Clearing message processing method and device, electronic equipment and readable storage medium

Abstract

The invention discloses a clearing message processing method and device, electronic equipment and a readable storage medium, and can be applied to the technical field of financial science and technology. The method comprises the steps that a clearing message is received, and the clearing message at least comprises transaction information and appendix data corresponding to a to-be-cancelled-after-verification transaction; inputting the appendix data in the liquidation message into a target cancel-after-verification model for identification to obtain an identification result of the appendix data; searching a target transaction record matched with the identification result from a transaction number table based on identification information related to the transaction to be canceled after verification included in the identification result; determining a matching result between the target transaction record and the liquidation message based on the identification result and transaction information in the liquidation message; and in response to the matching result indicating that the target transaction record is successfully matched with the liquidation message, updating the cancel-after-verification state corresponding to the liquidation message to a cancel-after-verification completion state. According to the invention, the technical problem of low processing efficiency of the clearing message is solved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and specifically, to a method, device, electronic device and readable storage medium for processing a clearing message. Background Art

[0002] At present, in the financial market, the clearing and cancellation of transactions involves the actual transfer and confirmation of funds between the two parties to the transaction, which is a crucial link. With the increase in transaction volume and the increase in transaction complexity, the traditional clearing and cancellation method is mainly based on the accurate matching of the date, amount, account number, and remarks of the report, especially when there are multiple transactions in the clearing report. Since the remarks depend on the clearing sender to fill in, if the remarks are not filled in accurately, it will bring great challenges to automatic cancellation. Therefore, there is a technical problem of low efficiency of clearing and cancellation.

[0003] With respect to the technical problem of low efficiency in liquidation and write-off in relevant technologies, no effective solution has been proposed so far. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, electronic device and readable storage medium for processing a clearing message, so as to solve the technical problem of low processing efficiency of clearing messages in the related art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for processing a clearing message is provided. The method comprises: receiving a clearing message, wherein the clearing message at least comprises transaction information and postscript data corresponding to the transaction to be written off, wherein the postscript data is unstructured description information of the transaction to be written off; inputting the postscript data in the clearing message into a target write-off model for recognition, and obtaining a recognition result of the postscript data, wherein the target write-off model is obtained by pre-training an initial write-off model using postscript data samples, and the recognition result at least comprises identification information related to the transaction to be written off; based on the identification information related to the transaction to be written off included in the recognition result, searching a target transaction record matching the recognition result from a transaction number table, wherein at least one transaction record is stored in the transaction number table; based on the recognition result and the transaction information in the clearing message, determining a matching result between the target transaction record and the clearing message, wherein the matching result is used to indicate the matching degree between the target transaction record and the clearing message; in response to the matching result indicating that the target transaction record matches the clearing message successfully, updating the write-off status corresponding to the clearing message to a write-off completion status.

[0006] Optionally, the identification information related to the transaction to be written off includes at least the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off. Based on the identification information related to the transaction to be written off included in the identification result, searching the transaction number table for a target transaction record that matches the identification result, including: constructing a query condition based on the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off, wherein the query condition includes at least one of the following information: the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off; based on the query condition, searching the transaction number table for a target transaction record that matches the identification result.

[0007] Optionally, based on the identification result and the transaction information in the clearing message, determining the matching result between the target transaction record and the clearing message, including: extracting key elements corresponding to the transaction to be written off from the identification result and the transaction information in the clearing message, and extracting key elements corresponding to the target transaction record from the target transaction record, wherein the key elements include at least the transaction currency, transaction date and transaction amount; matching the key elements corresponding to the transaction to be written off with the key elements corresponding to the target transaction record to obtain a matching result.

[0008] Optionally, the method for processing the clearing message also includes: comparing the matching degree between the clearing message and the target transaction record with a matching threshold to obtain a comparison result; in response to the comparison result indicating that the matching degree is greater than the matching threshold, determining that the target transaction record and the clearing message are successfully matched.

[0009] Optionally, the method for processing clearing messages also includes: obtaining remark data samples from historical clearing messages; marking identification information related to the transaction samples to be written off in the remark data samples to obtain label data; dividing the marked remark data samples into a training set and a test set; using the training set to train the initial write-off model to obtain the trained initial write-off model; using the test set to verify the recognition accuracy of the trained initial write-off model to obtain a verification result; in response to the verification result indicating that the recognition accuracy is greater than the accuracy threshold, determining the trained initial write-off model as the target write-off model.

[0010] Optionally, the method for processing the liquidation message also includes: in response to the recognition accuracy being not greater than the accuracy threshold, adjusting the model parameters of the trained initial write-off model; and continuing to train the adjusted initial write-off model using the training set until the recognition accuracy of the trained initial write-off model is greater than the accuracy threshold.

