CN110619574A - Remittance data processing method, remittance data processing apparatus, electronic device, and storage medium - Google Patents
Remittance data processing method, remittance data processing apparatus, electronic device, and storage medium Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention discloses a remittance data processing method, a remittance data processing device, electronic equipment and a storage medium, wherein the method comprises the following steps: receiving remittance application data from a client, the remittance application data including at least: transferor information and transferee information; judging whether the payee information accords with the preset customer attribute according to a pre-trained customer attribute model; responding to the information of the payee meeting the predetermined customer attribute, and judging whether the remittance application data meets the interception condition according to a preset application interception model; and in response to the remittance application data not meeting the interception condition, performing clearing operation processing on the remittance application data. By the method and the device, the consumption of human resources can be reduced, and the working efficiency is improved.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a remittance data processing method, apparatus, electronic device, and storage medium.
Background
At present, after a cross-border remittance service system of a bank receives a cross-border remittance application message initiated by a customer through an electronic channel such as an internet bank, a mobile phone bank, an intelligent terminal and the like, whether remittance of a payee-based enterprise relates to remittance under a capital item or not, whether the money relates to a sensitive country or not, and whether the money is in compliance or not are discriminated.
Disclosure of Invention
The present invention is directed to a remittance data processing method, apparatus, electronic device and storage medium that solve at least one of the problems set forth above.
According to a first aspect of the invention, there is provided a remittance data processing method, the method comprising: receiving remittance application data from a client, the remittance application data comprising at least: transferor information and transferee information; judging whether the payee information accords with the preset customer attribute according to a pre-trained customer attribute model; responding to the information of the payee meeting the predetermined customer attribute, and judging whether the remittance application data meets the interception condition according to a preset application interception model; and in response to the remittance application data not meeting the interception condition, performing clearing operation processing on the remittance application data.
According to a second aspect of the invention, there is provided a remittance data processing apparatus, the apparatus comprising: a data receiving unit for receiving remittance application data from a client, the remittance application data comprising at least: transferor information and transferee information; the customer attribute judging unit is used for judging whether the payee information accords with the preset customer attribute according to a pre-trained customer attribute model; the intercepting condition judging unit is used for responding to the condition that the payee information accords with the preset customer attribute and judging whether the remittance application data accords with the intercepting condition according to a preset application intercepting model; and the data processing unit is used for responding to the remittance application data which does not meet the interception condition and performing clearing operation processing on the remittance application data.
According to a third aspect of the invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the money transfer data processing method described above when executing the program.
According to a fourth aspect of the invention, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the remittance data processing method described above.
According to the technical scheme, the received remittance application is checked according to the pre-trained client attribute model and the application interception model, so that the client attribute of the receiver can be judged, the remittance application which does not accord with the remittance condition can be intercepted, when the receiver information accords with the preset client attribute (such as an individual client) and the remittance application does not accord with the interception condition, the remittance application is subjected to clearing operation processing, namely remittance application is subjected to routing, and finally the remittance application is sent to a preset bank, so that the consumption of human resources can be reduced, and the remittance operation efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow diagram of a method of remittance data processing according to an embodiment of the invention;
FIG. 2 is a block diagram of a money transfer data processing apparatus according to an embodiment of the invention;
FIG. 3 is a block diagram of the detailed construction of a money transfer data processing apparatus according to an embodiment of the invention;
fig. 4 is a block diagram of the structure of the customer attribute model training unit 27 according to an embodiment of the present invention;
FIG. 5 is a block diagram of the structure of the application compliance interception model training unit 25 according to the embodiment of the present invention;
fig. 6 is a block diagram of the first interception condition determining module 231 according to an embodiment of the present invention;
FIG. 7 is a block diagram of a machine learning based cross-border remittance intelligent audit system according to an embodiment of the invention;
FIG. 8 is a block diagram of an example architecture of an artificial intelligence platform 4 in accordance with an embodiment of the invention;
FIG. 9 is a block diagram of an example structure of a remittance application compliance intercept expert rules model, according to an embodiment of the invention;
FIG. 10 is a flow diagram of an audit based on the system of FIG. 7 in accordance with an embodiment of the present invention;
FIG. 11 is a flow diagram of a clearing process of clearing platform 6, according to an embodiment of the present invention;
FIG. 12 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In view of the fact that the existing cross-border remittance service system cannot judge whether the attribute of a receiver is public or personal, cannot judge whether to screen remittance under capital items or not, cannot automatically comply and check money, and can only manually judge whether to pass or not by service personnel, which causes great consumption of human resources, the embodiment of the invention provides a remittance data processing scheme combined with a machine learning technology to overcome the problems. Embodiments of the present invention are described in detail below with reference to the accompanying drawings.
