CN108694657B - Client identification apparatus, method and computer-readable storage medium - Google Patents
Client identification apparatus, method and computer-readable storage medium Download PDFInfo
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Abstract
The invention discloses a client identification device, which comprises a memory and a processor, wherein a client identification program which can run on the processor is stored in the memory, and the program realizes the following steps when being executed by the processor: determining a target service system to be scanned; acquiring a client data table and/or a transaction object data table of a target service system and a blacklist; according to the updating condition of the data in a preset time interval, matching the client data table and/or the transaction object data table with the blacklist list, and finding out abnormal clients; inquiring the transaction record of the abnormal customer from the corresponding database, and taking the transaction record meeting the preset conditions as the abnormal transaction record; and generating an abnormal customer case and an abnormal transaction case and sending the abnormal customer case and the abnormal transaction case to a preset institution node. The invention also provides a client identification method and a computer readable storage medium. The invention improves the efficiency of identifying suspicious customers by an organization, and then enhances the risk management and control capability.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a client identification apparatus, a client identification method, and a computer-readable storage medium.
Background
Some enterprises or institutions in the financial industry, such as banks, need to manage and control the security of transactions, authorities such as judicial authorities and the national banks can issue some blacklist information regularly, and financial institutions need to monitor transaction services of the institutions and detect whether suspicious customers may be on the blacklists. However, there is no centralized system in the existing banking system to identify suspicious customers and transactions, and each business system mainly uses its own method to perform blacklist management and control according to its own business requirements, even the identification modes of suspicious customers and transactions of some business systems remain on the manual identification level of counter clerks, each business system needs to maintain its own blacklist system, there is no uniform abnormal customer identification standard among systems, and blacklist management is disordered, resulting in low efficiency of identifying suspicious customers and low risk management and control capability.
Disclosure of Invention
The invention provides a client identification device, a client identification method and a computer readable storage medium, and mainly aims to improve identification efficiency of suspicious clients and enhance risk management and control capability.
In order to achieve the above object, the present invention provides a client identification device, which includes a memory and a processor, wherein the memory stores a client identification program operable on the processor, and the client identification program implements the following steps when executed by the processor:
when the time interval of the customer identification operation reaches a preset time interval, determining a target service system to be scanned;
acquiring a current customer data table and/or a transaction object data table of the target service system, and determining a blacklist corresponding to the target service system;
according to the updating condition of the customer data table and/or the transaction object data table in the preset time interval, matching the customer data table and/or the transaction object data table with the blacklist list, and finding out abnormal customers;
inquiring the transaction record of the abnormal customer from a database of the corresponding target business system, and taking the transaction record meeting the preset conditions as the abnormal transaction record;
and generating an abnormal customer case according to the matching condition of the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node.
Optionally, the step of matching the customer data table and/or the transaction object data table with the blacklist according to the update condition of the customer data table and/or the transaction object data table in the preset time interval, and finding an abnormal customer includes:
detecting whether the customer data table and/or the transaction object data table are updated within the preset time interval;
if so, acquiring incremental customer data and/or incremental transaction object data, matching the incremental customer data and/or the incremental transaction object data with the full blacklist according to the matching rule, and finding out abnormal customers;
if not, detecting whether the blacklist list is updated within the preset time interval;
if so, obtaining an increment blacklist, matching the increment blacklist with a full amount of the customer data table and/or the transaction object data table according to the matching rule, and finding out abnormal customers.
Optionally, according to a plurality of preset information items, matching the user information in the full amount of customer data tables and/or the transaction object data tables with the personnel information in the increment blacklist one by one;
if the contents of the preset information items are inconsistent, judging the client to be a normal client;
and if the contents of the preset information items are consistent, judging that the client is an abnormal client.
Optionally, the step of generating an abnormal customer case according to the matching condition of the abnormal customer includes:
acquiring the number of preset information items with consistent contents, and determining early warning levels according to the acquired number;
determining matching fields of preset information items with consistent contents;
and generating an abnormal customer case according to the information of the abnormal customer, the early warning level and the matching field.
Optionally, the step of querying the transaction record of the abnormal customer from the database of the corresponding target service system, and taking the transaction record meeting the preset condition as the abnormal transaction record of the customer includes:
inquiring the transaction record of the abnormal customer from a database of a corresponding target business system;
if the target business system is a first preset business system, taking a latest transaction record of the abnormal customer as an abnormal transaction record;
if the target business system is a second preset business system, detecting whether the abnormal customer generates a new transaction record within a preset time interval;
if so, taking the detected transaction record as an abnormal transaction record;
if not, taking the transaction record closest to the current time point in the current service validity period of the abnormal customer as the abnormal transaction record.
