Business and financial reconciliation method and system

Abstract

The present invention provides a business-finance reconciliation method and system, which extracts original transaction data, pre-processes the original transaction data, and generates a transaction data set with a unified data structure; matches the transaction data set according to a preset primary matching logic; when a record in which the primary matching logic fails to match is detected, at least one secondary matching logic is triggered according to the business scenario type, and the triggered secondary matching logic is used to match the transaction data set in which the primary matching logic fails to match; the transaction data set that is still unmatched after being processed by the primary matching logic and the secondary matching logic is fed back to an exception handling mechanism to perform at least one data correction operation; a timestamp-containing blending voucher is generated for the transaction data set that is finally successfully matched, and all the blending vouchers are aggregated to generate a settleable invoice, which significantly saves labor costs.

Description

Technical Field

[0001] The present invention belongs to the technical field of account reconciliation, and in particular relates to a business-finance account reconciliation method and system. Background Art

[0002] In a company's financial management, reconciliation is a core step in ensuring the consistency of business and financial data. Traditional reconciliation methods typically rely on manual verification or automated tools based on single rules, which present the following technical bottlenecks:

[0003] Existing technologies often rely on single-factor matching logic (e.g., relying solely on order or fund serial numbers for accurate matching). However, in practice, serial numbers often become unlinkable due to system interface anomalies, data entry errors, or cross-platform transactions (e.g., order number expiration in a refund scenario, or exchange rate fluctuations leading to amount discrepancies in cross-border payments). These anomalies require manual intervention, which is time-consuming, labor-intensive, and prone to errors. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a business-finance reconciliation method and system for solving the technical problems in the prior art.

[0005] In a first aspect, the invention provides the following technical solution: a method for reconciling business and finances, the method comprising:

[0006] Extracting original transaction data from the business system and the financial system using a data collection tool, wherein the original transaction data includes order data from the business system and fund flow data from the financial system;

[0007] Preprocessing the original transaction data to generate a transaction data set with a unified data structure;

[0008] Matching the transaction data set according to a preset primary matching logic, wherein the primary matching logic includes an exact match between the order serial number and the fund serial number;

[0009] When a record in which the primary matching logic fails to match is detected, at least one secondary matching logic is triggered according to the business scenario type, and the triggered secondary matching logic is used to match the transaction data set in which the primary matching logic fails to match;

[0010] Feeding back to the exception handling mechanism any unmatched transaction data set after being processed by the primary matching logic and the secondary matching logic to perform at least one data correction operation;

[0011] Feeding back the transaction data set processed by the exception handling mechanism to the main matching logic for re-matching;

[0012] A timestamp-bearing voucher is generated for the transaction data set that is finally matched successfully, and all the vouchers are aggregated to generate a settleable bill.

[0013] Compared with the existing technology, the beneficial effects of this application are: through the cooperation of primary matching logic and secondary matching logic, the workload of manual reconciliation of financial personnel is reduced, especially in high-frequency transaction scenarios (such as e-commerce and cross-border payments), labor costs are significantly saved, and it can adapt to complex and changing business scenarios.

[0014] Furthermore, the preprocessing includes data cleaning, data deduplication and standardization performed in sequence;

[0015] The data cleaning includes filtering and cleaning abnormal amount fields and unconventional date formats of the original transaction data using regular expressions;

[0016] The data deduplication includes generating a unique identifier for the cleaned original transaction data based on a hash algorithm, wherein the input parameters of the hash algorithm include the order number, the transaction amount, and the transaction timestamp;

[0017] The standardization process includes performing format unification processing on the fields of the original transaction data for generating the unique identifier.

[0018] Furthermore, the secondary matching logic includes:

[0019] When the business scenario is a refund, the combination matching rule of the business order number and the fund flow summary information is adopted;

[0020] When the business scenario is cross-border payment, fuzzy matching of the order's original currency amount and the exchange rate fluctuation threshold is used.

