WO2022073519A1 - 虚拟银行开户方法、装置、设备和计算机存储介质 - Google Patents

虚拟银行开户方法、装置、设备和计算机存储介质 Download PDF

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Publication number
WO2022073519A1
WO2022073519A1 PCT/CN2021/122908 CN2021122908W WO2022073519A1 WO 2022073519 A1 WO2022073519 A1 WO 2022073519A1 CN 2021122908 W CN2021122908 W CN 2021122908W WO 2022073519 A1 WO2022073519 A1 WO 2022073519A1
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Prior art keywords
account opening
information
account
user
result
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PCT/CN2021/122908
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English (en)
French (fr)
Inventor
安凯旋
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深圳壹账通智能科技有限公司
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Publication of WO2022073519A1 publication Critical patent/WO2022073519A1/zh

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/13—File access structures, e.g. distributed indices
    • G06F16/137—Hash-based
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/40—Spoof detection, e.g. liveness detection
    • G06V40/45—Detection of the body part being alive

Definitions

  • the present application relates to the technical field of artificial intelligence, and in particular, to a virtual bank account opening method, apparatus, device and computer storage medium.
  • the main purpose of the present application is to provide a virtual bank account opening method, device, equipment and computer storage medium, aiming at solving the technical problem that the virtual bank cannot effectively identify the risks of account opening users.
  • the present application provides a method for opening a virtual bank account, and the method for opening a virtual bank account includes the following steps:
  • a corresponding account opening result is sent to the terminal according to the classification result, wherein the account opening result includes account opening allowed, pending review, and account opening not allowed.
  • the account opening information before the step of receiving the account opening information sent by the account opening user based on the terminal, the account opening information includes basic information and authorization information, including:
  • the account opening type is determined according to the account opening request, and the account opening information collection method corresponding to the account opening type is returned to the terminal.
  • the account opening information before the step of receiving the account opening information sent by the account opening user based on the terminal, the account opening information includes basic information and authorization information, including:
  • Model training is performed using the training samples to generate a preset classification model.
  • the step of sending the corresponding account opening result to the terminal according to the classification result it includes:
  • the account-opening information of the account-opening user is added to the abnormal information database
  • the preset classification model is corrected and trained by using the account opening information of the abnormal information database.
  • the step of sending the corresponding account opening result to the terminal according to the classification result includes:
  • the step of sending the result to be reviewed to the terminal if the classification result is a potential risk includes:
  • the account opening result is re-determined according to the due diligence result and sent to the terminal.
  • the step includes:
  • the transaction information is broadcast on the entire network in the preset blockchain, so that each block in the blockchain records the transaction information;
  • the present application also provides a virtual bank account opening device, the virtual bank account opening device includes:
  • a receiving module configured to receive account opening information sent by the account opening user based on the terminal, where the account opening information includes basic information and authorization information;
  • a processing module configured to perform quantitative processing on the account opening information by using a preset score card, and generate user characteristics of the account opening user
  • a classification module configured to input the user characteristics into a preset classification model, and generate a classification result of the account-opening user, wherein the classification result is divided into low risk, potential risk and high risk;
  • a sending module configured to send a corresponding account opening result to the terminal according to the classification result, wherein the account opening result includes account opening allowed, pending review and account opening not allowed.
  • the present application also provides a virtual bank account opening device
  • the virtual bank account opening device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
  • the computer program when executed by the processor, implements the steps of the method for opening a virtual bank account as described above.
  • the present application also provides a computer storage medium
  • a computer program is stored on the computer storage medium, and when the computer program is executed by the processor, the steps of the above-mentioned virtual bank account opening method are implemented.
  • a virtual bank account opening method, device, equipment, and computer storage medium proposed in the embodiments of the present application receive account opening information sent by an account opening user based on a terminal, where the account opening information includes basic information and authorization information;
  • the account opening information is quantified to generate the user characteristics of the account opening users;
  • the user characteristics are input into a preset classification model to generate the classification results of the account opening users, wherein the classification results are divided into low risk, potential risk and high risk risk; send the corresponding account opening result to the terminal according to the classification result, wherein the account opening result includes account opening allowed, pending review and not allowed to open an account, by changing the traditional method of reviewing account opening by bank staff to
  • the pre-trained preset classification model is reviewed, which can effectively identify the risk status of account holders and reduce the risk of virtual banks.
  • FIG. 1 is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application;
  • FIG. 2 is a schematic flowchart of the first embodiment of the method for opening a virtual bank account in the application
  • FIG. 3 is a schematic diagram of the registration process of this application.
  • This application provides a solution to enable the virtual bank account opening device to receive account opening information sent by the account opening user based on the terminal, and use the account opening information as the basis for the user to review the account opening.
  • the virtual bank account opening device performs quantitative processing on the account opening information to generate the user characteristics of the account opening users.
  • the user characteristics are input into a preset classification model to generate According to the classification result of the account opening user, the corresponding account opening result is sent to the terminal according to the classification result.
  • FIG. 1 is a terminal (also called a virtual bank account opening device) of the hardware operating environment involved in the solution of the embodiment of the present application, wherein the virtual bank account opening device may be composed of a separate virtual bank account opening device, or may be composed of Other devices are combined with the virtual bank account opening device) structural diagram.
  • the virtual bank account opening device may be composed of a separate virtual bank account opening device, or may be composed of Other devices are combined with the virtual bank account opening device) structural diagram.
  • the terminal in the embodiment of the present application may be a fixed terminal or a mobile terminal, such as a smart air conditioner with networking function, a smart light bulb, a smart power supply, a smart speaker, an autonomous vehicle, a PC (personal computer) personal computer, a smart phone, a tablet computer , e-book readers, portable computers, etc.
  • a smart air conditioner with networking function such as a smart air conditioner with networking function, a smart light bulb, a smart power supply, a smart speaker, an autonomous vehicle, a PC (personal computer) personal computer, a smart phone, a tablet computer , e-book readers, portable computers, etc.
  • the terminal may include: a processor 1001 , for example, a central processing unit (Central Processing Unit, CPU), a network interface 1004 , a user interface 1003 , a memory 1005 , and a communication bus 1002 .
  • the communication bus 1002 is used to realize the connection and communication between these components.
  • the user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may include a standard wired interface and a wireless interface (eg, wireless fidelity WIreless-FIdelity, WIFI interface).
  • the memory 1005 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory). memory), for example, disk storage.
  • the memory 1005 may also be a storage device independent of the aforementioned processor 1001 .
  • the terminal may also include a camera, an RF (Radio Frequency, radio frequency) circuit, a sensor, an audio circuit, and a WiFi module; an input unit, a display screen, and a touch screen; the optional network interfaces include WiFi, Bluetooth, Probes, etc.
  • sensors such as light sensors, motion sensors and other sensors.
  • the light sensor may include an ambient light sensor and a proximity sensor; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.
  • terminal structure shown in FIG. 1 does not constitute a limitation on the terminal, and may include more or less components than the one shown, or combine some components, or arrange different components.
  • the computer software product is stored in a storage medium (storage medium: also known as computer storage medium, computer medium, readable medium, readable storage medium, computer-readable storage medium or directly called medium, etc., storage medium It can be a non-volatile readable storage medium, such as RAM, magnetic disk, optical disc), including several instructions to make a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the application
  • the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a computer program.
  • the network interface 1004 is mainly used to connect to the background server and perform data communication with the background server;
  • the user interface 1003 is mainly used to connect to the client (client) and perform data communication with the client;
  • the processor 1001 can be used to call the computer program stored in the memory 1005, and execute the steps in the virtual bank account opening method provided by the following embodiments of the present application.
  • the method for opening a virtual bank account includes:
  • Step S30 Receive account opening information sent by the account opening user based on the terminal, where the account opening information includes basic information and authorization information.
