Channel reconciliation methods, devices, electronic equipment and computer program products
By acquiring channel reconciliation documents and original transaction data, it is determined whether the transaction amount and number of transactions match. Key element weights and data extraction thresholds are queried, and detailed data of key reconciliation elements are extracted in a targeted manner. This solves the problem of low efficiency in channel reconciliation, enables rapid location of erroneous data, and improves reconciliation efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA MOBILE FINANCIAL TECHNOLOGY CO LTD
- Filing Date
- 2022-04-12
- Publication Date
- 2026-06-30
AI Technical Summary
Existing technologies are inefficient for channel reconciliation in scenarios with large amounts of data, failing to quickly locate discrepancies, resulting in excessive system load and excessively long reconciliation times.
By obtaining channel reconciliation documents and original transaction data, it is determined whether the transaction amount and number of transactions match. If they do not match, the key element weights and data extraction thresholds are queried, and detailed data of key reconciliation elements are extracted in a targeted manner to form target reconciliation data. Reconciliation is completed based on the consistency of the erroneous transaction amount and number of transactions.
It narrowed the data range for detailed reconciliation, improved the efficiency of channel reconciliation, quickly located the final erroneous data, and optimized the reconciliation process.
Smart Images
Figure CN116957785B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of IT application technology, specifically to a channel reconciliation method, electronic equipment, and computer program products. Background Technology
[0002] With the development of the internet and the transformation of people's payment methods, the number of transactions occurring daily is increasing rapidly, leading to a surge in the amount of data involved in channel reconciliation. Currently, the common channel reconciliation method involves comparing all data one by one with the details to check for errors. However, in scenarios with large amounts of data, the reconciliation process takes a long time to execute, overloading the system and making it impossible to quickly verify the final discrepancies, resulting in low efficiency in channel reconciliation.
[0003] In the prior art, a payment reconciliation method is proposed, which collects raw external reconciliation data through a log collection system; matches the raw external reconciliation data with the raw internal reconciliation data to obtain a first matching result; extracts the erroneous accounts that fail to match from the first matching result, processes the erroneous accounts through a preset programming model to achieve reconciliation, and obtains the reconciliation result.
[0004] The aforementioned prior art has the following disadvantages:
[0005] This solution requires a detailed comparison of all data to obtain matching results, which cannot narrow down the data range for detailed reconciliation, resulting in low efficiency of channel reconciliation. Summary of the Invention
[0006] This application provides a channel reconciliation method to solve the technical problem that the inability to narrow down the data range for detailed reconciliation leads to low efficiency in channel reconciliation.
[0007] In a first aspect, embodiments of this application provide a channel reconciliation method, including:
[0008] Obtain channel reconciliation documents and original transaction data;
[0009] Determine whether the transaction amount and number of transactions match based on the channel reconciliation documents and original transaction data. If they do not match, determine the total amount of error and the total number of errors based on the channel reconciliation documents and original transaction data.
[0010] The query provides the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is the data extraction permission parameter configured according to the weights of each key element.
[0011] The weights of key elements are compared with the data extraction thresholds one by one. If the weight of a key element is greater than the data extraction threshold, the detailed data of the key reconciliation elements corresponding to the weight of the key element are extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data.
[0012] The amount and number of erroneous transactions are determined based on the target reconciliation data. If the amount of erroneous transactions is consistent with the total amount of errors and the number of erroneous transactions is consistent with the total number of errors, then the channel reconciliation is completed.
[0013] In one embodiment, before obtaining the channel reconciliation file and the original transaction data, the method further includes:
[0014] The N preset reconciliation elements are sorted and combined in a preset order by a preset error data extraction model. Each preset reconciliation element is divided into several sub-elements according to business classification. The sub-elements corresponding to each preset reconciliation element are sorted and combined in a preset order to form M preset reconciliation element combinations.
[0015] Obtain historical channel reconciliation data, input the historical channel reconciliation data into a preset error data extraction model, so that the historical channel reconciliation data is arranged and combined according to the combination order of M preset reconciliation elements;
[0016] Based on the error data markings in historical channel reconciliation data, the error data ratio in each preset reconciliation element combination is determined by the preset error data extraction model.
[0017] If the error rate of the current preset reconciliation element combination is greater than zero, the current preset reconciliation element combination is determined as a key reconciliation element, and the weight of the key element is determined based on the error rate of the current preset reconciliation element combination.
[0018] In one embodiment, before obtaining the channel reconciliation file and the original transaction data, the method further includes:
[0019] The data extraction threshold for each key reconciliation element is determined based on the weight of each key element.