[0021] Furthermore, after the step of using the triggered secondary matching logic to match the transaction data set that failed to be matched by the primary matching logic, the method further includes:

[0022] If the secondary matching logic fails, extracting the semantic feature vector from the transaction data set where the secondary matching logic fails using natural language processing;

[0023] Calculating cosine similarity between the semantic feature vector and a preset label vector library, wherein the label vector library includes multi-dimensional feature representations of refunds, commissions, and taxes;

[0024] When the calculated similarity value exceeds the dynamic threshold, a temporary matching mapping relationship is generated and the confidence level is recorded;

[0025] Based on the confidence value, it is determined whether the transaction data set that failed to be matched by the secondary matching logic is matched successfully.

[0026] Furthermore, the confidence calculation formula includes:

[0027]

[0028] Where, Expressed as confidence, Expressed as historical matching accuracy, Expressed as business scenario weight, Expressed as data quality score, and is the weight coefficient;

[0029] The calculation formula of the dynamic threshold is:

[0030]

[0031] Where, Represents the current dynamic threshold, It represents the dynamic threshold reference value before adjustment. Expressed as the sensitivity coefficient, Represents the number of recent mismatches, Expressed as the total number of matches.

[0032] Furthermore, the exception handling mechanism includes:

[0033] Modify the order number or serial number of the transaction data set;

[0034] Adjust the amount deviation range of the transaction dataset or expand the time window.

[0035] Furthermore, the secondary matching logic is implemented by a dynamic rule engine, and the configuration of the rule engine includes:

[0036] A rule priority allocator, configured to dynamically adjust the priority execution order of multiple fuzzy matching rules in the secondary matching logic according to a business impact index;

[0037] The rule hot loading interface is used to support receiving new fuzzy matching rules pushed by external systems.

[0038] In a second aspect, the invention provides the following technical solution: a business-finance reconciliation system, the system comprising:

[0039] A collection module, configured to extract original transaction data from the business system and the financial system using a data collection tool, wherein the original transaction data includes order data from the business system and fund flow data from the financial system;

[0040] A unified module, configured to pre-process the original transaction data to generate a transaction data set having a unified data structure;

[0041] a first matching module, configured to match the transaction data set according to a preset primary matching logic, wherein the primary matching logic includes an exact match between an order serial number and a fund serial number;

[0042] a second matching module configured to, upon detecting a record for which the primary matching logic fails to match, trigger at least one secondary matching logic based on a business scenario type, and use the triggered secondary matching logic to match the transaction dataset for which the primary matching logic fails to match;

[0043] A correction module, configured to feed back to an exception handling mechanism at least one data correction operation for the transaction data set that is still unmatched after being processed by the primary matching logic and the secondary matching logic;

[0044] a re-matching module, configured to feed back the transaction data set processed by the exception handling mechanism to the main matching logic for re-matching;

[0045] The generation module is used to generate a timestamp of the transaction data set that is finally matched successfully, and aggregate all the blending vouchers to generate a settleable bill.

[0046] In a third aspect, the invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the business and financial reconciliation method as described above when executing the computer program.

[0047] In a fourth aspect, the invention provides the following technical solution: a storage medium storing a computer program, which implements the business and financial reconciliation method as described above when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A flowchart of the business-finance reconciliation method provided in the first embodiment of the present invention;

[0050] Figure 2 This is a structural block diagram of the business-finance reconciliation system provided by the second embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the hardware structure of a computer provided in the third embodiment of the present invention.

[0052] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout refer to the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0054] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0055] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0056] Example 1

[0057] In the first embodiment of the present invention, see Figure 1 A method for reconciling business and finances includes the following steps S01 to S07:

[0058] S01, using a data acquisition tool to extract original transaction data from the business system and the financial system respectively, wherein the original transaction data includes order data from the business system and capital flow data from the financial system.

[0059] In this embodiment, the specific collection methods of the data collection tool include API interface, database connection, and RPA (robotic process automation) and other automation means.

[0060] Collect data from business systems:

[0061] It mainly collects order data, including order number, transaction amount, transaction time and business summary.

[0062] Collect data from financial systems:

[0063] It mainly collects fund flow data, including serial number, amount received, transaction time and flow summary.

[0064] Through the above-mentioned data collection tools, the original transaction data of the business system and financial system can be efficiently obtained, providing a basis for subsequent analysis and processing.

