CN112884581A - Method and device for processing errors of automatic teller machine, electronic equipment and storage medium - Google Patents

Method and device for processing errors of automatic teller machine, electronic equipment and storage medium Download PDF

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CN112884581A
CN112884581A CN202110199205.7A CN202110199205A CN112884581A CN 112884581 A CN112884581 A CN 112884581A CN 202110199205 A CN202110199205 A CN 202110199205A CN 112884581 A CN112884581 A CN 112884581A
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information
transaction
account
machine
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曹翀
刘志浩
楚辰生
彭冲
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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Abstract

The present disclosure provides a method and apparatus for automated teller machine error handling, an electronic device, a readable storage medium, and a computer program product, which may be used in the financial, blockchain, or other fields. The method comprises the steps of obtaining end-machine transaction information from an automatic teller machine, wherein the end-machine transaction information comprises transaction information of at least one end-machine transaction of one or more customer accounts; performing text analysis mining on the end-machine transaction information and account transaction information which is stored in the business system and is associated with the end-machine transaction information to determine error processing element information in each end-machine transaction of each customer account; determining a target customer account and error information of the target customer account where an error occurred in at least one end-to-end transaction of one or more customer accounts based on the error processing factor information; and performing error processing on the target customer account and the internal account in the business system based on the error information of the target customer account.

Description

Method and device for processing errors of automatic teller machine, electronic equipment and storage medium
Technical Field
The present disclosure relates to the financial, blockchain, or other fields, and more particularly, to a method and apparatus for automated teller machine error handling, an electronic device, a readable storage medium, and a computer program product.
Background
At present, the Automatic Teller Machine (ATM) has more errors in end-machine transaction, the number of ATM errors reaches 3000 according to the statistical day average, at present, an error register is inquired and updated in a manual operation mode, and then manual error processing is carried out on the basis of the error register.
In implementing the disclosed concept, the inventors found that there are at least the following problems in the related art: the problem that the processing efficiency is low due to the fact that manual checking and processing errors lack rigid control, great risk hidden dangers exist, and a large number of manual inquiring and processing operations are achieved.
Disclosure of Invention
In view of the above, the present disclosure provides a method and apparatus for automated teller machine error handling, an electronic device, a readable storage medium, and a computer program product.
One aspect of the present disclosure provides a method of automated teller machine error handling, comprising: acquiring end-machine transaction information from an automatic teller machine, wherein the end-machine transaction information comprises transaction information of at least one end-machine transaction of one or more customer accounts; performing text analysis mining on the end-machine transaction information and account transaction information which is stored in a business system and is associated with the end-machine transaction information to determine error processing element information in each end-machine transaction of each customer account; determining a target customer account for which an error occurred in at least one end-to-end transaction of one or more of the customer accounts and error information for the target customer account based on the error handling factor information; and performing error processing on the target customer account and the internal account in the business system based on the error information of the target customer account.
According to an embodiment of the disclosure, the text analysis mining the end-machine transaction information and the account transaction information stored in the business system and associated with the end-machine transaction information to determine error processing element information in each end-machine transaction of each customer account comprises: and aggregating the end-machine transaction information and the account transaction information by using the screening keywords to determine error processing element information in each end-machine transaction of each customer account, wherein the error processing element information comprises the actual transaction amount of the end-machine associated with the customer account, the transaction amount recorded by the service system and the transaction timestamp.
According to an embodiment of the present disclosure, the method further comprises: acquiring face recognition information associated with the target customer account from an automatic teller machine; acquiring face feature information corresponding to the target customer account stored in the service system; and under the condition that the face identification information is matched with the face feature information, carrying out error processing on the target customer account and the internal account.
According to an embodiment of the present disclosure, the error information includes an error type and an error amount, wherein the determining a target customer account in which an error occurs in at least one end-machine transaction of one or more customer accounts and the error information of the target customer account based on the error processing element information includes:
for each terminal transaction of each customer account, under the condition that the actual transaction amount of the terminal is smaller than the transaction amount recorded by the service system, determining that the customer account is a target customer account with errors, the error type of the target customer account is a first error type, and the error amount is the difference value between the transaction amount recorded by the service system and the actual transaction amount of the terminal;
and aiming at each terminal machine transaction of each customer account, under the condition that the actual transaction amount of the terminal machine is larger than the transaction amount recorded by the service system, determining that the customer account is a target customer account with errors, the error type of the target customer account is a second error type, and the error amount is the difference value between the actual transaction amount of the terminal machine and the transaction amount recorded by the service system.
