WO2020253395A1 - 业务数据监控方法和装置 - Google Patents

业务数据监控方法和装置 Download PDF

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Publication number
WO2020253395A1
WO2020253395A1 PCT/CN2020/087740 CN2020087740W WO2020253395A1 WO 2020253395 A1 WO2020253395 A1 WO 2020253395A1 CN 2020087740 W CN2020087740 W CN 2020087740W WO 2020253395 A1 WO2020253395 A1 WO 2020253395A1
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business
approved
target user
flow data
data
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French (fr)
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李晨光
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OneConnect Smart Technology Co Ltd
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OneConnect Smart Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q40/03—Credit; Loans; Processing thereof

Definitions

  • This application relates to the field of big data technology, and in particular to a method and device for monitoring business data.
  • Banks, credit companies and other financial institutions need to review the user's credit, income, and assets before approving users' credit business such as loans, so as to determine reasonable credit lines and control business risks.
  • various financial institutions collect information about the user’s credit, income, and assets based on the various materials submitted by the user when submitting a business application, and verify the authenticity of the various materials in the subsequent procedures. The inventor found that this In this way, it is inevitable that false information will be confused, which may increase the business of various financial institutions.
  • a business data monitoring method and device are provided to enhance the risk control of business risks.
  • a business data monitoring method includes:
  • a predetermined measure is taken for the service to be approved applied by the target user.
  • a business data monitoring device including:
  • the data crawling module is used to capture the business flow data of the target user from the business network in a preset manner when receiving the request message requesting the approval of the pending service applied by the target user;
  • the risk calculation module is configured to extract N target parameters from the business flow data, and calculate the risk value of the business to be approved based on the N target parameters, where N ⁇ 1;
  • the judgment module is used to judge whether the risk value of the business to be approved exceeds the corresponding risk threshold
  • the measure implementation module is configured to, if the risk value of the business to be approved exceeds the corresponding risk threshold, take a predetermined measure for the business to be approved applied by the target user.
  • a computer-readable storage medium that stores a computer program, and when the computer program is executed by a processor, the steps of the following method are implemented:
  • a predetermined measure is taken for the service to be approved applied by the target user.
  • a computer device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the following method when the computer program is executed:
  • a predetermined measure is taken for the service to be approved applied by the target user.
  • FIG. 1 is a flowchart of an optional service data monitoring method provided by an embodiment of the present application
  • FIG. 2 is a schematic diagram of an optional service data monitoring device provided by an embodiment of the present application.
  • Fig. 3 is a schematic diagram of an optional computer device provided by an embodiment of the present application.
  • first, second, third, etc. may be used in the embodiments of the present application to describe the preset range, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other.
  • the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.
  • the word “if” as used herein can be interpreted as “when” or “when” or “in response to determination” or “in response to detection”.
  • the phrase “if determined” or “if detected (statement or event)” can be interpreted as “when determined” or “in response to determination” or “when detected (statement or event) )” or “in response to detection (statement or event)”.
  • Fig. 1 is a flowchart of an optional service data monitoring method provided by an embodiment of the present application. As shown in Figure 1, the method includes:
  • Step S101 When a request message requesting approval of the service to be approved applied by the target user is received, the service flow data of the target user is captured from the service network in a preset manner.
  • capturing the business flow data of the target user from the business network in a preset manner includes: using a preset web crawler to grab the business flow data of the target user from the business network.
  • Web crawlers also known as web spiders and web robots, can automatically grab information from the web. Different from the traditional keyword search, the web crawler starts from the URL of one or several initial web pages, and continuously extracts new URLs from the associated pages and puts them in the queue until the stop condition is met. Obtain target information based on the web pages continuously extracted from the above process.
  • a preset web crawler may be used to grab the target user's service flow data from the service network.
  • a pre-set web crawler is used to log in to the business network based on the login information authorized by the target user, and the business flow data of the target user is captured from the business network after the login is successful.
  • the selenium+scrapy crawler technology framework can be used to simulate the browser to log in to the business network.
  • the business flow data captured from the business network after a successful login includes user profile data and transaction flow data of the target user.
  • the business to be approved for the target user is a loan business
  • the business flow data of the target user captured from the bank network system includes user profile data and transaction flow data.
