Financial reconciliation intelligent automatic processing method based on RPA robot

Abstract

The invention discloses a financial reconciliation intelligent automatic processing method based on an RPA robot. The processing method comprises the following steps: carrying out data acquisition; transmitting the acquired data to an account checking platform; after the account checking platform receives the data, multi-dimensional data cleaning work is started; the account checking platform performs classification matching on the cleaned data according to a customized account checking rule; and outputting and storing a result. Full-process automatic processing is achieved, and efficiency and accuracy are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial reconciliation process, and particularly relates to a financial reconciliation intelligent automatic processing method based on an RPA robot. BACKGROUND

[0002] In the traditional financial reconciliation process, data collection, cleaning and reconciliation are mostly completed manually. Manual data collection is not only inefficient, but also prone to data omission or entry errors; in the data cleaning and reconciliation stage, manual processing of large amounts of complex data is prone to errors due to fatigue and other factors, and consumes a lot of time and labor cost. Although some existing financial processing systems introduce automation technology, the data collection, cleaning and reconciliation functions are often mixed to achieve high system function coupling, making maintenance and upgrading difficult, and it is difficult to fully utilize the technical advantages of each link.

[0003] In the current enterprise reconciliation scenario, the traditional technical solution has significant defects, and the high dependence on manual operation leads to low efficiency and frequent errors. In the data collection link, manual login to multiple systems (such as business systems, bank platforms, third-party payment systems, etc.) is not only time-consuming, but also prone to data omission or errors; in the data cleaning stage, manual judgment is prone to errors due to fatigue when facing a large amount of complex data, such as difficulty in continuously and accurately identifying abnormal transaction records; in the reconciliation link, manual operation is inefficient and the accuracy is difficult to guarantee, and the difference data is easy to be omitted.

[0004] Even if some systems introduce automation technology, some existing automation solutions and systems are highly integrated, which has high invasiveness to the system and high coupling degree with the system. This design makes maintenance and upgrading extremely difficult, and any change in any link may affect the stability of the whole system. At the same time, the mixed functions make it difficult to utilize the technical advantages of each link, such as the inability to introduce intelligent algorithms for data cleaning or to customize efficient matching engines for reconciliation, which ultimately restricts the improvement of overall reconciliation efficiency and accuracy.

[0005] Disadvantages of the prior art: 1. Low efficiency and high error rate of manual operation Traditional financial reconciliation relies heavily on manual operation, and data collection requires manual login to various systems (such as business systems, bank platforms, third-party payment systems, etc.), which is prone to errors and inefficient; in the data cleaning and reconciliation stage, manual processing of large amounts of complex data is prone to errors due to fatigue or negligence, and consumes a lot of time and labor cost.

[0006] 2. High system function coupling degree Some existing automated reconciliation systems highly couple data collection, cleaning, and reconciliation functions, resulting in excessive system integration and difficulties in maintenance and upgrades. Adjustments to any part of the system can affect overall operation, and it is difficult to optimize technical solutions for different parts, thus hindering improvements in reconciliation efficiency and accuracy. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed to provide an intelligent automated financial reconciliation method based on RPA robots to overcome or at least partially solve the above problems.

[0008] According to one aspect of the present invention, an intelligent automated processing method for financial reconciliation based on RPA robots is provided, the processing method comprising: Data collection is performed; The collected data will be transmitted to the reconciliation platform; After receiving the data, the reconciliation platform initiates a multi-dimensional data cleaning process; The reconciliation platform categorizes and matches the cleaned data according to customized reconciliation rules; Results output and storage.

[0009] Optionally, the data collection specifically includes: The RPA robot, as the core of data collection, acts according to preset rules; In real-world financial business scenarios, data sources are numerous and scattered. RPA robots can simulate human operations and automatically log into various systems. Capture online transaction data from third-party payment platforms; During the data collection process, tasks are executed according to the established procedures and time cycles, and key information such as transaction dates and transaction amounts are stored in a unified format in the data temporary storage area. Detailed data collection logs are kept. In case of any abnormality, data collection will be paused and the abnormality will be recorded. Data collection will resume after manual intervention.

[0010] Optionally, transmitting the collected data to the reconciliation platform specifically includes: Once data collection is complete, the transmission mechanism is automatically triggered. The system uses an encrypted transmission protocol to securely and completely transmit financial transaction data from the data storage area to the reconciliation platform.

[0011] Optionally, after receiving the data, the reconciliation platform initiates multi-dimensional data cleaning work, which specifically includes: format standardization, converting data with different formats collected from different systems into a unified format that includes standard fields for transaction date and transaction amount; Data verification algorithms are used to identify and remove duplicate data, such as duplicate transaction records. Filter out invalid data; Processing data that lacks critical information.

