Customer information intelligent acquisition method and system

Abstract

The invention relates to a customer information intelligent acquisition method and system. The method comprises the following steps: acquiring customer information through a big data platform, initiating customer information early warning, and executing customer identity recognition operation; carrying out differentiation comparison on the customer information, determining customer information needing to be updated, and carrying out updating operation on the customer information; and asynchronously consuming the client update information, and verifying the client update information based on the large model. A large amount of data from different sources is integrated through a big data platform, automatic and efficient processing and analysis of large-scale customer information are realized by using Sqoop, HDFS and a data warehouse, regular maintenance and updating of the customer information are realized through intelligent early warning and information acquisition, completeness and effectiveness of the customer information are guaranteed, and the customer information management efficiency is improved. And the reliability and accuracy of customer information are improved through data matching and a large model.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for intelligently collecting customer information. Background Art

[0002] Banks should establish an anti-money laundering information system that covers all businesses (including products and services) and customers, collect and record the information required for money laundering risk management in a timely, accurate and complete manner, and identify, evaluate, monitor and report money laundering risks. The anti-money laundering system includes but is not limited to supporting customer identification, registration, storage, query and use of anti-money laundering information such as customer identity information and transaction records.

[0003] Through smart push and channel message notifications, customer information can be regularly updated and maintained, which can better protect the integrity, validity and accuracy of the identity information of customers and their beneficial owners. Summary of the invention

[0004] The present invention realizes regular maintenance and update of customer information through intelligent early warning and information collection, ensures the integrity and effectiveness of customer information, and uses a large model to verify customer information to ensure the reliability of customer information.

[0005] To achieve the above objectives, the technical solutions adopted by the present invention include:

[0006] A method for intelligently collecting customer information, characterized by comprising:

[0007] Step S1. Collect customer information through the big data platform, initiate customer information warning, and perform customer identity recognition operations;

[0008] Step S2. Perform differential comparison on the customer information, determine the customer information that needs to be updated, and update the customer information;

[0009] Step S3: asynchronously consume customer update information and verify the customer update information based on the big model.

[0010] Furthermore, the collecting of customer information through the big data platform, initiating customer information warning, and performing customer identity recognition operations include:

[0011] Collect data from relational databases of various business systems through Sqoop;

[0012] Store the collected data in HDFS for distributed storage;

[0013] Use the Hive data warehouse tool to write HQL to generate MR tasks, process the data offline, and label the data that meets the preset rules;

[0014] Process all data and filter by rules, and provide a query interface to the outside world through Elasticsearch;

[0015] Perform data matching according to preset rules to obtain customer information that requires early warning;

[0016] Initiate customer information warnings and update reminders through interface query and silent control.

[0017] Furthermore, the data matching according to the preset rules to obtain the customer information that needs early warning includes:

[0018] Perform data cleansing and labeling on customer information, set warning labels for customers who meet preset rules, and display them in a differentiated manner to determine customer information that requires warning.

[0019] Furthermore, the initiation of customer information warning and update reminder through interface query and silent control includes:

[0020] Customized push notifications are provided for different anti-money laundering risk levels and customer risk levels, through SMS, WeChat, and mini-program channels;

[0021] Jump to the customer identity identification operation page through the short link.

[0022] Furthermore, the step of performing differential comparison on the customer information, determining the customer information that needs to be updated, and performing an update operation on the customer information includes:

[0023] Confirm and update customer information on the H5 interface, query big data warning information, display the data corresponding to the big data warning information and the data warehouse difference data in a differentiated manner, and determine the customer information that needs to be updated;

[0024] Update the customer-filled information to the core system, and verify and record the information through the core system.

[0025] Furthermore, the asynchronous consumption of customer update information verifies the customer update information based on the big model, including:

[0026] Query the external system to obtain customer-related information and match the customer's updated information for verification;

[0027] Through large model rule processing, the collected customer information is compared with the customer's reported information to determine whether it complies with the preset rules;

[0028] The bank occupation code is output based on the big model, and further operations are performed on the customer's credit limit according to the bank occupation code.

[0029] The present invention also relates to a system for intelligently collecting customer information, which is characterized by comprising:

[0030] The early warning module is used to collect customer information through the big data platform, initiate customer information early warning, and perform customer identity recognition operations;

[0031] The update module is used to compare customer information, determine the customer information that needs to be updated, and update the customer information;

[0032] The verification module is used to asynchronously consume customer update information and verify the customer update information based on the big model.