[0011] In order to achieve the above objective, according to another aspect of the present application, a device for processing a clearing message is provided. The device comprises: a receiving unit, used for receiving a clearing message, wherein the clearing message at least comprises transaction information and postscript data corresponding to the transaction to be written off, wherein the postscript data is unstructured description information of the transaction to be written off; an identification unit, used for inputting the postscript data in the clearing message into a target write-off model for identification, and obtaining an identification result of the postscript data, wherein the target write-off model is obtained by pre-training an initial write-off model using postscript data samples, and the identification result at least comprises identification information related to the transaction to be written off; a searching unit, used for searching a target transaction record matching the identification result from a transaction number table based on the identification information related to the transaction to be written off included in the identification result, wherein at least one transaction record is stored in the transaction number table; a determining unit, used for determining a matching result between the target transaction record and the clearing message based on the identification result and the transaction information in the clearing message, wherein the matching result is used to indicate a matching degree between the target transaction record and the clearing message; and an updating unit, used for updating the write-off status corresponding to the clearing message to a write-off completion status in response to the matching result indicating that the target transaction record matches the clearing message successfully.

[0012] Optionally, the search unit is also used to: construct query conditions based on the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off, wherein the query conditions include at least one of the following information: the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off; based on the query conditions, query the transaction number table for a target transaction record that matches the identification result.

[0013] Optionally, the determination unit is also used to: extract key elements corresponding to the transaction to be written off from the identification result and the transaction information in the settlement message, and extract key elements corresponding to the target transaction record from the target transaction record, wherein the key elements include at least the transaction currency, transaction date and transaction amount; match the key elements corresponding to the transaction to be written off with the key elements corresponding to the target transaction record to obtain a matching result.

[0014] Optionally, the clearing message processing device is further used to: compare the matching degree between the clearing message and the target transaction record with a matching threshold to obtain a comparison result; in response to the comparison result indicating that the matching degree is greater than the matching threshold, determine that the target transaction record and the clearing message are successfully matched.

[0015] Optionally, the processing device of the clearing message is also used to: obtain remark data samples from historical clearing messages; mark the identification information related to the transaction samples to be written off in the remark data samples to obtain label data; divide the marked remark data samples into a training set and a test set; use the training set to train the initial write-off model to obtain the trained initial write-off model; use the test set to verify the recognition accuracy of the trained initial write-off model; in response to the recognition accuracy being greater than the accuracy threshold, determine the trained initial write-off model as the target write-off model.

[0016] Optionally, the processing device of the clearing message is also used to: in response to the recognition accuracy being not greater than the accuracy threshold, adjust the model parameters of the trained initial write-off model; and continue to train the adjusted initial write-off model using the training set until the recognition accuracy of the trained initial write-off model is greater than the accuracy threshold.

[0017] To achieve the above object, according to another aspect of the present application, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any of the above method embodiments.

[0018] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the steps in any one of the above-mentioned method embodiments.

[0019] In order to achieve the above objective, according to another aspect of the present application, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0020] In an embodiment of the present application, a clearing message is received, wherein the clearing message at least includes transaction information and postscript data corresponding to the transaction to be written off, wherein the postscript data is unstructured description information of the transaction to be written off; the postscript data in the clearing message is input into a target write-off model for recognition, and a recognition result of the postscript data is obtained, wherein the target write-off model is obtained by pre-training an initial write-off model using a postscript data sample, and the recognition result at least includes identification information related to the transaction to be written off; based on the identification information related to the transaction to be written off included in the recognition result, a target transaction record matching the recognition result is searched from a transaction number table, wherein at least one transaction record is stored in the transaction number table; based on the recognition result and the transaction information in the clearing message, a matching result between the target transaction record and the clearing message is determined, wherein the matching result is used to indicate a matching degree between the target transaction record and the clearing message; in response to the matching result indicating that the target transaction record and the clearing message are successfully matched, the write-off status corresponding to the clearing message is updated to a write-off completion status. That is to say, in the embodiment of the present application, by inputting the postscript data in the clearing message into the target write-off model for automatic identification, the identification information related to the transaction to be written off can be accurately extracted from the free-format postscript data, thereby improving the accuracy of matching with the transaction records in the transaction number table, reducing the errors and omissions that may be introduced by traditional manual verification, achieving efficient processing of unstructured descriptive information, reducing the need for manual analysis, greatly improving the automation level and write-off efficiency of financial market transaction clearing message processing, and thus solving the technical problem of low write-off efficiency of clearing messages. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0022] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a method for processing a clearing message according to an embodiment of the present application;

[0023] Figure 2 is a flow chart of a method for processing a clearing message according to an embodiment of the present application;

[0024] Figure 3 is a flowchart of another method for processing a clearing message according to an embodiment of the present application;

[0025] Figure 4 is a schematic diagram of a clearing message processing device according to an embodiment of the present application;

[0026] Figure 5 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0030] Example 1

[0031] According to an embodiment of the present application, an embodiment of a method for processing a clearing message is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 11 is a hardware structure block diagram of a computer terminal for implementing a method for processing a clearing message according to an embodiment of the present application. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (shown in the figure as 102a, 102b, ..., 102n) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Microcontroller Unit, referred to as MCU) or a programmable logic device (Field-Programmable Gate Array, referred to as FPGA)), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (Input / Output, referred to as I / O), a Universal Serial Bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0033] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0034] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the processing method of the clearing message in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the processing method of the clearing message. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] The display may be, for example, a touch screen liquid crystal display (LCD), which enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0037] Under the above operating environment, this application provides Figure 2 The processing method of the clearing message is shown. Figure 2 It is a flowchart of the method for processing clearing messages according to Example 1 of the present application.

[0038] Step S201, creating a database execution object.