[0012] Optionally, the invalid data refers to records where the transaction amount is negative and there is no reasonable explanation.

[0013] Optionally, the reconciliation platform classifies and matches the cleaned data according to customized reconciliation rules, specifically including: The rules can be set flexibly; For data that is successfully matched automatically, the system automatically marks it as reconciled and generates reconciliation results; For discrepancies that cannot be automatically matched, the reasons are analyzed, and detailed information about the discrepancies is presented to finance personnel through a visual interface. Finance personnel can then edit the data, supplement information, or confirm discrepancies on the interface, thereby ensuring the accuracy of reconciliation.

[0014] Optionally, the rules can be flexibly configured to include matching based on transaction date, transaction amount, and transaction object.

[0015] Optionally, the result output and storage specifically include: After the reconciliation is completed, the reconciliation platform generates a comprehensive summary of financial reconciliation data, details of discrepancies, and information on their handling. All financial transaction data, reconciliation process data, and reconciliation results data are stored in the database.

[0016] This invention provides an intelligent automated financial reconciliation method based on RPA robots. The method includes: data collection; transmitting the collected data to a reconciliation platform; the reconciliation platform initiating multi-dimensional data cleaning upon receiving the data; the reconciliation platform classifying and matching the cleaned data according to customized reconciliation rules; and outputting and storing the results. This achieves fully automated processing, improving efficiency and accuracy.

[0017] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of an intelligent automatic financial reconciliation method based on an RPA robot provided in this embodiment of the invention; Figure 2 A detailed architecture diagram of the platform provided for embodiments of the present invention; Figure 3 This is a schematic diagram of the RPA robot execution process provided in an embodiment of the present invention. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0021] The terms "comprising" and "having," and any variations thereof, in the specification, embodiments, claims, and drawings of this invention are intended to cover non-exclusive inclusion, such as including a series of steps or units.

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0023] like Figure 1 As shown, the present invention provides an intelligent automated processing method for financial reconciliation based on RPA robots, the processing method comprising: Data collection is performed; The collected data will be transmitted to the reconciliation platform; After receiving the data, the reconciliation platform initiates a multi-dimensional data cleaning process; The reconciliation platform categorizes and matches the cleaned data according to customized reconciliation rules; Results output and storage.

[0024] Intelligent automation is achieved through the collaborative work of RPA robots and financial reconciliation, specifically including: Data Acquisition: The RPA robot, as the core of data acquisition, acts according to preset rules. In actual financial business scenarios, data sources are numerous and scattered. The RPA robot can simulate human operation and automatically log into various systems. It captures online transaction data from third-party payment platforms. During the acquisition process, it executes tasks according to a predetermined process and time cycle, storing key information such as transaction date and transaction amount in a unified format in a data temporary storage area. Simultaneously, it records detailed acquisition logs. In case of abnormalities, acquisition is paused and the anomaly is recorded, resuming only after manual handling, ensuring the acquisition process is traceable and problems are easily troubleshootable.

[0025] Data transmission: Once data collection is complete, the transmission mechanism is automatically triggered. The system employs an encrypted transmission protocol to securely and completely transmit the financial transaction data from the data storage area to the reconciliation platform. This process prevents data from being stolen or tampered with during transmission, ensuring data security and laying a solid foundation for subsequent data processing.

[0026] Data Cleaning: After receiving the data, the reconciliation platform initiates a multi-dimensional data cleaning process. First, format standardization is performed, converting the inconsistently formatted data collected from different systems into a unified format that includes standard fields such as transaction date and transaction amount. Next, data validation algorithms are used to accurately identify and remove duplicate data, such as repeatedly entered transaction records; invalid data, such as records with negative transaction amounts without reasonable explanation; and data lacking crucial information is processed.

[0027] Data reconciliation: The reconciliation platform categorizes and matches the cleaned data according to customized reconciliation rules. Rules can be flexibly set, such as matching by transaction date, transaction amount, or transaction counterparty. For automatically matched data, the system automatically marks it as reconciled and generates reconciliation results. For discrepancies that cannot be automatically matched, the platform conducts in-depth analysis of the reasons and presents detailed information about the discrepancies to finance personnel through a visual interface. Finance personnel can edit the data, supplement information, or confirm discrepancies on the interface, ensuring the accuracy of the reconciliation.

[0028] Results Output and Storage: After reconciliation is completed, the reconciliation platform generates a comprehensive summary of financial reconciliation data, details of discrepancies, and processing status, making it convenient for finance personnel to view and use. Simultaneously, all financial transaction data, reconciliation process data, and reconciliation results data are stored in a database for subsequent data retrieval, auditing, and analysis, providing data support for financial decision-making.