In addition, to achieve the above object, the present invention further provides a client identifying method, including:
when the time interval of the client identification operation reaches a preset time interval, determining a target service system to be scanned;
acquiring a current customer data table and/or a transaction object data table of the target service system, and determining a blacklist corresponding to the target service system;
according to the updating condition of the customer data table and/or the transaction object data table in the preset time interval, matching the customer data table and/or the transaction object data table with the blacklist list, and finding out abnormal customers;
inquiring the transaction record of the abnormal customer from a database of the corresponding target business system, and taking the transaction record meeting the preset conditions as the abnormal transaction record;
and generating an abnormal customer case according to the matching condition of the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node.
Further, to achieve the above object, the present invention also provides a computer readable storage medium having stored thereon a client identification program, which is executable by one or more processors to implement the steps of the client identification method as described above.
When the time interval of the customer identification operation reaches the preset time interval, the customer identification device, the customer identification method and the computer readable storage medium determine a target service system needing to be scanned, further acquire a customer data table and/or a transaction object data table of the target service system, and determine a blacklist to be matched. And matching the client data table and/or the transaction object data table with the blacklist according to a preset matching rule, searching abnormal clients, inquiring transaction records of the abnormal clients from a database of the corresponding target business system, and taking the transaction records meeting preset conditions as abnormal transaction records. The abnormal customer cases are generated according to the abnormal customers, the abnormal transaction cases are generated according to the abnormal transaction records, and the abnormal customer cases and the abnormal transaction cases are sent to the preset organization nodes.
Drawings
FIG. 1 is a schematic diagram of a subscriber identity device according to an embodiment of the present invention;
FIG. 2 is a block diagram of a client identification program according to an embodiment of the present invention;
fig. 3 is a flowchart of an embodiment of a client identification method according to the invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a client identification device. Fig. 1 is a schematic diagram of a subscriber identity device according to an embodiment of the invention.
In the present embodiment, the client identifying apparatus 1 may be a PC (Personal Computer), or may be a terminal device such as a smartphone, a tablet Computer, or a portable Computer.
The client identification device 1 comprises at least a memory 11, a processor 12, a communication bus 13, and a network interface 14.
The memory 11 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the client identification device 1, for example a hard disk of the client identification device 1. The memory 11 may be an external storage device of the subscriber identity device 1 in other embodiments, such as a plug-in hard disk provided on the subscriber identity device 1, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 11 may also include both an internal storage unit of the client recognition apparatus 1 and an external storage device. The memory 11 may be used not only to store application software installed in the client identifying apparatus 1 and various types of data, such as a code of the client identifying program 01, but also to temporarily store data that has been output or is to be output.
The communication bus 13 is used to realize connection communication between these components.
The network interface 14 may optionally include a standard wired interface, a wireless interface (e.g., a WI-FI interface), and is typically used to establish a communication link between the apparatus 1 and other electronic devices.
Fig. 1 shows only the client identification device 1 with the components 11-14 and the client identification program 01, but it is to be understood that not all shown components are required to be implemented, and that more or fewer components may be implemented instead.
In the embodiment of the apparatus 1 shown in fig. 1, a client identification program 01 is stored in the memory 11; the processor 12, when executing the client identification program 01 stored in the memory 11, implements the following steps:
and when the time interval of the customer identification operation reaches a preset time interval, determining a target business system needing to be scanned.
And acquiring a current customer data table and/or a transaction object data table of the target service system, and determining a blacklist corresponding to the target service system.
In the embodiment of the present invention, the user may preset a time interval for performing the abnormal customer identification operation, for example, 24 hours, and the customer identification device performs real-time monitoring on the time interval for performing the abnormal customer identification operation, and starts the abnormal customer identification operation when the preset time interval is reached. In addition, the client identification device of the embodiment can simultaneously manage and control a plurality of service systems. The business system refers to each independently operated business system in a company, such as an insurance business system, a security business system, a banking business system, and the like. The user can preset a service system needing scanning as a target service system and acquire the current latest client data table and/or transaction object data table of the target service system. In some embodiments, only the customer data tables may be scanned, and in other embodiments, both the customer data tables and the transaction object data tables comprising the transaction objects of the customers in these data tables may be scanned. Taking the health insurance business system as an example, the clients refer to individual and group insurance applicants, insured persons and casualty beneficiaries, and the transaction objects refer to the third party to the public involved in the transaction in the group insurance business system. The customer data sheet mainly comprises information such as the name, the certificate type and the certificate number of a customer, and the transaction object data sheet mainly comprises information such as the name, the certificate type and the certificate number of a transaction object.