  • the virtual bank account opening device receives the account opening information sent by the account opening user based on the terminal.
  • the virtual bank account opening device is usually the core system of the virtual bank, and the terminal can be a PC, a mobile phone, a dedicated bank counter, etc.
  • the account opening information includes There are two parts, one part is the information directly filled in by the user on the account opening page of the terminal and collected by means of ocr scanning or liveness detection through the camera. This part of the information becomes the basic information in this embodiment, and the other part is the virtual bank client device.
  • the account opening instructions displayed to the user on the registration interface and the authority to obtain the electronic contract are only one-step collection of user information from the external data interface and the cooperative data interface, and this part of information is referred to as authorization information in this embodiment.
  • step S30 it may further include:
  • Step S10 receiving an account opening request sent by the user based on the terminal
  • the virtual bank account opening device receives the account opening request sent by the user based on the terminal.
  • account opening There are many types of account opening in the virtual bank, such as debit account, credit account and loan account, etc., and the account opening request includes the account opening type specified by the user.
  • the above account types include debit accounts, credit accounts, and debit accounts.
  • step S10 the user needs to perform the registration operation in advance.
  • the registration instructions are displayed before the user submits the registration, so that the user is aware of the relevant terms.
  • the online electronic contract is carried out in the form of a message and a traditional signature as the appearance.
  • the step of completing the electronic contract is to obtain the user's authorization to further capture from the cooperation interface or external interface.
  • authorization information it also includes ID card scanning. For example, ocr scans the ID card to complete the real-name authentication. In order to confirm that the account holder and the ID card information are unified, the personal ID card is compared.
  • the camera is turned on to collect the account holder's information.
  • the image is compared with the preset face database, and further, in order to ensure that the image of the account holder is a real image rather than a photo, live detection is performed, that is, the account holder is required to turn his head and open his mouth when collecting the image of the account holder. , blinking and other actions, after the authentication and comparison, the information of the account holder is first collided with the data in the blacklist. If the account holder is in the blacklist, the user's further account opening operation is directly rejected. .
  • step S30 it may further include:
  • Step a1 preprocessing the user information in the preset user information database to generate training samples
  • the virtual bank account opening device preprocesses the user information in the preset user information database, generates training samples, and performs quantitative processing on the information, such as education information, records it as junior high school and high school in the training sample information, and quantifies the education information. After quantification, the features represented by numbers are formed.
  • the information of each dimension can be processed by the above method and can be in a certain range.
  • the preset scorecard can be constructed using statistical methods according to the user information database.
  • the establishment process of the scorecard model mainly includes: The process includes data acquisition, exploratory data analysis, and data preprocessing.
  • the user information database contains trustworthy user information and untrustworthy user information. To ensure the accuracy of subsequent model training results, both trustworthy user information and untrustworthy user information need to be selected.
  • Certain samples are combined as training samples, and some samples are reserved for model verification. Perform null value processing and normalization processing on the determined training samples; with regard to null value processing, specifically, after determining the training samples, even if the dimensions with serious information loss are filled, they will not have reference value in subsequent training. Therefore, This dimension is removed, and the empty values in the important dimension are filled, for example, the median value can be selected as the filling.
  • normalization specifically, unquantified variable selection, model development, model evaluation, model implementation, model testing, etc. in training samples.
  • the main steps of building a scorecard based on the summary of the establishment process of the above scorecard model may include: selecting modeling users; selecting modeling variables; setting modeling coefficients for each modeling variable; coefficients, and build a scorecard model in combination with preset modeling rules. Understandably, the modeling user refers to the user used to build the model. In order to ensure the accuracy of the scorecard model's prediction of new users, the modeling information base needs to be guaranteed.
  • Modeling variables are one or more variables selected from user variables.
  • the selection method of modeling variables can be: divide multiple modeling users into multiple groups of modeling samples according to certain rules, such as time rules, and then according to each user variable For the performance in multiple modeling samples, the variables that perform well in each group of modeling samples are selected from the user variables as modeling variables.
  • Step a2 using the training samples to perform model training to generate a preset classification model.
  • the virtual bank account opening device uses the training samples for model training to generate a preset sub-model, and after processing by the above method, the training samples can be used to construct a user classification model. Extract various dimensions in the training sample, such as income, education, mobile phone network access time, e-commerce platform refund rate, number of credit cards under the name, number of times of contract performance and other influencing factors together as the characteristics of the user classification model, using such as support vector machine (Support Vector Machine) , SVM), clustering algorithm (Clustering Algorithms) and other machine learning algorithms initially build a user classification model, and use other samples other than the training samples as verification data to verify the reliability of the user classification model.
  • support vector machine Small Vector Machine
  • SVM Support Vector Machine
  • Clustering Algorithms Clustering Algorithms
  • Step S20 Determine the account opening type according to the account opening request, and return the account opening information collection method corresponding to the account opening type to the terminal.
  • the virtual bank account opening device determines the account opening type according to the account opening request, and returns the account opening information collection method corresponding to the account opening type to the terminal. Due to the different account opening types, the examination conditions for the account opener are also different. The review required for opening a debit account may not be as strict as that of a credit account and a loan account. Reducing some unnecessary information filling and authentication steps can effectively improve the account opening efficiency of low-risk accounts such as debit accounts.
  • the terminal select the account or business type to open an account, and display different information collection pages, such as debit account, credit card account, and loan account. According to the possible risks of different accounts granted to users by the institution, the accounts are divided into low-risk accounts, that is, debit accounts.
  • Accounts and medium-risk accounts are credit card accounts, high-risk accounts such as loan accounts, and display different information collection pages respectively.
  • the server receives the personal information entered by the user based on the mobile terminal, and enters basic personal information such as mobile phone number and identity when opening a debit account.
  • basic personal information such as mobile phone number and identity
  • the input includes not only basic personal information, but also the company, income, social security, provident fund and other information.
  • additional asset information is included.
  • This step also includes the user's electronic signing, so that the customer knows the relevant terms, and obtains the user's authorization operation through the electronic signing, so as to carry out deeper information mining, such as the length of time the mobile phone number has been connected to the network, the number of credit cards under the name, and the performance of the contract. , purchase records of e-commerce accounts, refunds, consumption details, regular contacts on phone (whether the regular contacts are on the blacklist or untrustworthy list), and regular contacts on social applications (whether the regular contacts on social applications have a blacklist or untrustworthy list) medium) and other dimensions.
  • Step S40 using a preset scorecard to quantify the account opening information to generate user characteristics of the account opening user.
  • the virtual bank account opening device quantifies the account opening information to generate the user characteristics of the account opening user.
  • the account opening information is not only the information that the user fills in when registering or opening an account (basic information). ), and also includes the user's information (authorization information) obtained by the virtual bank account opening device through the external interface or cooperation interface, and preprocesses the data input by the user and the data obtained through authorization, such as null value, normalization, and if inconsistent Standard filled information and unfilled information are inserted with blank values, and the preset scorecard is used to determine the score for each dimension. It is understandable that the information of users is often in various text styles, or lacks quantitative standards.
  • the quantification methods include, for example, targeting A preset scorecard is established for the characteristics of each dimension or the method of dictionary mapping is adopted, and the collected account opening information is converted into a unified standard to generate the user characteristics of the account opening user, which is stored in the form of a multi-dimensional vector.
  • Step S50 inputting the user characteristics into a preset classification model to generate a classification result of the account opening user, wherein the classification result is classified into low risk, potential risk and high risk.
  • the virtual bank account opening device inputs the user characteristics into a preset classification model, and generates a classification result of the account opening user, and the preset classification model is a model established in pre-training that can determine the user's credit or risk status according to the user characteristics , the establishment method of the preset classification model will be described in the subsequent embodiments, and will not be repeated here.