[0020] In one embodiment, after determining the amount and number of erroneous transactions based on the target reconciliation data, the method further includes:
[0021] If the amount of the erroneous transaction is inconsistent with the total amount of the error, or the number of erroneous transactions is inconsistent with the total number of errors, the remaining detailed data will be extracted from the channel reconciliation file and the original transaction data to form the remaining reconciliation data.
[0022] The remaining error amount and the remaining number of errors are determined based on the remaining reconciliation data. If the sum of the error transaction amount and the remaining error amount is consistent with the total error amount, and the sum of the error transaction number and the remaining error number is consistent with the total error number, then the channel reconciliation is completed.
[0023] In one embodiment, after completing channel reconciliation, the following is also included:
[0024] Import the channel reconciliation files and original transaction data into the historical channel reconciliation data and update the historical channel reconciliation data.
[0025] In one embodiment, after comparing the weights of key elements with the data extraction thresholds one by one, the method further includes:
[0026] If the weights of all key elements are less than the data extraction threshold, then the channel reconciliation file will be reconciled with all data in the original transaction data.
[0027] In one embodiment, after determining whether the transaction amount and number of transactions match based on the channel reconciliation file and the original transaction data, the method further includes:
[0028] If the transaction amount in the channel reconciliation file matches the transaction amount in the original transaction data, and the number of transactions in the channel reconciliation file matches the number of transactions in the original transaction data, then the channel reconciliation is complete.
[0029] Secondly, embodiments of this application provide a channel reconciliation device, comprising:
[0030] The reconciliation data acquisition module is used to acquire channel reconciliation files and raw transaction data;
[0031] The error data determination module is used to determine whether the transaction amount and number of transactions match based on the channel reconciliation file and the original transaction data. If they do not match, the module determines the total error amount and the total number of errors based on the channel reconciliation file and the original transaction data.
[0032] The query module is used to query the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is the data extraction permission parameter configured according to the weights of each key element.
[0033] The reconciliation data extraction module is used to compare the weight of key elements with the data extraction threshold one by one. If the weight of a key element is greater than the data extraction threshold, the detailed data of the key reconciliation elements corresponding to the weight of the key element are extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data.
[0034] The reconciliation module is used to determine the amount and number of erroneous transactions based on the target reconciliation data. If the amount of erroneous transactions matches the total amount of errors and the number of erroneous transactions matches the total number of errors, then the channel reconciliation is completed.
[0035] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the channel reconciliation method described in the first aspect.
[0036] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the channel reconciliation method described in the first aspect.
[0037] The channel reconciliation method, apparatus, electronic device, and computer program product provided in this application obtain channel reconciliation files and original transaction data. Based on these files, the system determines whether the transaction amount and number of transactions match. If they do not match, it determines the total amount and number of errors based on the same data. Then, it queries a preset error data extraction model based on the key element weights output from historical channel reconciliation data and the data extraction thresholds configured according to each key element weight. The system compares each key element weight with its corresponding data extraction threshold. If the key element weight is greater than the data extraction threshold, the system determines the error. By extracting thresholds, detailed data of key reconciliation elements corresponding to the weights of key elements are extracted from channel reconciliation files and original transaction data to form target reconciliation data. Data prone to errors is extracted in a targeted manner, and the validity of the selected data is ensured through comparison. The amount and number of erroneous transactions are determined based on the target reconciliation data. If the amount of erroneous transactions is consistent with the total amount of errors, and the number of erroneous transactions is consistent with the total number of errors, then channel reconciliation is completed. Prioritizing the extraction of target reconciliation data with high-frequency errors narrows the data range of detailed reconciliation, which is conducive to quickly locating the final error data and improving the efficiency of channel reconciliation. Attached Figure Description
[0038] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 This is one of the flowcharts illustrating the channel reconciliation method provided in the embodiments of this application;
[0040] Figure 2This is the second flowchart illustrating the channel reconciliation method provided in the embodiments of this application;
[0041] Figure 3 This is the third flowchart illustrating the channel reconciliation method provided in the embodiments of this application;
[0042] Figure 4 This is a schematic diagram of the channel reconciliation device provided in the embodiments of this application;
[0043] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0045] Figure 1 This is one of the flowcharts illustrating the channel reconciliation method provided in this application embodiment. (Refer to...) Figure 1 This application provides a channel reconciliation method, which may include:
[0046] Step 101: Obtain channel reconciliation documents and original transaction data.
[0047] In this embodiment, the channel reconciliation file can be obtained from the file servers of the NetsUnion and UnionPay platforms, while the original transaction data can be obtained from the internal system. Furthermore, the obtained channel reconciliation file and original transaction data can be stored in the reconciliation database for later retrieval. It is understood that in practical applications, the methods for obtaining and storing channel reconciliation files and original transaction data are diverse, and the appropriate method should be determined based on the specific application; no single limitation is made here.