[0033] The present invention also relates to a computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and the computer program implements the above method when executed by a processor.

[0034] The present invention also relates to an electronic device, characterized in that it comprises a processor and a memory;

[0035] The memory is used to store program instructions;

[0036] The processor is used to execute the above method by calling program instructions.

[0037] The present invention also relates to a computer program product, comprising a computer program and / or instructions, characterized in that the computer program and / or instructions implement the steps of the above method when executed by a processor.

[0038] The beneficial effects of the present invention are:

[0039] By adopting the method and system for intelligent collection of customer information described in the present invention, a large amount of data from different sources is integrated through a big data platform, so as to realize efficient processing and analysis of large-scale customer information, use Sqoop to realize automatic data collection, use HDFS to meet the storage requirements of large-scale data, use data warehouse tools to realize efficient data processing, use big models to process and analyze customer information, improve the consistency and accuracy of customer information through data matching and big models, and realize automatic customer information collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0041] Figure 1 A schematic diagram of a method for intelligently collecting customer information according to the present invention;

[0042] Figure 2A schematic diagram of the system structure of intelligent collection of customer information according to the present invention;

[0043] Figure 3 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. It should be understood that the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments, and these embodiments are only used to illustrate the present application and are not used to limit the scope of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0045] In the present invention, the following techniques can be used:

[0046] Operating system: open platform x86 architecture;

[0047] Basic framework: Spring Boot;

[0048] Decision engine: risk decision engine;

[0049] Web server: Spring Boot built-in tomcat;

[0050] Basic components (pluggable and extensible): service registration center (Eureka Server), configuration center (CloudConfig), log management (Agent+Kafka+ELK), link tracking (Brave+zipkin+Skywalking), monitoring and alarm (Prometheus+Grafana), continuous integration (Jenkins+kubernetes), data bus (Kafka), security monitoring, and others;

[0051] Database: Elasticsearch;

[0052] Middleware used: Tomcat, Redis.

[0053] like Figure 1 As shown, the embodiment of the present invention discloses a method for intelligently collecting customer information, and the steps of the method include:

[0054] Step S1. Collect customer information through the big data platform, initiate customer information warning, and perform customer identity recognition operations.

[0055] The big data platform combines PBOC reports, head office retail data, PBOC / Public Security document verification information, and customer cycle update information to remind customers to perform customer identity verification operations.

[0056] Specifically, the data processing process during the customer identification operation is carried out by collecting customer information through the big data platform, initiating customer information warnings, and executing customer identity identification operations, including:

[0057] Step S101, collecting data from relational databases of various business systems through Sqoop;

[0058] Step S102: Storing the collected data in HDFS for distributed storage;

[0059] Step S103: Use the Hive data warehouse tool to write HQL to generate MR tasks, process the data offline, and label the data that meets the preset rules;

[0060] Step S104: Process and filter all data according to rules, and provide a query interface to the outside through Elasticsearch;

[0061] Step S105: perform data matching according to preset rules to obtain customer information that requires early warning;

[0062] Step S106: Initiate customer information warning and update reminder through interface query and silent control.

[0063] In the embodiment of the present invention, by collecting data regularly through Sqoop and aggregating data from various business systems, customer information can be well collected, matching processing can be performed according to preset rules, customer information that needs to be warned can be obtained, and intelligent reminders for customers to update their personal information can be realized through interface query and silent control. Without the customer's awareness, data cleaning and label processing are performed on customer information, and corresponding warning labels are added to all customers who meet the conditions for differentiated display. Customers can automatically receive information reminders, and are actively reminded to update their information.

[0064] Furthermore, data matching is performed according to preset rules to obtain customer information that requires early warning, which also includes:

[0065] Perform data cleansing and labeling on customer information, set warning labels for customers who meet preset rules, and display them in a differentiated manner to determine customer information that requires warning.

[0066] Furthermore, customer information warnings and update reminders are initiated through interface query and silent control, which also includes:

[0067] Customized push notifications are provided for different anti-money laundering risk levels and customer risk levels, through SMS, WeChat, and mini-program channels;

[0068] Jump to the customer identity identification operation page through the short link.

[0069] The online banking department uses batch SMS, WeChat, and mini-programs to push messages for high, medium, and low-risk customers and corresponding information modification records, and can jump to the customer identity recognition operation page through a short link. This embodiment can push and remind targeted messages for different anti-money laundering risk levels, and regularly customize push reminders for customers who need to update their information.