  • the classification results of the users are divided into three categories, namely low risk, potential risk and high risk, and for different risks Account-opening users have different countermeasures, which will be described in subsequent embodiments.
  • Step S60 Send a corresponding account opening result to the terminal according to the classification result, wherein the account opening result includes account opening allowed, pending review, and account opening not allowed.
  • the virtual bank account opening device sends the corresponding account opening result to the terminal according to the classification result.
  • the classification result includes three categories: low risk, potential risk and high risk, and the corresponding account opening results also include account opening, pending review, Three types of account opening are not allowed.
  • the virtual bank account opening device feeds back the classification result to the terminal to know the current account opening status of the user.
  • step S60 also includes:
  • Step b1 if the classification result is low risk, send the account opening result to the terminal;
  • Step b2 if the classification result is a potential risk, send the result to be examined to the terminal;
  • Step b3 if the classification result is high risk, send an account opening result not allowed to the terminal.
  • the virtual bank account opening device sends an account opening result to the terminal; if the classification result is a potential risk, the virtual bank account opening device sends the pending review result to the terminal. If the result is high risk, the virtual bank account opening device sends the result of not allowing account opening to the terminal. For high risk classification, only the result of not allowing account opening is returned. For potential risk classification, it returns the result to be reviewed, and the account opening information enters the list to be reviewed. , further assign customer service personnel to conduct investigations, and decide whether to allow users to open accounts according to the investigation results.
  • step b2 it may include:
  • Step b4 Send the account opening information whose classification result is a potential risk to the customer service system, so that the customer service system assigns customer service personnel to perform due diligence on the account opening users corresponding to the account opening information to generate due diligence results.
  • Step b5 Re-determine the account opening result according to the due diligence result and send it to the terminal.
  • the result to be reviewed is returned, the user information is entered into the list to be reviewed, and customer service personnel are further assigned to conduct an investigation. Based on the investigation results, it is decided whether to allow the user to open an account. For authorized users, return to the client for the next step of business selection.
  • the content of the investigation conducted by the customer service personnel includes the customer's occupation, industry, income, contact address and telephone number, the situation on the external sanctions list, the external investigation situation, etc.
  • the information can be obtained through video.
  • the business personnel can select the current investigation conclusion of the target bank customer according to the above information, and the investigation conclusion is a set of limited options. For example: keep the current classification results, adjust the customer classification results, etc.
  • the virtual bank account opening device receives the account opening information sent by the account opening user based on the terminal, and uses the account opening information as the basis for the user to review the account opening. It can be understood that the user's account opening information may not all be directly available for data processing. Therefore, the virtual bank account opening device performs quantitative processing on the account opening information to generate the user characteristics of the account opening user. After the user characteristic vector is constructed, the user characteristics are input into a preset classification model to generate the account opening. According to the classification result of the user, the corresponding account opening result is sent to the terminal according to the classification result.
  • an effective method is achieved. Identify the risk status of account holders and reduce the effect of virtual bank risks.
  • a second embodiment of the virtual bank account opening method of the present application is further proposed.
  • This embodiment is a post-step of step b1 in the first embodiment.
  • the virtual bank account opening Methods include:
  • Step b6 generating an account opening account corresponding to the account opening user.
  • Step b7 Receive account usage information sent by the account opening user based on the terminal.
  • Step b8 Divide the account opening account into at least two sub-accounts according to the account usage information and a preset agreement corresponding to the account usage information, wherein the funds in each sub-account are independent.
  • the virtual bank account opening device sends a result of allowing account opening to the terminal, and then generates an account opening account corresponding to the account opening user, and the account opening account is the bank account obtained by the account opening user after being reviewed.
  • the virtual bank account opening device receives the account usage information sent by the account opening user based on the terminal, and the usage information virtual bank account opening device opens the account according to the account usage information and the preset agreement corresponding to the account user information.
  • the account is divided into at least two sub-accounts, wherein the funds in each of the sub-accounts are independent, and the preset agreement is an agreement pre-established by the virtual bank to guide account opening according to the account usage information, which is actually a record based on account usage. Information on the method by which account opening accounts are divided.
  • the tuition fee is used as an example for description.
  • the tuition fee also includes sub-categories such as book fees, accommodation fees, clothing fees, etc. If the total tuition fee is 5,000 yuan, among which, books are 1,000 yuan, accommodation is 3,000 yuan, and clothing is 1,000 yuan. If you have 4,500 yuan, the tuition payment will fail due to insufficient balance. If you open an account, it will be divided into three sub-accounts for books, accommodation, and clothing according to the usage information (tuition fee) of the account, and the funds of the three accounts are independent.
  • the books and accommodation fees can be paid first, and the clothing fees can be paid later, thus realizing the details of the charging items. Management, and will not cause the payment to fail completely because the total amount in the account is insufficient to pay the fee.
  • the account opening account usage information is for medical and other purposes related to personal health, the user's rights can be effectively protected by dividing the account opening account into several sub-accounts to ensure the stable payment of some funds.
  • a third embodiment of the virtual bank account opening method of the present application is further proposed.
  • This embodiment is a post-step of step S60 in the first embodiment.
  • the virtual bank account opening Methods include:
  • Step c1 if the untrustworthy behavior of the account-opening user is detected after the user opens the account, the account-opening information of the account-opening user is added to the abnormal information database.
  • the account opening information of the account opening user is added to the abnormal information database.
  • Recognition of the user's credit, and the user's untrustworthy behavior is to determine that the user's account opening information is data that cannot be accurately identified as a blind spot for the classification model, and the detection of the account-opening user's untrustworthy behavior means that the virtual bank account opening equipment is querying.
  • the account-opening user has abnormal behavior, such as overdue, the data has great significance for improving the classification ability of the user classification model, so it is added to the abnormal information database for subsequent use in improving the preset classification model.
  • Step c2 correcting and training the preset classification model by using the account opening information of the abnormal information database.
  • the virtual bank account opening device uses the account opening information of the abnormal information database to correct and train the preset classification model, records the account opening information of the untrustworthy user as special data, and uses the special data to further perform the preset classification model. Correction and improvement of the risk identification ability of the preset classification model can to a certain extent prevent users who have similar situations in the future from easily passing the audit bank and cause losses due to loopholes in account opening audit.
  • the account opening information of the user who has untrustworthy behavior after the user opens an account is recorded as abnormal information, and the abnormal information is used to correct and train the preset classification model, thereby improving the risk identification ability of the preset classification model.
  • abnormal information is used to correct and train the preset classification model, thereby improving the risk identification ability of the preset classification model.
  • a fourth embodiment of the method for opening a virtual bank account of the present application is further proposed.
  • This embodiment is a post-step of step S60 in the first embodiment, and the method for opening a virtual bank account includes: :
  • step d1 when the user generates transaction information, the transaction information is broadcast on the entire network in the preset blockchain, so that each block in the blockchain records the transaction information.
  • Step d2 build a Merkle tree based on its own block.
  • Step d3 when receiving a user's transaction information query instruction, query the Merkle tree based on the transaction information to obtain transaction information corresponding to the transaction information query instruction.
  • the virtual bank account opening device broadcasts the transaction information in the preset blockchain, and the virtual bank establishes an initial block to issue a unique public key and private key to the authorized user.
  • the private key is kept by the user and can be modified by the user.
  • the public key and private key are formed by asymmetric encryption based on the user's information such as the account opening bank card number and other unique information.
  • the bank will The transaction information is broadcast to the whole network, and all nodes in the whole network are responsible for maintaining the blockchain, write the transaction information into their own blocks, and write the transaction information into the blockchain when a consensus is reached, and complete the transaction operation. , the user can choose whether the transaction data is on the chain or not.