Regarding the blacklist in this embodiment, a plurality of blacklists issued from different mechanism nodes may be preset. Such as suspicious client lists issued by authorities such as judicial authorities and the China's people's bank. For a service system, the service system may be matched with one or more of the preset blacklists, and a user may preset a corresponding relationship between the service system and the blacklists of each source.
And matching the customer data table and/or the transaction object data table with the blacklist according to the updating condition of the customer data table and/or the transaction object data table in a preset time interval, and finding out the abnormal customer.
For business systems, new customers may perform transactions every day, such as depositing and withdrawing money, buying insurance, purchasing investment products, etc., or some old customers may also perform some new transaction objects, so that the update of the customer data table and/or the transaction object data table needs to be considered when performing customer identification. Moreover, the blacklist issued by the authority has a possibility of being updated within a preset time interval, such as deleting, adding some personnel information to the blacklist, or modifying the personnel information on the blacklist. Therefore, in order to accurately identify the abnormal client, the client identification operation needs to be executed again at intervals according to the update condition of the data.
Specifically, the step of matching the customer data table and/or the transaction object data table with the blacklist according to the update condition of the customer data table and/or the transaction object data table within the preset time interval may include the following detailed steps: detecting whether a customer data table and/or a transaction object data table are updated within a preset time interval; if so, acquiring incremental customer data and/or incremental transaction object data, matching the incremental customer data and/or the incremental transaction object data with a full blacklist according to a matching rule, and finding out abnormal customers; if not, detecting whether the blacklist list is updated within a preset time interval; if yes, obtaining the increment blacklist, matching the increment blacklist with the full amount of customer data tables and/or the transaction object data tables according to the matching rule, and finding out the abnormal customers.
In the above step, when the customer data table and/or the transaction object data table are updated, the incremental customer data and/or the incremental transaction object data may be matched with the full amount of blacklist regardless of whether the blacklist is updated. And under the condition that the client data table and/or the transaction object data table are not updated and the blacklist is updated, matching the full amount of the client data table and/or the transaction object data table with the incremental blacklist data and finding out the abnormal client. It can be understood that, if the customer data table and/or the transaction object data are not updated and the blacklist is not updated within the preset time interval, the current identification result may be the same as the previous identification result, and therefore, the scanning may be performed to obtain the previous identification result. Or in other embodiments, the full amount of customer data tables and/or transaction object data tables may be matched against the full amount of blacklist data again to find anomalous customers. Specifically, when matching is performed, if the customer information is consistent with the person information on the blacklist, it is determined that the customer is an abnormal customer, and the information of the customer is recorded as an abnormal customer.
Further, matching the incremental blacklist with a full amount of customer data tables and/or transaction object data tables according to a matching rule, and finding out abnormal customers comprises the following steps: according to a plurality of preset information items, matching user information in a full amount of customer data tables and/or transaction object data tables with personnel information in an increment blacklist one by one; if the contents of the preset information items are inconsistent, judging the client to be a normal client; and if the contents of the preset information items are consistent, judging that the client is an abnormal client. Specifically, the preset information items mainly include three information items of certificate type, certificate number and customer name. Acquiring certificate types, certificate numbers and client names in client information, matching the acquired information items with data in a blacklist one by one according to a preset sequence, and sending out early warning information of corresponding levels according to the matching conditions of the three information items. The user can preset early warning levels corresponding to different matching conditions. For example, if the certificate numbers are the same and the names are matched accurately successfully, a primary early warning is given; if the certificate numbers are the same, the name fuzzy matching is successful, a secondary early warning is performed, and if the certificate numbers and/or the certificate type information do not exist and the name is accurately matched successfully, a tertiary early warning is performed; if no certificate number and/or certificate type information exists and the name is matched in a fuzzy way successfully, four-stage early warning is carried out.
After the client is judged to be an abnormal client, acquiring the number of preset information items with consistent content, and determining an early warning level according to the acquired number; determining matching fields of preset information items with consistent contents; and generating an abnormal customer case according to the information, the early warning level and the matching field of the abnormal customer.
And inquiring the transaction record of the abnormal customer from the database of the corresponding target business system, and taking the transaction record meeting the preset conditions as the abnormal transaction record.
And generating an abnormal customer case according to the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node.
If a plurality of target service systems exist, after the abnormal customers are found, the target service systems corresponding to the customer information are determined. The transaction record of the customer information is queried from a database of the target business system. And screening out the transaction records meeting the conditions as abnormal transaction records. Specifically, inquiring transaction records of abnormal customers from a database of a corresponding target business system; if the target business system is a first preset business system, taking the latest transaction record of the abnormal customer as an abnormal transaction record; if the target business system is a second preset business system, detecting whether an abnormal customer generates a new transaction record within a preset time interval; if so, taking the detected transaction record as an abnormal transaction record; if not, taking the transaction record closest to the current time point in the current service validity period of the abnormal customer as the abnormal transaction record.