[0039] In the technical solution provided in the above step S201 of the present application, the clearing message at least includes the transaction information corresponding to the transaction to be written off and the postscript data is the unstructured descriptive information of the transaction to be written off. Among them, the transaction information corresponding to the transaction to be written off is structured information, which includes the date, time, transaction amount, currency, clearing counterparty information, transaction account and other key data of the transaction. This part of information is usually the basis for automatic reading and processing by the electronic clearing system. The postscript data is different from the transaction information. The postscript data is unstructured descriptive information and can include additional information about the transaction, such as the background of the transaction, the nature of the transaction, the specific transaction number, the name of the clearing counterparty, the description of the transaction product, etc. However, since the postscript data is filled in by different institutions or individuals, there are great differences in its format and content style, which makes it difficult for the postscript data to be directly parsed and used by the traditional automation system. This is only an illustrative example, and the specific content included in the transaction information and postscript data is not limited.

[0040] In this embodiment, when one or more financial transactions reach settlement conditions, the financial institution or clearing system will receive clearing messages. These messages are electronic documents used by the transaction parties or their agents to perform transaction confirmation and fund transfer, and contain detailed transaction information, such as transaction date, amount, currency, account information of the transaction parties, etc., as well as postscript data.

[0041] Step S202: input the postscript data in the liquidation message into the target write-off model for recognition, and obtain the recognition result of the postscript data.

[0042] In the technical solution provided in the above step S202 of the present application, the target write-off model is obtained by pre-training the initial write-off model using the postscript data samples. That is, the target write-off model is obtained by training and optimizing the initial write-off model based on a large number of labeled postscript data samples. In other words, the target write-off model has learned how to identify key transaction information from the postscript data.

[0043] In this embodiment, after receiving the liquidation message, the postscript data in the liquidation message may be input into the target write-off model for recognition to obtain a recognition result of the postscript data, so as to improve the recognition accuracy of the relevant information in the postscript data.

[0044] Optionally, during the training phase of the target write-off model, the initial write-off model gradually optimizes parameters and improves recognition accuracy by learning the association between the postscript data samples and the labeled transaction identification information (such as transaction number, clearing counterparty, product code, etc.). After the recognition accuracy of the initial write-off model meets the conditions, the initial write-off model is determined as the target write-off model for subsequent transaction matching and write-off confirmation.

[0045] Step S203: based on the identification information related to the transaction to be written off included in the identification result, searching the transaction number table for a target transaction record that matches the identification result.

[0046] In the technical solution provided in the above step S203 of the present application, at least one transaction record is stored in the transaction number table.

[0047] In this embodiment, after obtaining the recognition result of the postscript data in the clearing message, the target transaction record matching the recognition result can be searched from the transaction number table according to the identification information related to the transaction to be written off included in the recognition result.

[0048] For example, the recognition result may include a series of identification information related to the transaction to be written off, such as the transaction number, the name of the clearing counterparty, the specific bond name or abbreviation, the bond principal repayment, etc. The transaction number table is a database that stores all current or historical transaction records, each of which includes a unique transaction number and all information associated with the transaction, such as the specific bond name or abbreviation, the bond principal repayment, the transaction date, the transaction amount, the information of the participants, the product type, etc. Based on the identification information related to the transaction to be written off extracted from the recognition result, the transaction number table can be automatically queried, and then the transaction record matching the identification information can be retrieved from the transaction number table.

[0049] In this step, since at least one transaction record is stored in the transaction number table, and each transaction record includes specific transaction information, based on this, the transaction record matching the transaction to be written off can be quickly retrieved from the transaction number using the recognition result of the postscript data included in the settlement message of the transaction to be written off, thereby improving the writing-off efficiency of the transaction to be written off.

[0050] Step S204: determining a matching result between the target transaction record and the clearing message based on the recognition result and the transaction information in the clearing message.

[0051] In the technical solution provided in the above step S204 of the present application, the matching result is used to indicate the matching degree between the target transaction record and the settlement message.

[0052] In this embodiment, after obtaining the target transaction record, the identification result and the transaction information in the settlement message can be matched with the corresponding information stored in the target transaction record to obtain a matching result. The matching degree between the target transaction record and the settlement message is determined according to the matching result, and then whether the target transaction record is a transaction record corresponding to the transaction to be written off is determined according to the matching degree.

[0053] For example, matching involves comparisons of multiple dimensions, including but not limited to amount, currency, transaction date, information of both parties to the transaction, transaction number, etc. If the matching degree of each piece of information is greater than the matching threshold, it means that the clearing message and the target transaction record are successfully matched, and the target transaction record can be determined as the transaction record corresponding to the transaction to be written off. On the contrary, if there is inconsistent information, it means that the matching degree between the clearing message and the target transaction record is low. In this case, further manual confirmation or re-calling of the large model for in-depth analysis is required.

[0054] In this step, by comparing the identification results of the transaction information and postscript data corresponding to the clearing message with the corresponding information in the target transaction record in multiple dimensions, it is possible to confirm whether the target transaction record corresponds to the transaction that has not been written off, thereby improving the accuracy of clearing and write-off.

[0055] Step S205 , in response to the matching result indicating that the target transaction record matches the clearing message successfully, the write-off status corresponding to the clearing message is updated to a write-off completion status.