  • the virtual bank will only flow funds after the transaction data is on the chain after the whole network consensus, if it is like a quick transfer to an acquaintance, etc. You can choose to operate without being on the chain. Regardless of whether it is on the chain or not, the transaction data is the virtual bank record as its own backup. If the transaction occurs after the other party is a fraudster or the account is stolen, the user encrypts the transaction record together with the private key through the public key and sends it to the virtual bank. After the virtual bank receives the encrypted information with the public key , use the private key to decrypt, and then match with the private key entrained in the decrypted information.
  • the transaction record is determined to be an abnormal transaction, the transaction record is broadcast to the whole network, and the transaction is marked as abnormal and recorded. , as a backup of its own abnormal information. After the node in the network receives the transaction record with the logo, it can determine that the previous transaction record without the logo is stolen, and remove it from the block maintained by itself. Finally, due to the consensus of the whole network, the transaction record is For abnormal transaction records, the data finally written into the blockchain will not contain the consumption record, so the transaction process of the virtual bank will not be triggered.
  • the virtual bank integrates all transaction records about users, including abnormal records, into the same Merkle tree, which is stored in the blockchain. Consumers can retrieve the Merkle tree query to obtain accurate transaction information that cannot be tampered with.
  • a Merkle tree is a data structure, and the leaf nodes in the Merkle tree are hash values of data blocks. In this method, the hash values correspond to the hash values of transaction records.
  • the virtual bank can build a transaction data file system.
  • the transaction data file system is used to store block information and transaction detailed data.
  • the file size in the file system can be configured.
  • the block data is written into the next file, and the file system index is constructed based on the file system.
  • the index includes the location index information of the block and transaction data in the file system, and can also include historical transaction data. And account transaction data and transaction tree, metadata index, etc., the index can be implemented by the index database LevelDB.
  • an embodiment of the present application also proposes a virtual bank account opening device, where the virtual bank account opening device includes:
  • a receiving module configured to receive account opening information sent by the account opening user based on the terminal, where the account opening information includes basic information and authorization information;
  • a processing module configured to perform quantitative processing on the account opening information by using a preset score card, and generate user characteristics of the account opening user
  • a classification module configured to input the user characteristics into a preset classification model, and generate a classification result of the account-opening user, wherein the classification result is divided into low risk, potential risk and high risk;
  • a sending module configured to send a corresponding account opening result to the terminal according to the classification result, wherein the account opening result includes account opening allowed, pending review and account opening not allowed.
  • the present application also provides a virtual bank account opening device
  • the virtual bank account opening device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein:
  • the computer program when executed by the processor, implements the operations of the method for opening a virtual bank account as described above.
  • an embodiment of the present application also provides a computer storage medium, where the computer-readable storage medium may be non-volatile or volatile.
  • a computer program is stored on the computer storage medium, and when the computer program is executed by the processor, the operations in the method for opening a virtual bank account provided by the foregoing embodiments are implemented.
  • the description is relatively simple, and for related parts, please refer to the partial description of the method embodiment.