  • the user profile data includes: basic account information, such as account name, currency, card number, card type (debit or credit card), card issuance address, account balance
  • transaction flow data includes online banking flow, such as total repayment amount, Minimum repayment amount, due date of repayment, bill date, current repayment amount, current minimum repayment amount, credit limit, cash advance limit, currency short code, consumption category, etc.
  • the aforementioned service network may be a service network corresponding to the service to be approved applied by the target user, and may also include a third-party service network associated with the service to be approved.
  • the business to be approved by the target user is a loan business
  • the business network includes the bank network system of the loan business applied for, as well as the bank network systems and third-party payment platforms of other banks associated with the target user.
  • capturing the target user’s business flow data from the business network includes: capturing a transaction page in a preset business system based on the target user’s characteristic data; and extracting the target user’s first transaction page from the transaction page.
  • Transaction flow data and characteristic data of the third-party platform associated with the target user based on the characteristic data of the third-party platform, grab the target user’s transaction page from the third-party platform system and extract the target user’s
  • the second transaction flow data the transaction flow data of the target user is obtained according to the first transaction flow data and the second transaction flow data.
  • the third-party platform refers to a third-party bank or a third-party payment platform.
  • the transaction data of other payment platforms associated with the target user can be captured from the banking system of the loan business that the user applies for, for example, based on the user's ID number , Face avatars, bank card accounts, transfer records, etc. as characteristic data to search the transaction pages of third-party platforms.
  • Step S102 Extract N target parameters from the business flow data, and calculate the risk value of the business to be approved based on the N target parameters, where N ⁇ 1.
  • the business flow data is applied to the risk evaluation of the pending business applied for by the target user to determine the risk value of the pending business applied for by the target user.
  • extract N target parameters from the business flow data of the target user and assign the N target parameters or assign score weights corresponding to at least one evaluation value calculated based on the N parameters, based on the N target parameters
  • Step S103 Determine whether the risk value of the service to be approved exceeds the corresponding risk threshold.
  • Step S104 If it is determined that the risk value of the service to be approved exceeds its corresponding risk threshold, take predetermined measures for the service to be approved applied by the target user.
  • a corresponding risk threshold is set corresponding to the business to be approved applied by the target user.
  • the target user can apply for
  • the business to be approved takes predetermined measures.
  • the predetermined measures may be to refuse to review and pass the business to be approved, request target users to add evaluation data, etc., thereby adding business risk evaluation factors on the basis of existing business risk control, and enhancing business risk control .
  • extracting N target parameters from the business flow data, and calculating the risk value of the business to be approved based on the N target parameters includes: determining the current processing stage of the business to be approved; Extract N target parameters from the business flow data in the preset time period associated with the current processing stage of the business to be approved, and calculate the risk value of the current processing stage of the business to be approved based on the N target parameters.
  • the processing stages of the business to be approved include: the admission stage before approval, the inspection stage after the approval, and the monitoring stage after approval.
  • the current business to be approved is calculated based on the target flow data.
  • the risk values at the processing stage include:
  • calculating the risk value of the business to be approved in the entry stage includes: extracting the business flow data of the previous M months when the target user submits the application for approval of the business to be approved Transaction flow data and default record data, M>1; according to the transaction flow data and the default record data, calculate the risk value of the pending business in the admission phase.
  • the association relationship between the transaction flow data and the income score and the association relationship between the storage default record data and the default score are stored in advance; the target users are respectively calculated based on the transaction flow data and default record data extracted from the M months of business flow data
  • the above calculation of the risk value of the business to be approved at the admission stage includes:
  • Calculating the default score y includes: determining the total number of defaults x 1 in M months; if x 1 is greater than X 1 , the value of y is y 1 ; if x 1 is in the range of (0, X 1 )
  • the step size a 1 , x 1 is reduced by a step size a 1
  • the above x 1 , X 1 , y 1 , a 1 and b 1 are all positive numbers and can be determined according to actual needs.
  • the business to be approved by the target user is a loan business
  • the calculation of the risk value of the target user's loan business admission stage includes: the default record based on the previous six months of the flow data when the target user submits the loan application The data calculates the default score of the target user.
  • the default score is 5; if the number of default records in the previous six months is 2 times, the default score is 3; If the number of default records in the previous six months is 1, the default score is 1; if there is no default record, the default score is 0; if the default score is less than or equal to 3, it is based on the transaction flow of the first six months when the target user submits the loan application
  • the data calculates the income score of the target user. Different income data corresponds to different income scores. For example, the six-month average income is less than 3000.