An embodiment of the present invention provides a remittance data processing method, fig. 1 is a flowchart of the method, and as shown in fig. 1, the method includes:
step 101, receiving remittance application data from a client, wherein the remittance application data at least comprises: transferor information and transferee information;
102, judging whether the payee information accords with the preset customer attribute according to a pre-trained customer attribute model;
step 103, in response to the receiving information meeting the predetermined customer attributes, determining whether the remittance application data meets the intercepting conditions according to a preset application intercepting model, where the customer attributes include: for a public client and an individual client, reserving the client attribute as the individual client;
and 104, in response to the remittance application data not meeting the interception condition, performing clearing operation processing on the remittance application data.
The received remittance application is checked according to the pre-trained client attribute model and the application interception model, so that the client attribute of a receiver can be judged, the remittance application which does not accord with the remittance condition can be intercepted, and when the receiver information accords with the preset client attribute (such as an individual client) and the remittance application does not accord with the interception condition, the remittance application is subjected to clearing operation processing, namely, the remittance application is subjected to routing and is finally sent to a preset bank.
In actual practice, the customer attribute model in step 102 may be trained in advance by: obtaining historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical payee information, e.g., payee name, address, etc.; carrying out similar word quantitative processing on the historical payee information; and training the customer attribute model according to the historical payee information and the historical remittance feedback data after the similar words are subjected to quantization processing.
After the customer attribute model training is completed, inputting information such as payee name and address, and correspondingly outputting: for public/personal customers. When the output is for a public customer, it indicates that the payee information does not conform to the predetermined customer attributes, then a manual review stage is entered, and when the output is for a personal customer, it indicates that the payee information conforms to the predetermined customer attributes, then step 103 is entered.
Specifically, the application interception model in step 103 includes: applying for a compliance interception model and applying for a compliance expert model, when the remittance application data do not accord with the interception conditions of the compliance interception model and the compliance expert model, the remittance application data are subjected to clearing operation processing, otherwise, when the remittance application data accord with the interception conditions of the compliance interception model or the compliance expert model, risks are prompted, and at the moment, a manual auditing stage is started to ensure the remittance safety. The interception condition herein is, for example, information that the remittance currency is U.S. dollars and the remittance statement contains HAMAS (Hamas), and further, for example, the remittance currency is U.S. dollars and the payee's account opening country is RU (Russia).
The risk of remittance applications meeting the intercepting conditions can be prompted through the application compliance intercepting model. Specifically, the application compliance interception model can be trained in advance through the following modes:
obtaining historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical transferor information, historical transferor appendage information, and historical transferee information; performing feature extraction processing on the historical remittance application data; and training the application compliance interception model according to the historical remittance application data and the historical remittance feedback data after the characteristic extraction processing.
That is, the application compliance intercept model relies on machine learning, trained with a large number of historical remittance applications and their feedback information. The application compliance interception model is trained by acquiring historical remittance application data, extracting characteristic values such as a receiver account number, a receiver name, a receiver address, a remitter account number, a remitter name, a remitter address and a remitter postscript and combining corresponding feedback information of the historical remittance application data.
After the application of the compliance interception model training is completed, the account number, name, address and postscript of the remitter are input, and then the following are correspondingly output: intercept/no intercept, or hit/miss, indicates a pass or fail audit of the money transfer application.
Through the application compliance interception model, remittance applications meeting conditions can be intercepted, and workers are prompted to conduct manual check to avoid remittance risks.
To further reduce the risk of remittance, the remittance application of the embodiments of the present invention also requires an audit by the application compliance expert model. The application compliance expert model may be generated as follows: intercepting keywords are respectively set for the information of the sender, the information of the epilogue of the sender, the money transfer currency and the information of the receiver according to preset rules so as to generate an application compliance expert model. The predetermined rules may be anti-money laundering rules, for example, if the money transfer currency in a money transfer application is U.S. dollars, and the transferor's statement contains HAMAS, then the money transfer application meets the interception requirements.