As an embodiment, the first preset business system may be a security business system, a letter insurance business system, and the latest transaction record of the customer may be obtained as the abnormal transaction record. Taking the second preset service system as an insurance service system as an example, if the abnormal customer has no transaction record within a preset time interval, acquiring the transaction record within the current policy validity period of the customer information as the abnormal transaction record. Further, if no transaction record exists in the policy validity period, the transaction record closest to the current time point in the historical transaction records is obtained and used as the abnormal transaction record.
The generated abnormal customer case comprises fields matched with the blacklist in the customer information, early warning level and other information, and the information reflects the abnormal degree of the customer. The abnormal transaction case includes the obtained abnormal transaction record, which reflects the abnormal transaction condition of the abnormal customer. The case is sent to the preset mechanism node, and it can be understood that blacklists from different sources correspond to different mechanism nodes, so that when the case is generated, a plurality of case tables can be generated according to the matched blacklist corresponding to the abnormal customer and sent to the corresponding mechanism node.
The client identifying device provided in the above embodiment determines the target service system to be scanned when the time interval of the client identifying operation reaches the preset time interval, further obtains the client data table and/or the transaction object data table of the target service system, and determines the blacklist to be matched. And matching the client data table and/or the transaction object data table with the blacklist according to a preset matching rule, finding out abnormal clients, inquiring transaction records of the abnormal clients from a corresponding database of the target business system, and taking the transaction records meeting preset conditions as the abnormal transaction records. The abnormal customer case is generated according to the abnormal customer, the abnormal transaction case is generated according to the abnormal transaction record, and the abnormal customer case and the abnormal transaction case are sent to the preset organization node.
Alternatively, in other embodiments, the client identification program may be divided into one or more modules, and the one or more modules are stored in the memory 11 and executed by one or more processors (in this embodiment, the processor 12) to implement the present invention, where the module referred to in the present invention refers to a series of computer program instruction segments capable of performing a specific function for describing the execution process of the client identification program in the client identification device.
For example, referring to fig. 2, a schematic diagram of program modules of a customer identification program in an embodiment of the customer identification device of the present invention is shown, in which the customer identification program may be divided into a first determining module 10, a second determining module 20, a customer matching module 30, a transaction querying module 40, and a case generating module 50, and exemplarily:
the first determination module 10 is configured to: when the time interval of the customer identification operation reaches a preset time interval, determining a target service system to be scanned;
the second determination module 20 is configured to: acquiring a current customer data table and/or a transaction object data table of the target service system, and determining a blacklist corresponding to the target service system;
the client matching module 30 is configured to: according to the updating condition of the customer data table and/or the transaction object data table in the preset time interval, matching the customer data table and/or the transaction object data table with the blacklist list, and finding out abnormal customers;
the case generation module 50 is configured to: and generating an abnormal customer case according to the matching condition of the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node.
The functions or operation steps implemented by the first determining module 10, the second determining module 20, the customer matching module 30, the transaction querying module 40, and the case generating module 50 when executed are substantially the same as those of the above embodiments, and are not described herein again.
In addition, the invention also provides a client identification method. Referring to fig. 3, a flow chart of an embodiment of the client identification method of the present invention is shown. The method may be performed by an apparatus, which may be implemented by software and/or hardware.
In this embodiment, the client identification method includes:
and step S10, when the time interval of the client identification operation reaches a preset time interval, determining a target service system needing to be scanned.
Step S20, obtaining the current client data sheet and/or transaction object data sheet of the target service system, and determining the blacklist corresponding to the target service system.
In the embodiment of the present invention, the user may set in advance a time interval, for example, 24 hours, at which the abnormal customer recognition operation is performed. The abnormal customer recognition operation is started when the time interval of the abnormal customer recognition operation reaches the preset time interval. In addition, the embodiment can simultaneously manage and control a plurality of service systems. The business system refers to a business system operated independently in a company, such as an insurance business system, a security business system, a banking business system, and the like. The user can preset a service system needing scanning as a target service system and obtain the current latest client data table and/or transaction object data table of the target service system. In some embodiments, only the customer data tables may be scanned, and in other embodiments, both the customer data tables and the transaction object data tables comprising the transaction objects of the customers in these data tables may be scanned. Taking the health insurance business system as an example, the customers refer to individual and group insurance applicants, insured persons and physical and casualty beneficiaries, and the transaction objects refer to the third parties of the public involved in the transaction in the group. The customer data sheet mainly comprises information such as the name, the certificate type and the certificate number of a customer, and the transaction object data sheet mainly comprises information such as the name, the certificate type and the certificate number of a transaction object.