[0065] S02: Preprocess the original transaction data to generate a transaction data set with a unified data structure.

[0066] Furthermore, the preprocessing process includes the following three main steps:

[0067] 1. Data cleaning;

[0068] Regular expressions are used to filter abnormal amount fields and unusual date formats in raw transaction data to ensure data accuracy and consistency.

[0069] 2. Data deduplication;

[0070] A unique identifier is generated for the cleaned raw transaction data using a hash algorithm. The input parameters of the hash algorithm include the order number, transaction amount, and transaction timestamp to ensure data uniqueness.

[0071] 3. Standardized processing;

[0072] The field format of the original transaction data that generates the unique identifier is unified to ensure that the format of all data fields is consistent, providing standardized data support for subsequent analysis and processing.

[0073] Through the above preprocessing steps, the data quality can be effectively improved, laying the foundation for subsequent analysis and application.

[0074] S03, matching the transaction data set according to a preset primary matching logic, wherein the primary matching logic includes an exact matching of the order serial number and the fund serial number.

[0075] In this embodiment, the primary matching logic is configured to accurately compare the order number in the order data with the serial number in the fund flow data;

[0076] Matching process:

[0077] Traverse the transaction data set and compare the order number provided by the business system (such as T20231128001) with the fund serial number of the financial system (such as F20231128001) one by one;

[0078] If the order number and the serial number are exactly the same, it is considered a successful match.

[0079] If the match is successful, it will be marked as "blended" and a blending record will be generated;

[0080] When the order number is not associated with the serial number (for example, the serial number is not bound to the original order in the example), the matching logic in step S04 is triggered.

[0081] S04: When a record in which the primary matching logic fails to match is detected, at least one secondary matching logic is triggered according to the business scenario type, and the transaction data set in which the primary matching logic fails to match is matched using the triggered secondary matching logic.

[0082] Specifically, the secondary matching logic includes:

[0083] When the business scenario is a refund, the combination matching rule of the business order number and the fund flow summary information is adopted;

[0084] When the business scenario is cross-border payment, fuzzy matching of the order's original currency amount and the exchange rate fluctuation threshold is used.

[0085] The specific implementation is as follows:

[0086] 1. Scenario judgment and rule calling;

[0087] Scene Recognition:

[0088] Identify business scenario types by parsing business summaries (such as "User Refund Application") and transaction summaries (such as "Original Return"), including but not limited to:

[0089] Refund scenarios (keywords: refund, return to original route);

[0090] Cross-border payment scenarios (keywords: exchange rate conversion, cross-border fees).

[0091] Rule matching:

[0092] Call the sub-matching logic corresponding to the scenario type:

[0093] Refund scenario: Use the "business order number + fund transaction summary keyword" combination matching rule (for example, order number T20231128001 is associated with transactions containing "original return" in the summary);

[0094] Cross-border payment scenario: Use the fuzzy matching rule of "order original currency amount ± exchange rate fluctuation threshold" (for example, if the order amount is 100 USD, the tolerance range is calculated based on the real-time exchange rate ± 0.5%).

[0095] 2. Secondary matching logic execution;

[0096] Data association: Perform secondary association on transaction data sets that failed primary matching based on secondary matching logic;

[0097] If the match is successful, a redemption record is generated and the matching type is marked (e.g., "refund scenario match");

[0098] If it still fails, enter the semantic matching or manual correction process.

[0099] Specifically, the secondary matching logic is implemented by a dynamic rule engine, and the configuration of the rule engine includes:

[0100] 1. Rule priority assignment:

[0101] Dynamically adjust the execution order of sub-matching logic based on business impact indicators (such as order amount and urgency):

[0102] Risk control rules are prioritized for high-value orders;

[0103] Refund scenarios take priority over normal transactions.

[0104] 2. Rule hot loading interface:

[0105] Supports receiving new rules pushed by external systems (such as risk control platforms and settlement centers):

[0106] Example: Add "Logistics Freight Matching Rule" (match by waybill number + freight amount);

[0107] Real-time effectiveness mechanism: New rules take effect immediately after being loaded into the rule base through the API interface, without the need for system restart.

