enterprise journal collection task starts, enter the financial sharing system one by one according to the order of account arrangement, enter the account management, query the enterprise journal data of the corresponding account from September 1, 2024 to September 30, 2024 and download it, then parse the downloaded file, standardize the data according to the created mapping relationship, and then store the standardized data in the enterprise journal management module.

[0059] At this time, the intelligent bank-enterprise reconciliation platform will automatically associate and match the corporate online banking flow and electronic receipt data to establish a one-to-one association relationship; The intelligent bank-enterprise reconciliation platform will automatically reconcile online banking transactions and enterprise journals according to the configured reconciliation rules. This step will take from a few seconds to more than ten seconds depending on the amount of data. After the automatic reconciliation is completed, the reconciliation results will be automatically generated, including the blended list, the unblended list, and the balance adjustment table; in the blended list, three types of blending relationships appear, some online banking statements and corporate journals are one-to-one, some are one-to-many, and some are many-to-many; Xiaomei saw that the automatic blending rate was 98%. There were 2 data that could not be automatically blended according to the reconciliation rules due to irregular accounting. So she went to the unblended list page and performed manual blending to bring the reconciliation success rate to 100%.

[0060] A one-to-one, one-to-many or many-to-many relationship is established between the online banking transaction and the enterprise journal data according to the blending result, that is, a one-to-one, one-to-many or many-to-many relationship is established between the online banking transaction and the accounting voucher number, and then combined with the association relationship established between the online banking transaction and the electronic receipt, the voucher number and the electronic receipt are finally associated, and a closed loop is established between the transaction voucher and the accounting voucher; After Xiaomei confirmed that the reconciliation results were correct, she submitted a review / confirmation for the account period results of the eight accounts.

[0061] Note: The automation technology involved is not the patent applied for this invention, but the supporting software or method in this example.

[0062] This application also provides a bank-enterprise reconciliation system, including: The creation module is used to create collection tasks, including online banking flow collection tasks and corporate diary accounting tasks; Configuration module, used to configure the header fields and mapping of collected data based on collection tasks; The reconciliation module is used to establish a reconciliation model table based on the collected data according to the key elements of financial account type, summary, amount, date, accounting voucher number, online banking account, online banking account name, bank name, time, debit and credit direction, and amount; The association module is used to configure the reconciliation relationship between each data item of the online banking flow and the data item of the enterprise journal account. The association relationships include containment, greater than, less than, equal to, greater than or equal to, less than or equal to, and combined equal to; The weight module is used to set the weight parameters of the data items in the reconciliation model table based on the association relationship. The weight parameter for equality, equals and combined equals is s, the weight parameter for inclusion is p, and the weight parameter for greater than or equal to and less than or equal to is e; The matching module is used to match the online banking transaction data items with the enterprise journal accounting data items based on weight parameters to successfully complete the reconciliation.

[0063] The present invention also provides an electronic device, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit executes the above method.

[0064] The present invention also provides a storage medium storing a computer program executable by an electronic device. When the program runs on the electronic device, the electronic device executes the above method.

[0065] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A bank-enterprise reconciliation method, characterized in that: include: Creating collection tasks, including online banking transaction collection tasks and corporate diary accounting tasks; Based on the collection task, respectively configure the header fields and mapping of the collected data; Based on the collected data, a reconciliation model table is established according to the key elements of financial subject type, summary, amount, date, accounting voucher number, online banking account, online banking account name, bank name, time, debit and credit direction, and amount; Configure the reconciliation relationship between each data item of online banking flow and the data item of enterprise diary accounting, and the said relationship includes including, greater than, less than, equal to, greater than or equal to, less than or equal to, and combined equal to; The weight parameters of the data items in the reconciliation model table are set based on the association relationship, the weight parameters of equal and combined equal are s, the weight parameters of inclusion are p, and the weight parameters of greater than or equal to and less than or equal to are e; Based on the weight parameters, the online banking transaction data items and the enterprise journal accounting data items are matched to successfully complete the reconciliation.

2. The bank-enterprise reconciliation method according to claim 1, characterized in that: The data source for collecting online banking transactions is an online banking system or a treasury system, and the data source for corporate diary accounting is a financial system or an ERP system.

3. The bank-enterprise reconciliation method according to claim 2, characterized in that: Creating a collection task includes the collection time, collection cycle, collection account, and collection of running virtual machine resource information, where the running virtual machine resource information includes IP address, number of CPU cores, memory size, operating system, and storage size.

4. The bank-enterprise reconciliation method according to claim 3, characterized in that: The mapping relationship between the entity table corresponding to the collected data and the reconciliation model table is stored in the corresponding flow management module.

5. The bank-enterprise reconciliation method according to claim 4, characterized in that: Based on the weight parameter, successfully completing the reconciliation by matching the online banking transaction data item with the enterprise journal account data item includes: Calculate the total comparison score using the formula: t=g+q+(s+p+e)*j; In the formula: g is the result value of semantic comparison data; q is the weight score of the amount constraint condition; j is the number of auxiliary comparison fields; Calculate the matching result, the formula is: o=x+t; In the formula: x is the correlation relationship, if there is a correlation relationship, x=1, if there is no correlation relationship, x=0; If t / o≥0.9, the weight of the corresponding data item in the reconciliation model table is not increased and the match is judged to be successful. When t / o<0.9, the weight of the corresponding data item in the reconciliation model table is increased and recalculated until the match is successful.

6. The bank-enterprise reconciliation method according to claim 5, characterized in that: g is the formula for the semantic comparison data result value: ɡ=θ+n*θ; Where: θ is the similarity; n is the character text distance.

7. The bank-enterprise reconciliation method according to claim 6, characterized in that: n is the character text distance formula: n = α - β / k; Where: α is 1, β is the number of edited characters in the text, and k is the maximum number of characters.

8. A bank-enterprise reconciliation system, characterized in that: include: A creation module is used to create collection tasks, including online banking flow collection tasks and corporate diary accounting tasks; A configuration module, used to configure the header fields and mapping of the collected data based on the collection tasks; A reconciliation module is used to establish a reconciliation model table based on the collected data according to the key elements of financial subject type, summary, amount, date, accounting voucher number, online banking account, online banking account name, bank name, time, debit and credit direction, and amount; An association module is used to configure the reconciliation association relationship between each data item of the online banking flow and the data item of the enterprise diary account, and the association relationship includes including, greater than, less than, equal to, greater than or equal to, less than or equal to, and combined equal to; A weight module, used to set weight parameters of the data items in the reconciliation model table based on the association relationship, the weight parameter of equal and combined equal is s, the weight parameter of inclusion is p, and the weight parameter of greater than or equal to and less than or equal to is e; A matching module is used to match the online banking transaction data item with the enterprise diary accounting data item based on the weight parameter to successfully complete the reconciliation.

9. An electronic device, characterized in that: The method comprises at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the program is executed by the processing unit, the processing unit executes the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: It stores a computer program executable by an electronic device, and when the program runs on the electronic device, the electronic device executes the method described in any one of claims 1 to 7.

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