[0056] In the technical solution provided in the above step S205 of the present application, after the matching result is obtained through step S204, if the matching result indicates that the target transaction record matches the clearing message successfully, the write-off status corresponding to the clearing message is updated from "pending write-off status" to "write-off completed status".

[0057] In this step, after the clearing message is successfully matched with the target transaction record, the write-off status of the clearing message is updated to the write-off completion status to avoid repeated write-offs of the same clearing message. Moreover, when the write-off status corresponding to the clearing message is in the write-off completion status, it can ensure that the transaction is quickly confirmed and settled, reducing the risk of transaction delays.

[0058] In the above steps S201 to S205, by inputting the postscript data in the clearing message into the target write-off model for automatic identification, the identification information related to the transaction to be written off can be accurately extracted from the free-format postscript data, thereby improving the accuracy of matching with the transaction records in the transaction number table, reducing the errors and omissions that may be introduced by traditional manual verification, achieving efficient processing of unstructured descriptive information, reducing the need for manual analysis, greatly improving the automation level and write-off efficiency of financial market transaction clearing message processing, and thus solving the technical problem of low write-off efficiency of clearing messages.

[0059] The embodiment of the present invention is described in detail below in combination with the above steps.

[0060] As an optional implementation, the identification information related to the transaction to be written off includes at least the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off. Step S203, based on the identification information related to the transaction to be written off included in the identification result, searching the transaction number table for a target transaction record that matches the identification result, including: constructing a query condition based on the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off, wherein the query condition includes at least one of the following information: the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off; based on the query condition, searching the transaction number table for a target transaction record that matches the identification result.

[0061] In this embodiment, after identifying the postscript data in the clearing message and obtaining the identification result, a query condition can be constructed based on the identification information of the transaction to be written off, the identification information of the clearing object, and the identification information of the transaction product corresponding to the transaction to be written off included in the identification result. The query condition is used to find transaction records that match these identification information in the transaction number table.

[0062] Optionally, the transaction number table is a database used by financial institutions to record each transaction, which stores all structured information related to the transaction, including transaction number, clearing object information, transaction product type, etc., and is used to confirm the consistency between the clearing message and the backend transaction record. According to the constructed query conditions, the transaction record matching the identification information of the clearing message can be searched in the transaction number table, and the found transaction record can be determined as the target transaction record.

[0063] In this step, the unstructured information identified by the large model and the structured transaction information in the clearing message are used to construct query conditions and locate the target transaction records, thereby achieving intelligent matching between the clearing message and the transaction record, thereby improving clearing efficiency, reducing operational risks, and laying the foundation for subsequent transaction confirmation and status updates.

[0064] As an optional implementation, step S204, based on the identification result and the transaction information in the clearing message, determines the matching result between the target transaction record and the clearing message, including: extracting key elements corresponding to the transaction to be written off from the identification result and the transaction information in the clearing message, and extracting key elements corresponding to the target transaction record from the target transaction record, wherein the key elements at least include the transaction currency, transaction date and transaction amount; matching the key elements corresponding to the transaction to be written off with the key elements corresponding to the target transaction record to obtain a matching result.

[0065] In this embodiment, after obtaining the recognition result of the postscript data, key elements such as transaction currency, transaction date and transaction amount can be extracted from the recognition result and the transaction information in the settlement message. This is only an illustrative example and does not limit the specific content of the key elements.

[0066] Optionally, while extracting key elements from the identification results and the transaction information in the clearing message, the corresponding key elements can also be extracted from the target transaction record. The key elements of the transaction to be written off extracted from the identification results and the clearing message are then compared one by one with the key elements in the target transaction record to determine whether the target transaction record is the transaction record corresponding to the clearing message. If all key elements are completely matched, the matching result is successful; if any element does not match, the matching fails, requiring additional manual review or in-depth analysis through the large model again.

[0067] In this step, by matching the key elements extracted from the recognition results and clearing messages with the key elements in the target transaction records, the cancellation confirmation of financial market clearing messages can be completed automatically and accurately, greatly improving the efficiency and accuracy of the clearing process.

[0068] As an optional implementation, the method for processing the clearing message also includes: comparing the degree of matching between the clearing message and the target transaction record with a matching threshold to obtain a comparison result; in response to the comparison result indicating that the degree of matching is greater than the matching threshold, determining that the target transaction record and the clearing message are successfully matched.

[0069] In this embodiment, after obtaining the matching result between the clearing message and the target transaction record, since the matching result is used to indicate the matching degree between the clearing message and the target transaction record, based on this, after obtaining the matching degree between the clearing message and the target transaction record, the matching degree can be compared with the matching threshold to obtain a comparison result. Then, according to the comparison result, it is determined whether the target transaction record and the clearing message are successfully matched.

[0070] For example, if the comparison result indicates that the matching degree between the clearing message and the target transaction record is greater than the matching threshold, it can be determined that the target transaction record and the clearing message are matched successfully. If the comparison result indicates that the matching degree between the clearing message and the target transaction record is not greater than the matching threshold, it is determined that the matching degree between the target transaction record and the clearing message fails.