Specifically, matching remittance application data with an application compliance expert model; when the interception keyword is matched, determining that the remittance application data meets the interception condition; intercept information is generated corresponding to the money transfer application data to alert the worker that the money transfer application is at risk.
In the embodiment of the invention, the machine learning model (such as the customer attribute model and the application compliance interception model) is used for deeply learning the non-compliant transactions in the historical remittance application and mining information main bodies such as the epilogue, the username and the like, so that the abnormal remittance application can be accurately identified, and the auditing efficiency is improved. Meanwhile, keyword information is preset according to expert experience by applying for a compliance expert model, keywords are accurately matched according to cross-border remittance information, if the matching is successful, an alarm is given, the situation that the keyword needs to be intercepted is shown, and the auditing efficiency can be further improved.
Embodiments of the present invention also provide a remittance data processing apparatus, which may preferably be used to implement the methods of the above embodiments. Fig. 2 is a block diagram of the apparatus, and as shown in fig. 2, the apparatus includes: a data receiving unit 21, a client attribute judging unit 22, an interception condition judging unit 23, and a data processing unit 24, wherein:
a data receiving unit 21, configured to receive remittance application data from a client, where the remittance application data at least includes: transferor information and transferee information;
a customer attribute judging unit 22, configured to judge whether the payee information conforms to a predetermined customer attribute according to a pre-trained customer attribute model;
an interception condition determining unit 23, configured to determine, in response to that the payee information conforms to a predetermined customer attribute, whether the remittance application data conforms to an interception condition according to a preset application interception model;
and a data processing unit 24 for performing a clearing operation process on the remittance application data in response to the remittance application data failing to meet the interception condition.
The received remittance application is checked by the client attribute judging unit 22 according to a pre-trained client attribute model and the interception condition judging unit 23 according to a pre-set application interception model, so that the client attribute of the remittee can be judged, the remittance application which does not accord with the remittance condition can be intercepted, and when the remittee information accords with the preset client attribute (such as an individual client) and the remittance application does not accord with the interception condition, the remittance application is subjected to clearing operation processing, namely, the remittance application is subjected to remittance route selection, and finally the remittance application is sent to a preset bank.
The application interception model specifically includes: the remittance request data is judged by the interception condition judging unit 23 according to the interception conditions of the application compliance interception model and the application compliance expert model, and when the remittance request data does not accord with the interception conditions of the application compliance interception model and the application compliance expert model, the remittance request data is judged by the interception condition judging unit 23 to not accord with the interception conditions, which indicates that the remittance request data passes the audit, and a subsequent remittance process can be performed.
As shown in fig. 3, the above apparatus further includes: and a customer attribute model training unit 27 for training the customer attribute model. As shown in fig. 4, the customer attribute model training unit 27 includes: a historical data obtaining module 271, a similar word quantifying module 272 and a client attribute model training module 273, wherein:
a historical data acquisition module 271 for acquiring historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical payee information;
a similar word quantization module 272, configured to perform similar word quantization processing on the historical payee information;
a customer attribute model training module 273, configured to train the customer attribute model according to the historical payee information and the historical remittance feedback data after similar word quantization processing, where the customer attribute includes: for both public and individual customers.
With continued reference to fig. 3, the apparatus further comprises: and the application compliance interception model training unit 25 is used for training the application compliance interception model. As shown in fig. 5, the application for the compliant interception model training unit 25 specifically includes: a historical data acquisition module 251, a feature extraction module 252, and an application compliance interception model training module 253, wherein:
a historical data retrieval module 251 for retrieving historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical transferor information, historical transferor appendage information, and historical transferee information;
a feature extraction module 252, configured to perform feature extraction processing on the historical remittance application data;
and an application compliance interception model training module 253 for training the application compliance interception model according to the historical remittance application data and the historical remittance feedback data after feature extraction processing.