Regarding the blacklist in this embodiment, a plurality of blacklists issued from different mechanism nodes may be preset. Such as suspicious client lists issued by authorities such as judicial authorities and the China's people's bank. For a service system, it may be matched with one or more of the preset blacklists, and the user may preset the corresponding relationship between the service system and the blacklists of each source.
And step S30, according to the updating condition of the customer data table and/or the transaction object data table in a preset time interval, matching the customer data table and/or the transaction object data table with the blacklist list, and finding out abnormal customers.
For business systems, new customers may be available for transactions each day, such as depositing and withdrawing money, purchasing insurance, purchasing investment products, etc., or old customers may have new transaction objects, and therefore, the update of the customer data table and/or the transaction object data table is considered when identifying customers. Moreover, the blacklist issued by the authority also has the possibility of being updated within a preset time interval, such as deleting, adding some personnel information to the blacklist, or modifying the personnel information on the blacklist, etc. Therefore, in order to accurately identify the abnormal client, the client identification operation needs to be executed again at intervals according to the update condition of the data.
Specifically, the step of matching the customer data table and/or the transaction object data table with the blacklist according to the update condition of the customer data table and/or the transaction object data table within the preset time interval may include the following refinement steps: detecting whether a customer data table and/or a transaction object data table are updated within a preset time interval; if so, acquiring incremental customer data and/or incremental transaction object data, matching the incremental customer data and/or the incremental transaction object data with a full blacklist according to a matching rule, and finding out abnormal customers; if not, detecting whether the blacklist list is updated within a preset time interval; if so, obtaining an increment blacklist, matching the increment blacklist with a full amount of customer data tables and/or transaction object data tables according to a matching rule, and finding out abnormal customers.
In the above step, when the customer data table and/or the transaction object data table are updated, the incremental customer data and/or the incremental transaction object data may be matched with the full amount of blacklist regardless of whether the blacklist is updated. And under the condition that the client data table and/or the transaction object data table are not updated and the blacklist is updated, matching the full amount of the client data table and/or the transaction object data table with the incremental blacklist data and finding out the abnormal client. It can be understood that, if the customer data table and/or the transaction object data are not updated and the blacklist is not updated within the preset time interval, the current identification result may be the same as the previous identification result, so that the scanning may be performed to obtain the previous identification result. Or in other embodiments, the full amount of customer data tables and/or transaction object data tables may be matched against the full amount of blacklist data again to find anomalous customers. Specifically, when matching is performed, if the customer information is consistent with the person information on the blacklist, it is determined that the customer is an abnormal customer, and the information of the customer is recorded as an abnormal customer.
Further, matching the incremental blacklist with a full amount of customer data tables and/or transaction object data tables according to a matching rule, and the step of finding out abnormal customers comprises the following steps: according to a plurality of preset information items, matching user information in a full amount of customer data tables and/or transaction object data tables with personnel information in an increment blacklist one by one; if the contents of the preset information items are inconsistent, judging the client to be a normal client; and if the contents of the preset information items are consistent, judging that the client is an abnormal client. Specifically, the preset information items mainly include three information items of certificate types, certificate numbers and client names. Acquiring certificate types, certificate numbers and client names in client information, matching the acquired information items with data in a blacklist one by one according to a preset sequence, and sending out early warning information of corresponding levels according to the matching conditions of the three information items. The user can preset early warning levels corresponding to different matching conditions. For example, if the certificate numbers are the same and the names are matched accurately successfully, a primary early warning is given; if the certificate numbers are the same, the name fuzzy matching is successful, a secondary early warning is performed, and if the certificate numbers and/or the certificate type information do not exist and the name is accurately matched successfully, a tertiary early warning is performed; if no certificate number and/or certificate type information exists and the name is matched in a fuzzy way successfully, four-stage early warning is carried out.
After the client is judged to be an abnormal client, acquiring the number of preset information items with consistent content, and determining an early warning level according to the acquired number; determining matching fields of preset information items with consistent contents; and generating an abnormal customer case according to the information, the early warning level and the matching field of the abnormal customer.
And S40, inquiring the transaction record of the abnormal customer from the database of the corresponding target business system, and taking the transaction record meeting the preset conditions as the abnormal transaction record.
And S50, generating an abnormal customer case according to the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node.