[0071] In this step, the matching threshold is used as a standard to measure the matching degree between the clearing message and the target transaction record, which can ensure that the target transaction record is a transaction record corresponding to the clearing message.

[0072] Next, the training process of the target write-off model is introduced.

[0073] As an optional implementation, the method for processing the clearing message also includes: obtaining a postscript data sample from a historical clearing message; marking the identification information related to the transaction sample to be written off in the postscript data sample to obtain label data; dividing the marked postscript data sample into a training set and a test set; using the training set to train the initial write-off model to obtain a trained initial write-off model; using the test set to verify the recognition accuracy of the trained initial write-off model to obtain a verification result; in response to the verification result indicating that the recognition accuracy is greater than the accuracy threshold, determining the trained initial write-off model as the target write-off model.

[0074] In this embodiment, samples can be extracted from the historical settlement messages of financial institutions. These samples contain various postscript information, reflecting the actual diversity and complexity of settlement messages. The selection of historical data should cover different transaction types, settlement objects, products, and various possible postscript formats to ensure the comprehensiveness and generalization ability of model training. The extracted postscript data samples are manually labeled by business experts or system engineers, focusing on the identification information related to the transactions to be written off. This identification information may include transaction number, settlement counterparty name, product code, transaction date, transaction amount, etc. The labeling process forms a set of label data to guide the learning of the model.

[0075] Optionally, after obtaining the postscript data samples and label data, the labeled postscript data samples can be divided into a training set and a test set. The training set is used to train the initial write-off model so that the initial write-off model learns how to identify key identification information from the postscript of the clearing message; the test set is used to verify the performance and accuracy of the initial write-off model after the training is completed.

[0076] Optionally, the test set data is used to verify the trained initial write-off model. By comparing the recognition results of the initial write-off model with the manually annotated label data, the recognition accuracy of the initial write-off model is evaluated. An accuracy threshold is set as the evaluation standard for the performance of the initial write-off model. If the recognition accuracy verified by the test set is greater than the set threshold, it means that the performance of the initial write-off model meets the requirements. In this case, the trained initial write-off model can be determined as the target write-off model. The target write-off model can effectively identify and process the postscript data in the clearing message, providing support for automatic write-off confirmation.

[0077] In this step, data collection, labeling, model training and model verification ensure that the establishment of the target write-off model is based on sufficient historical data and strict performance verification. Once the target write-off model is determined, it can be used to automatically process newly received clearing messages, improve clearing efficiency and reduce manual operation risks.

[0078] As an optional implementation, the method for processing the liquidation message also includes: in response to the recognition accuracy being not greater than the accuracy threshold, adjusting the model parameters of the trained initial write-off model; and continuing to train the adjusted initial write-off model using the training set until the recognition accuracy of the trained initial write-off model is greater than the accuracy threshold.

[0079] In this embodiment, if the recognition accuracy of the initial write-off model on the test set is not greater than the accuracy threshold, it means that the current performance of the model has not yet met the standards of business requirements and needs further optimization. In this case, the model parameters of the trained initial write-off model will be adjusted automatically or manually by the developer. Parameter adjustment may include but is not limited to modification of the learning rate, increase in the number of neural network layers, optimization of feature selection, etc., to improve the recognition ability and accuracy of the model.

[0080] Optionally, the model is trained again using the adjusted model parameters and training set data. This process may require multiple iterations, and the recognition accuracy of the model is gradually improved through continuous training and parameter fine-tuning. After each model training, the recognition accuracy of the initial write-off model is re-verified using the test set data to check whether the adjustment is effective and whether the initial write-off model is close to or reaches the preset accuracy threshold. When the recognition accuracy of the trained initial write-off model is finally greater than the accuracy threshold, the system confirms that the model training is complete, and the model is considered to be the target write-off model and can be put into the actual clearing message processing process.

[0081] In this step, the closed-loop mechanism of model training and verification ensures that the recognition accuracy of the target write-off model can meet the high standard requirements of automatic write-off confirmation of financial market clearing messages before it is put into practical application. By continuously adjusting model parameters and iterative training, the initial write-off model can continuously learn and optimize, and ultimately have the ability to process complex postscript data, providing financial institutions with efficient and accurate automatic write-off confirmation services.

[0082] Another optional specific implementation is described in detail below.

[0083] At present, financial market-related businesses flow into the backend system for clearing and processing after the transaction is completed. The backend system will generate payment and collection information of the fund collection and payment flow according to the transaction time plan, and process the fund collection and payment according to the delivery time. For the scenario of our collection, it is necessary to associate and confirm the clearing report with the original transaction one by one to ensure the accuracy of the transaction funds. At present, the financial market clearing report write-off business is mainly based on the accurate matching of the date, amount, account number, and remarks of the report. In particular, there are multiple transactions in the clearing report, and the remarks depend on the clearing sender to fill in, resulting in the inability to accurately match. The payer may fill in a transaction number or the payer's abbreviation / full name or a bond name or abbreviation or the bond principal repayment, etc., resulting in the inability to match successfully. Business personnel are required to manually identify the content of the remarks and match and confirm with our collection and payment flow. Because the remarks filled in by each counterparty have no fixed format, the manual workload is relatively large. In this scenario, automatic identification and processing cannot be achieved, and it depends on manual confirmation, which is time-consuming and labor-intensive and has operational risks.