  • the apparatus embodiments described above are merely illustrative, wherein units described as separate components may or may not be physically separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present application. Those of ordinary skill in the art can understand and implement it without creative effort.

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Abstract

一种虚拟银行开户方法、装置、设备和计算机存储介质，涉及人工智能技术领域，该方法包括以下步骤：接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息（S30）；采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征（S40）；将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险（S50）；根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户（S60）。该方法实现了准确识别虚拟银行开户者的风险状况，降低虚拟银行风险的目的。

Description

虚拟银行开户方法、装置、设备和计算机存储介质
本申请要求于2020年10月9日提交中国专利局、申请号为202011074728.0、发明名称为“虚拟银行开户方法、装置、设备和计算机存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及人工智能技术领域，尤其涉及虚拟银行开户方法、装置、设备和计算机存储介质。
 背景技术
随着社会的发展，银行业也在发生巨大的变革，传统银行模式中银行的业务均在线下进行，用户办理业务时一般需要提前预约，很大程度限制有客户的自由性也浪费了客户的时间，对客户而言体验非常差，对银行而言也需要付出高昂的成本。
为解决传统银行的痛点，虚拟银行应运而生，虚拟银行基本上无须依赖线下网点即可受理客户的各种请求，给客户带来了巨大的便利，但发明人意识到，在线上经营业务也存在问题，如作为入口的线上开户业务对于开户用户缺乏有效的审查机制，给了部分信用状况差或别有用心者可乘之机，给银行造成损失，如何有效地在虚拟银行开户时甄别不同风险的开户者成了亟待解决的问题。
 技术问题
本申请的主要目的在于提供一种虚拟银行开户方法、装置、设备和计算机存储介质，旨在解决虚拟银行无法有效的甄别开户用户的风险的技术问题。
 技术解决方案
为实现上述目的，本申请提供虚拟银行开户方法，所述虚拟银行开户方法包括以下步骤：
接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；
采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；
将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；
根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
在一实施例中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
接收用户基于所述终端发送的开户请求；
根据所述开户请求确定开户类型，并返回与所述开户类型对应的开户信息采集方式至所述终端。
在一实施例中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
对预设用户信息库中的与用户信息进行预处理，生成训练样本；
采用所述训练样本进行模型训练，生成预设分类模型。
在一实施例中，所述根据所述分类结果发送对应的开户结果至所述终端步骤之后，包括：
若在用户开户后检测到所述开户用户的失信行为，则将所述开户用户的开户信息加入异常信息库；
采用所述异常信息库的开户信息对所述预设分类模型进行校正训练。
在一实施例中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤，包括：
若所述分类结果为低风险，则发送允许开户结果至所述终端；
若所述分类结果为潜在风险，则发送待审查结果至所述终端；
若所述分类结果为高风险，则发送不允许开户结果至所述终端。
在一实施例中，所述若所述分类结果为潜在风险，则发送待审查结果至所述终端的步骤之后，包括：
将所述分类结果为潜在风险的开户信息发送至客服系统，以使所述客服系统分配客服人员对所述开户信息对应的开户用户进行尽职调查生成尽职调查结果；
根据所述尽职调查结果重新确定开户结果并发送至所述终端。
在一实施例中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤之后，包括：
当用户产生交易信息时，将所述交易信息在预设区块链中进行全网广播，以使区块链中的各区块记录所述交易信息；
以自身区块为基准构建默克尔树；
在接收到用户的交易信息查询指令时，基于所述交易信息查询所述默克尔树以获取所述交易信息查询指令对应的交易信息。
此外，为实现上述目的，本申请还提供一种虚拟银行开户装置，所述虚拟银行开户装置包括：
接收模块，用于接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；
处理模块，用于采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；
分类模块，用于将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；
发送模块，用于根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
此外，为实现上述目的，本申请还提供一种虚拟银行开户设备；
所述虚拟银行开户设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其中：
所述计算机程序被所述处理器执行时实现如上所述的虚拟银行开户方法的步骤。
此外，为实现上述目的，本申请还提供计算机存储介质；
所述计算机存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如上述的虚拟银行开户方法的步骤。
 有益效果
本申请实施例提出的一种虚拟银行开户方法、装置、设备和计算机存储介质，接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户，通过将传统的由银行工作人员进行审核开户的方式转变至由预先训练的预设分类模型进行审核，实现了有效地甄别开户者的风险状况，降低虚拟银行的风险的效果。
 附图说明
图1是本申请实施例方案涉及的硬件运行环境的装置结构示意图；
图2为本申请虚拟银行开户方法第一实施例的流程示意图；
图3为本申请注册流程示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
 本发明的实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
由于虚拟银行中作为入口的线上开户业务对于开户用户缺乏有效的审查机制，给了部分信用状况差或别有用心者可乘之机，给银行造成损失，或是因审查机制不合理将部分信用状况一般的潜在用户拒之门外，如何有效地在虚拟银行开户时甄别不同风险的开户者并避免潜在用户的流失成了亟待解决的问题。
本申请提供一种解决方案，使虚拟银行开户设备接收开户用户基于终端发送的开户信息，将所述开户信息作用户为开户审核的依据，可以理解的是，用户的开户信息未必均为可直接做数据处理的信息，因此虚拟银行开户设备对所述开户信息进行量化处理，生成所述开户用户的用户特征，在所述用户特征向量构建后，将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，根据所述分类结果发送对应的开户结果至所述终端，通过将传统的由银行工作人员进行审核开户的方式转变至由预先训练的预设分类模型进行审核，实现了有效地甄别开户者的风险状况，降低虚拟银行风险的效果。
如图1所示，图1是本申请实施例方案涉及的硬件运行环境的终端（又叫虚拟银行开户设备，其中，虚拟银行开户设备可以是由单独的虚拟银行开户装置构成，也可以是由其他装置与虚拟银行开户装置组合形成）结构示意图。
本申请实施例终端可以固定终端，也可以是移动终端，如，带联网功能的智能空调、智能电灯、智能电源、智能音箱、自动驾驶汽车、PC (personal computer)个人计算机、智能手机、平板电脑、电子书阅读器、便携计算机等。
如图1所示，该终端可以包括：处理器1001，例如，中央处理器Central Processing Unit，CPU），网络接口1004，用户接口1003，存储器1005，通信总线1002。其中，通信总线1002用于实现这些组件之间的连接通信。用户接口1003可以包括显示屏（Display）、输入单元比如键盘（Keyboard），可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口（如无线保真WIreless-FIdelity，WIFI接口）。存储器1005可以是高速RAM存储器，也可以是稳定的存储器（non-volatile memory），例如，磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
可选地，终端还可以包括摄像头、RF（Radio Frequency，射频）电路，传感器、音频电路、WiFi模块；输入单元，比显示屏，触摸屏；网络接口可选除无线接口中除WiFi外，蓝牙、探针等等。其中，传感器比如光传感器、运动传感器以及其他传感器。具体地，光传感器可包括环境光传感器及接近传感器；当然，移动终端还可配置陀螺仪、气压计、湿度计、温度计、红外线传感器等其他传感器，在此不再赘述。
本领域技术人员可以理解，图1中示出的终端结构并不构成对终端的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图1所示，该计算机软件产品存储在一个存储介质（存储介质：又叫计算机存储介质、计算机介质、可读介质、可读存储介质、计算机可读存储介质或者直接叫介质等，存储介质可以是非易失性可读存储介质，如RAM、磁碟、光盘）中，包括若干指令用以使得一台终端设备（可以是手机，计算机，服务器，空调器，或者网络设备等）执行本申请各个实施例所述的方法，作为一种计算机存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及计算机程序。
在图1所示的终端中，网络接口1004主要用于连接后台服务器，与后台服务器进行数据通信；用户接口1003主要用于连接客户端（用户端），与客户端进行数据通信；而处理器1001可以用于调用存储器1005中存储的计算机程序，并执行本申请以下实施例提供的虚拟银行开户方法中的步骤。