[0048] Step 102: Determine whether the transaction amount and number of transactions match based on the channel reconciliation documents and original transaction data.
[0049] Specifically, the total amount and number of receivables and payables of the channel can be summarized separately according to the channel and business identifier, and the total amount and number of receivables and payables of the internal system can be summarized. Then, a comparison and matching are performed. If there is no match, the total amount of error and the total number of error are determined according to the channel reconciliation documents and the original transaction data.
[0050] Understandably, if the transaction amount in the channel reconciliation file matches the transaction amount in the original transaction data, and the number of transactions in the channel reconciliation file matches the number of transactions in the original transaction data, then the channel reconciliation is complete.
[0051] Step 103: Query the weights of key elements and the data extraction threshold.
[0052] In this embodiment, the key element weight is defined as the weight value output by the preset error data extraction model based on historical channel reconciliation data, and corresponding to each key reconciliation element. The preset error data extraction model is a pre-set and trained model, which may include, but is not limited to, a data import module, an anomaly data identification module, and a key element weight determination module. The data import module is used to save the externally input reconciliation dataset, i.e., historical channel reconciliation data, to the reconciliation data detail table. The anomaly data identification module is used to import the data with anomalies in the reconciliation data detail table into the anomaly data record table according to the error data marking. The key element weight determination module is used to analyze the anomaly data appearing in each key reconciliation element from the anomaly data record table and calculate the proportion of error data in each key reconciliation element, update the key element weight according to the proportion of error data, and store the result in the key element weight record table. It is understandable that in practical applications, the construction method of the preset error data extraction model can be diverse and needs to be constructed according to the actual application situation. The preset error data extraction model should be able to determine the key reconciliation elements and the key element weights corresponding to each key reconciliation element based on historical channel reconciliation data. There is no single limitation here.
[0053] Furthermore, a key reconciliation element is defined as a pre-defined combination of reconciliation elements containing erroneous reconciliation data. These pre-defined reconciliation elements may include, but are not limited to, payment channels, payment channel business, transaction time, and payment channel transaction monitoring error reporting time periods. It can be understood that a pre-defined reconciliation element combination is a combination of various pre-defined reconciliation elements, such as "payment channel - payment channel business - transaction time". Therefore, a pre-defined reconciliation element combination containing erroneous reconciliation data can be understood as the reconciliation data belonging to the pre-defined reconciliation element combination "payment channel - payment channel business - transaction time" containing erroneous reconciliation data. This pre-defined reconciliation element combination can then be identified as a key reconciliation element.
[0054] In addition, the data extraction threshold is a data extraction permission parameter configured based on the weight of each key element. It can be configured before reconciliation. The configuration method is automatic based on the key element weight. For example, if the key element weight is greater than or equal to 0.5, the data extraction threshold is automatically configured as the key element weight minus 0.1, allowing key reconciliation elements with higher weights to be extracted. Conversely, if the key element weight is less than 0.5, the data extraction threshold is automatically configured as the key element weight plus 0.1, preventing key reconciliation elements with lower weights from being extracted. It should be understood that the above automatic configuration method for the data extraction threshold is only an example to better understand the technical solution. In actual applications, there are various ways to automatically configure the data extraction threshold, and the appropriate automatic configuration method must be determined based on the actual application situation. This is not a single limitation. The data extraction threshold can also be configured by operations personnel. Again, it should be understood that the configuration method for the data extraction threshold is also diverse and must be selected based on the actual application situation. This is not a single limitation.
[0055] Step 104: Compare the weights of the key elements with the data extraction thresholds one by one.
[0056] If the weight of a key element is greater than the data extraction threshold, it means that the detailed data of the key reconciliation element corresponding to the weight of that key element has been granted extraction permission. Then, the detailed data of the key reconciliation element corresponding to the weight of the key element is extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data. It can be understood that the reconciliation operation can only be performed when both the channel reconciliation file and the original transaction data have the same reconciliation element. Therefore, the detailed data of the same key reconciliation element needs to be extracted from both the channel reconciliation file and the original transaction data and combined to form the target reconciliation data.
[0057] In this embodiment, during the extraction of detailed data, detailed data of key reconciliation elements with a key element weight greater than the data extraction threshold can be extracted simultaneously, or the detailed data of key reconciliation elements can be extracted sequentially in descending order of key element weight; no single limitation is imposed. The detailed data extracted from the channel reconciliation file and the detailed data extracted from the original transaction data can be stored on a Redis storage server or other storage devices; no single limitation is imposed here.
[0058] Step 105: Determine the amount and number of erroneous transactions based on the target reconciliation data, and determine whether channel reconciliation is complete.
