Accounting automatic supplementing method and system for account error data

Abstract

The application provides a kind of accounting automatic supplementing of account processing method and system of account error data, it is related to financial service technical field, comprising: extracting data, judge whether data can be supplemented to account, mark the data that can be supplemented to account;Marked data is batch imported into supplementing of account processing flow, and flow is according to the rule set that is set in advance, specifically judge whether the marked data that batch imports meets the requirement of supplementing of account;For the marked data that can be supplemented to account, according to the information required when customer enters account, supplementing of account is carried out according to the rule configured in the system by the customer;For the marked data that does not meet the requirement of supplementing of account, the information of marked data is counted, and whether the statistical result exceeds the set abnormal data early warning standard is judged;For the abnormal data that does not meet the requirement of supplementing of account, and the supplementing of account detail state of the day is gradually processed, the data that can be adjusted is marked, and the adjustment rule is changed to supplement the rule set of account.The application can reduce the proportion of the number of system abnormal transactions.

Description

Technical Field

[0001] This invention relates to the field of financial services technology, and more specifically, to a method and system for automatically correcting accounting errors. Background Technology

[0002] With the increasing number of business types supported by the system and the rapid growth of daily transaction data, a new logic for automatically correcting and recording common errors in the system has been developed. This logic addresses the challenges of handling daily discrepancies, particularly those that are difficult for business personnel to process quickly or even completely. Based on current operational habits of business personnel in handling discrepancies, prior experience in processing various types of discrepancies, and the existing automated batch processing workflow, this paper presents a framework for automatically correcting and recording common errors in the system.

[0003] The existing mechanism for correcting reconciliation errors is as follows: During reconciliation, it determines whether a transaction is one where the system has pre-funded the transaction. For pre-funded transactions, the data is stored in a database pre-processing table using a unified data structure. Then, each data item in the table is matched against the corrective accounting rules set in the system's accounting rules. Data that does not match is submitted to the business platform for analysis and processing by relevant business personnel, who then provide rules for subsequent iteration and optimization. For data that matches the rules, the accounting rules within the rules query and complete the corresponding information according to the current daily fund transfer data structure for merchants, and then enter it into the merchant fund transfer table. Finally, the data entered into the merchant fund transfer table by the corrective accounting logic is submitted to the core system for fund transfer. Subsequent processing of merchant funds by related systems does not distinguish whether the data in the table is generated by the corrective accounting logic; therefore, subsequent processing is unaware of this type of error data compared to normal merchant fund transfer data.

[0004] In summary, existing technologies suffer from the following drawbacks: For infrequently occurring and unprocessable erroneous data, handling must be left to subsequent business personnel. However, the lack of such data prevents business personnel from developing corresponding correction rules for an extended period, necessitating manual processing and hindering efficiency in handling such system errors. Furthermore, the inability of business personnel to provide correction rules fundamentally prevents subsequent rule iteration and optimization. Moreover, for new business types, without pre-configured correction rules, existing logic cannot correct erroneous data for that business type and facilitate subsequent rule iteration and optimization. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for automatically correcting accounting errors.

[0006] According to the present invention, an automatic accounting correction method and system for reconciliation error data is provided, the solution of which is as follows:

[0007] Firstly, a method for automatically correcting accounting errors by correcting them is provided, the method comprising:

[0008] Step S1: Extract data that is not reconciled or has been written off based on the reconciliation results. Based on the factors that caused the discrepancy during data verification, make a preliminary judgment on whether the data can be added back into the accounts, and mark the data that can be added back into the accounts to form marked data.

[0009] Step S2: Import the marked data into the batch entry processing flow. The batch entry processing flow determines whether the imported marked data meets the entry requirements based on a pre-set set of rules.

[0010] Step S3: For the marked data that can be added to the account, add the data according to the rules configured by the customer in the system, based on the information required by the customer when making the account entry.

[0011] Step S4: For marked data that does not meet the requirements for supplementary accounting, statistically analyze the marked data information based on multiple dimensions, and determine whether the statistical results exceed the set abnormal data warning standards; if they exceed the standards, synchronize the information to the warning platform to remind business personnel to handle the abnormal data in a timely manner.

[0012] Step S5: Process the abnormal data submitted to the business platform that does not meet the requirements for supplementary entries, as well as the status of the supplementary entries details for the day, step by step, mark the data that can be adjusted according to the specific situation, and provide adjustment rules to change the supplementary entries rule set.