参照图2，本申请一种虚拟银行开户方法的第一实施例中，所述虚拟银行开户方法包括：
步骤S30，接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息。
虚拟银行开户设备接收开户用户基于终端发送的开户信息，所述虚拟银行开户设备通常为虚拟银行的核心系统，所述终端则可为pc、手机和专用的银行柜机等，所述开户信息包含有两部分，一部分是用户直接在终端的开户页面填写以及通过摄像头进行ocr扫描或活体检测等手段采集的信息，此部分信息在本实施例中均成为基础信息，另一部分则为虚拟银行客户设备以注册界面向用户展示的开户说明以及进行电子签约获取的权限仅一步对外部数据接口和合作数据接口进行的用户信息采集，此部分信息在本实施例中均称为授权信息。
进一步的，所述步骤S30之前还可包括：
步骤S10，接收用户基于所述终端发送的开户请求；
虚拟银行开户设备接收用户基于终端发送的开户请求，虚拟银行的开户类型包含多种，如借记账户、信用账户和借贷账户等，而所述开户请求中即包含有用户指定的开户类型，所述开户类型包含借记账户、信用账户和借贷账户等。
可以理解的是，在步骤S10之前，用户需提前进行注册操作，参照图3，如用户采用手机号注册的方式，则在用户提交注册前展示注册说明，以使用户知悉相关的条款，在初步的注册完成后，以电文为形式，以传统的签字为外观进行线上的电子签约，事实上，完成该电子签约的步骤即实现了获取用户的授权，以进一步从合作接口或外部接口抓取更多的授权信息，此后还包括身份证扫描，如ocr对身份证进行扫描完成实名认证，为确定开户者与身份证信息统一，再进行人证比对，具体的，开启摄像头采集开户者的图像，与预设人脸数据库比对的，进一步，为确保所述开户者的图像为真人图像而非照片，再进行活体检测，即在采集开户者图像时要求开户者做出转头、张嘴、眨眼等动作，认证比对过后，首先将所述开户者的信息与黑名单中的数据进行碰撞，若所述开户者处于所述黑名单内，则直接拒绝所述用户的进一步的开户操作。
进一步的，所述步骤S30之前还可包括：
步骤a1，对预设用户信息库中的与用户信息进行预处理，生成训练样本；
虚拟银行开户设备对预设用户信息库中的与用户信息进行预处理，生成训练样本，的信息进行量化处理，如学历信息，在训练样本信息中记录为初中、高中，将所述学历信息进行量化后形成用数字表示的特征，各维度的信息均可通过上述方法处理后处于某一区间，可根据用户信息库使用统计学方法构建预设评分卡，对评分卡模型的建立过程主要包括的流程有数据获取、探索性数据分析、数据预处理，所述用户信息库中包含有守信用户信息和失信用户信息，为确保后续模型训练结果的准确性，守信用户信息和失信用户信息均需要选择一定样本进行组合作为训练样本，保留部分样本以进行模型校验。对确定的训练样本进行如空值处理，归一化处理；关于空值处理，具体的，确定训练样本后，对于信息缺失较为严重的维度即便进行填充在后续训练中也不具备参考价值，因此去除该维度，而对于重要维度内的空值，则进行填充处理，如可选取中位值作为填充。关于归一化，具体的，对于训练样本中未量化变量选择、模型开发、模型评估、模型实施、模型检测等。
针对上述评分卡模型的建立流程总结得到构建评分卡的主要步骤可包括：选取建模用户；选取建模变量；为各个建模变量设置建模系数；根据各个建模变量及其对应的建模系数，结合预设建模规则构建评分卡模型。可以理解的，建模用户是指用来构建模型的用户，为了保证评分卡模型对新用户预测的准确性，需要保证建模信息基数。建模变量是从用户变量中选择的一个或多个变量，建模变量的选择方式可以为：将多个建模用户按照一定规则比如时间规则分为多组建模样本，然后根据每个用户变量在多个建模样本中的表现情况，从用户变量中选择在各组建模样本中都表现良好的变量作为建模变量，可以根据各个建模变量在建模用户中所占的权重为各个建模变量设置建模系数，也即根据各个建模变量对用户预测的影响情况来为各个建模变量设置建模系数，后根据选择的建模变量及其对应建模系数构建评分卡模型，建立评分卡模型后，使用评分卡模型对各维度数据根据评分卡标准进行量化处理。
步骤a2，采用所述训练样本进行模型训练，生成预设分类模型。
虚拟银行开户设备采用所述训练样本进行模型训练，生成预设分模型，经过上述方法处理，所述训练样本已可用于用户分类模型的构建。提取训练样本中的各维度如收入、学历、手机入网时长、电商平台退款率、名下信用卡数量、履约次数等影响因素一起作为用户分类模型的特征，使用如支持向量机（Support Vector Machine，SVM）、聚类算法（Clustering Algorithms）等机器学习算法初步构建用户分类模型，使用训练样本外的其他样本作为验证数据，验证用户分类模型可靠性。
步骤S20，根据所述开户请求确定开户类型，并返回与所述开户类型对应的开户信息采集方式至所述终端。
虚拟银行开户设备根据所述开户请求确定开户类型，并返回与所述开户类型对应的开户信息采集方法至所述终端，由于开户类型的不同，对于开户人的审查条件也不一样，如在用户进行借记账户开户时需要的审查未必需要如同信用账户和借贷账户一般严格，减少部分不必要的资料填写和认证的步骤提有效提升如借记账户这样的低风险账户的开户效率，而根据用户在终端的选取开户的账户或业务类型展示不同的信息采集页面，如借记账户、信用卡账户、借贷账户，其中根据机构授予用户不同账户的可能存在的风险将账户分为低风险账户即借记账户、中风险账户即信用卡账户、高风险账户如借贷账户并分别展示不同的信息采集页面，服务器接收用户基于移动终端输入的个人信息，在开通借记账户时输入基础个人信息如手机号、身份证信息，在开通信用卡账户时输入除包含基础个人信息外，还包含任职公司、收入、社保、公积金等信息，在开通借贷账户时输入除前述信息外，额外包含资产信息等。本步骤中还包含有用户电子签约，以使客户知悉相关条款，并通过电子签约取得用户的授权操作，以进行更深层次的信息挖掘，挖掘如手机号入网时间长度，名下信用卡数量，履约情况，电商账户购买记录，退款情况，消费明细，电话常联系人（常联系人是否存在黑名单、失信名单中）、社交应用常联系人（社交应用常联系人是否存在黑名单、失信名单中）等维度。
步骤S40，采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征。
虚拟银行开户设备对所述开户信息进行量化处理，生成所述开户用户的用户特征，如前述步骤中对于开户信息的解释，所述开户信息不仅为用户在注册或开户时填写的信息（基础信息），还包含虚拟银行开户设备通过外部接口或合作接口获取的所述用户的信息（授权信息），将用户输入的数据和授权取得的数据进行预处理，如空值、归一化，如不合规范填写的信息和未填写的信息插入空值，使用预设评分卡为各维度确定分值。可以理解的是，用户的信息往往为各式的文本样式，或者是缺乏量化的标准，如电话常联系人此类无法准确作为分类的特征的维度应当进行量化后使用，量化的方法包含如针对每一维度的特征建立预设评分卡或采用字典映射的方式，将采集到的开户信息转化为统一标准以生成所述开户用户的用户特征，以多维向量的形式保存。
步骤S50，将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险。
虚拟银行开户设备将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，所述预设分类模型为预先训练建立的可根据用户特征确定所述用户的信用或风险状况的模型，关于所述预设分类模型的建立方法将在后续实施例中说明，此处不作赘述，所述用户的分类结果分为三类，即低风险、潜在风险和高风险，并且针对不同风险的开户用户具有不同的应对举措，将在后续实施例中说明。
步骤S60，根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
虚拟银行开户设备根据所述分类结果发送对应的开户结果至所述终端，如前述步骤所述，分类结果包含低风险、潜在风险和高风险三类，对应的开户结果也有允许开户、待审查、不允许开户三种，虚拟银行开户设备将所述分类结果反馈至终端知悉用户当前的开户状况。
进一步的，步骤S60还包括：
步骤b1，若所述分类结果为低风险，则发送允许开户结果至所述终端；
步骤b2，若所述分类结果为潜在风险，则发送待审查结果至所述终端；
步骤b3，若所述分类结果为高风险，则发送不允许开户结果至所述终端。
若所述分类结果为低风险，虚拟银行开户设备则发送允许开户结果至所述终端，若所述分类结果为潜在风险，虚拟银行开户设备则发送待审查结果至所述终端，若所述分类结果为高风险，虚拟银行开户设备则发送不允许开户结果至所述终端，对于高风险分类，只给返回不允许开户结果，对于潜在风险分类，返回待审查的结果，开户信息进入待审核列表中，进一步分配客户服务人员进行调查，根据调查结果决定是否允许用户开户。
进一步的，所述步骤b2之后可包括：
步骤b4，将所述分类结果为潜在风险的开户信息发送至客服系统，以使所述客服系统分配客服人员对所述开户信息对应的开户用户进行尽职调查生成尽职调查结果。
步骤b5，根据所述尽职调查结果重新确定开户结果并发送至所述终端。
返回待审核的结果，用户信息进入待审核列表中，进一步分配客户服务人员进行调查，根据调查结果决定是否允许用户开户，对于可授权用户，返回客户端进行下一步业务选择。所述客户服务人员进行调查的内容包括可对客户的职业、行业、收入、联系地址以及联系电话、在外部制裁名单上的情况、外部调查情况等，此外，还可通过视频方式以已掌握信息询问客户进行比对的方式，并记录所述视频进行眼动、头部姿态、肌肉动作等微表情评估，得出评估值供客户服务人员作为调查结果参考，当输出目标银行客户的尽职调查结果后，业务人员可根据上述信息，选择该目标银行客户当期的调查结论，调查结论是有一个有限选项的集合。例如：保持当前分类结果、调整客户分类结果等。
在本实施例中，虚拟银行开户设备接收开户用户基于终端发送的开户信息，将所述开户信息作用户为开户审核的依据，可以理解的是，用户的开户信息未必均为可直接做数据处理的信息，因此虚拟银行开户设备对所述开户信息进行量化处理，生成所述开户用户的用户特征，在所述用户特征向量构建后，将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，根据所述分类结果发送对应的开户结果至所述终端，通过将传统的由银行工作人员进行审核开户的方式转变至由预先训练的预设分类模型进行审核，实现了有效地甄别开户者的风险状况，降低虚拟银行风险的效果。
进一步的，在本申请第一实施例的基础上，进一步提出了本申请虚拟银行开户方法的第二实施例，本实施例为第一实施例中步骤b1的后置步骤，所述虚拟银行开户方法包括：
步骤b6，生成与所述开户用户对应的开户账户。
步骤b7，接收所述开户用户基于所述终端发送的账户用途信息。