[0108] Through the above-mentioned sub-matching logic, we can flexibly respond to data matching needs in different business scenarios and improve the accuracy and efficiency of matching.

[0109] S05, feeding back the transaction data set that is still unmatched after being processed by the primary matching logic and the secondary matching logic to the exception handling mechanism to perform at least one data correction operation;

[0110] The exception handling mechanism includes:

[0111] Manually modify the order number or serial number of the transaction data set;

[0112] Adjust the amount deviation range of the transaction dataset or expand the time window.

[0113] In this embodiment, when there are still unmatched records in the transaction data set after being processed by the primary matching logic ( S03 ) and the secondary matching logic ( S04 ), the records are pushed to the exception handling mechanism.

[0114] Exception handling mechanism execution process:

[0115] 1. Manual correction operation;

[0116] Operation Type:

[0117] Field modification: supports manual modification of order numbers and serial numbers (e.g., changing T2023112800X to T20231128001);

[0118] Parameter adjustment:

[0119] Expand the amount tolerance range (e.g., from ±0.5% to ±2%);

[0120] Expand the trading time window (e.g. from ±1 hour to ±24 hours).

[0121] Pending account processing: Records that cannot be corrected immediately are marked as "pending" and a pending account list is generated.

[0122] Operation interface functions:

[0123] Batch processing: supports selecting multiple records and then modifying fields or parameters in a unified manner;

[0124] Smart Recommendation: Recommends a correction plan based on historical correction records and similar scenarios (for example, "The historical correction success rate for order number T2023112800X is 92%, so it is recommended to correct it to T20231128001").

[0125] (2) Operational audit and tracking;

[0126] Logging:

[0127] Record the user ID, operation time, original value, and revised value (e.g., [User A] 2023-11-28 15:00:00 Order No. T2023112800X → T20231128001);

[0128] Generate exportable audit reports containing action summaries and data snapshots.

[0129] (3) Data rematching;

[0130] Corrected data will trigger a partial retry (only for the records containing the corrected fields) instead of rematching the entire data.

[0131] Retry logic:

[0132] If the correction satisfies the main matching logic, the blending voucher is generated directly;

[0133] If it still fails, only the secondary matching logic related to the modified field will be executed (for example, only checking the amount tolerance).

[0134] S06, feeding back the transaction data set processed by the exception handling mechanism to the main matching logic for re-matching;

[0135] In this embodiment, when the exception handling mechanism (S05) completes the data correction operation, the rematching process is automatically triggered, a corrected tag is added to the corrected transaction data set, and the transaction data set marked as corrected is re-injected into the main matching logic (S03) queue.

[0136] S07, generating a timestamped redemption voucher for the transaction data set that is finally successfully matched, and aggregating all the redemption vouchers to generate a settleable bill.

[0137] In this embodiment, for transaction records successfully matched by the primary matching logic (S03), secondary matching logic (S04), or exception handling mechanism (S05), a timestamped redemption voucher is generated. All generated redemption vouchers are aggregated into a complete, billable invoice, which is then pushed to the financial system for subsequent settlement and financial processing.

[0138] Through the above steps, it can be ensured that the blending results of transaction data are recorded and transmitted in a standardized form, providing an accurate and reliable basis for financial settlement.

[0139] In an optional embodiment, when the secondary matching logic fails, natural language processing (NLP) technology is introduced to perform semantic analysis on the transaction data set to further improve the matching accuracy. The method further includes:

[0140] S71, if the secondary matching logic fails, extracting a semantic feature vector from the transaction data set where the secondary matching logic fails using natural language processing;

[0141] S72, performing cosine similarity calculation on the semantic feature vector and a preset label vector library, wherein the label vector library includes multi-dimensional feature representations of refunds, commissions, and taxes;

[0142] S73, when the calculated similarity value exceeds the dynamic threshold, a temporary matching mapping relationship is generated and the confidence level is recorded;

[0143] S74: Based on the confidence value, determine whether the transaction data set that failed the secondary matching logic matching is successfully matched.