[0013] Preferably, step S1 includes:

[0014] Step S1.1: When there is a discrepancy in the reconciliation, determine whether the current transaction is a discrepancy transaction in which the system has advanced funds, based on relevant factors including the transaction source system and transaction type;

[0015] Step S1.2: For unbalanced transactions where funds have been advanced, determine whether the transaction can be credited or deducted based on relevant factors, including the business type, transaction type, and error type.

[0016] Preferably, the determination of whether the data meets the requirements for supplementary accounting in step S2 includes: for data that meets the requirements, marking it as eligible for supplementary accounting after statistical analysis of the detailed supplementary accounting data; for data that does not meet the requirements, marking the reasons for non-compliance and submitting the marked data to the business platform for display.

[0017] Preferably, step S2 specifically includes:

[0018] Step S2.1: Based on the required transaction addition markers, obtain the configured accounting rules according to relevant factors including business type, transaction type, and reconciliation status. Then, based on the accounting rules and specific transaction institution information, obtain the relevant information required for addition, including fee type, account, and debit / credit identifiers, and generate addition details data.

[0019] Step S2.2: For the data that has been marked as true and obtained from the accounting rules and generated detailed data elements, submit it to the business platform for display and processing according to the business report data structure of the failed data entry, as it does not match the current data entry type.

[0020] Preferably, step S4 includes:

[0021] Step S4.1: Statistically analyze the transactions added to the account on the same day based on relevant dimensions, including transaction amount, transaction quantity, and transaction type data that requires warning. If the data exceeds the set warning value, issue an abnormal warning to the warning system.

[0022] Step S4.2: Statistically analyze the transactions for which deductions are made on the same day, including transaction amount, transaction quantity, and transaction type data that require warning. If the data exceeds the set warning value, issue an abnormal warning to the warning system.

[0023] Secondly, a system for automatically correcting accounting errors is provided, the system comprising:

[0024] Module M1: Extracts data that is not reconciled or has been written off based on the reconciliation results. Based on the factors that caused the discrepancy during data verification, it makes a preliminary judgment on whether the data can be added back into the accounts and marks the data that can be added back into the accounts, forming marked data.

[0025] Module M2: Imports the marked data in batches into the supplementary accounting process. The supplementary accounting process determines whether the batch-imported marked data meets the supplementary accounting requirements based on a pre-set set of rules.

[0026] Module M3: For marked data that can be added to the system, add the data according to the rules configured by the customer in the system, based on the information required by the customer when adding the data.

[0027] Module M4: For marked data that does not meet the requirements for supplementary accounting, statistical information on the marked data is calculated based on multiple dimensions, and it is determined whether the statistical results exceed the set abnormal data warning standards; if they exceed the standards, the information is synchronized to the warning platform to remind business personnel to handle the abnormal data in a timely manner.

[0028] Module M5: Processes abnormal data submitted to the business platform that does not meet the requirements for supplementary entries step by step, as well as the status of supplementary entries details for the day, and marks the data that can be adjusted according to the specific situation, and provides adjustment rules to change the set of supplementary entries rules.

[0029] Preferably, the module M1 includes:

[0030] Module M1.1: When reconciliation is unbalanced, determine whether the current transaction is an unbalanced transaction for which the system has advanced funds, based on relevant factors including the transaction source system and transaction type;

[0031] Module M1.2: For unbalanced transactions where funds have been advanced, determine whether the transaction can be credited or deducted based on relevant factors including the business type, transaction type, and error type.

[0032] Preferably, the determination of whether the data meets the requirements for supplementary accounting in module M2 includes: for data that meets the requirements, marking it as eligible for supplementary accounting after statistical analysis of the detailed supplementary accounting data; for data that does not meet the requirements, marking the reasons for non-compliance and submitting the marked data to the business platform for display.

[0033] Preferably, the module M2 specifically includes:

[0034] Module M2.1: Based on the required transaction addition markers, the module obtains the configured accounting rules according to relevant factors including business type, transaction type, and reconciliation status. Then, based on the accounting rules and specific transaction institution information, the module obtains the relevant information required for addition, including fee type, account, and debit / credit identifiers, and generates detailed addition data.

[0035] Module M2.2: For the data that has been marked as true and obtained from the accounting rules and generated detailed data elements, the data is submitted to the business platform for display and processing according to the business report data structure of the failed data entry, and is not consistent with the current data entry type.

[0036] Preferably, the module M4 includes:

[0037] Module M4.1: Statistically analyzes transactions added to the account on the same day based on relevant dimensions, including transaction amount, transaction quantity, and transaction type data that requires alerts. If the data exceeds the set warning threshold, an abnormal warning will be issued to the alert system.