Further, as shown in fig. 3, the apparatus further includes: the application compliance expert model generating unit 26 is configured to set interception keywords for the remitter information, the remitter epilog information, the remittance currency, and the remitter information according to predetermined rules, respectively, to generate an application compliance expert model. The predetermined rule may be an anti-money laundering rule.
In actual operation, the interception condition judgment unit includes: the system comprises a first interception condition judgment module and a second interception condition judgment module, wherein the first interception condition judgment module is used for judging whether the remittance application data meet the interception conditions according to the application compliance expert model, and the second interception condition judgment module is used for judging whether the remittance application data meet the interception conditions according to the application compliance interception model.
Fig. 6 is a block diagram illustrating a structure of the first interception condition determining module 231, and as shown in fig. 6, the first interception condition determining module 231 includes: matching operation submodule 2311, interception condition determining submodule 2312 and interception information generating submodule 2313, wherein:
a matching operation sub-module 2311 for matching the remittance application data with the application compliance expert model;
an interception condition determining sub-module 2312, configured to determine that the remittance application data meets an interception condition in response to the matching result being that the interception keyword is matched;
intercept information generation submodule 2313 generates intercept information corresponding to the money transfer application data.
For specific execution functions of the units, the modules, and the sub-modules, reference may be made to the description of the method embodiment, and details are not described here again.
In a specific implementation process, the units, the modules, and the sub-modules may be arranged singly or in combination, but the invention is not limited thereto.
For a better understanding of embodiments of the present invention, an example is given below.
Fig. 7 is a block diagram of a cross-border remittance intelligent auditing system based on machine learning according to an embodiment of the present invention, as shown in fig. 7, the system includes: the system comprises a client 1, a channel terminal 2 (such as a personal internet bank, a mobile phone bank and an intelligent terminal), a remittance auditing system 3, an artificial intelligence platform 4, a big data platform 5 and a clearing platform 6. The client 1 is connected with the channel terminal 2, the channel terminal 2 is connected with the remittance auditing system 3, the remittance auditing system 3 is connected with the artificial intelligent platform 4, the big data platform 5 is connected with the remittance auditing system 3 and the artificial intelligent platform 4, the remittance auditing system 3 is connected with the settlement platform 6, wherein:
the client 1 is used for realizing the client to submit the remittance application data.
The channel terminal 2 is responsible for processing remittance application data filled by the customer and sending the remittance application to the remittance auditing system 3.
The remittance auditing system 3 is responsible for receiving remittance applications, calling a customer attribute intelligent model (such as the customer attribute model) provided by the artificial intelligence platform, a remittance application compliance interception model (such as the application compliance interception model) and calling a remittance application compliance interception expert rule model (such as the application compliance expert model) to realize intelligent auditing treatment of the remittance applications.
And the artificial intelligence platform 4 is responsible for constructing a customer attribute intelligent model and a remittance application compliance interception model, evaluating the model, issuing the model and self-learning the model, and providing customer attribute intelligent model service and remittance application compliance interception model service.
The big data platform 5 is responsible for storing remittance application data (e.g., remitter information).
And the clearing platform 6 is responsible for supplementing the remittance information of the remittance application and sending the remittance application to the adversary.
FIG. 8 is a block diagram of an example architecture of the artificial intelligence platform 4 in an embodiment of the present invention, in this example, illustrating an architecture for implementing the customer attribute intelligence model functionality.
As shown in fig. 8, the artificial intelligence platform 4 specifically includes: the model building module 41, the model self-learning module 42 and the intelligent service module 43, the model building module 41 is connected with the model self-learning module 42, the model self-learning module 42 is connected with the intelligent service module 43, wherein:
the model building module 41 is responsible for performing similar word quantization processing on the account name and address information of the payee of the historical remittance, and building a customer attribute classification learning model.
And the model self-learning module 42 is responsible for analyzing and comparing the actual feedback results of the account names and the address information of the increment payee with the learning model, retraining the learning model, performing increment self-learning updating and improving the identification accuracy of the model.
And the intelligent service module 43 is responsible for providing a service for intelligently identifying the client attribute, and the clearing platform identifies the client attribute type by calling the service after receiving the remittance application to realize the automatic identification function.