According to an embodiment of the present disclosure, the performing error processing on the target customer account and the internal account based on the error information of the target customer account includes: when the error type is a first error type, carrying out deduction processing on the error amount of the internal account, and carrying out posting processing on the error amount of the customer account; and when the error type is a second error type, carrying out deduction processing on the error amount of the target customer account, and carrying out posting processing on the error amount of the internal account.
According to an embodiment of the present disclosure, the method further comprises: broadcasting the results of the error handling within a blockchain network, the results of the error handling including an error amount, a customer account, and a handling timestamp of the error handling.
Another aspect of the present disclosure provides an apparatus for automated teller machine error handling, comprising: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring end-machine transaction information from an automatic teller machine and account transaction information which is stored in a business system and is associated with the end-machine transaction information, and the end-machine transaction information comprises transaction information of at least one end-machine transaction of one or more customer accounts; the analysis module is used for performing text analysis mining on the end-machine transaction information and the account transaction information to determine error processing element information in each end-machine transaction of each customer account, and determining a target customer account with an error in at least one end-machine transaction of one or more customer accounts and error information of the target customer account based on the error processing element information; and the processing module is used for carrying out error processing on the target customer account and the internal account in the service system based on the error information of the target customer account.
According to an embodiment of the disclosure, the analysis module performs aggregation processing on the end-machine transaction information and the account transaction information by using a screening keyword to determine error processing element information in each end-machine transaction of each customer account, wherein the error processing element information comprises an actual transaction amount of an end-machine associated with the customer account, a transaction amount recorded by a service system and a transaction timestamp.
According to an embodiment of the disclosure, the obtaining module is further configured to obtain face recognition information associated with the target customer account from an automatic teller machine, and obtain face feature information corresponding to the target customer account stored in the business system; and the processing module is also used for carrying out error processing on the target customer account and the internal account under the condition that the face identification information is matched with the face characteristic information.
According to the embodiment of the disclosure, the error information comprises an error type and an error amount, the analysis module is used for determining that the client account is a target client account with an error and the error type of the target client account is a first error type in the case that the actual transaction amount of the client is smaller than the transaction amount recorded by the service system for each client transaction of each client account, and the error amount is the difference value between the transaction amount recorded by the service system and the actual transaction amount of the client; and aiming at each terminal machine transaction of each customer account, under the condition that the actual transaction amount of the terminal machine is larger than the transaction amount recorded by the service system, determining that the customer account is a target customer account with errors, the error type of the target customer account is a second error type, and the error amount is the difference value between the actual transaction amount of the terminal machine and the transaction amount recorded by the service system.
According to the embodiment of the disclosure, the processing module is configured to, if the error type is a first error type, perform deduction processing of the error amount on the internal account, and perform posting processing of the error amount on the customer account; and when the error type is a second error type, carrying out deduction processing on the error amount of the target customer account, and carrying out posting processing on the error amount of the internal account.
According to an embodiment of the present disclosure, the apparatus further comprises a validation module for broadcast validation of the result of the error handling within the blockchain network, the result of the error handling including an error amount, a customer account, and a handling timestamp of the error handling.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program product comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, because the technical means of determining the error processing element information by text analysis and mining of the end-machine transaction information from the automatic teller machine and the account transaction information stored in the service system is adopted, the error processing element information can be determined quickly and accurately, the target customer account and the error information which have errors can be determined quickly based on the error processing element information, then the automatic error processing can be performed on the target customer account and the internal account of the service system based on the error information, the whole process does not need manual participation, so that the technical problems of potential risk and low processing efficiency in manual error processing are at least partially overcome, the accuracy of error processing is improved, and the technical effect of optimizing the efficiency is realized.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an exemplary system architecture to which the disclosed automated teller machine error handling method and apparatus may be applied;
FIG. 2 schematically illustrates a flow diagram of a method of automated teller machine error handling in accordance with an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow diagram of text analytics mining, according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow diagram of face recognition information matching according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow diagram of a method of automated teller machine error handling according to another embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of an apparatus for automated teller machine error handling in accordance with an embodiment of the present disclosure;
fig. 7 schematically illustrates a block diagram of an electronic device suitable for implementing a method of automated teller machine error handling in accordance with an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the process of realizing the disclosure, text analysis mining is used for replacing manual confirmation of error elements, error processing element information can be determined quickly and accurately, and a target customer account to be subjected to error processing and error information are determined through the error processing element information, so that error processing is carried out automatically, and human resources are released.