If a plurality of target service systems exist, after the abnormal customers are found, the target service systems corresponding to the customer information are determined. The transaction record of the customer information is queried from a database of the target business system. And screening out the transaction records meeting the conditions as abnormal transaction records. Specifically, inquiring transaction records of abnormal customers from a database of a corresponding target business system; if the target business system is a first preset business system, taking a latest transaction record of the abnormal customer as an abnormal transaction record; if the target business system is a second preset business system, detecting whether an abnormal customer generates a new transaction record within a preset time interval; if so, taking the detected transaction record as an abnormal transaction record; if not, taking the transaction record which is closest to the current time point in the current service validity period of the abnormal customer as the abnormal transaction record.
As an embodiment, the first preset business system may be a security business system, a credit insurance business system, and may obtain a latest transaction record of the customer as an abnormal transaction record. Taking the second preset service system as an insurance service system as an example, if the abnormal customer has no transaction record within a preset time interval, acquiring the transaction record within the current policy validity period of the customer information as the abnormal transaction record. Further, if no transaction record exists in the policy validity period, the transaction record closest to the current time point in the historical transaction records is obtained as the abnormal transaction record.
The generated abnormal customer case comprises fields matched with the blacklist in the customer information, information such as early warning level and the like, and the information reflects the abnormal degree of the customer. The abnormal transaction case includes the obtained abnormal transaction record, which reflects the abnormal transaction condition of the abnormal customer. The case is sent to the preset mechanism node, and it can be understood that blacklists from different sources correspond to different mechanism nodes, so that when the case is generated, a plurality of case tables can be generated according to the matched blacklist corresponding to the abnormal customer and sent to the corresponding mechanism node.
In the customer identification method provided in the above embodiment, when the time interval of the customer identification operation reaches the preset time interval, the target service system to be scanned is determined, and then the customer data table and/or the transaction object data table of the target service system are obtained, and the blacklist to be matched is determined. And matching the client data table and/or the transaction object data table with the blacklist according to a preset matching rule, finding out abnormal clients, inquiring transaction records of the abnormal clients from a corresponding database of the target business system, and taking the transaction records meeting preset conditions as the abnormal transaction records. The abnormal customer cases are generated according to the abnormal customers, the abnormal transaction cases are generated according to the abnormal transaction records, and the abnormal customer cases and the abnormal transaction cases are sent to the preset organization nodes.
Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, where a client identification program is stored on the computer-readable storage medium, and the client identification program is executable by one or more processors to implement the following operations:
when the time interval of the customer identification operation reaches a preset time interval, determining a target service system to be scanned;
acquiring a current customer data table and/or a transaction object data table of the target service system, and determining a blacklist corresponding to the target service system;
according to the updating condition of the customer data table and/or the transaction object data table in the preset time interval, matching the customer data table and/or the transaction object data table with the blacklist list, and finding out abnormal customers;
inquiring the transaction record of the abnormal customer from a database of the corresponding target business system, and taking the transaction record meeting the preset conditions as the abnormal transaction record;
and generating an abnormal customer case according to the matching condition of the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node. The embodiment of the computer readable storage medium of the present invention is substantially the same as the embodiments of the client identifying apparatus and method, and will not be described herein again.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, apparatus, article, or method that comprises the element.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A client identification device, comprising a memory and a processor, the memory having stored thereon a client identification program executable on the processor, the client identification program when executed by the processor implementing the steps of:
when the time interval of the customer identification operation reaches a preset time interval, determining a target service system to be scanned;
acquiring a current customer data table and/or a transaction object data table of the target service system, and determining a blacklist corresponding to the target service system;
according to the updating condition of the customer data table and/or the transaction object data table in the preset time interval, matching the customer data table and/or the transaction object data table with the blacklist list, and finding out abnormal customers;
inquiring the transaction record of the abnormal customer from a database of the corresponding target business system, and taking the transaction record meeting the preset conditions as the abnormal transaction record;
and generating an abnormal customer case according to the matching condition of the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node.
2. The customer identification device of claim 1 wherein the step of matching the customer data table and/or the transaction object data table with the blacklist based on the update of the customer data table and/or the transaction object data table within the predetermined time interval comprises:
detecting whether the customer data table and/or the transaction object data table are updated within the preset time interval;
if yes, obtaining incremental customer data and/or incremental transaction object data, matching the incremental customer data and/or the incremental transaction object data with the full blacklist according to a matching rule, and finding out abnormal customers;
if not, detecting whether the blacklist list is updated within the preset time interval;
if so, obtaining an increment blacklist, matching the increment blacklist with a full amount of the customer data table and/or the transaction object data table according to the matching rule, and finding out abnormal customers.