The remittance application compliance interception expert rule model may be used for anti-money laundering identification, and fig. 9 is a block diagram illustrating an example structure of the remittance application compliance interception expert rule model, in which the model performs expert rule determination based on existing business characteristics, and is an expert model intercepting an unregulated remittance application according to rules set by a remittance currency, a remittance type, a remitter username, a remitter address, a payee username, a payee country, and a remitter caption. In actual operation, the remittance application compliance interception expert rule model can flexibly adjust the threshold value of each rule in real time (for example, in the anti-money laundering identification, the threshold value is A, and in other rule identifications, other threshold values can be set), so as to achieve effective compliance interception effect.
As shown in fig. 9, the remittance application compliance intercept expert rules model includes: an expert rule parameter setting module 51, a rule judging module 52 and a result processing module 53, wherein:
the expert rule parameter setting module 51 is configured to set a hit rule (i.e., an interception rule) according to a remittance application type, a remittance currency, a recipient name, a remitter epilogue, and the like. For example, if the money transfer currency in a money transfer application is U.S. dollars and the written sentence contains HAMAS, then the money transfer application complies with the hit rules, i.e., the money transfer application is intercepted.
The rule judging module 52 is responsible for matching the hit rule in the expert rule parameter setting module 51 according to each information in the remittance application.
And a result processing module 53 for processing the remittance application according to the matching result.
Fig. 10 is a flow chart of auditing based on the system shown in fig. 7, according to an embodiment of the present invention, after receiving a remittance instruction from the client 1, the channel terminal 2 sends the remittance instruction to the remittance auditing system 3, the remittance auditing system 3 invokes a customer attribute intelligent model of the artificial intelligent platform 4 to identify whether the customer attribute is public or private, and automatically intercepts the remittance instruction according to the type of the customer attribute; then, continuously calling a remittance application compliance interception model of the artificial intelligent platform 4 to identify whether the remittance instruction hits the rules, and simultaneously calling a remittance application compliance interception expert rule model to identify whether the remittance instruction hits the rules, if one party hits, handing the remittance instruction to a worker for processing; if none are hit, a money transfer instruction is sent to the clearance platform 6.
As shown in fig. 10, the specific auditing process includes:
step 1001, the channel terminal receives the remittance instruction of the customer and sends the remittance instruction to the remittance auditing system.
Step 1002, the remittance auditing system calls a customer attribute intelligent identification model provided by an artificial intelligent platform to identify the customer attribute of the name and address of the recipient of the remittance instruction.
Step 1003, if the client attribute of the receiver corresponding to the remittance instruction is identified as a 'to public' client, automatically intercepting the remittance instruction, delivering the remittance instruction to a teller for processing, and manually confirming the type of the receiver again; if it is an "individual" customer, the next money transfer application compliance intercept process is performed.
Step 1004, obtain the remittance order identified as "personal" customer and manually review the account number, name, address and postscript of the remitter of the remittance order to the public customer after release.
Step 1005, calling a remittance application compliance interception model provided by the artificial intelligence platform, and auditing the account number, name, address and postscript of the remitter. If so, the money transfer instruction is intercepted and manually processed. For example, a remittance application originated from a domestic client and remitted to the asian region of industrial and silver, the remittance application is processed by a worker if the remittance instruction has a recipient name containing HAMAS and is recognized as a compliance hit by calling a remittance application compliance interception model.
Step 1006, calling the remittance application compliance interception expert rule model, and auditing the account number, name, address and epilogue of the remitter. If so, the money transfer instruction is intercepted and processed by the staff member. For example, the hit rule is preset such that the money transfer application currency is U.S. dollar and the payee account opening country is RU, and when the currency of the foreign money transfer application originated by the domestic client is U.S. dollar and the payee account opening country is RU, the hit expert rule intercepts the money transfer instruction for processing by the staff.
Step 1007, if neither step 1005 nor 1006 hit, then a money transfer instruction is sent to the clearing platform.
Fig. 11 is a flow chart of the clearing process of clearing platform 6 according to an embodiment of the present invention, as shown in fig. 11, the flow chart includes:
step 1101, the clearing platform 6 receives the remittance message sent by the remittance auditing system 3, and selects the message to be sent.
Step 1102, the clearing platform performs a sending process to judge whether the sending is direct or not.
Step 1103, the clearing platform performs routing on the remittance instruction, and finally sends the message to the adversary.