[0059] The target reconciliation data is compared one by one in detail to locate the erroneous data. The erroneous transaction amount and the number of erroneous transactions are summarized. If the erroneous transaction amount is consistent with the total erroneous amount and the number of erroneous transactions is consistent with the total number of erroneous transactions, the channel reconciliation is completed.
[0060] The following beneficial effects can be seen from the above embodiments:
[0061] By acquiring channel reconciliation documents and original transaction data, the system determines whether the transaction amount and number of transactions match. If they do not match, it determines the total amount and number of errors based on the same documents. Then, it queries the preset error data extraction model based on the key element weights output from historical channel reconciliation data and the data extraction thresholds configured for each key element weight. The key element weights are compared with the data extraction thresholds one by one. If a key element weight is greater than the threshold, detailed data of the key reconciliation elements corresponding to the key element weights are extracted from the channel reconciliation documents and original transaction data to form target reconciliation data. This targeted extraction of error-prone data ensures the validity of the selected data through comparison. Based on the target reconciliation data, the error transaction amount and number of errors are determined. If the error transaction amount matches the total error amount and the number of errors matches the total number of errors, the channel reconciliation is complete. Prioritizing the extraction of high-frequency error target reconciliation data narrows the data range for detailed reconciliation, facilitating rapid location of the final error data and improving the efficiency of channel reconciliation.
[0062] To facilitate understanding, an example of the channel reconciliation method is provided below. In practical applications, before obtaining the channel reconciliation file and the original transaction data, a preset error data extraction model is used to determine the key reconciliation elements and the key element weights corresponding to the key reconciliation elements. The data extraction threshold is also configured based on the key element weights.
[0063] Figure 2 This is the second flowchart illustrating the channel reconciliation method provided in this application's embodiments. (Refer to...) Figure 2 This application provides a channel reconciliation method, which may include:
[0064] Step 201: Form M preset reconciliation element combinations through a preset error data extraction model.
[0065] At any time before executing the channel reconciliation task, for example, the early morning hours of each day, N preset reconciliation elements are sorted and combined in a preset order using a preset error data extraction model. For example, "payment channel - payment channel business - transaction time" is used. Then, each preset reconciliation element is divided into several sub-elements according to business categories. For example, payment channels can be divided into sub-elements including but not limited to UnionPay omnichannel, UnionPay Quick, NetsUnion Quick, and NetsUnion Gateway; payment channel business can be divided into sub-elements including but not limited to tokenized business and bank installment business; transaction time can be divided into sub-elements including but not limited to... Sub-elements such as 22:00-23:00, 23:00-24:00, and 24:00-00:10 are then sorted and combined according to a preset order to form M preset reconciliation element combinations. For example, the preset reconciliation element combinations can be such as "UnionPay Omnichannel - Tokenized Business - (22:00-23:00)" or "UnionPay Omnichannel - Bank Installment Business - (23:00-24:00)", etc., ultimately forming M preset reconciliation element combinations. N and M are both positive integers, and the values of N and M need to be determined according to the actual application situation and are not uniquely limited.
[0066] Step 202: Obtain historical channel reconciliation data and input the historical channel reconciliation data into the preset error data extraction model.
[0067] This allows historical channel reconciliation data to be arranged and combined according to the combination order of M preset reconciliation element combinations. Each preset reconciliation element combination will have corresponding reconciliation data. For example, the preset reconciliation element combination "UnionPay Omnichannel - Tokenized Business - (22:00-23:00)" will have 100 reconciliation data entries for payment channels of UnionPay Omnichannel, payment channel business of tokenized business, and transaction time of 22:00-23:00. It is understood that the number of reconciliation data entries under each preset reconciliation element combination needs to be determined based on the actual application situation, and is not uniquely limited here.
[0068] Step 203: Based on the error data markings of historical channel reconciliation data, determine the proportion of error data in each preset reconciliation element combination using the preset error data extraction model.
[0069] It is understandable that historical channel reconciliation data represents data from reconciliation processes that have already been completed. This data inevitably includes erroneous entries, which can be flagged using a dedicated field to create error data tags. Therefore, these error data tags can be used to determine the number of erroneous entries in each pre-defined combination of reconciliation elements, thereby determining the error data ratio.
[0070] Step 204: Determine the key reconciliation elements and determine the key element weights corresponding to the key reconciliation elements.