[0038] Module M4.2: Statistically analyzes transactions that are deducted on the same day based on relevant dimensions, including transaction amount, transaction quantity, and transaction type data that requires warning. If the data exceeds the set warning value, an abnormal warning will be issued to the warning system.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. This invention, based on the steps of obtaining accounting rules from the supplementary entry mark and subsequently generating entry details data, transforms the different empirical rules for manually handling unbalanced transactions into a unified and operable data structure.

[0041] 2. By processing the addition of unbalanced data, the system can make a portion of the unbalanced data equal to the clearing result during reconciliation, thereby reducing the proportion of abnormal transactions during system operation.

[0042] 3. This invention enables the system to issue early warnings for supplementary accounting data and allows business personnel to review the supplementary accounting data. The system can further optimize the supplementary accounting rules under the influence of subsequent business iterations and personnel changes, thereby reducing the subsequent development workload and the number of code deployments.

[0043] Other beneficial effects of the present invention will be explained in detail through the introduction of specific technical features and technical solutions in specific embodiments. Those skilled in the art should be able to understand the beneficial technical effects brought about by these technical features and technical solutions through the introduction of these technical features and technical solutions. Attached Figure Description

[0044] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0046] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0047] This invention provides a method for automatically correcting accounting errors by adding incorrect data to the books. First, it extracts data where the reconciliation result is not balanced or the transactions have already been closed. After these data are matched using a rule set, the system automatically processes their accounting issues, thereby reducing system errors, minimizing manual intervention, and improving system stability. (Refer to...) Figure 1 As shown, the method specifically includes the following:

[0048] Step S1: When the system performs reconciliation for various business types, it extracts data that is not reconciled or has been written off. Based on the factors that caused the discrepancy during data verification, it makes a preliminary judgment on whether the data can be added back into the accounts and marks the data that can be added back into the accounts, forming marked data.

[0049] If there are discrepancies in the reconciliation, the system determines whether the transaction involved funds advanced by the system, based on the transaction source system and transaction type. For discrepancies where funds were advanced by the system, the system determines whether the transaction can be re-entered or deducted based on the transaction's business type, transaction type, and error type.

[0050] Step S2: Import the marked data in batches into the supplementary accounting processing flow. This flow, based on a pre-defined set of rules, specifically determines whether the imported marked data meets the supplementary accounting requirements. For data that meets the requirements, the system statistically analyzes the detailed supplementary accounting data and marks it as eligible for supplementary accounting. For data that does not meet the requirements, the system will indicate the reason for the non-compliance and submit the data to the business platform for display.

[0051] Based on the required supplementary entry (supplementary deduction) markers for transactions, the system obtains the configured accounting rules according to the business type, transaction type, and reconciliation status. Then, based on the accounting rules and the specific transaction institution information, it obtains the necessary information for supplementary entry (supplementary deduction), such as the fee type, account, and debit / credit identifier, to generate supplementary entry (supplementary deduction) details.

[0052] For data that accurately identifies accounting rules and generates detailed data elements, the system submits it to the business platform for processing based on the business report data structure of the failed data entry, indicating that it does not match the current data entry type.

[0053] Step S3: For the marked data that can be added to the account, add it to the account according to the rules configured by the merchant in the system, based on the information required by the merchant when adding the data.

[0054] Step S4: For data that does not meet the requirements for data entry, the system statistically analyzes data from multiple dimensions and determines whether the statistical results exceed the set abnormal data warning standards. If the standards are exceeded, the system will synchronize the information to the warning platform to remind business personnel to handle the abnormal data in a timely manner.

[0055] The system tracks daily transactions by metrics such as transaction amount, number of transactions, and types of transactions requiring alerts. If these metrics exceed set thresholds, an anomaly warning is sent to the alert system. Conversely, the system tracks daily deductions by metrics such as transaction amount, number of transactions, and types of transactions requiring alerts. If these metrics exceed set thresholds, an anomaly warning is sent to the alert system.

[0056] Step S5: Business personnel can process abnormal data that does not meet the requirements for supplementary entries submitted to the business platform step by step, as well as the status of supplementary entries details for the day, and mark the data that can be adjusted according to the specific situation, and provide adjustment rules to change the supplementary entry rule set.