[0084] However, the embodiment of the present application proposes a method for processing a clearing message, extracting a postscript data sample from a historical clearing message in advance, and marking the postscript data sample, training an initial write-off model based on the marked postscript data sample, and then obtaining a target write-off model. After receiving the clearing message, the postscript data in the clearing message can be input into the target write-off model for identification, and then obtaining the identification result of the postscript data in the clearing message, and according to the identification result of the target write-off model, querying an external transaction number table to determine the target transaction record corresponding to the clearing message, and further verifying the matching degree between the target transaction record and the clearing message, and confirming the matching success when the matching degree is greater than the matching threshold, and updating the status of the clearing message to the completion of the write-off confirmation, realizing the automatic write-off confirmation of the clearing message, reducing the input of human resources, improving the processing efficiency and accuracy of the clearing message transaction matching, reducing the operational risk, and realizing efficient and automated processing.

[0085] Next, the method for processing the clearing message in the embodiment of the present application is further introduced.

[0086] Figure 3 1 is a flowchart of another method for processing a clearing message according to an embodiment of the present application. The method comprises the following steps:

[0087] Step S301, receiving a liquidation report.

[0088] In this embodiment, the bank or financial institution receives a clearing message that has not yet completed the write-off confirmation. These messages may contain information on multiple transactions, and the remarks field may be incomplete or inconsistent in format, making it difficult for traditional automatic matching methods to accurately process them.

[0089] Step S302, marking the verification data.

[0090] In this embodiment, the financial institution processes the unwritten-off clearing messages, mainly marking the key information in the messages, including identifying and extracting information such as transaction date, amount, account number, and remarks, to prepare data for subsequent model training.

[0091] Step S303: construct, train and optimize the large model.

[0092] In this embodiment, a deep learning model (large model) is constructed and trained based on the data marked in step S302. This model is designed to understand the content of the postscript of the settlement message, even if the postscript information is vague or non-standard. Through a large amount of data training, the model can learn the patterns of different settlement postscripts and improve the accuracy of recognition and matching.

[0093] Step S304, large model interface.

[0094] In this embodiment, the trained big model is encapsulated into a callable interface for use by other components of the financial system. This means that any system that receives a clearing message can pass the message data to the big model through this interface for intelligent analysis and processing.

[0095] Step S305: large model strategy execution.

[0096] In this embodiment, the large model conducts in-depth analysis of the postscript of the clearing message to identify key information related to the transaction (such as transaction number, counterparty name, bond name, etc.). The recognition results of the model will be used to guide the subsequent transaction matching process.

[0097] Step S306, confirm whether the verification data has been completed.

[0098] In this embodiment, the system matches the clearing message with the actual transaction record. If the match is successful and the clearing message is confirmed to be completely consistent with a transaction, it means that the intelligent write-off confirmation has been completed, and step S307 is executed. If the match fails, the whole process ends.

[0099] Step S307, the status of the reported write-off data is set to write-off completed.

[0100] In this embodiment, if the liquidation message is written off, the status of the liquidation message is set to the written off completed status, and then the whole process ends.

[0101] In the above steps S301 to S307, the automatic processing of clearing reports that cannot be solved by conventional means is completed through the construction of a large system model, report identification, strategy execution, transaction matching, and automatic write-off confirmation, thereby reducing human resource investment, improving the processing efficiency and accuracy of back-end clearing report transaction matching, and realizing the function of efficient and automated processing of clearing report write-offs, automatic write-off confirmation matching, saving labor costs, and reducing operational risks.

[0102] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0103] Example 2

[0104] The embodiment of the present application also provides a clearing message processing device. It should be noted that the clearing message processing device of the embodiment of the present application can be used to execute the clearing message processing method provided in the embodiment of the present application. The clearing message processing device provided in the embodiment of the present application is introduced below.

[0105] According to an embodiment of the present application, a device for implementing the above-mentioned method for processing a clearing message is also provided. Figure 4 As shown, the clearing message processing device 400 includes: a receiving unit 401, an identification unit 402, a searching unit 403, a determining unit 404 and an updating unit 405.

[0106] The receiving unit 401 is used to receive a clearing message, wherein the clearing message at least includes transaction information and postscript data corresponding to the transaction to be written off, and the postscript data is unstructured description information of the transaction to be written off.

[0107] The identification unit 402 is used to input the postscript data in the clearing message into the target write-off model for identification, and obtain the identification result of the postscript data, wherein the target write-off model is obtained by pre-training the initial write-off model with the postscript data sample, and the identification result at least includes the identification information related to the transaction to be written off.

[0108] The searching unit 403 is used to search for a target transaction record matching the identification result from the transaction number table based on the identification information related to the transaction to be written off included in the identification result, wherein the transaction number table stores at least one transaction record.

[0109] The determination unit 404 is used to determine a matching result between the target transaction record and the clearing message based on the identification result and the transaction information in the clearing message, wherein the matching result is used to indicate a matching degree between the target transaction record and the clearing message.

[0110] The updating unit 405 is configured to update the write-off status corresponding to the clearing message to a write-off completion status in response to the matching result indicating that the target transaction record matches the clearing message successfully.