In actual operation, after receiving cross-border remittance and payment application initiated by an individual customer through electronic channels such as an internet bank, a mobile phone bank, an intelligent terminal and the like, the clearing platform determines whether the remittance of a payee-based enterprise needs to be remittance under a capital item. Because the existing system can not identify the customer attribute of the payee, the business personnel can only select whether to pass or not after judging. Through the machine learning model in the embodiment of the invention, the client attribute of the receiver can be intelligently judged, and the remittance application compliance interception can be simultaneously carried out, so that the accurate prompt of the personal to the automatic release of the trans-border remittance and the risk of remittance instruction can be realized, the automatic processing capacity of a business operation system can be improved, the remittance risk can be reduced, the business cost related to business processing can be reduced, and the profit rate can be ensured.
FIG. 12 is a schematic diagram of an electronic device according to an embodiment of the invention. The electronic device shown in fig. 12 is a general-purpose data processing apparatus comprising a general-purpose computer hardware structure including at least a processor 1201 and a memory 1202. The processor 1201 and the memory 1202 are connected by a bus 1203. The memory 1202 is adapted to store one or more instructions or programs executable by the processor 1201. The one or more instructions or programs are executed by processor 1201 to perform the steps in the money transfer data processing method described above.
The processor 1201 may be a single microprocessor or a set of one or more microprocessors. Thus, the processor 1201 implements processing of data and control of other devices by executing commands stored in the memory 1202 to perform the method flows of embodiments of the present invention as described above. The bus 1203 connects the above components together, as well as connecting the above components to a display controller 1204 and a display device and input/output (I/O) device 1205. Input/output (I/O) devices 1205 may be a mouse, keyboard, modem, network interface, touch input device, motion-sensing input device, printer, and other devices known in the art. Typically, an input/output (I/O) device 1205 is connected to the system through an input/output (I/O) controller 1206.
Memory 1202 may store, among other things, software components such as an operating system, communication modules, interaction modules, and application programs. Each of the modules and applications described above corresponds to a set of executable program instructions that perform one or more functions and methods described in embodiments of the invention.
Embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the money transfer data processing method described above.
In summary, the embodiments of the present invention utilize machine learning technology to realize personal cross-border automatic checking of remittance and accurate prompt of remittance instruction risk, and can solve the problems that the compliance checking service of remittance under the current suspected capital item is large in labor consumption, and the system cannot judge whether the client attribute of the recipient is personal or public, and the remittance data processing scheme provided by the embodiments of the present invention has the following advantages:
1. the manual operation link is greatly reduced, manual processing errors are reduced, automatic and intelligent processing of the service is realized, and an intelligent operation process is promoted;
2. the automatic processing capacity of the banking operation system can be greatly improved, the business cost related to business processing is continuously reduced, and the profit margin is guaranteed.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
The terms "comprises," "comprising," or any other variation thereof, in the embodiments of this specification are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.
Claims (14)
1. A method of remittance data processing, the method comprising:
receiving remittance application data from a client, the remittance application data comprising at least: transferor information and transferee information;
judging whether the payee information accords with the preset customer attribute according to a pre-trained customer attribute model;
responding to the information of the payee meeting the predetermined customer attribute, and judging whether the remittance application data meets the interception condition according to a preset application interception model;
and in response to the remittance application data not meeting the interception condition, performing clearing operation processing on the remittance application data.
2. The remittance data processing method according to claim 1, wherein the application intercept model comprises: applying for a compliance intercept model and applying for a compliance expert model, the remittance application data not meeting the intercept condition comprising:
the remittance application data does not meet the interception conditions of the application compliance interception model and the application compliance expert model.
3. The remittance data processing method of claim 2, wherein the application compliance intercept model is trained by:
obtaining historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical transferor information, historical transferor appendage information, and historical transferee information;
performing feature extraction processing on the historical remittance application data;
and training the application compliance interception model according to the historical remittance application data and the historical remittance feedback data after the characteristic extraction processing.
4. The remittance data processing method according to claim 2, wherein the application compliance expert model is generated by:
intercepting keywords are respectively set for the information of the sender, the information of the epilogue of the sender, the money transfer currency and the information of the receiver according to preset rules so as to generate an application compliance expert model.