The embodiment of the disclosure provides an automatic teller machine error processing method and device. The method comprises the steps of obtaining end-machine transaction information from an automatic teller machine, wherein the end-machine transaction information comprises transaction information of at least one end-machine transaction of one or more customer accounts; performing text analysis mining on the end-machine transaction information and account transaction information which is stored in a business system and is associated with the end-machine transaction information to determine error processing element information in each end-machine transaction of each customer account; determining a target customer account for which an error occurred in at least one end-to-end transaction of one or more of the customer accounts and error information for the target customer account based on the error handling factor information; and performing error processing on the target customer account and the internal account in the business system based on the error information of the target customer account. The method and the device can be used in the financial field, the block chain field and any field except the financial field and the block chain field, and the application field of the method and the device for processing the errors of the automatic teller machine is not limited.
Fig. 1 schematically illustrates an exemplary system architecture 100 to which the method and apparatus for automated teller machine error handling may be applied, according to an embodiment of the disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, a system architecture 100 according to this embodiment may include automated teller machines 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium to provide a communication link between the automated teller machines 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired and/or wireless communication links, and so forth.
The user may interact with a server 105 via a network 104 using the automated teller machines 101, 102, 103 to receive or send messages or the like. The automatic teller machines 101, 102, 103 may be an outdoor automatic teller machine or an indoor automatic teller machine depending on the installation location thereof, and are not particularly limited. Most of the automatic teller machines are used for depositing and withdrawing money by users, and therefore, may be called as automatic teller machines, or automatic teller machines. Various applications such as deposit, withdrawal, transfer, inquiry and the like are installed on the automatic teller machine. The automatic teller machines 101, 102, 103 generally have display screens and support face information acquisition, so that the user can conveniently perform operations such as depositing and withdrawing, and the transaction safety is ensured.
The server 105 may be a server providing various services, such as a background management server (for example only) that supports operations such as depositing, withdrawing, transferring, querying, etc. performed by users using the automatic teller machines 101, 102, 103. The background management server may analyze and otherwise process data such as the received user request, and feed back a processing result (for example, account query information obtained or generated according to the user request, and the like) to the automatic teller machine.
It should be noted that the method for error handling of the automatic teller machine provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the automatic teller machine error handling apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The method of automated teller machine error handling provided by the embodiments of the present disclosure may also be performed by a server or a cluster of servers different from the server 105 and capable of communicating with the automated teller machines 101, 102, 103 and/or the server 105. Accordingly, the apparatus for error handling of the automated teller machine provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the automated teller machines 101, 102, 103 and/or the server 105.
It should be understood that the number of automated teller machines, networks, and servers in fig. 1 is merely illustrative. There may be any number of automated teller machines, networks, and servers, as desired for an implementation.
Fig. 2 schematically shows a flow chart of a method of automated teller machine error handling according to an embodiment of the present disclosure. As shown in fig. 2, the method includes operations S201 to S204.
In operation S201, on-line transaction information from an automated teller machine is obtained, wherein the on-line transaction information includes transaction information of at least one on-line transaction occurring in one or more customer accounts.
In operation S202, text analysis mining is performed on the terminal transaction information and account transaction information associated with the terminal transaction information stored in the business system to determine error handling element information in each terminal transaction of each customer account.
In operation S203, a target customer account and error information of the target customer account where an error occurs in at least one end-to-end transaction of one or more customer accounts are determined based on the error processing element information.
In operation S204, error processing is performed on the target customer account and the internal account in the business system based on the error information of the target customer account.