3. The customer identification device of claim 2 wherein matching the incremental blacklist against a full amount of the customer data tables and/or transaction object data tables according to the matching rules comprises:
according to a plurality of preset information items, matching user information in a full amount of customer data tables and/or transaction object data tables with personnel information in an increment blacklist one by one;
if the contents of the preset information items are inconsistent, judging the client to be a normal client;
and if the contents of the preset information items are consistent, judging that the client is an abnormal client.
4. The customer identification device of claim 3 wherein the step of generating an anomalous customer case based on the anomalous customer match comprises:
acquiring the number of preset information items with consistent contents, and determining early warning levels according to the acquired number;
determining matching fields of preset information items with consistent contents;
and generating an abnormal customer case according to the information of the abnormal customer, the early warning level and the matching field.
5. The customer identification device according to any one of claims 1 to 4, wherein the step of querying the database of the corresponding target business system for the transaction record of the abnormal customer, and using the transaction record meeting the preset condition as the abnormal transaction record of the customer comprises:
inquiring the transaction record of the abnormal customer from a database of a corresponding target business system;
if the target business system is a first preset business system, taking a latest transaction record of the abnormal customer as an abnormal transaction record;
if the target service system is a second preset service system, detecting whether the abnormal customer generates a new transaction record within a preset time interval;
if so, taking the detected transaction record as an abnormal transaction record;
if not, taking the transaction record closest to the current time point in the current service validity period of the abnormal customer as the abnormal transaction record.
6. A method for identifying a customer, the method comprising:
when the time interval of the client identification operation reaches a preset time interval, determining a target service system to be scanned;
acquiring a current customer data table and/or a transaction object data table of the target service system, and determining a blacklist corresponding to the target service system;
according to the updating condition of the customer data table and/or the transaction object data table in the preset time interval, matching the customer data table and/or the transaction object data table with the blacklist list, and finding out abnormal customers;
inquiring the transaction record of the abnormal customer from a database of the corresponding target business system, and taking the transaction record meeting the preset conditions as the abnormal transaction record;
and generating an abnormal customer case according to the matching condition of the abnormal customer, generating an abnormal transaction case according to the abnormal transaction record, and sending the abnormal customer case and the abnormal transaction case to a preset institution node.
7. The customer identification method of claim 6, wherein the step of matching the customer data table and/or the transaction object data table with the blacklist according to the update condition of the customer data table and/or the transaction object data table within the preset time interval comprises:
detecting whether the customer data table and/or the transaction object data table are updated within the preset time interval;
if so, acquiring incremental customer data and/or incremental transaction object data, matching the incremental customer data and/or the incremental transaction object data with the full blacklist according to a matching rule, and finding out abnormal customers;
if not, detecting whether the blacklist list is updated within the preset time interval;
if so, obtaining an increment blacklist, matching the increment blacklist with a full amount of the customer data table and/or the transaction object data table according to the matching rule, and finding out abnormal customers.
8. The customer identification method of claim 7 wherein matching said incremental blacklist to a full number of said customer data tables and/or transaction object data tables according to said matching rules, the step of finding anomalous customers comprises:
according to a plurality of preset information items, matching user information in a full amount of customer data tables and/or transaction object data tables with personnel information in an increment blacklist one by one;
if the contents of the preset information items are inconsistent, judging the client to be a normal client;
and if the contents of the preset information items are consistent, judging that the client is an abnormal client.
9. The customer identification method according to any one of claims 6 to 8, wherein the step of querying the database of the corresponding target business system for the transaction record of the abnormal customer, and using the transaction record meeting the preset condition as the abnormal transaction record of the customer comprises:
inquiring the transaction record of the abnormal customer from a database of a corresponding target business system;
if the target business system is a first preset business system, taking the latest transaction record of the abnormal customer as an abnormal transaction record;
if the target business system is a second preset business system, detecting whether the abnormal customer generates a new transaction record within a preset time interval;
if so, taking the detected transaction record as an abnormal transaction record;
if not, taking the transaction record closest to the current time point in the current service validity period of the abnormal customer as the abnormal transaction record.
10. A computer-readable storage medium, having stored thereon a client identification program executable by one or more processors to perform the steps of the client identification method of any one of claims 6 to 9.