[0071] If the error rate of the current preset reconciliation element combination is greater than zero, it indicates that there is erroneous data in the reconciliation data belonging to the current preset reconciliation element combination. Therefore, the current preset reconciliation element combination is determined as a critical reconciliation element, considered as one prone to errors. The weight of the critical element is determined based on the error rate of the current preset reconciliation element combination. In this embodiment, the error rate can be directly used as the weight of the critical element. In practical applications, the method for determining the weight of the critical element is varied and needs to be determined based on the actual application situation. No single method is limited here. For example, assuming the current preset reconciliation element combination is "UnionPay Omnichannel - Tokenized Business - (22:00-23:00)", and its error rate is 90%, then the current preset reconciliation element combination can be determined as a critical reconciliation element, and the weight of the critical element can be determined to be 0.9.
[0072] Conversely, if the error rate of the current preset reconciliation element combination is zero, it means that there is no error data in the reconciliation data belonging to the current preset reconciliation element combination. Therefore, the current preset reconciliation element combination is not a key reconciliation element and the probability of error is low.
[0073] Step 205: Determine the data extraction threshold for each key reconciliation element based on the weight of each key element.
[0074] In this embodiment, key reconciliation elements and their weights can be displayed on the data extraction threshold configuration interface of the payment operation management system. The data extraction threshold configuration can be achieved through automatic configuration or manual configuration by operators. For example, if the key reconciliation element "UnionPay Omnichannel - Tokenized Business - (22:00-23:00)" has a key element weight of 0.9, then the data extraction threshold can be configured to 0.8. In this case, when performing channel reconciliation tasks, reconciliation data with payment channel being UnionPay Omnichannel, payment channel business being tokenized business, and transaction time being 22:00-23:00 will be extracted from the channel reconciliation file and the original transaction data for comparison. On the other hand, if the key reconciliation element "UnionPay Omnichannel - Tokenized Business - (20:00-21:00)" has a key element weight of 0.01, then the data extraction threshold can be configured to 0.02. In this case, when performing channel reconciliation tasks, reconciliation data with payment channel being UnionPay Omnichannel, payment channel business being tokenized business, and transaction time being 20:00-21:00 will not be extracted from the channel reconciliation file and the original transaction data for comparison, thus reducing the amount of reconciliation data extracted and compared.
[0075] The following beneficial effects can be seen from the above embodiments:
[0076] M preset reconciliation element combinations are formed by a preset error data extraction model. Historical channel reconciliation data is obtained and input into the preset error data extraction model. Based on the error data markings in the historical channel reconciliation data, the preset error data extraction model determines the proportion of error data in each preset reconciliation element combination. The data extraction threshold corresponding to each key reconciliation element is determined according to the weight of each key element. This helps to avoid extracting too much invalid reconciliation data during subsequent channel reconciliation tasks, which would lead to an excessive amount of reconciliation data comparison. This improves the effectiveness of the extracted reconciliation data and enhances the reconciliation efficiency when performing channel reconciliation tasks.
[0077] To facilitate understanding, the following provides an example of a channel reconciliation method. Figure 3 This is the third flowchart illustrating the channel reconciliation method provided in this application's embodiments. (Refer to...) Figure 3 This application provides a channel reconciliation method, which may include:
[0078] Step 301: Compare the weights of the key elements with the data extraction thresholds one by one.
[0079] If the weights of all key elements are less than the data extraction threshold, then the channel reconciliation file will be reconciled with all data in the original transaction data.
[0080] Step 302: Determine the amount and number of erroneous transactions based on the target reconciliation data, and determine whether channel reconciliation is complete.
[0081] If the amount of the erroneous transaction is inconsistent with the total amount of the erroneous transaction, or the number of erroneous transactions is inconsistent with the total number of erroneous transactions, it indicates that there are also erroneous reconciliation data in other reconciliation data besides the target reconciliation data. In this case, the remaining detailed data is extracted from the channel reconciliation file and the original transaction data. The remaining detailed data is the detailed data of the non-critical reconciliation elements in the channel reconciliation file and the original transaction data, forming the remaining reconciliation data.
[0082] The remaining error amount and the remaining number of errors are determined based on the remaining reconciliation data. If the sum of the error transaction amount and the remaining error amount is consistent with the total error amount, and the sum of the error transaction number and the remaining error number is consistent with the total number of errors, then the channel reconciliation is completed.
[0083] Step 303: Update historical channel reconciliation data.
[0084] Importing channel reconciliation files and raw transaction data into historical channel reconciliation data and updating the historical channel reconciliation data means that, before the next channel reconciliation task is executed, the preset error data extraction model will generate updated key reconciliation elements and updated key element weights corresponding to the updated key reconciliation elements based on the updated historical channel reconciliation data.