[0057] This invention also provides an automatic accounting error correction system. This system can be implemented by executing the steps of the automatic accounting error correction method. That is, those skilled in the art can understand the automatic accounting error correction method as a preferred embodiment of the automatic accounting error correction system. Specifically, the system includes:

[0058] Module M1: When the system performs reconciliation for various business types, it extracts data that is not reconciled or has been written off. Based on the factors that caused the discrepancy during data verification, it makes a preliminary judgment on whether the data can be added back into the accounts and marks the data that can be added back into the accounts.

[0059] If there are discrepancies in the reconciliation, the system determines whether the transaction involved funds advanced by the system, based on the transaction source system and transaction type. For discrepancies where funds were advanced by the system, the system determines whether the transaction can be re-entered or deducted based on the transaction's business type, transaction type, and error type.

[0060] Module M2: This module imports tagged data in batches into the data replenishment process. The replenishment process uses a pre-defined set of rules to determine whether the imported tagged data meets the replenishment requirements. For data that meets the requirements, the system statistically analyzes the detailed replenishment data and marks it as eligible for replenishment. For data that does not meet the requirements, the system identifies the reasons for the non-compliance and submits the data to the business platform for display.

[0061] Based on the required supplementary entry (supplementary deduction) markers for transactions, the system obtains the configured accounting rules according to the business type, transaction type, and reconciliation status. Then, based on the accounting rules and the specific transaction institution information, it obtains the necessary information for supplementary entry (supplementary deduction), such as the fee type, account, and debit / credit identifier, to generate supplementary entry (supplementary deduction) details.

[0062] For data that accurately identifies accounting rules and generates detailed data elements, the system submits it to the business platform for processing based on the business report data structure of the failed data entry, indicating that it does not match the current data entry type.

[0063] Module M3: For tagged data that can be added to the account, add the data according to the rules configured by the merchant in the system, based on the information required by the merchant when adding the data.

[0064] Module M4: For data that does not meet the requirements for data entry, the system statistically analyzes data from multiple dimensions and determines whether the statistical results exceed the set abnormal data warning standards. If the standards are exceeded, the system will synchronize the information to the warning platform, reminding business personnel to handle the abnormal data in a timely manner.

[0065] The system tracks daily transactions by metrics such as transaction amount, number of transactions, and types of transactions requiring alerts. If these metrics exceed set thresholds, an anomaly warning is sent to the alert system. Conversely, the system tracks daily deductions by metrics such as transaction amount, number of transactions, and types of transactions requiring alerts. If these metrics exceed set thresholds, an anomaly warning is sent to the alert system.

[0066] Module M5: Business personnel can process abnormal data submitted to the business platform that does not meet the requirements for supplementary entries, as well as the status of supplementary entries details for the day, step by step, and mark the data that can be adjusted according to the specific situation, and provide adjustment rules to change the supplementary entry rule set.

[0067] This invention provides a method and system for automatically correcting accounting errors, where the acquiring system advances funds to merchants and subsequently reconciles accounts. For accounting errors, the system automatically processes the erroneous portion of the accounting data according to a predetermined set of correction rules. For data not belonging to the rule set, the system submits the data to the business platform for review and processing, after which it can be included in the rule set for handling such erroneous data.

[0068] For transactions involving advance payments from the acquiring system, the system can identify and capture data that can be automatically credited (deducted) by the system, and process the accounts according to the crediting and clearing format at the time of the advance payment, ultimately achieving the technical effect of reducing the proportion of abnormal transactions in the system.

[0069] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0070] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for automatically correcting accounting errors, characterized in that, include: Step S1: Extract data that is not reconciled or has been written off based on the reconciliation results. Based on the factors that caused the discrepancy during data verification, make a preliminary judgment on whether the data can be added back into the accounts, and mark the data that can be added back into the accounts to form marked data. Step S2: Import the marked data into the batch entry processing flow. The batch entry processing flow determines whether the imported marked data meets the entry requirements based on a pre-set set of rules. Step S3: For the marked data that can be added to the account, add the data according to the rules configured by the customer in the system, based on the information required by the customer when making the account entry. Step S4: For marked data that does not meet the requirements for supplementary accounting, statistically analyze the marked data information based on multiple dimensions, and determine whether the statistical results exceed the set abnormal data warning standards; If the data exceeds the standard, the information will be synchronized to the early warning platform to remind business personnel to handle the abnormal data in a timely manner. Step S5: Process the abnormal data submitted to the business platform that does not meet the requirements for supplementary entries, as well as the status of the supplementary entries details for the day, step by step, and mark the data that can be adjusted according to the specific situation, and provide adjustment rules to change the supplementary entries rule set; The step S2, determining whether the data meets the requirements for data replenishment, includes: for data that meets the requirements, marking it as eligible for replenishment after statistical analysis of the detailed data; for data that does not meet the requirements, indicating the reasons for non-compliance and submitting the marked data to the business platform for display. Step S2 specifically includes: Step S2.1: Based on the required transaction addition markers, obtain the configured accounting rules according to relevant factors including business type, transaction type, and reconciliation status. Then, based on the accounting rules and specific transaction institution information, obtain the relevant information required for addition, including fee type, account, and debit / credit identifiers, and generate addition details data. Step S2.2: For the marked data that confirms the acquisition of accounting rules and the generation of detailed data elements, submit it to the business platform for display and processing according to the business report data structure of the failed supplementary entry, as it does not match the current supplementary entry type; Step S4 includes: Step S4.1: Statistically analyze the transactions added to the account on the same day based on relevant dimensions, including transaction amount, transaction quantity, and transaction type data that requires warning. If the data exceeds the set warning value, issue an abnormal warning to the warning system. Step S4.2: Statistically analyze the transactions for which deductions are made on the same day, including transaction amount, transaction quantity, and transaction type data that require warning. If the data exceeds the set warning value, issue an abnormal warning to the warning system.