According to an embodiment of the present disclosure, the automatic teller machine may be an automatic deposit machine, an automatic teller machine, an automated teller machine, or the like. The end-machine transaction occurring at the atm may be a deposit or withdrawal transaction, wherein the end-machine transaction information includes information such as a customer account, a deposit amount, a withdrawal amount, a time stamp, etc. of each end-machine transaction. The customer account can be an individual account or a public account, and different customer accounts can be distinguished by identifiers such as card numbers and account names.
According to the embodiment of the disclosure, because the automatic teller machine and the service system are networked, each piece of end-to-end machine transaction information can find the corresponding account transaction information stored in the service system, however, when the end-to-end machine transaction occurs on the automatic teller machine, the situations of paper money clamping, paper money swallowing, paper money multi-discharging or paper money missing and the like can occur due to hardware failure or personal negligence, and at the moment, the actual transaction amount of the end-to-end machine in the end-to-end machine transaction information is inconsistent with the transaction amount recorded by the service system in the account transaction information.
For example, if a 1000 yuan withdrawal transaction occurs in the atm, a cash jam occurs, which results in an actual withdrawal amount of the terminal being 900, at this time, the withdrawal amount in the terminal transaction information is 900, and the withdrawal amount recorded in the account transaction information is still 1000 yuan, thereby generating a situation that the terminal transaction information and the account transaction information are inconsistent.
Due to the large data volume, the manual confirmation and check between the end-machine transaction information and the account transaction information consumes a large amount of manpower, and the risk of missed check exists.
According to the embodiment of the disclosure, after the end-machine transaction has the situations of paper money clamping, paper money swallowing, paper money spitting more or paper money missing, etc., which are listed above, the account of the client or the internal account of the business system is hung, and the error processing needs to be simultaneously performed on the account of the client and the internal account which have errors based on the error information so as to clear the hanging account, so that the internal account and the target client account are matched with the actually occurring end-machine transaction.
According to the embodiment of the disclosure, by means of the technical means of determining the error processing element information by text analysis and mining of the end-machine transaction information from the automatic teller machine and the account transaction information stored in the service system, the error processing element information can be determined quickly and accurately, the target customer account with errors and the error information are determined quickly based on the error processing element information, wherein the error information comprises the error type and the error amount, then the target customer account and the internal account of the service system can be subjected to automatic error processing based on the error information, and the whole process does not need manual participation, so that the technical problems of potential risks in manual error processing and low processing efficiency are at least partially overcome, the accuracy of error processing is improved, and the technical effect of efficiency optimization is realized.
The method shown in fig. 2 is further described with reference to fig. 3-4 in conjunction with specific embodiments.
FIG. 3 schematically shows a flow diagram of text analytics mining, in accordance with an embodiment of the present disclosure.
As shown in FIG. 3, operation S202 specifically includes sub-operations S2021-S2022.
In sub-operation S2021, the terminal transaction information and the account transaction information are screened using the keywords.
For example, for end-machine transaction information, keywords include, but are not limited to, "card number," "deposit," "withdraw," "timestamp," and the like; for account transaction information, the keywords include, but are not limited to, "timestamp," "amount," "card number," and the like.
In sub-operation S2022, the filtered end-machine transaction information and the account transaction information are subjected to aggregation processing to determine error processing element information.
Specifically, the data screening, cleaning and merging are performed on the terminal transaction information and the account transaction information, so that the error processing element information in each terminal transaction of each customer account can be determined. The aggregated information includes the aforementioned keyword information, wherein the deposit or withdrawal information determines the actual transaction amount of the terminal associated with the customer account, and the amount information determines the transaction amount recorded by the business system associated with the customer account.
According to an embodiment of the present disclosure, operation S203 may specifically include the following operations:
and for each terminal transaction of each customer account, in the case that the actual transaction amount of the terminal is smaller than the transaction amount recorded by the service system, for example, in the case that the automatic teller machine generates a paper money, determining that the customer account is a target customer account with an error, the error type of the target customer account is a first error type, and the error amount is the difference value between the transaction amount recorded by the service system and the actual transaction amount of the terminal.
And for each terminal transaction of each client account, determining that the client account is a target client account with errors in the case that the actual transaction amount of the terminal is larger than the transaction amount recorded by the service system, for example, in the case that the ATM generates too much money, and the error type of the target client account is a second error type, wherein the error amount is the difference value between the actual transaction amount of the terminal and the transaction amount recorded by the service system.