[0085] The following beneficial effects can be seen from the above embodiments:
[0086] When the specific details to be extracted cannot be determined, a full reconciliation of all data is performed to avoid errors in the reconciliation results. If the amount of the erroneous transaction is inconsistent with the total amount of the erroneous transaction, or the number of erroneous transactions is inconsistent with the total number of erroneous transactions, a detailed comparison of the remaining reconciliation data is conducted. Channel reconciliation is only completed when the sum of the erroneous transaction amount and the remaining erroneous amount matches the total amount of the erroneous transaction, and the sum of the number of erroneous transactions and the remaining erroneous number matches the total number of erroneous transactions, ensuring the accuracy of the reconciliation results. After completing the channel reconciliation, the channel reconciliation file and original transaction data for the current channel reconciliation task are imported into the historical channel reconciliation data. The historical channel reconciliation data is updated, and the output accuracy of the key reconciliation elements and key element weights of the preset error data extraction model is optimized. This allows the preset error data extraction model to achieve a self-correcting state of continuous improvement while running, thereby improving the efficiency of channel reconciliation.
[0087] The channel reconciliation device provided in the embodiments of this application is described below. The channel reconciliation device described below can be referred to in correspondence with the channel reconciliation method described above.
[0088] Figure 4 This is a schematic diagram of the channel reconciliation device provided in an embodiment of this application. (Refer to...) Figure 4 This application provides a channel reconciliation device, which may include:
[0089] The reconciliation data acquisition module is used to acquire channel reconciliation files and raw transaction data;
[0090] The error data determination module is used to determine whether the transaction amount and number of transactions match based on the channel reconciliation file and the original transaction data. If they do not match, the module determines the total error amount and the total number of errors based on the channel reconciliation file and the original transaction data.
[0091] The query module is used to query the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is the data extraction permission parameter configured according to the weights of each key element.
[0092] The reconciliation data extraction module is used to compare the weight of key elements with the data extraction threshold one by one. If the weight of a key element is greater than the data extraction threshold, the detailed data of the key reconciliation elements corresponding to the weight of the key element are extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data.
[0093] The reconciliation module is used to determine the amount and number of erroneous transactions based on the target reconciliation data. If the amount of erroneous transactions matches the total amount of errors and the number of erroneous transactions matches the total number of errors, then the channel reconciliation is completed.
[0094] The channel reconciliation device provided in this application embodiment acquires channel reconciliation files and original transaction data. It determines whether the transaction amount and number of transactions match based on the channel reconciliation files and original transaction data. If they do not match, it determines the total amount and total number of errors based on the channel reconciliation files and original transaction data. Then, it queries the key element weights output by the preset error data extraction model based on historical channel reconciliation data and the data extraction thresholds configured according to each key element weight. It compares each key element weight with the data extraction threshold. If the key element weight is greater than the data extraction threshold, then... The system extracts detailed data of key reconciliation elements corresponding to the weights of key elements from the reconciliation documents and original transaction data to form target reconciliation data. It also extracts data that is prone to errors and ensures the validity of the selected data through comparison. Based on the target reconciliation data, it determines the amount and number of erroneous transactions. If the amount of erroneous transactions is consistent with the total amount of errors and the number of erroneous transactions is consistent with the total number of errors, the channel reconciliation is completed. Prioritizing the extraction of target reconciliation data with high-frequency errors narrows the data range of detailed reconciliation, which is conducive to quickly locating the final error data and improving the efficiency of channel reconciliation.
[0095] In one embodiment, before obtaining the channel reconciliation file and the original transaction data, the method further includes:
[0096] The N preset reconciliation elements are sorted and combined in a preset order by a preset error data extraction model. Each preset reconciliation element is divided into several sub-elements according to business classification. The sub-elements corresponding to each preset reconciliation element are sorted and combined in a preset order to form M preset reconciliation element combinations.
[0097] Obtain historical channel reconciliation data, input the historical channel reconciliation data into a preset error data extraction model, so that the historical channel reconciliation data is arranged and combined according to the combination order of M preset reconciliation elements;
[0098] Based on the error data markings in historical channel reconciliation data, the error data ratio in each preset reconciliation element combination is determined by the preset error data extraction model.
[0099] If the error rate of the current preset reconciliation element combination is greater than zero, the current preset reconciliation element combination is determined as a key reconciliation element, and the weight of the key element is determined based on the error rate of the current preset reconciliation element combination.
[0100] In one embodiment, before obtaining the channel reconciliation file and the original transaction data, the method further includes:
[0101] The data extraction threshold for each key reconciliation element is determined based on the weight of each key element.
[0102] In one embodiment, after determining the amount and number of erroneous transactions based on the target reconciliation data, the method further includes:
[0103] If the amount of the erroneous transaction is inconsistent with the total amount of the error, or the number of erroneous transactions is inconsistent with the total number of errors, the remaining detailed data will be extracted from the channel reconciliation file and the original transaction data to form the remaining reconciliation data.