[0144] It is worth noting that the specific steps of step S74 include:

[0145] If the confidence level is greater than 90% (high confidence), it is directly marked as "matched" and the final blending voucher is generated (no manual intervention is required);

[0146] If the confidence level is between 70% and 90% (medium confidence), a secondary verification rule (such as amount / time range review) is triggered and the application is automatically confirmed upon passing.

[0147] If the confidence level is lower than 70 percent (low confidence), it will be sent to the manual review queue (exception handling mechanism) for financial personnel to verify the matching relationship.

[0148] Specifically, the confidence calculation formula includes:

[0149]

[0150] Where, Expressed as confidence, Expressed as historical matching accuracy (e.g., 95% success rate in the past 30 days), Expressed as business scenario weight (e.g. refund scenario weight 1.2, cross-border payment 1.0), Expressed as data quality score, and is the weight coefficient ( and );

[0151] The calculation formula of the dynamic threshold is:

[0152]

[0153] Where, Represents the current dynamic threshold, It represents the dynamic threshold reference value before adjustment (such as the initial value 0.8). Expressed as a sensitivity coefficient (such as 0.1), Indicates the number of recent mismatches (e.g., 10 mismatches in the past 24 hours). Expressed as the total number of matches (e.g. 1000 matches in the past 24 hours).

[0154] Specifically, a pre-trained word vector model (such as Word2Vec) is used to parse the transaction summary in the transaction dataset and generate a semantic feature vector.

[0155] It is worth noting that, through steps S71 to S74, semantic analysis of the original transaction data is added to support intelligent matching of unstructured data, thereby improving matching accuracy.

[0156] In summary, a method for reconciling business and finances has the following effects:

[0157] 1. Improved automation capabilities;

[0158] Automatically trigger the secondary matching logic when the primary matching logic fails (such as in refund and cross-border payment scenarios), significantly reducing the need for manual intervention and improving automated processing capabilities.

[0159] 2. Data quality assurance;

[0160] Regular expressions are used to clean abnormal data (such as incorrect amount format) and hash algorithms are used to deduplicate data (generating a unique identifier based on order number + amount + time) to ensure data consistency and reduce mismatches caused by dirty data.

[0161] 3. Matching accuracy optimization;

[0162] Through natural language processing (NLP) and confidence formulas (combining historical accuracy and scenario weights), the misjudgment rate is reduced and the matching accuracy in complex scenarios (such as cross-border payments) is improved.

[0163] 4. Enhanced system adaptability;

[0164] Through the rule priority assigner and hot loading interface, it can respond to business changes (such as adding new promotion rules) in real time and improve system adaptability.

[0165] 5. Save labor costs;

[0166] By coordinating primary and secondary matching logic, the manual reconciliation workload of financial personnel is reduced, especially in high-frequency transaction scenarios (such as e-commerce and cross-border payments), resulting in significant savings in labor costs.

[0167] 6. Technological advantages and innovation;

[0168] The combination of primary matching logic, secondary matching logic, and semantic analysis significantly differs from existing technologies (such as single serial number matching) and improves efficiency.

[0169] The above advantages can effectively improve the efficiency and accuracy of transaction data matching, reduce labor costs and compliance risks, and adapt to complex and changing business scenarios.

[0170] Example 2

[0171] like Figure 2 As shown, a second embodiment of the present invention provides a business-finance reconciliation system, the system comprising:

[0172] The acquisition module 10 is used to extract original transaction data from the business system and the financial system respectively using a data acquisition tool, wherein the original transaction data includes order data from the business system and fund flow data from the financial system;

[0173] A unification module 20 is configured to pre-process the original transaction data to generate a transaction data set having a unified data structure;

[0174] A first matching module 30 is configured to match the transaction data set according to a preset primary matching logic, wherein the primary matching logic includes an exact match between the order serial number and the fund serial number;

[0175] The second matching module 40 is configured to, upon detecting a record for which the primary matching logic fails to match, trigger at least one secondary matching logic based on the business scenario type, and use the triggered secondary matching logic to match the transaction dataset for which the primary matching logic fails to match;

[0176] A correction module 50 is configured to feed back to an exception handling mechanism at least one data correction operation for the transaction data set that is still unmatched after being processed by the primary matching logic and the secondary matching logic;

[0177] A re-matching module 60 is configured to feed back the transaction data set processed by the exception handling mechanism to the main matching logic for re-matching;

[0178] The generation module 70 is used to generate a timestamp-carrying voucher for the transaction data set that is finally matched successfully, and aggregate all the vouchers to generate a settleable bill.