According to an embodiment of the present disclosure, operation S203 may specifically include the following operations:
when the error type is the first error type, the internal account is subjected to deduction processing of the error amount, and account posting processing of the error amount is performed on the customer account. Specifically, when the error type is the first error type, the internal account is a debit and needs to be deducted, and the customer account is a credit and needs to be credited.
And when the error type is the second error type, carrying out deduction processing of the error amount on the target customer account, and carrying out posting processing of the error amount on the internal account. Specifically, when the error type is the second error type, the internal account is a credit and the error amount needs to be credited, and the customer account is a debit and the error amount needs to be deducted.
Fig. 4 schematically shows a flow chart of face recognition information matching according to an embodiment of the present disclosure.
As shown in fig. 4, in order to improve reliability of the error confirmation in operations S201 to S203, the method further includes operations S401 to S403.
Acquiring face recognition information associated with a target customer account from an automated teller machine in operation S401; in operation S402, face feature information corresponding to a target customer account stored in a business system is acquired; in operation S403, in case that the face recognition information matches the face feature information, error processing is performed again on the target customer account and the internal account.
Optionally, under the condition that the face identification information is not matched with the face feature information, manual confirmation can be performed on the pen-end transaction.
In the case of the terminal transaction, the user of the client account usually performs the deposit and withdrawal operations, so that the user can further confirm whether the user performs the deposit and withdrawal operations by face recognition to ensure the reliability of the error confirmation process in the terminal transaction with errors.
Optionally, after the error processing in operation S204, broadcasting the result of the error processing in the blockchain network for confirmation, where the result of the error processing includes the error amount, the customer account, and the processing timestamp of the error processing. Based on the characteristics of information disclosure and non-tampering in the blockchain network, the method and the system can carry out broadcast confirmation on the error processing result through the blockchain network, so as to ensure the authenticity and reliability of the error processing.
Fig. 5 schematically shows a flow chart of a method of automated teller machine error handling according to another embodiment of the present disclosure. As shown in fig. 5, taking the customer account as the individual account, the method includes operations S501 to S511.
In operation 501, end-of-line transaction information and face recognition information from an automated teller machine are acquired by a business system, the end-of-line transaction information including cash transaction information occurring at the automated teller machine.
At operation 502, the business system performs text analysis mining on the end-to-end transaction information and account transaction information stored by the business system in association with the end-to-end transaction information to determine error handling element information. The error processing element information includes core information such as a customer account, an end machine generation amount, a host generation amount, an automatic teller machine end machine number, a transaction time stamp and the like.
In operation 503, a target customer account where the error occurred and error information thereof are determined based on the error processing element information, the error information including an error type and an error amount, wherein the error type is divided into a first error type (internal account is debit) and a second error type (internal account is credit), and the error amount is a difference between the end-machine generation amount and the host generation amount.
In operation 504, it is determined whether the face recognition information from the automated teller machine matches the face feature information corresponding to the target customer account, and if so, automatic error handling is performed, and if not, an error is manually confirmed.
In operation 505, in the event the error type is a first error type, an internal account credit check is performed to determine if the internal account is capable of normally performing the posting process, which is typically performed if the internal account has a preset transaction limit.
At operation 506, a deduction of the error amount is made from the personal account as the debit.
In operation 507, an internal account that is a lender is subjected to posting processing of an error amount.
In operation 508, in the case where the error type is the second error type, an internal account debit check is performed to determine whether the internal account can normally perform the deduction process.
At operation 509, the internal account that is the debit is subject to a deduction of the error amount.
At operation 510, a personal account that is credited with an error amount.
In operation 511, the result of the error handling is broadcast acknowledged within the blockchain network to prevent repudiation.
Fig. 6 schematically shows a block diagram of an apparatus for automated teller machine error handling according to an embodiment of the present disclosure.
As shown in fig. 6, the apparatus 600 for error handling of an automatic teller machine includes an acquisition module 610, an analysis module 620, and a processing module 630.
The acquiring module 610 is used for acquiring end-machine transaction information from an automatic teller machine and account transaction information stored in a business system and associated with the end-machine transaction information, wherein the end-machine transaction information comprises transaction information of at least one end-machine transaction of one or more customer accounts.