[0070] Step S2: Perform differential comparison on the customer information, determine the customer information that needs to be updated, and perform an update operation on the customer information.

[0071] Specifically, the customer information is compared by differentiation, the customer information that needs to be updated is determined, and the customer information is updated, including:

[0072] Confirm and update customer information on the H5 interface, query big data warning information, display the data corresponding to the big data warning information and the data warehouse difference data in a differentiated manner, and determine the customer information that needs to be updated;

[0073] Update the customer-filled information to the core system, and verify and record the information through the core system.

[0074] In this embodiment, the H5 interface is used to confirm and update customer information, query big data warning information, and display the difference data between the People's Bank of China, the head office, and the data warehouse in a differentiated manner, so that customers can modify and submit targeted data. In the specific implementation process, if it involves updating customer ID information, it is necessary to retain the front and back photos of the customer's ID card, synchronize the photos to the image platform, and update them to the core system at the same time, so that they can be checked and left traces through the core system.

[0075] Step S3: asynchronously consume customer update information and verify the customer update information based on the big model.

[0076] Specifically, asynchronously consume customer update information and verify the customer update information based on the big model, including:

[0077] Query the external system to obtain customer-related information and match the customer's updated information for verification;

[0078] Through large model rule processing, the collected customer information is compared with the customer's reported information to determine whether it complies with the preset rules;

[0079] The bank occupation code is output based on the big model, and further operations are performed on the customer's credit limit according to the bank occupation code.

[0080] In the embodiment of the present invention, asynchronous consumption of customer update information, risk decision-making through data matching and data collection process of various dimensions to verify the accuracy and credibility of customer update information, risk decision engine based on the big model can be continuously improved and learned to ensure that customer information can be updated to the core system. Among them, the risk decision engine mainly has the following stages:

[0081] Collect data, query peripheral systems (big data, electronic approval system, core system, credit reporting system) to obtain relevant information, and use auxiliary information to the maximum extent possible to match the reliability of customer-reported information;

[0082] Use big model rules to process and calculate a large amount of information collected by objective institutions and information reported by customers. Configure big model rules to ensure the reliability of information calculation, keep learning, and ensure the credibility of customer reported information through big models.

[0083] Highly credible customer information is synchronously updated to the core system to realize regular customer information collection and ensure information reliability. At the same time, the bank occupation code is output according to the big model, and the next step is to determine the customer's credit limit based on the bank occupation code.

[0084] like Figure 2 As shown, the embodiment of the present invention discloses a system for intelligently collecting customer information, and its structure includes:

[0085] The early warning module is used to collect customer information through the big data platform, initiate customer information early warning, and perform customer identity recognition operations;

[0086] The update module is used to compare customer information, determine the customer information that needs to be updated, and update the customer information;

[0087] The verification module is used to asynchronously consume customer update information and verify the customer update information based on the big model.

[0088] By using this system, the above-mentioned operation processing method can be executed and the corresponding technical effects can be achieved.

[0089] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the method in the above embodiment, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all the steps in the method in the above embodiment are implemented.

[0090] The embodiment of the present invention further provides an electronic device for executing the above method, as a device for implementing the method, and its structure is as follows: Figure 3 As shown, the electronic device 300 includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one. It is understandable that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the specific structure of the electronic device 300. In other embodiments of the present application, the electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. Optionally, the electronic device may also include a display screen 305 for displaying images, or receiving user operation instructions when necessary.

[0091] The processor 301 is applied in the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 304 may include a receiver and a transmitter. The transceiver 304 is applied in the embodiment of the present application to implement the function of the electronic device of the embodiment of the present application communicating with other devices when executed.

[0092] Processor 301 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0093] The processor 301 may also include one or more processing units, for example, the processor 301 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processor (NPU). Among them, different processing units may be independent devices or integrated into one or more processors. Among them, the controller may be the nerve center and command center of the electronic device 300. The controller may generate an operation control signal according to the instruction opcode and the timing signal to complete the control of fetching and executing instructions. A memory may also be set in the processor 301 for storing instructions and data. In some embodiments, the memory in the processor 301 is a high-speed cache memory. The memory may store instructions or data that the processor 301 has just used or circulated. If the processor 301 needs to use the instruction or data again, it may be directly called from the memory. Repeated access is avoided, the waiting time of the processor 301 is reduced, and the efficiency of the system is improved.