步骤b8，根据所述账户用途信息和与所述账户用途信息对应的预设协议将所述开户账户划分为至少两个子账户，其中，所述各子账户内的资金独立。
若所述分类结果为低风险，虚拟银行开户设备则发送允许开户结果至所述终端，随后生成与所述开户用户对应的开户账户，所述开户账户即为开户用户通过审核后获取的银行账户，虚拟银行开户设备接收所述开户用户基于所述终端发送的账户用途信息，所述用途信息虚拟银行开户设备根据所述账户用途信息和与所述账户用户信息对应的预设协议将所述开户账户划分为至少两个子账户，其中，所述各子账户内的资金独立，所述预设协议是虚拟银行根据账户用途信息预先建立的指导开户账户进行划分的协议，实际即为记录基于账户用途信息对开户账户进行划分的方法。
可以理解的是，众多开户用户在银行开户时都有明确的开户账户用途，如用于缴纳学费、用于购买股票或基金等金融产品、用于支付在医院的开销、用于支付养老机构的开销。
本实施例以学费为例进行说明，学费又包含有书本费、住宿费、服装费等子类目，若学费总额为5000元，其中书本1000元、住宿3000元、服装1000元，而账户中存有4500元，则会由于余额不足导致学费缴纳失败，若在开户时根据其开户账户用途信息（学费）将其划分为书本、住宿、服装三个子账户，且三个账户的资金分别独立，如书本子账户存有1000、住宿子账户存有3000、服装账户存有500，则在进行学费缴纳时，可先将书本和住宿费用缴纳，服装费用则后补，由此实现收费项目的明细化管理，而不会因为账户内的总额不足以缴纳费用而导致缴纳完全失败。推而广之，当所述开户账户用途信息为医疗等关乎人身健康的用途时，通过将开户账户划分为数子账户以保证部分款项的稳定缴纳可有效保障用户的权益。
在本实施例中，通过根据开户用户提供的账户用途信息将开户账户划分为若干个资金独立的子账户，实现了同一笔费用下不同款项的分别缴纳，对于开户用户而言，避免因为余额不足而导致的所有款项均无法缴纳的问题，对于收款方而言，实现了收费款项的精细化管理。
进一步的，在本申请第一实施例的基础上，进一步提出了本申请虚拟银行开户方法的第三实施例，本实施例为第一实施例中步骤S60的后置步骤，所述虚拟银行开户方法包括：
步骤c1，若在用户开户后检测到所述开户用户的失信行为，则将所述开户用户的开户信息加入异常信息库。
若虚拟银行开户设备在用户开户后检测到所述开户用户的失信行为，则将所述开户用户的开户信息加入异常信息库，如前述实施例所述，用户完成开户则说明预设分类模型对于用户信用的认可，而所述用户发生失信行为即判定该用户的开户信息对于分类模型而言即为存在盲区无法准确认定的数据，而检测所述开户用户的失信行为即虚拟银行开户设备在查询到所述开户用户异常行为，如逾期，因此该数据对于改善用户分类模型的分类能力存在较大意义，因此将其加入异常信息库以便后续用于改善所述预设分类模型。
步骤c2，采用所述异常信息库的开户信息对所述预设分类模型进行校正训练。
虚拟银行开户设备采用所述异常信息库的开户信息对所述预设分类模型进行校正训练，将所述失信用户的开户信息作为特殊数据记录，并使用所述特殊数据对预设分类模型进行进一步校正，提升预设分类模型的风险识别能力，可一定程度避免今后出现存在类似情况的用户轻易通过审核银行因开户审查的漏洞造成损失。
在本实施例中，通过将用户开户后发生失信行为的用户的开户信息作为异常信息记录，并采用所述异常信息对预设分类模型进行校正训练，提升了预设分类模型的风险识别能力，可一定程度避免今后出现存在类似情况的用户轻易通过审核，降低因开户审查的漏洞造成虚拟银行的损失。
进一步的，在本申请以上实施例的基础上，进一步提出了本申请虚拟银行开户方法的第四实施例，本实施为第一实施例中步骤S60的后置步骤，所述虚拟银行开户方法包括：
步骤d1，当用户产生交易信息时，将所述交易信息在预设区块链中进行全网广播，以使区块链中的各区块记录所述交易信息。
步骤d2，以自身区块为基准构建默克尔树。
步骤d3，在接收到用户的交易信息查询指令时，基于所述交易信息查询所述默克尔树以获取所述交易信息查询指令对应的交易信息。
当用户产生交易信息时，虚拟银行开户设备将所述交易信息在预设区块链中进行全网广播，虚拟银行建立一初始区块，为可授权用户发放唯一的公钥和私钥，所述私钥则为用户保管且可自行修改，所述公钥和私钥均为基于用户的信息如开户银行卡号等唯一的信息进行非对称加密形成，每当该用户进行交易操作时，银行将交易信息向全网广播，全网所有节点负责维护区块链，将该条交易信息写入自身区块中，当达成共识后将所述交易信息写入区块链，并完成该次交易操作，对于交易数据是否上链用户可以自行选择，可选的，若为防范诈骗等情形则应当上链，交易数据上链在全网共识后虚拟银行才进行资金流动，若如同快速转账给熟人等操作则可选择无须上链，不论是否上链，交易数据均为虚拟银行记录作为自身备份。若在交易中出现对方为诈骗人员或账户被盗后产生交易的情形，用户将该次交易记录连同私钥通过公钥进行加密，一同发送至虚拟银行，虚拟银行收到该公钥加密信息之后，使用私钥进行解密，再与解密信息中夹带的私钥进行匹配，匹配成功则认定该交易记录为异常交易，向全网广播该次交易记录，并将该次交易标记为异常交易并记录，作为自身异常信息备份。网络中的节点在收到带有标识的交易记录之后，则可判定先前不带标志的交易记录为盗刷，从自身所维护的区块当中剔除，最终由于全网达成共识，该交易记录为异常的交易记录，最终写入区块链当中的数据将不包含该条消费记录，从而将不会触发虚拟银行的交易过程。
虚拟银行将关于用户的所有交易记录，包含异常记录整合为同一棵默克尔树，存放于区块链中，消费者可检索默克尔树查询得到无法篡改准确的交易信息，所述默克尔树为一种数据结构，所述默克尔树中的树叶节点为数据块的哈希值，在本方法中，哈希值与交易记录的哈希值对应。
可选的，为实现用户快速检索交易记录，虚拟银行可构建一交易数据文件系统，所述交易数据文件系统用于存储区块信息和交易详细数据，文件系统中文件大小可配置，当前文件写满后，区块数据写入下一文件中，基于所述文件系统再构建文件系统索引，所述索引包含区块和交易数据处于文件系统中的位置索引信息，此外，还可包含历史交易数据和账户交易数据和交易树、元数据索引等，该索引可采用索引数据库LevelDB实现。
此外，本申请实施例还提出一种虚拟银行开户装置，所述虚拟银行开户装置包括：
接收模块，用于接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；
处理模块，用于采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；
分类模块，用于将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；
发送模块，用于根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
其中，虚拟银行开户装置的各个功能模块实现的步骤可参照本申请虚拟银行开户方法的各个实施例，此处不再赘述。
此外，为实现上述目的，本申请还提供一种虚拟银行开户设备；
所述虚拟银行开户设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其中：
所述计算机程序被所述处理器执行时实现如上所述的虚拟银行开户方法的操作。
此外，本申请实施例还提出一种计算机存储介质，所述计算机可读存储介质可以是非易失性，也可以是易失性。
所述计算机存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现上述实施例提供的虚拟银行开户方法中的操作。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体/操作/对象与另一个实体/操作/对象区分开来，而不一定要求或者暗示这些实体/操作/对象之间存在任何这种实际的关系或者顺序；术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者系统不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者系统所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者系统中还存在另外的相同要素。
对于装置实施例而言，由于其基本相似于方法实施例，所以描述得比较简单，相关之处参见方法实施例的部分说明即可。以上所描述的装置实施例仅仅是示意性的，其中作为分离部件说明的单元可以是或者也可以不是物理上分开的。可以根据实际的需要选择中的部分或者全部模块来实现本申请方案的目的。本领域普通技术人员在不付出创造性劳动的情况下，即可以理解并实施。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种虚拟银行开户方法，其中，所述虚拟银行开户方法包括以下步骤：
    接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；
    采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；
    将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；
    根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
  2. 如权利要求1所述的虚拟银行开户方法，其中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
    接收用户基于所述终端发送的开户请求；
    根据所述开户请求确定开户类型，并返回与所述开户类型对应的开户信息采集方式至所述终端。
  3. 如权利要求1所述的虚拟银行开户方法，其中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
    对预设用户信息库中的与用户信息进行预处理，生成训练样本；
    采用所述训练样本进行模型训练，生成预设分类模型。
  4. 如权利要求1-3中任一项所述的虚拟银行开户方法，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤之后，包括：
    若在用户开户后检测到所述开户用户的失信行为，则将所述开户用户的开户信息加入异常信息库；
    采用所述异常信息库的开户信息对所述预设分类模型进行校正训练。
  5. 如权利要求1所述的虚拟银行开户方法，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤，包括：
    若所述分类结果为低风险，则发送允许开户结果至所述终端；
    若所述分类结果为潜在风险，则发送待审查结果至所述终端；
    若所述分类结果为高风险，则发送不允许开户结果至所述终端。
  6. 如权利要求5所述的虚拟银行开户方法，其中，所述若所述分类结果为潜在风险，则发送待审查结果至所述终端的步骤之后，包括：