[0104] The remaining error amount and the remaining number of errors are determined based on the remaining reconciliation data. If the sum of the error transaction amount and the remaining error amount is consistent with the total error amount, and the sum of the error transaction number and the remaining error number is consistent with the total error number, then the channel reconciliation is completed.
[0105] In one embodiment, after completing channel reconciliation, the following is also included:
[0106] Import the channel reconciliation files and original transaction data into the historical channel reconciliation data and update the historical channel reconciliation data.
[0107] In one embodiment, after comparing the weights of key elements with the data extraction thresholds one by one, the method further includes:
[0108] If the weights of all key elements are less than the data extraction threshold, then the channel reconciliation file will be reconciled with all data in the original transaction data.
[0109] In one embodiment, after determining whether the transaction amount and number of transactions match based on the channel reconciliation file and the original transaction data, the method further includes:
[0110] If the transaction amount in the channel reconciliation file matches the transaction amount in the original transaction data, and the number of transactions in the channel reconciliation file matches the number of transactions in the original transaction data, then the channel reconciliation is complete.
[0111] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call a computer program in the memory 530 to execute steps of the channel reconciliation method, such as:
[0112] Obtain channel reconciliation documents and original transaction data;
[0113] Determine whether the transaction amount and number of transactions match based on the channel reconciliation documents and original transaction data. If they do not match, determine the total amount of error and the total number of errors based on the channel reconciliation documents and original transaction data.
[0114] The query provides the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is the data extraction permission parameter configured according to the weights of each key element.
[0115] The weights of key elements are compared with the data extraction thresholds one by one. If the weight of a key element is greater than the data extraction threshold, the detailed data of the key reconciliation elements corresponding to the weight of the key element are extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data.
[0116] The amount and number of erroneous transactions are determined based on the target reconciliation data. If the amount of erroneous transactions is consistent with the total amount of errors and the number of erroneous transactions is consistent with the total number of errors, then the channel reconciliation is completed.
[0117] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0118] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the channel reconciliation method provided in the above embodiments, such as including:
[0119] Obtain channel reconciliation documents and original transaction data;
[0120] Determine whether the transaction amount and number of transactions match based on the channel reconciliation documents and original transaction data. If they do not match, determine the total amount of error and the total number of errors based on the channel reconciliation documents and original transaction data.
[0121] The query provides the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is the data extraction permission parameter configured according to the weights of each key element.
[0122] The weights of key elements are compared with the data extraction thresholds one by one. If the weight of a key element is greater than the data extraction threshold, the detailed data of the key reconciliation elements corresponding to the weight of the key element are extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data.
[0123] The amount and number of erroneous transactions are determined based on the target reconciliation data. If the amount of erroneous transactions is consistent with the total amount of errors and the number of erroneous transactions is consistent with the total number of errors, then the channel reconciliation is completed.
[0124] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the methods provided in the above embodiments, such as including:
[0125] Obtain channel reconciliation documents and original transaction data;
[0126] Determine whether the transaction amount and number of transactions match based on the channel reconciliation documents and original transaction data. If they do not match, determine the total amount of error and the total number of errors based on the channel reconciliation documents and original transaction data.
[0127] The query provides the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is the data extraction permission parameter configured according to the weights of each key element.
[0128] The weights of key elements are compared with the data extraction thresholds one by one. If the weight of a key element is greater than the data extraction threshold, the detailed data of the key reconciliation elements corresponding to the weight of the key element are extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data.
[0129] The amount and number of erroneous transactions are determined based on the target reconciliation data. If the amount of erroneous transactions is consistent with the total amount of errors and the number of erroneous transactions is consistent with the total number of errors, then the channel reconciliation is completed.
[0130] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).
[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0132] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A channel reconciliation method, characterized by, include: Obtain channel reconciliation documents and original transaction data; The transaction amount and number of transactions are determined based on the channel reconciliation file and the original transaction data. If they do not match, the total amount of error and the total number of errors are determined based on the channel reconciliation file and the original transaction data. The query provides the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is a data extraction permission parameter configured according to the weights of each key element. The weights of the key elements are compared one by one with the data extraction threshold. If the weight of the key element is greater than the data extraction threshold, the detailed data of the key reconciliation elements corresponding to the weight of the key element are extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data. The amount of erroneous transactions and the number of erroneous transactions are determined based on the target reconciliation data. If the amount of erroneous transactions is consistent with the total amount of errors and the number of erroneous transactions is consistent with the total number of errors, then the channel reconciliation is completed. Before obtaining the channel reconciliation file and original transaction data, the following is also included: The preset error data extraction model sorts and combines N preset reconciliation elements in a preset order, divides each preset reconciliation element into several sub-elements according to business classification, and sorts and combines the sub-elements corresponding to each preset reconciliation element in the preset order to form M preset reconciliation element combinations. The historical channel reconciliation data is obtained, and the historical channel reconciliation data is input into the preset error data extraction model so that the historical channel reconciliation data is arranged and combined according to the combination order of the M preset reconciliation elements. The error data extraction model determines the proportion of error data in each preset reconciliation element combination based on the error data markings of the historical channel reconciliation data. If the error data ratio of the current preset reconciliation element combination is greater than zero, then the current preset reconciliation element combination is determined as the key reconciliation element, and the weight of the key element is determined according to the error data ratio of the current preset reconciliation element combination.