  • calculating the risk value of the business to be approved in the verification stage includes: extracting user profile data from the business flow data of the target user, based on the extracted user profile data Verify the true evaluation value of the data submitted by the target user. If the true evaluation value is lower than the preset reference value, the verification during review fails; if the true evaluation value is greater than or equal to the preset reference value, the business of the first K months when the target user submits the review application
  • the flow data calculates the credit evaluation value of the target user, K>1; the business quota approved for the business to be approved is determined according to the credit evaluation value.
  • the user profile data includes: identity information, income information, and loan information; the profile data submitted by the user includes: identity verification documents, income certification documents, and loan certification documents; from the identity verification documents, income certification documents, and loan certification documents Extract the user’s identity verification information, income verification information, and loan verification information, respectively, and compare the identity verification information, income verification information, and loan verification information with the captured identity information, income information, and loan information, according to the comparison The result determines the true evaluation value.
  • the true evaluation value is 0, and the income information and loan information are no longer compared; if the identity information verification is consistent, one of the income information and loan information is not verified, then The true evaluation value is 3; if the identity information verification is consistent, the two verifications of the income information and the loan information are not passed, the true evaluation value is 1; if the three information verifications are passed, the true evaluation value is 5, if the true evaluation value If the value is less than 3, the verification during review fails; if the true evaluation value is greater than or equal to 3, the credit evaluation value of the target user is calculated according to the business flow data of the first K months when the target user submits the review application.
  • calculating the credit evaluation value of the target user according to the business flow data of the first K months when the target user submits the application for under review includes: extracting the business flow data of the first K months
  • the target user s credit limit, total consumption amount, and performance information of each performance period in A performance cycles included in the previous K months, A>1; according to the credit limit, total consumption amount, and each of the A performance cycles
  • the credit score corresponding to the performance information of the performance cycle is calculated to calculate the credit evaluation value of the target user.
  • calculating the credit evaluation value of the target user includes:
  • the implementation steps include: judging whether the credit limit is greater than the preset value B 1 , and if it is greater than B 1 , determining that the value of z 1 is k 1 ; the above B 1 and k 1 are all positive numbers, and B 1 , k 1 can be set according to actual needs.
  • the implementation steps include: judging whether the total consumption amount is greater than B 2 , and if it is greater than B 2 , then determining that the value of z 2 is k 2 ; the foregoing B 2 and k 2 are both positive numbers, and B 2 and k 2 can be Set according to actual needs.
  • the implementation steps include: determining z 3 based on the consumption amount included in the performance information of each performance cycle, agreed currency consumption information, installment consumption information, and performance status information, including: if the consumption amount is greater than the number of performance cycles of B 3 If greater than v 1 , the value of z 3 is added by k 3 ; if there is no default in a single performance cycle, the value of z 3 is added by k 4 , if the number of consecutive performance cycles without default is greater than v 2, then z 3 Add k 5 to the value of A; if the number of performance cycles in which consumption of the agreed currency occurs in a performance cycle is greater than v 3, then the value of z 3 is added to k 6 ; if there is a default in a single performance cycle, then z 3 is taken Decrease k 7 ; if there is an installment performance in a single performance cycle, the value of
  • the business flow data of the first 12 months of the target user is obtained.
  • the 12-month business flow data is divided into 12 performance periods, and the target user's business flow data is extracted from the 12-month business flow data. Credit limit, total consumption amount, and performance information for each performance period in 12 performance periods.
  • the target user if the target user’s credit limit is greater than 60,000, the credit score is increased by 2; the total consumption amount is greater than 50,000, then the credit score is increased by 5; if there is more than 1 performance cycle, the consumption amount is greater than 5000, the credit score Add 1; add 2 to the credit score of the billing cycle for successful performance without overdue in each performance cycle; add 1 to the credit score of foreign currency consumption that occurs more than 1 time; deduct 2.5 for the credit score for overdue in each performance cycle; in each performance cycle If there is an installment behavior, the credit score will be deducted 0.5; the credit score of overdue for more than 3 consecutive performance cycles will be deducted 3 extra.
  • the final credit evaluation value of the target user is calculated based on the business flow data of 12 performance cycles, and the loan amount is determined according to the correspondence between the preset credit evaluation value and the loan amount.