[0179] The business and financial reconciliation system provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0180] Example 3

[0181] like Figure 3 As shown, in the third embodiment of the present invention, the embodiment of the present invention provides the following technical solution: a computer, including a memory 202, a processor 201, and a computer program stored in the memory 202 and executable on the processor 201, wherein the processor 201 implements the business and financial reconciliation method as described above when executing the computer program.

[0182] Specifically, the processor 201 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0183] Memory 202 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 202 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 202 may include removable or non-removable (or fixed) media. Where appropriate, memory 202 may be internal or external to the data processing device. In certain embodiments, memory 202 is non-volatile memory. In certain embodiments, memory 202 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Where appropriate, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0184] The memory 202 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 201 .

[0185] The processor 201 implements the above-mentioned business-financial reconciliation method by reading and executing the computer program instructions stored in the memory 202.

[0186] In some embodiments, the computer may further include a communication interface 203 and a bus 200. Figure 3 As shown, the processor 201 , the memory 202 , and the communication interface 203 are connected via a bus 200 and communicate with each other.

[0187] The communication interface 203 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 203 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0188] The bus 200 includes hardware, software, or both, and couples the components of the computer to each other. The bus 200 includes, but is not limited to, at least one of the following: a data bus (DataBus), an address bus (AddressBus), a control bus (ControlBus), an expansion bus (ExpansionBus), and a local bus (LocalBus). By way of example and not limitation, the bus 200 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, or a 10,000 Pin Count bus. Bus 200 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0189] Example 4

[0190] In a fourth embodiment of the present invention, in combination with the above-mentioned business and financial reconciliation method, the embodiment of the present invention provides the following technical solution: a storage medium having a computer program stored thereon, and the computer program implements the above-mentioned business and financial reconciliation method when executed by a processor.

[0191] Those skilled in the art will appreciate that the logic and / or steps represented by data in flowcharts or otherwise described herein, for example, can be considered as a sequenced data table of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0192] More specific examples (not an exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0193] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0194] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0195] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for reconciling business and finance, characterized in that: The method comprises: Extracting original transaction data from the business system and the financial system using a data collection tool, wherein the original transaction data includes order data from the business system and fund flow data from the financial system; Preprocessing the original transaction data to generate a transaction data set with a unified data structure; Matching the transaction data set according to a preset primary matching logic, wherein the primary matching logic includes an exact match between the order serial number and the fund serial number; When a record in which the primary matching logic fails to match is detected, at least one secondary matching logic is triggered according to the business scenario type, and the triggered secondary matching logic is used to match the transaction data set in which the primary matching logic fails to match; Feeding back to the exception handling mechanism any unmatched transaction data set after being processed by the primary matching logic and the secondary matching logic to perform at least one data correction operation; Feeding back the transaction data set processed by the exception handling mechanism to the main matching logic for re-matching; Generate a timestamped redemption voucher for the transaction data set that is finally matched successfully, and aggregate all the redemption vouchers to generate a settleable bill; The secondary matching logic includes: When the business scenario is a refund, the combination matching rule of the business order number and the fund flow summary information is adopted; When the business scenario involves cross-border payments, fuzzy matching is used based on the order's original currency amount and the exchange rate fluctuation threshold. After the step of using the triggered secondary matching logic to match the transaction data set that failed to be matched by the primary matching logic, the method further includes: If the secondary matching logic fails, extracting the semantic feature vector from the transaction data set where the secondary matching logic fails using natural language processing; Calculating cosine similarity between the semantic feature vector and a preset label vector library, wherein the label vector library includes multi-dimensional feature representations of refunds, commissions, and taxes; When the calculated similarity value exceeds the dynamic threshold, a temporary matching mapping relationship is generated and the confidence level is recorded; Based on the confidence value, determining whether the transaction data set that failed to be matched by the secondary matching logic is successfully matched; The calculation formula of the confidence level includes: Where, Expressed as confidence, Expressed as historical matching accuracy, Expressed as business scenario weight, Expressed as data quality score, and is the weight coefficient; The calculation formula of the dynamic threshold is: Where, Represents the current dynamic threshold, It represents the dynamic threshold reference value before adjustment. Expressed as the sensitivity coefficient, Represents the number of recent mismatches, Expressed as the total number of matches.