2. The channel reconciliation method according to claim 1, characterized in that, Before obtaining the channel reconciliation file and original transaction data, the following is also included: The data extraction threshold for each key reconciliation element is determined based on the weight of each key element.
3. The channel reconciliation method according to claim 1, characterized in that, After determining the amount and number of erroneous transactions based on the target reconciliation data, the process further includes: If the amount of the erroneous transaction is inconsistent with the total amount of the error, or if the number of erroneous transactions is inconsistent with the total number of errors, then the remaining detailed data is extracted from the channel reconciliation file and the original transaction data to form the remaining reconciliation data. The remaining error amount and the remaining number of errors are determined based on the remaining reconciliation data. If the sum of the error transaction amount and the remaining error amount is consistent with the total error amount, and the sum of the error transaction number and the remaining error number is consistent with the total error number, then the channel reconciliation is completed.
4. The channel reconciliation method according to claim 1, characterized in that, After completing the channel reconciliation, the following is also included: Import the channel reconciliation file and the original transaction data into the historical channel reconciliation data, and update the historical channel reconciliation data.
5. The channel reconciliation method according to claim 1, characterized in that, After comparing the weights of the key elements with the data extraction thresholds one by one, the process further includes: If the weights of all the key elements are less than the data extraction threshold, then the channel reconciliation file will be reconciled with all the data in the original transaction data.
6. The channel reconciliation method according to claim 1, characterized in that, After determining whether the transaction amount and number of transactions match based on the channel reconciliation file and the original transaction data, the process further includes: If the transaction amount in the channel reconciliation file matches the transaction amount in the original transaction data, and the number of transactions in the channel reconciliation file matches the number of transactions in the original transaction data, then the channel reconciliation is complete.
7. A channel reconciliation apparatus, characterized by, include: The reconciliation data acquisition module is used to acquire channel reconciliation files and raw transaction data; The error data determination module is used to determine whether the transaction amount and the number of transactions match based on the channel reconciliation file and the original transaction data. If they do not match, the module determines the total error amount and the total number of errors based on the channel reconciliation file and the original transaction data. The query module is used to query the weights of key elements and the data extraction threshold. The weights of key elements are output by the preset error data extraction model based on historical channel reconciliation data, and are respectively associated with the weight values of each key reconciliation element. The key reconciliation elements are preset reconciliation element combinations that contain erroneous reconciliation data. The data extraction threshold is a data extraction permission parameter configured according to the weights of each key element. The reconciliation data extraction module is used to compare the weight of the key element with the data extraction threshold one by one. If the weight of the key element is greater than the data extraction threshold, the detailed data of the key reconciliation element corresponding to the weight of the key element is extracted from the channel reconciliation file and the original transaction data to form the target reconciliation data. The reconciliation module is used to determine the amount of erroneous transactions and the number of erroneous transactions based on the target reconciliation data. If the amount of erroneous transactions is consistent with the total amount of errors and the number of erroneous transactions is consistent with the total number of errors, then the channel reconciliation is completed. Before obtaining the channel reconciliation file and original transaction data, the following is also included: The preset error data extraction model sorts and combines N preset reconciliation elements in a preset order, divides each preset reconciliation element into several sub-elements according to business classification, and sorts and combines the sub-elements corresponding to each preset reconciliation element in the preset order to form M preset reconciliation element combinations. The historical channel reconciliation data is obtained, and the historical channel reconciliation data is input into the preset error data extraction model so that the historical channel reconciliation data is arranged and combined according to the combination order of the M preset reconciliation elements. The error data extraction model determines the proportion of error data in each preset reconciliation element combination based on the error data markings of the historical channel reconciliation data. If the error data ratio of the current preset reconciliation element combination is greater than zero, then the current preset reconciliation element combination is determined as the key reconciliation element, and the weight of the key element is determined according to the error data ratio of the current preset reconciliation element combination.
8. An electronic device comprising a processor and a memory having a computer program stored therein, characterized in that, When the processor executes the computer program, it implements the steps of the channel reconciliation method according to any one of claims 1 to 6.
9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the channel reconciliation method according to any one of claims 1 to 6.