  • the corresponding relationship between the credit evaluation value and the loan line can be: the credit evaluation value is less than 10, the loan line is 0, the loan line is greater than or equal to 10 and less than 20, and the loan line is 5000, and the corresponding relationship table is formed in turn.
  • calculating the risk value of the business to be approved in the monitoring stage after the approval includes: from the business flow data of the first L performance cycles of the target user’s current business Extract the number of defaults and the amount of default, L ⁇ 1; calculate the early warning score value of the target user according to the number of defaults and the default amount; if the early warning score value is greater than the agreed value, a warning message is generated.
  • Calculating the target user’s early warning score value based on the number of defaults and default amount includes: different default times and default amounts correspond to different early warning scores, and the target user’s early warning scores are determined based on the number of defaults extracted from the business flow data and the default amount Warning score value.
  • calculating the early warning score value of the target user includes:
  • the specific implementation steps include: determining whether there is a default in the current performance cycle, if there is a default, the early warning score is increased by l 1 . Determine the percentage of the default limit of the current performance period to the total amount of the applied business, and determine the increase in the early warning score based on the percentage.
  • the percentage is greater than B 3
  • the early warning score is increased by l 2 ; if the percentage is less than If it is equal to B 3 and greater than B 4 , the warning score increases by l 3 .
  • the early warning score Increase l 4. If the total amount of the current installment performance behavior is less than or equal to B 5 and greater than B 6 , the early warning score is increased by l 5 .
  • the above l 1 , B 3 , l 2 , B 4 , l 3 , B 5 , l 4 , B 6 , l 5 are all positive numbers and can be set according to actual needs, B 3 is greater than B 4 , B 5 is greater than B 6 .
  • the current flow data of the target user’s current performance cycle is obtained. If it is overdue, the warning score is increased by 1, if the overdue amount is greater than 30% of the current user’s loan limit, the warning score is increased by 2, and if it is greater than 50%, the warning score is increased Add 3, if it is greater than 70%, the warning score is increased by 5; if there is installment behavior, and the total bill consumption amount in the current period is greater than 50% of the current user loan amount, the warning score is increased by 1, if it is greater than 70%, the warning score is increased by 3; 2 in a row If a performance cycle shows overdue, the early warning score is increased by 2, and the overdue early warning score for 3 consecutive performance cycles is increased by 3, and the early warning score for normal repayment is not overdue is reduced by 0.5; the target user’s early warning score is calculated according to the rules, and once it is greater than or equal to 5. Trigger an alarm.
  • adding user business flow data to the business risk evaluation system can increase business risk evaluation factors.
  • the program has the following
  • Using web crawler technology to grab the business flow data of the target user from the business network system can not only improve the convenience of obtaining user business data, but also grab the data from the business system to ensure the credibility of the business flow data;
  • Fig. 2 is a schematic diagram of an optional service data monitoring device provided by an embodiment of the present application.
  • the device shown in Fig. 2 is used to execute the above-mentioned service data monitoring method.
  • the device includes:
  • the data crawling module 11 is configured to use a preset method to capture the business flow data of the target user from the business network when receiving a request message requesting approval of the pending business application for the target user;
  • the risk calculation module 12 is configured to extract N target parameters from the business flow data, and calculate the risk value of the business to be approved based on the N target parameters, where N ⁇ 1;
  • the judgment module 13 is used to judge whether the risk value of the business to be approved exceeds the corresponding risk threshold
  • the measure implementation module 14 is configured to, if the risk value of the business to be approved exceeds the corresponding risk threshold, take a predetermined measure for the business to be approved applied by the target user.
  • the risk calculation module 12 is specifically configured to: determine the current processing stage of the business to be approved; extract N from business flow data in a preset time period associated with the current processing stage of the business to be approved And calculate the risk value of the current processing stage of the pending business based on the N target parameters.
  • the processing stage of the business to be approved includes: the admission stage before approval; calculating the risk value of the business to be approved in the admission stage includes: submitting an application for approval of the business to be approved from the target user Extract transaction flow data and default record data from the business flow data of the first M months at that time, M>1; calculate the risk value of the pending business in the admission phase according to the transaction flow data and the default record data.