[0111] Optionally, the search unit 403 is also used to: construct a query condition based on the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off, wherein the query condition includes at least one of the following information: the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off; based on the query condition, query the transaction number table for a target transaction record that matches the identification result.

[0112] Optionally, the determination unit 404 is also used to: extract key elements corresponding to the transaction to be written off from the identification result and the transaction information in the settlement message, and extract key elements corresponding to the target transaction record from the target transaction record, wherein the key elements include at least the transaction currency, transaction date and transaction amount; match the key elements corresponding to the transaction to be written off with the key elements corresponding to the target transaction record to obtain a matching result.

[0113] Optionally, the clearing message processing device 400 is further used to: compare the matching degree between the clearing message and the target transaction record with a matching threshold to obtain a comparison result; in response to the comparison result indicating that the matching degree is greater than the matching threshold, determine that the target transaction record and the clearing message are matched successfully.

[0114] Optionally, the processing device 400 of the clearing message is also used to: obtain remark data samples from historical clearing messages; mark the identification information related to the transaction samples to be written off in the remark data samples to obtain label data; divide the marked remark data samples into a training set and a test set; use the training set to train the initial write-off model to obtain the trained initial write-off model; use the test set to verify the recognition accuracy of the trained initial write-off model to obtain a verification result; in response to the verification result indicating that the recognition accuracy is greater than the accuracy threshold, determine the trained initial write-off model as the target write-off model.

[0115] Optionally, the clearing message processing device 400 is also used to: in response to the recognition accuracy being not greater than the accuracy threshold, adjust the model parameters of the trained initial write-off model; and continue to train the adjusted initial write-off model using the training set until the recognition accuracy of the trained initial write-off model is greater than the accuracy threshold.

[0116] The clearing message processing device provided in the embodiment of the present application can accurately extract identification information related to the transaction to be written off from the free-format postscript data by inputting the postscript data in the clearing message into the target write-off model for automatic identification, thereby improving the accuracy of matching with the transaction records in the transaction number table, reducing errors and omissions that may be introduced by traditional manual verification, achieving efficient processing of unstructured descriptive information, reducing the need for manual analysis, greatly improving the automation level and write-off efficiency of financial market transaction clearing message processing, and thus solving the technical problem of low write-off efficiency of clearing messages.

[0117] It should be noted that the receiving unit 401, the identifying unit 402, the searching unit 404, the determining unit 404 and the updating unit 405 correspond to steps S201 to S205 in Embodiment 1, and the five modules and the corresponding steps implement the same examples and application scenarios, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the modules or units may be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the modules may also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0118] Example 3

[0119] An embodiment of the present application may provide an electronic device, Figure 5 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 5 As shown, the electronic device may include: one or more ( Figure 5 (only one is shown) processor 502, memory 504, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0120] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0121] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: receiving a clearing message, wherein the clearing message at least includes transaction information and postscript data corresponding to the transaction to be written off, and the postscript data is unstructured description information of the transaction to be written off; inputting the postscript data in the clearing message into a target write-off model for recognition to obtain a recognition result of the postscript data, wherein the target write-off model is obtained by pre-training an initial write-off model using a postscript data sample, and the recognition result at least includes identification information related to the transaction to be written off; based on the identification information related to the transaction to be written off included in the recognition result, searching for a target transaction record matching the recognition result from a transaction number table, wherein at least one transaction record is stored in the transaction number table; determining a matching result between the target transaction record and the clearing message based on the recognition result and the transaction information in the clearing message, wherein the matching result is used to indicate a matching degree between the target transaction record and the clearing message; in response to the matching result indicating that the target transaction record matches the clearing message successfully, updating the write-off status corresponding to the clearing message to a write-off completion status.

[0122] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: construct a query condition based on the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off, wherein the query condition includes at least one of the following information: the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off; based on the query condition, query the transaction number table for a target transaction record that matches the identification result.

[0123] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: extract the key elements corresponding to the transaction to be written off from the identification results and the transaction information in the clearing message, and extract the key elements corresponding to the target transaction record from the target transaction record, wherein the key elements include at least the transaction currency, transaction date and transaction amount; match the key elements corresponding to the transaction to be written off with the key elements corresponding to the target transaction record to obtain a matching result.

[0124] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: compare the degree of match between the clearing message and the target transaction record with a matching threshold to obtain a comparison result; in response to the comparison result indicating that the degree of match is greater than the matching threshold, determine that the target transaction record and the clearing message are successfully matched.

[0125] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain a postscript data sample from a historical settlement message; mark the identification information related to the transaction sample to be written off in the postscript data sample to obtain label data; divide the marked postscript data sample into a training set and a test set; use the training set to train the initial write-off model to obtain the trained initial write-off model; use the test set to verify the recognition accuracy of the trained initial write-off model to obtain a verification result; in response to the verification result indicating that the recognition accuracy is greater than the accuracy threshold, determine the trained initial write-off model as the target write-off model.

[0126] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: in response to the recognition accuracy being not greater than the accuracy threshold, adjust the model parameters of the trained initial write-off model; continue to train the adjusted initial write-off model using the training set until the recognition accuracy of the trained initial write-off model is greater than the accuracy threshold.