An analysis module 620, configured to perform text analysis mining on the end-machine transaction information and the account transaction information to determine error processing element information in each end-machine transaction of each customer account, and determine a target customer account in which an error occurs in at least one end-machine transaction of one or more customer accounts and error information of the target customer account based on the error processing element information.
And the processing module 630 is configured to perform error processing on the target customer account and the internal account in the business system based on the error information of the target customer account.
According to the embodiment of the disclosure, by means of a technical means of text analysis and mining of the end-machine transaction information from the automatic teller machine and the account transaction information stored in the service system to determine the error processing element information, the error processing element information is determined quickly and accurately, and the target customer account and the error information with errors are determined quickly based on the error processing element information, wherein the error information comprises the error type and the error amount, then the target customer account and the internal account of the service system can be subjected to automatic error processing based on the error information, and the whole process does not need manual participation, so that the technical problems of potential risks in manual error processing and low processing efficiency are at least partially overcome, the accuracy of error processing is improved, and the technical effect of efficiency optimization is realized.
According to an embodiment of the disclosure, the analysis module 620 performs aggregation processing on the terminal transaction information and the account transaction information by using the filtering keyword to determine error processing element information in each terminal transaction of each customer account, wherein the error processing element information includes an actual transaction amount of a terminal associated with the customer account, a transaction amount recorded by a service system, and a transaction timestamp.
According to an embodiment of the present disclosure, the obtaining module 610 is further configured to obtain face recognition information associated with a target customer account from an automatic teller machine, and obtain face feature information corresponding to the target customer account stored in a business system; the processing module is also used for carrying out error processing on the target customer account and the internal account under the condition that the face recognition information is matched with the face characteristic information.
According to an embodiment of the present disclosure, the error information includes an error type and an error amount, the analysis module 620 is configured to determine, for each client transaction of each client account, that the client account is a target client account in which an error occurs, and that the error type of the target client account is a first error type in the case that the actual transaction amount of the client is smaller than the transaction amount recorded by the service system, and the error amount is a difference between the transaction amount recorded by the service system and the actual transaction amount of the client; and aiming at each terminal machine transaction of each client account, under the condition that the actual transaction amount of the terminal machine is larger than the transaction amount recorded by the service system, determining that the client account is a target client account with errors, the error type of the target client account is a second error type, and the error amount is the difference value between the actual transaction amount of the terminal machine and the transaction amount recorded by the service system.
According to the embodiment of the disclosure, the processing module 630 is configured to, if the error type is the first error type, perform deduction processing on the error amount from the internal account, and perform posting processing on the error amount from the customer account; and when the error type is the second error type, carrying out deduction processing of the error amount on the target customer account, and carrying out posting processing of the error amount on the internal account.
According to an embodiment of the disclosure, the apparatus 600 for automatic teller machine error processing further comprises a confirmation module for broadcasting confirmation of the result of the error processing in the blockchain network, the result of the error processing comprising an error amount of the error processing, a customer account and a processing time stamp.
According to an embodiment of the present disclosure, the automatic teller machine error processing apparatus 600 may be disposed in a business system.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any number of the obtaining module 610, the analyzing module 620 and the processing module 630 may be combined and implemented in one module/unit/sub-unit, or any one of the modules/units/sub-units may be split into a plurality of modules/units/sub-units. Alternatively, at least part of the functionality of one or more of these modules/units/sub-units may be combined with at least part of the functionality of other modules/units/sub-units and implemented in one module/unit/sub-unit. According to an embodiment of the present disclosure, at least one of the obtaining module 610, the analyzing module 620, and the processing module 630 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware by any other reasonable manner of integrating or packaging a circuit, or may be implemented in any one of or a suitable combination of software, hardware, and firmware. Alternatively, at least one of the obtaining module 610, the analyzing module 620 and the processing module 630 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.