    将所述分类结果为潜在风险的开户信息发送至客服系统，以使所述客服系统分配客服人员对所述开户信息对应的开户用户进行尽职调查生成尽职调查结果；
    根据所述尽职调查结果重新确定开户结果并发送至所述终端。
  7. 如权利要求1所述的虚拟银行开户方法，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤之后，包括：
    当用户产生交易信息时，将所述交易信息在预设区块链中进行全网广播，以使区块链中的各区块记录所述交易信息；
    以自身区块为基准构建默克尔树；
    在接收到用户的交易信息查询指令时，基于所述交易信息查询所述默克尔树以获取所述交易信息查询指令对应的交易信息。
  8. 一种虚拟银行开户装置，其中，所述虚拟银行开户装置包括：
    接收模块，用于接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；
    处理模块，用于采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；
    分类模块，用于将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；
    发送模块，用于根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
  9. 一种虚拟银行开户设备，其中，所述虚拟银行开户设备包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被所述处理器执行时实现如下步骤：
    接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；
    采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；
    将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；
    根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
  10. 如权利要求9所述的虚拟银行开户设备，其中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
    接收用户基于所述终端发送的开户请求；
    根据所述开户请求确定开户类型，并返回与所述开户类型对应的开户信息采集方式至所述终端。
  11. 如权利要求9所述的虚拟银行开户设备，其中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
    对预设用户信息库中的与用户信息进行预处理，生成训练样本；
    采用所述训练样本进行模型训练，生成预设分类模型。
  12. 如权利要求9-11中任一项所述的虚拟银行开户设备，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤之后，包括：
    若在用户开户后检测到所述开户用户的失信行为，则将所述开户用户的开户信息加入异常信息库；
    采用所述异常信息库的开户信息对所述预设分类模型进行校正训练。
  13. 如权利要求9所述的虚拟银行开户设备，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤，包括：
    若所述分类结果为低风险，则发送允许开户结果至所述终端；
    若所述分类结果为潜在风险，则发送待审查结果至所述终端；
    若所述分类结果为高风险，则发送不允许开户结果至所述终端。
  14. 如权利要求13所述的虚拟银行开户设备，其中，所述若所述分类结果为潜在风险，则发送待审查结果至所述终端的步骤之后，包括：
    将所述分类结果为潜在风险的开户信息发送至客服系统，以使所述客服系统分配客服人员对所述开户信息对应的开户用户进行尽职调查生成尽职调查结果；
    根据所述尽职调查结果重新确定开户结果并发送至所述终端。
  15. 如权利要求9所述的虚拟银行开户设备，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤之后，包括：
    当用户产生交易信息时，将所述交易信息在预设区块链中进行全网广播，以使区块链中的各区块记录所述交易信息；
    以自身区块为基准构建默克尔树；
    在接收到用户的交易信息查询指令时，基于所述交易信息查询所述默克尔树以获取所述交易信息查询指令对应的交易信息。
  16. 一种计算机存储介质，其中，所述计算机存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如下步骤：
    接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息；
    采用预设评分卡对所述开户信息进行量化处理，生成所述开户用户的用户特征；
    将所述用户特征输入预设分类模型，生成所述开户用户的分类结果，其中，所述分类结果分为低风险、潜在风险和高风险；
    根据所述分类结果发送对应的开户结果至所述终端，其中，所述开户结果包括允许开户、待审查和不允许开户。
  17. 如权利要求16所述的计算机存储介质，其中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
    接收用户基于所述终端发送的开户请求；
    根据所述开户请求确定开户类型，并返回与所述开户类型对应的开户信息采集方式至所述终端。
  18. 如权利要求16所述的计算机存储介质，其中，所述接收开户用户基于终端发送的开户信息，所述开户信息包含基础信息和授权信息的步骤之前，包括：
    对预设用户信息库中的与用户信息进行预处理，生成训练样本；
    采用所述训练样本进行模型训练，生成预设分类模型。
  19. 如权利要求16-18中任一项所述的计算机存储介质，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤之后，包括：
    若在用户开户后检测到所述开户用户的失信行为，则将所述开户用户的开户信息加入异常信息库；
    采用所述异常信息库的开户信息对所述预设分类模型进行校正训练。
  20. 如权利要求16所述的计算机存储介质，其中，所述根据所述分类结果发送对应的开户结果至所述终端的步骤，包括：
    若所述分类结果为低风险，则发送允许开户结果至所述终端；
    若所述分类结果为潜在风险，则发送待审查结果至所述终端；
    若所述分类结果为高风险，则发送不允许开户结果至所述终端。
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CN112766974A (zh) * 2021-01-19 2021-05-07 中国农业银行股份有限公司广州分行 风险账户识别方法、装置、计算机设备和存储介质
CN113159460A (zh) * 2021-05-24 2021-07-23 中国平安人寿保险股份有限公司 一种用户数据处理的方法、装置、计算机设备及存储介质
CN113191890A (zh) * 2021-05-27 2021-07-30 中国工商银行股份有限公司 客户风险确定方法、装置及设备
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CN113628030A (zh) * 2021-08-11 2021-11-09 中国银行股份有限公司 基于区块链的个人海外见证开户业务处理系统及方法
CN113610324A (zh) * 2021-08-27 2021-11-05 中国银行股份有限公司 一种基于LightGBM的开户审核方法及相关设备
TWI813066B (zh) * 2021-11-12 2023-08-21 金腦數位股份有限公司 遠距身分認證與對保簽約的方法及其設備
CN114358920A (zh) * 2022-01-07 2022-04-15 北京百度网讯科技有限公司 迭代信用评分卡模型的方法、装置、电子设备及存储介质
CN115984021B (zh) * 2023-03-20 2023-11-14 福建至简至一信息科技有限公司 网络货运平台注册方法、电子设备及存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100145834A1 (en) * 2008-12-10 2010-06-10 Citizens Financial Group, Inc. Method and system for identifying fraudulent account activity
CN105303442A (zh) * 2015-11-04 2016-02-03 中国民生银行股份有限公司 网上银行开户账号检测方法和装置
CN110060068A (zh) * 2019-02-14 2019-07-26 阿里巴巴集团控股有限公司 商户评估方法、装置、电子设备及可读存储介质
CN110335144A (zh) * 2019-07-10 2019-10-15 中国工商银行股份有限公司 个人电子银行账户安全检测方法及装置
CN112199575A (zh) * 2020-10-09 2021-01-08 深圳壹账通智能科技有限公司 虚拟银行开户方法、装置、设备和计算机存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100145834A1 (en) * 2008-12-10 2010-06-10 Citizens Financial Group, Inc. Method and system for identifying fraudulent account activity
CN105303442A (zh) * 2015-11-04 2016-02-03 中国民生银行股份有限公司 网上银行开户账号检测方法和装置
CN110060068A (zh) * 2019-02-14 2019-07-26 阿里巴巴集团控股有限公司 商户评估方法、装置、电子设备及可读存储介质
CN110335144A (zh) * 2019-07-10 2019-10-15 中国工商银行股份有限公司 个人电子银行账户安全检测方法及装置
CN112199575A (zh) * 2020-10-09 2021-01-08 深圳壹账通智能科技有限公司 虚拟银行开户方法、装置、设备和计算机存储介质

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