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| CN201810768273.9A CN108694657B (en) | 2018-07-13 | 2018-07-13 | Client identification apparatus, method and computer-readable storage medium |
| PCT/CN2018/107720 WO2020010714A1 (en) | 2018-07-13 | 2018-09-26 | Client identification apparatus and method, and computer-readable storage medium |
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| CN201810768273.9A CN108694657B (en) | 2018-07-13 | 2018-07-13 | Client identification apparatus, method and computer-readable storage medium |
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Families Citing this family (16)
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|---|---|---|---|---|
| CN109840316A (en) * | 2018-12-21 | 2019-06-04 | 上海诺悦智能科技有限公司 | A kind of customer information sanction list matching system |
| CN109918408A (en) * | 2019-03-01 | 2019-06-21 | 成都农村商业银行股份有限公司 | A kind of blacklist update method, device, equipment and computer readable storage medium |
| CN109902747B (en) * | 2019-03-01 | 2023-08-29 | 成都农村商业银行股份有限公司 | Identity recognition method, device, equipment and computer readable storage medium |
| CN110070445B (en) * | 2019-04-28 | 2024-03-01 | 深圳前海微众银行股份有限公司 | Transaction processing method and device based on blockchain system |
| CN110363644A (en) * | 2019-06-17 | 2019-10-22 | 深圳壹账通智能科技有限公司 | Exception information recognition methods, device, computer equipment and storage medium |
| CN110335069B (en) * | 2019-06-19 | 2024-07-02 | 中国平安财产保险股份有限公司 | Method, device, computer equipment and storage medium for counting first dial progress |
| CN111212073B (en) * | 2020-01-02 | 2022-07-05 | 中国银行股份有限公司 | Public cloud-based blacklist account sharing method and device |
| CN114066378A (en) * | 2020-08-05 | 2022-02-18 | 中国联合网络通信集团有限公司 | Satisfaction survey method and device |
| CN112184410A (en) * | 2020-09-15 | 2021-01-05 | 中信银行股份有限公司 | Method, system and storage medium for identifying high-risk client |
| CN112486964B (en) * | 2020-11-26 | 2024-04-26 | 中国人寿保险股份有限公司 | Target identification method and device |
| CN113344584A (en) * | 2021-06-02 | 2021-09-03 | 中国工商银行股份有限公司 | Data feedback method, device and system based on blacklist and storage medium |
| CN113487431A (en) * | 2021-07-05 | 2021-10-08 | 中国工商银行股份有限公司 | Centralized trigger identity discrimination method and device |
| CN113962817B (en) * | 2021-11-11 | 2024-07-19 | 泰康保险集团股份有限公司 | Abnormal person identification method and device, electronic equipment and storage medium |
| CN114549193A (en) * | 2021-12-21 | 2022-05-27 | 上海金仕达软件科技有限公司 | List screening method, apparatus, device, storage medium and program product |
| CN116809652B (en) * | 2023-03-28 | 2024-04-26 | 材谷金带(佛山)金属复合材料有限公司 | Abnormality analysis method and system for hot rolling mill control system |
| CN117557211A (en) * | 2023-10-23 | 2024-02-13 | 广东电网有限责任公司 | Intelligent financial business processing method, platform and medium based on flow automation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017152765A1 (en) * | 2016-03-10 | 2017-09-14 | 阿里巴巴集团控股有限公司 | Automatic fusing-based message sending method, device and system |
| CN107993006A (en) * | 2017-11-30 | 2018-05-04 | 平安科技（深圳）有限公司 | Warning grade determines method, apparatus, equipment and readable storage medium storing program for executing |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102270334A (en) * | 2011-09-08 | 2011-12-07 | 成都讯业科技有限公司 | Safety management method and system for financial service |
| US20130212680A1 (en) * | 2012-01-12 | 2013-08-15 | Arxceo Corporation | Methods and systems for protecting network devices from intrusion |
| CN106649845A (en) * | 2016-12-30 | 2017-05-10 | 上海富聪金融信息服务有限公司 | Transaction information service platform and information processing method thereof |
| CN106920170B (en) * | 2017-03-02 | 2021-07-27 | 北京小米移动软件有限公司 | Transaction reminding method and device |
| CN107437179A (en) * | 2017-08-09 | 2017-12-05 | 中国银行股份有限公司 | A kind of banking channels system that risk monitoring and control and special service are provided |
| CN107767021A (en) * | 2017-09-12 | 2018-03-06 | 阿里巴巴集团控股有限公司 | A kind of risk control method and equipment |
-
2018
- 2018-07-13 CN CN201810768273.9A patent/CN108694657B/en active Active
- 2018-09-26 WO PCT/CN2018/107720 patent/WO2020010714A1/en active Application Filing
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017152765A1 (en) * | 2016-03-10 | 2017-09-14 | 阿里巴巴集团控股有限公司 | Automatic fusing-based message sending method, device and system |
| CN107993006A (en) * | 2017-11-30 | 2018-05-04 | 平安科技（深圳）有限公司 | Warning grade determines method, apparatus, equipment and readable storage medium storing program for executing |
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