[0029] like Figure 2 As shown, the RPA robot technology architecture includes key technical components such as a central control management platform, a process code development framework, and robot actuators. (1) Central control platform, which supports full lifecycle management of RPA processes and supports multi-user and multi-role management; (2) Process code development engine, used for writing and arranging processes, and adapting to the central control platform and actuators; (3) Actuator, responsible for executing specific automated processes, and supports process execution progress management and log viewing functions.

[0030] like Figure 3 As shown, the RPA robot execution flow is as follows: Users initiate an automation process request, sending task instructions, task ID, and execution parameters to the RPA control platform. The RPA control platform creates the task and encapsulates the parameters. Based on the task situation, it schedules a robot from the RPA robot resource pool to execute the task. The robot collaboration business system executes the automated workflow and then feeds back the execution results (text / file stream) to the user and stores them in the local repository, ultimately completing the RPA robot workflow.

[0031] Beneficial effects: The use of RPA (Robotic Processing Automation) technology automates the collection of financial data. Pre-defined verification rules and full-process logging ensure the integrity and accuracy of the collected data. Simultaneously, an independent reconciliation platform is built to standardize and clean the collected data, supporting intelligent reconciliation processing based on configurable rules. Ultimately, it achieves automated output and storage of discrepancy analysis results. Through a decoupled architecture of "RPA collection + platform processing," an innovative collaborative work model is formed, realizing intelligent processing of the entire financial reconciliation process while effectively reducing the coupling between systems, significantly improving system maintainability and scalability.

[0032] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent and automated financial reconciliation based on RPA robots, characterized in that, The processing method includes: Data collection is performed; The collected data will be transmitted to the reconciliation platform; After receiving the data, the reconciliation platform initiates a multi-dimensional data cleaning process; The reconciliation platform categorizes and matches the cleaned data according to customized reconciliation rules; Results output and storage.

2. The intelligent automatic processing method for financial reconciliation based on RPA robots according to claim 1, characterized in that, The data collection process specifically includes: The RPA robot, as the core of data collection, acts according to preset rules; In real-world financial business scenarios, data sources are numerous and scattered. RPA robots can simulate human operations and automatically log into various systems. Capture online transaction data from third-party payment platforms; During the data collection process, tasks are executed according to the established procedures and time cycles, and key information such as transaction dates and transaction amounts are stored in a unified format in the data temporary storage area. Detailed data collection logs are kept. In case of any abnormality, data collection will be paused and the abnormality will be recorded. Data collection will resume after manual intervention.

3. The intelligent automatic processing method for financial reconciliation based on RPA robots according to claim 1, characterized in that, The process of transmitting the collected data to the reconciliation platform specifically includes: Once data collection is complete, the transmission mechanism is automatically triggered. The system uses an encrypted transmission protocol to securely and completely transmit financial transaction data from the data storage area to the reconciliation platform.

4. The intelligent automatic processing method for financial reconciliation based on RPA robots according to claim 1, characterized in that, After receiving the data, the reconciliation platform initiates a multi-dimensional data cleaning process, which specifically includes: Format standardization converts data collected from different systems with varying formats into a unified format that includes standard fields for transaction date and transaction amount. Data verification algorithms are used to identify and remove duplicate data, such as duplicate transaction records. Filter out invalid data; Processing data that lacks critical information.

5. The intelligent automatic processing method for financial reconciliation based on RPA robots according to claim 4, characterized in that, The invalid data refers to records where the transaction amount is negative and there is no reasonable explanation.

6. The intelligent automatic processing method for financial reconciliation based on RPA robots according to claim 1, characterized in that, The reconciliation platform categorizes and matches the cleaned data according to customized reconciliation rules, specifically including: The rules can be set flexibly; For data that is successfully matched automatically, the system automatically marks it as reconciled and generates reconciliation results; For discrepancies that cannot be automatically matched, the reasons are analyzed, and detailed information about the discrepancies is presented to finance personnel through a visual interface. Finance personnel can then edit the data, supplement information, or confirm discrepancies on the interface, thereby ensuring the accuracy of reconciliation.

7. The intelligent automatic processing method for financial reconciliation based on RPA robots according to claim 6, characterized in that, The rules can be flexibly set, specifically including matching by transaction date, transaction amount, and transaction object.

8. The intelligent automatic processing method for financial reconciliation based on RPA robots according to claim 1, characterized in that, The output and storage of the results specifically include: After the reconciliation is completed, the reconciliation platform generates a comprehensive summary of financial reconciliation data, details of discrepancies, and information on their handling. All financial transaction data, reconciliation process data, and reconciliation results data are stored in the database.

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