[0094] The processor 301 can run the method provided in the embodiment of the present application. The processor 301 can include different devices. For example, when a CPU and a GPU are integrated, the CPU and the GPU can cooperate to execute the method provided in the embodiment of the present application, for example, part of the algorithm in the method is executed by the CPU, and another part of the algorithm is executed by the GPU to obtain faster processing efficiency.

[0095] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0096] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or may include a high-speed random access memory, or may include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0097] Optionally, the memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the method provided in any embodiment of the present application.

[0098] The memory 303 can be used to store computer executable program codes, which include instructions. The processor 301 executes various functional applications and data processing of the electronic device 300 by running the instructions stored in the memory 303. The memory 303 may include a program storage area and a data storage area. Among them, the program storage area may store the code of the operating system, application program, etc. The data storage area may store data created during the use of the electronic device 300 (such as images, videos, etc. collected by the camera application), etc.

[0099] The memory 303 may also store one or more computer programs corresponding to the method provided in the embodiment of the present application. The one or more computer programs are stored in the above-mentioned memory 303 and are configured to be executed by the one or more processors 301. The one or more computer programs include instructions, and the above-mentioned instructions can be used to execute each step in the above-mentioned corresponding embodiment.

[0100] Of course, the code of the method provided in the embodiment of the present application can also be stored in an external memory. In this case, the processor 301 can run the code of the method stored in the external memory through the external memory interface, and the processor 301 can control the running process.

[0101] The display screen 305 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 300 may include 1 or N display screens 305, where N is a positive integer greater than 1. The display screen 305 can be used to display information input by a user or information provided to a user and various graphical user interfaces (GUI). For example, the display screen 305 can display photos, videos, web pages, or files.

[0102] The electronic device provided in the embodiment of the present application is applicable to any embodiment of the above method. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0105] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0107] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for intelligently collecting customer information, characterized in that: include: S1. Collect customer information through the big data platform, initiate customer information warning, and perform customer identity recognition operations; S2. Perform differential comparison on the customer information, determine the customer information that needs to be updated, and update the customer information; S3. Asynchronously consume customer update information and verify the customer update information based on the big model.

2. The method according to claim 1, characterized in that The above-mentioned collecting customer information through the big data platform, initiating customer information warning, and performing customer identity recognition operations include: Collect data from relational databases of various business systems through Sqoop; Store the collected data in HDFS for distributed storage; Use the Hive data warehouse tool to write HQL to generate MR tasks, process the data offline, and label the data that meets the preset rules; Process all data and filter by rules, and provide a query interface to the outside world through Elasticsearch; Perform data matching according to preset rules to obtain customer information that requires early warning; Initiate customer information warnings and update reminders through interface query and silent control.

3. The method according to claim 2, characterized in that The data matching according to the preset rules to obtain the customer information that needs warning includes: Perform data cleansing and labeling on customer information, set warning labels for customers who meet preset rules, and display them in a differentiated manner to determine customer information that requires warning.

4. The method according to claim 2, characterized in that The method of initiating customer information warning and update reminder through interface query and silent control includes: Customized push notifications are provided for different anti-money laundering risk levels and customer risk levels, through SMS, WeChat, and mini-program channels; Jump to the customer identity identification operation page through the short link.

5. The method according to claim 1, characterized in that The step of performing differential comparison on the customer information, determining the customer information that needs to be updated, and performing an update operation on the customer information includes: Confirm and update customer information on the H5 interface, query big data warning information, display the data corresponding to the big data warning information and the data warehouse difference data in a differentiated manner, and determine the customer information that needs to be updated; Update the customer-filled information to the core system, and verify and record the information through the core system.

6. The method according to claim 1, characterized in that The asynchronous consumption of customer update information and verification of the customer update information based on the big model include: Query the external system to obtain customer-related information and match the customer's updated information for verification; Through large model rule processing, the collected customer information is compared with the customer's reported information to determine whether it complies with the preset rules; The bank occupation code is output based on the big model, and further operations are performed on the customer's credit limit according to the bank occupation code.

7. A system for intelligent collection of customer information, characterized in that: include: The early warning module is used to collect customer information through the big data platform, initiate customer information early warning, and perform customer identity recognition operations; The update module is used to compare customer information, determine the customer information that needs to be updated, and update the customer information; The verification module is used to asynchronously consume customer update information and verify the customer update information based on the big model.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store program instructions; The processor is configured to execute the method according to any one of claims 1 to 6 by calling program instructions.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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