2. The method for automatically correcting accounting errors according to claim 1, characterized in that, Step S1 includes: Step S1.1: When there is a discrepancy in the reconciliation, determine whether the current transaction is a discrepancy transaction in which the system has advanced funds, based on relevant factors including the transaction source system and transaction type; Step S1.2: For unbalanced transactions where funds have been advanced, determine whether the transaction can be credited or deducted based on relevant factors, including the business type, transaction type, and error type.

3. An automatic accounting error correction system, characterized in that, include: Module M1: Extracts data that is not reconciled or has been written off based on the reconciliation results. Based on the factors that caused the discrepancy during data verification, it makes a preliminary judgment on whether the data can be added back into the accounts and marks the data that can be added back into the accounts, forming marked data. Module M2: Imports the marked data in batches into the supplementary accounting process. The supplementary accounting process determines whether the batch-imported marked data meets the supplementary accounting requirements based on a pre-set set of rules. Module M3: For marked data that can be added to the system, add the data according to the rules configured by the customer in the system, based on the information required by the customer when adding the data. Module M4: For marked data that does not meet the requirements for supplementary accounting, statistical information on the marked data is collected from multiple dimensions, and it is determined whether the statistical results exceed the set abnormal data warning standards. If the data exceeds the standard, the information will be synchronized to the early warning platform to remind business personnel to handle the abnormal data in a timely manner. Module M5: Processes abnormal data submitted to the business platform that does not meet the requirements for supplementary accounting step by step, as well as the status of supplementary accounting details for the day, and marks the data that can be adjusted according to the specific situation, and provides adjustment rules to change the set of supplementary accounting rules; The determination of whether the data meets the requirements for data replenishment in module M2 includes: for data that meets the requirements, marking it as eligible for replenishment after statistical analysis of the detailed data; for data that does not meet the requirements, marking the reasons for non-compliance and submitting the marked data to the business platform for display. The module M2 specifically includes: Module M2.1: Based on the required transaction addition markers, the module obtains the configured accounting rules according to relevant factors including business type, transaction type, and reconciliation status. Then, based on the accounting rules and specific transaction institution information, the module obtains the relevant information required for addition, including fee type, account, and debit / credit identifiers, and generates detailed addition data. Module M2.2: For the marked data that has been obtained from the accounting rules and generated detailed data elements, the data structure of the business report that failed to be entered into the accounts is submitted to the business platform for display and processing as it does not match the current entry type. The module M4 includes: Module M4.1: Statistically analyzes transactions added to the account on the same day based on relevant dimensions, including transaction amount, transaction quantity, and transaction type data that requires alerts. If the data exceeds the set warning threshold, an abnormal warning will be issued to the alert system. Module M4.2: Statistically analyzes transactions that are deducted on the same day based on relevant dimensions, including transaction amount, transaction quantity, and transaction type data that requires warning. If the data exceeds the set warning value, an abnormal warning will be issued to the warning system.

4. The automatic accounting error correction system according to claim 3, characterized in that, The module M1 includes: Module M1.1: When reconciliation is unbalanced, determine whether the current transaction is an unbalanced transaction for which the system has advanced funds, based on relevant factors including the transaction source system and transaction type; Module M1.2: For unbalanced transactions where funds have been advanced, determine whether the transaction can be credited or deducted based on relevant factors including the business type, transaction type, and error type.

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