5. The remittance data processing method according to claim 4, wherein determining whether the remittance application data meets an interception condition based on the application compliance expert model comprises:
matching the remittance application data with the application compliance expert model;
in response to the matching result being that the interception keyword is matched, determining that the remittance application data meets the interception condition;
generating interception information corresponding to the money transfer application data.
6. The remittance data processing method of claim 1, wherein the customer property model is trained by:
obtaining historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical payee information;
carrying out similar word quantitative processing on the historical payee information;
training the customer attribute model according to the historical payee information and the historical remittance feedback data after the similar words are subjected to quantization processing, wherein the customer attributes comprise: for both public and individual customers.
7. A remittance data processing apparatus, comprising:
a data receiving unit for receiving remittance application data from a client, the remittance application data comprising at least: transferor information and transferee information;
the customer attribute judging unit is used for judging whether the payee information accords with the preset customer attribute according to a pre-trained customer attribute model;
the intercepting condition judging unit is used for responding to the condition that the payee information accords with the preset customer attribute and judging whether the remittance application data accords with the intercepting condition according to a preset application intercepting model;
and the data processing unit is used for responding to the remittance application data which does not meet the interception condition and performing clearing operation processing on the remittance application data.
8. The remittance data processing apparatus according to claim 7, wherein the application intercept model comprises: applying for a compliance interception model and applying for a compliance expert model, the interception condition determining unit determining that the remittance application data does not meet an interception condition includes:
the remittance application data does not meet the interception conditions of the application compliance interception model and the application compliance expert model.
9. The remittance data processing apparatus according to claim 8, further comprising:
an application compliance interception model training unit for training the application compliance interception model,
the application compliance interception model training unit specifically comprises:
a historical data acquisition module for acquiring historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical transferor information, historical transferor appendage information, and historical transferee information;
a feature extraction module for performing feature extraction processing on the historical remittance application data;
and the application compliance interception model training module is used for training the application compliance interception model according to the historical remittance application data and the historical remittance feedback data after feature extraction processing.
10. The remittance data processing apparatus according to claim 8, further comprising:
and the application compliance expert model generating unit is used for respectively setting intercepting keywords for the remitter information, the remitter appendix information, the remittance currency and the remittee information according to a preset rule so as to generate an application compliance expert model.
11. The remittance data processing apparatus according to claim 10, wherein the interception condition determining unit includes:
a first interception condition judgment module for judging whether the remittance application data meets the interception condition according to the application compliance expert model,
the first interception condition judgment module comprises:
a matching operation sub-module for matching the remittance application data with the application compliance expert model;
the intercepting condition determining submodule is used for determining that the remittance application data meets the intercepting condition in response to the matching result that the intercepting keyword is matched;
and the interception information generation submodule is used for generating interception information corresponding to the remittance application data.
12. The remittance data processing apparatus according to claim 7, further comprising:
a customer attribute model training unit for training the customer attribute model,
the customer attribute model training unit includes:
a historical data acquisition module for acquiring historical remittance data, the historical remittance data comprising: historical remittance application data and historical remittance feedback data, the historical remittance application data including at least: historical payee information;
the similar word quantization module is used for carrying out similar word quantization processing on the historical payee information;
a customer attribute model training module, configured to train the customer attribute model according to the historical payee information and the historical remittance feedback data after similar word quantization processing, where the customer attribute includes: for both public and individual customers.
13. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the steps of the remittance data processing method according to any one of claims 1-6 are performed by the processor when executing the program.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the remittance data processing method according to any one of claims 1 to 6.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111178914A (en) * | 2019-12-31 | 2020-05-19 | 中国银行股份有限公司 | Method, device, equipment and storage medium for identifying splitting and purchasing behavior |
| CN111260363A (en) * | 2020-01-14 | 2020-06-09 | 上海和数软件有限公司 | Public benefit fund supervision method, device, equipment and medium based on block chain |
| CN111383094A (en) * | 2020-03-06 | 2020-07-07 | 深圳前海微众银行股份有限公司 | Product service full-chain driving method, equipment and readable storage medium |
| CN112712429A (en) * | 2020-12-28 | 2021-04-27 | 中电金信软件有限公司 | Remittance service auditing method, remittance service auditing device, computer equipment and storage medium |
| CN117436820A (en) * | 2023-12-15 | 2024-01-23 | 广州敏行数字科技有限公司 | Control method and system based on artificial intelligence |
| CN117436820B (en) * | 2023-12-15 | 2024-04-30 | 广州敏行数字科技有限公司 | Control method and system based on artificial intelligence |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101504753A (en) * | 2009-03-27 | 2009-08-12 | 中国工商银行股份有限公司 | Packet processing method, apparatus and system |
| CN101957974A (en) * | 2009-07-15 | 2011-01-26 | 中国工商银行股份有限公司 | Multi-payment and clearing system supporting remittance processing device and method |
| KR20160115188A (en) * | 2015-03-26 | 2016-10-06 | 네이버 주식회사 | Transaction processing for direct remittance using user id |
| CN109767327A (en) * | 2018-12-20 | 2019-05-17 | 平安科技（深圳）有限公司 | Customer information acquisition and its application method based on anti money washing |
| CN109767322A (en) * | 2018-12-20 | 2019-05-17 | 平安科技（深圳）有限公司 | Suspicious transaction analysis method, apparatus and computer equipment based on big data |
| CN110020938A (en) * | 2019-01-23 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Exchange information processing method, device, equipment and storage medium |
| CN110050286A (en) * | 2016-10-20 | 2019-07-23 | 三星电子株式会社 | System and method for mobile wallet remittance |
| CN110135829A (en) * | 2019-05-10 | 2019-08-16 | 中国银行股份有限公司 | It directly keeps accounts the data processing method and device of judgement for cross-border inward remittance |
-
2019
- 2019-09-23 CN CN201910899169.8A patent/CN110619574A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101504753A (en) * | 2009-03-27 | 2009-08-12 | 中国工商银行股份有限公司 | Packet processing method, apparatus and system |
| CN101957974A (en) * | 2009-07-15 | 2011-01-26 | 中国工商银行股份有限公司 | Multi-payment and clearing system supporting remittance processing device and method |
| KR20160115188A (en) * | 2015-03-26 | 2016-10-06 | 네이버 주식회사 | Transaction processing for direct remittance using user id |
| CN110050286A (en) * | 2016-10-20 | 2019-07-23 | 三星电子株式会社 | System and method for mobile wallet remittance |
| CN109767327A (en) * | 2018-12-20 | 2019-05-17 | 平安科技（深圳）有限公司 | Customer information acquisition and its application method based on anti money washing |
| CN109767322A (en) * | 2018-12-20 | 2019-05-17 | 平安科技（深圳）有限公司 | Suspicious transaction analysis method, apparatus and computer equipment based on big data |
| CN110020938A (en) * | 2019-01-23 | 2019-07-16 | 阿里巴巴集团控股有限公司 | Exchange information processing method, device, equipment and storage medium |
| CN110135829A (en) * | 2019-05-10 | 2019-08-16 | 中国银行股份有限公司 | It directly keeps accounts the data processing method and device of judgement for cross-border inward remittance |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111178914A (en) * | 2019-12-31 | 2020-05-19 | 中国银行股份有限公司 | Method, device, equipment and storage medium for identifying splitting and purchasing behavior |
| CN111178914B (en) * | 2019-12-31 | 2023-09-22 | 中国银行股份有限公司 | Method, device, equipment and storage medium for identifying split purchase and sink behaviors |
| CN111260363A (en) * | 2020-01-14 | 2020-06-09 | 上海和数软件有限公司 | Public benefit fund supervision method, device, equipment and medium based on block chain |
| CN111383094A (en) * | 2020-03-06 | 2020-07-07 | 深圳前海微众银行股份有限公司 | Product service full-chain driving method, equipment and readable storage medium |
| CN112712429A (en) * | 2020-12-28 | 2021-04-27 | 中电金信软件有限公司 | Remittance service auditing method, remittance service auditing device, computer equipment and storage medium |
| CN117436820A (en) * | 2023-12-15 | 2024-01-23 | 广州敏行数字科技有限公司 | Control method and system based on artificial intelligence |
| CN117436820B (en) * | 2023-12-15 | 2024-04-30 | 广州敏行数字科技有限公司 | Control method and system based on artificial intelligence |
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