2. The business-finance reconciliation method according to claim 1, characterized in that: The preprocessing includes data cleaning, data deduplication and standardization performed in sequence; The data cleaning includes filtering and cleaning abnormal amount fields and unconventional date formats of the original transaction data using regular expressions; The data deduplication includes generating a unique identifier for the cleaned original transaction data based on a hash algorithm, wherein the input parameters of the hash algorithm include the order number, the transaction amount, and the transaction timestamp; The standardization process includes performing format unification processing on the fields of the original transaction data for generating the unique identifier.

3. The business-finance reconciliation method according to claim 1, characterized in that: The exception handling mechanism includes: Modify the order number or serial number of the transaction data set; Adjust the amount deviation range of the transaction dataset or expand the time window.

4. The business-finance reconciliation method according to claim 1, characterized in that: The secondary matching logic is implemented by a dynamic rule engine, and the configuration of the rule engine includes: A rule priority allocator, configured to dynamically adjust the priority execution order of multiple fuzzy matching rules in the secondary matching logic according to a business impact index; The rule hot loading interface is used to support receiving new fuzzy matching rules pushed by external systems.

5. A business and financial reconciliation system, characterized by: The system comprises: A collection module, configured to extract original transaction data from the business system and the financial system using a data collection tool, wherein the original transaction data includes order data from the business system and fund flow data from the financial system; A unified module, configured to pre-process the original transaction data to generate a transaction data set having a unified data structure; a first matching module, configured to match the transaction data set according to a preset primary matching logic, wherein the primary matching logic includes an exact match between an order serial number and a fund serial number; a second matching module configured to, upon detecting a record for which the primary matching logic fails to match, trigger at least one secondary matching logic based on a business scenario type, and use the triggered secondary matching logic to match the transaction dataset for which the primary matching logic fails to match; A correction module, configured to feed back to an exception handling mechanism at least one data correction operation for the transaction data set that is still unmatched after being processed by the primary matching logic and the secondary matching logic; a re-matching module, configured to feed back the transaction data set processed by the exception handling mechanism to the main matching logic for re-matching; A generation module, configured to generate a timestamp-bearing redemption voucher for the transaction data set that is finally matched successfully, and to aggregate all the redemption vouchers to generate a settleable bill; The secondary matching logic includes: When the business scenario is a refund, the combination matching rule of the business order number and the fund flow summary information is adopted; When the business scenario involves cross-border payments, fuzzy matching is used based on the order's original currency amount and the exchange rate fluctuation threshold. After the step of using the triggered secondary matching logic to match the transaction data set that failed to be matched by the primary matching logic, the method further includes: If the secondary matching logic fails, extracting the semantic feature vector from the transaction data set where the secondary matching logic fails using natural language processing; Calculating cosine similarity between the semantic feature vector and a preset label vector library, wherein the label vector library includes multi-dimensional feature representations of refunds, commissions, and taxes; When the calculated similarity value exceeds the dynamic threshold, a temporary matching mapping relationship is generated and the confidence level is recorded; Based on the confidence value, determining whether the transaction data set that failed to be matched by the secondary matching logic is successfully matched; The calculation formula of the confidence level includes: Where, Expressed as confidence, Expressed as historical matching accuracy, Expressed as business scenario weight, Expressed as data quality score, and is the weight coefficient; The calculation formula of the dynamic threshold is: Where, Represents the current dynamic threshold, It represents the dynamic threshold reference value before adjustment. Expressed as the sensitivity coefficient, Represents the number of recent mismatches, Expressed as the total number of matches.

6. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the business and financial reconciliation method as described in any one of claims 1 to 4 is implemented.

7. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the business-financial reconciliation method according to any one of claims 1 to 4.

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