[0127] By adopting the embodiment of the present application, a method for processing clearing messages is provided. By inputting the remark data in the clearing message into the target write-off model for automatic identification, the identification information related to the transaction to be written off can be accurately extracted from the free-format remark data, thereby improving the accuracy of matching with the transaction records in the transaction number table, reducing the errors and omissions that may be introduced by traditional manual verification, achieving efficient processing of unstructured descriptive information, reducing the need for manual analysis, greatly improving the automation level and write-off efficiency of financial market transaction clearing message processing, and thus solving the technical problem of low write-off efficiency of clearing messages.

[0128] It can be understood by those skilled in the art that Figure 5 The structure shown is for illustration only, and the electronic device may also be a terminal device such as a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, abbreviated as MID), a personal access device (Personal Access Device, abbreviated as PAD), etc. Figure 5 The structure of the electronic device is not limited. Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 5 Different configurations are shown.

[0129] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0130] Example 4

[0131] The embodiment of the present application further provides a computer-readable storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the method for processing the clearing message provided in the first embodiment.

[0132] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0133] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program steps of the method for processing a clearing message.

[0134] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0135] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0140] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for processing a clearing message, characterized in that: include: receiving a clearing message, wherein the clearing message includes at least transaction information and postscript data corresponding to the transaction to be written off, wherein the postscript data is unstructured description information of the transaction to be written off; Inputting the postscript data in the clearing message into a target write-off model for recognition, and obtaining a recognition result of the postscript data, wherein the target write-off model is obtained by pre-training an initial write-off model using a sample of the postscript data, and the recognition result at least includes identification information related to the transaction to be written off; Based on the identification information related to the transaction to be written off included in the identification result, searching a target transaction record matching the identification result from a transaction number table, wherein the transaction number table stores at least one transaction record; Based on the identification result and the transaction information in the settlement message, determining a matching result between the target transaction record and the settlement message, wherein the matching result is used to indicate a matching degree between the target transaction record and the settlement message; In response to the matching result indicating that the target transaction record matches the settlement message successfully, updating the write-off status corresponding to the settlement message to a write-off completion status.

2. The method according to claim 1, characterized in that The identification information related to the transaction to be written off includes at least the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off. Based on the identification information related to the transaction to be written off included in the identification result, searching the transaction number table for a target transaction record matching the identification result includes: Based on the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off, a query condition is constructed, wherein the query condition includes at least one of the following information: the identification information of the transaction to be written off, the identification information of the liquidation object, and the identification information of the transaction product corresponding to the transaction to be written off; Based on the query condition, the transaction number table is searched for a target transaction record that matches the recognition result.

3. The method according to claim 1, characterized in that Determining a matching result between the target transaction record and the settlement message based on the identification result and the transaction information in the settlement message includes: Extracting key elements corresponding to the transaction to be written off from the identification result and the transaction information in the settlement message, and extracting key elements corresponding to the target transaction record from the target transaction record, wherein the key elements at least include transaction currency, transaction date, and transaction amount; The key elements corresponding to the transaction to be written off are matched with the key elements corresponding to the target transaction record to obtain the matching result.

4. The method according to claim 3, characterized in that The method further comprises: Comparing the matching degree between the settlement message and the target transaction record with a matching threshold to obtain a comparison result; In response to the comparison result indicating that the matching degree is greater than the matching threshold, it is determined that the target transaction record matches the settlement message successfully.

5. The method according to claim 1, characterized in that The method further comprises: Obtaining the postscript data sample from the historical clearing message; Marking the identification information related to the transaction sample to be written off in the postscript data sample to obtain label data; Dividing the annotated postscript data samples into a training set and a test set; Using the training set to train the initial write-off model to obtain the trained initial write-off model; Using the test set, verifying the recognition accuracy of the trained initial write-off model to obtain a verification result; In response to the verification result indicating that the recognition accuracy is greater than an accuracy threshold, the trained initial write-off model is determined as the target write-off model.

6. The method according to claim 5, characterized in that The method further comprises: In response to the recognition accuracy being not greater than the accuracy threshold, adjusting model parameters of the trained initial write-off model; The adjusted initial write-off model is continuously trained using the training set until the recognition accuracy of the trained initial write-off model is greater than the accuracy threshold.

7. A device for processing a clearing message, characterized in that: include: A receiving unit, configured to receive a clearing message, wherein the clearing message at least includes transaction information and postscript data corresponding to the transaction to be written off, wherein the postscript data is unstructured description information of the transaction to be written off; an identification unit, configured to input the postscript data in the clearing message into a target write-off model for identification, and obtain an identification result of the postscript data, wherein the target write-off model is obtained by pre-training an initial write-off model using a sample of the postscript data, and the identification result at least includes identification information related to the transaction to be written off; a search unit, configured to search a target transaction record matching the identification result from a transaction number table based on identification information related to the transaction to be written off included in the identification result, wherein the transaction number table stores at least one transaction record; a determining unit, configured to determine a matching result between the target transaction record and the clearing message based on the identification result and the transaction information in the clearing message, wherein the matching result is used to indicate a matching degree between the target transaction record and the clearing message; An updating unit is configured to update a write-off status corresponding to the clearing message to a write-off completion status in response to the matching result indicating that the target transaction record matches the clearing message successfully.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 6 when running.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

Images