Fig. 7 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 7, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. The processor 701 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. It is noted that the programs may also be stored in one or more memories other than the ROM 702 and RAM 703. The processor 701 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
Electronic device 700 may also include input/output (I/O) interface 705, which input/output (I/O) interface 705 is also connected to bus 704, according to an embodiment of the present disclosure. The system 700 may also include one or more of the following components connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the processor 701, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples may include, but are not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 702 and/or the RAM 703 and/or one or more memories other than the ROM 702 and the RAM 703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method provided by the embodiments of the present disclosure, when the computer program product is run on an electronic device, the program code being adapted to cause the electronic device to carry out the method of automated teller machine error handling provided by the embodiments of the present disclosure.
The computer program, when executed by the processor 701, performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (10)

1. A method of automated teller machine error handling, comprising:
acquiring end-machine transaction information from an automatic teller machine, wherein the end-machine transaction information comprises transaction information of at least one end-machine transaction of one or more customer accounts;
performing text analysis mining on the end-machine transaction information and account transaction information which is stored in a business system and is associated with the end-machine transaction information to determine error processing element information in each end-machine transaction of each customer account;
determining a target customer account for which an error occurred in at least one end-to-end transaction of one or more of the customer accounts and error information for the target customer account based on the error handling factor information; and
and carrying out error processing on the target customer account and the internal account in the business system based on the error information of the target customer account.
2. The method of claim 1, wherein the text-parsing mining the end-of-machine transaction information and account transaction information stored in a business system in association with the end-of-machine transaction information to determine error-handling factor information in each end-of-machine transaction for each of the customer accounts comprises:
screening the terminal machine transaction information and the account transaction information by using keywords;
aggregating the screened end-machine transaction information and the account transaction information to determine the error processing element information;
wherein the error processing element information comprises the actual transaction amount of the terminal machine associated with the customer account, the transaction amount recorded by the business system and the transaction time stamp.
3. The method of claim 2, further comprising:
acquiring face recognition information associated with the target customer account from an automatic teller machine;
acquiring face feature information corresponding to the target customer account stored in the service system;
and under the condition that the face identification information is matched with the face feature information, carrying out error processing on the target customer account and the internal account.
4. The method of claim 2, wherein the error information includes an error type and an error amount, and wherein the determining a target customer account for which an error occurred in at least one end-machine transaction of one or more of the customer accounts based on the error handling factor information and the error information for the target customer account comprises:
for each terminal transaction of each customer account, under the condition that the actual transaction amount of the terminal is smaller than the transaction amount recorded by the service system, determining that the customer account is a target customer account with errors, the error type of the target customer account is a first error type, and the error amount is the difference value between the transaction amount recorded by the service system and the actual transaction amount of the terminal;
and aiming at each terminal machine transaction of each customer account, under the condition that the actual transaction amount of the terminal machine is larger than the transaction amount recorded by the service system, determining that the customer account is a target customer account with errors, the error type of the target customer account is a second error type, and the error amount is the difference value between the actual transaction amount of the terminal machine and the transaction amount recorded by the service system.
5. The method of claim 4, wherein the error handling of the target customer account and internal account based on the error information of the target customer account comprises:
when the error type is a first error type, carrying out deduction processing on the error amount of the internal account, and carrying out posting processing on the error amount of the customer account;
and when the error type is a second error type, carrying out deduction processing on the error amount of the target customer account, and carrying out posting processing on the error amount of the internal account.
6. The method of claim 1, further comprising:
and broadcasting and confirming the error processing result in the blockchain network, wherein the error processing result comprises the error amount, the customer account and the processing time stamp of the error processing.
7. An automated teller machine error handling apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring end-machine transaction information from an automatic teller machine and account transaction information which is stored in a business system and is associated with the end-machine transaction information, and the end-machine transaction information comprises transaction information of at least one end-machine transaction of one or more customer accounts;
the analysis module is used for performing text analysis mining on the end-machine transaction information and the account transaction information to determine error processing element information in each end-machine transaction of each customer account, and determining a target customer account with an error in at least one end-machine transaction of one or more customer accounts and error information of the target customer account based on the error processing element information; and
and the processing module is used for carrying out error processing on the target customer account and the internal account in the service system based on the error information of the target customer account.
8. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.
9. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.
10. A computer program product comprising computer executable instructions for implementing the method of any one of claims 1 to 6 when executed.
CN202110199205.7A 2021-02-22 2021-02-22 Method and device for processing errors of automatic teller machine, electronic equipment and storage medium Pending CN112884581A (en)

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