  • the processing stage of the business to be approved includes: an inspection stage; correspondingly, the risk calculation module 12 calculating the risk value of the business to be approved in the inspection stage includes: from the target The user profile data is extracted from the user's business flow data, and the true evaluation value of the profile data submitted by the target user is verified based on the extracted user profile data; if the true evaluation value is lower than the preset reference value, the verification fails; if If the true evaluation value is greater than or equal to the preset reference value, the credit evaluation value of the target user is calculated according to the business flow data of the first K months when the target user submits the application for mid-review, K>1; The credit evaluation value determines the business quota approved for the pending business.
  • the processing stage of the business to be approved includes: a monitoring stage after approval; accordingly, the risk calculation module 12 calculating the risk value of the business to be approved in the monitoring stage after approval includes: The number of defaults and the amount of default are extracted from the business flow data of the first L performance cycles of the target user's current business, L ⁇ 1; the early warning score value of the target user is calculated according to the number of defaults and the default amount; if the early warning score If the value is greater than the agreed value, a warning message will be generated.
  • the business flow data is transaction flow data
  • the data crawling module 11 uses a preset method to grab the business flow data of the target user from the business network, including: based on the characteristics of the target user Data grabs the transaction page in the preset business system; extracts the first transaction flow data of the target user and the characteristic data of the third-party platform associated with the target user from the transaction page; based on the third-party platform
  • the characteristic data of the target user is captured from the third-party platform system and the target user’s second transaction flow data is extracted from it; the results are obtained according to the first transaction flow data and the second transaction flow data State the transaction flow data of the target user.
  • the business data monitoring device of the embodiment of the application grabs the business flow data of the target user from the business network system, which not only improves the convenience of obtaining user data, but also ensures the credibility of the business flow data by grabbing data from the business system ; Further, the program adds the captured business flow data to the business risk evaluation link, realizes the full automatic monitoring of the user's business, adds evaluation factors for the business system to evaluate the user's performance ability, so that the business system can reasonably control business risks .
  • the device shown in FIG. 2 can execute the method of the embodiment shown in FIG. 1.
  • parts that are not described in detail in this embodiment please refer to the related description of the embodiment shown in FIG.
  • the implementation process and technical effects of this technical solution refer to the description in the embodiment shown in FIG. 1, which will not be repeated here.
  • an embodiment of the present application provides a computer-readable storage medium that stores a computer program that, when executed by a processor, implements the following steps of the foregoing business data monitoring method:
  • a predetermined measure is taken for the service to be approved applied by the target user.
  • the following steps are further implemented when the computer program is executed by the processor: extracting N target parameters from the business flow data, and calculating the risk value of the business to be approved based on the N target parameters, including: determining The current processing stage of the business to be approved; extract N target parameters from the business flow data in a preset time period associated with the current processing stage of the business to be approved, and calculate the business to be approved based on the N target parameters The risk value of the current processing stage.
  • the processing stage of the business to be approved includes: the admission stage before approval; correspondingly, the risk value of the business to be approved in the admission stage is calculated Including: extracting transaction flow data and default record data from the business flow data of the first M months when the target user submits the approval application for the pending business approval, M>1; calculating the office based on the transaction flow data and the default record data State the risk value of the business to be approved at the admission stage.
  • the processing stage of the pending business includes: an inspection stage; correspondingly, calculating the risk value of the pending business at the stage of inspection includes : Extract user profile data from the target user’s business flow data, and verify the true evaluation value of the profile data submitted by the target user based on the extracted user profile data; if the true evaluation value is lower than the preset reference value, review If the true evaluation value is greater than or equal to the preset reference value, the credit evaluation value of the target user is calculated according to the business flow data of the first K months when the target user submits the application >1; Determine the business quota approved for the business to be approved according to the credit evaluation value.
  • the processing stage of the business to be approved includes: a monitoring stage after approval; accordingly, the risk of the business to be approved in the monitoring stage after approval is calculated
  • the value includes: extracting the number of defaults and the amount of default from the business flow data of the previous L performance cycles of the target user's current business, L ⁇ 1; calculating the early warning score value of the target user according to the number of defaults and the amount of default; If the warning score value is greater than the agreed value, a warning message is generated.
  • the business flow data is transaction flow data
  • the use of a preset method to capture the target user's business flow data from the business network includes: The characteristic data of the target user is captured in the preset business system to capture the transaction page; the first transaction flow data of the target user and the characteristic data of the third-party platform associated with the target user are extracted from the transaction page; The characteristic data of the third-party platform grabs the target user’s transaction page from the third-party platform system and extracts the target user’s second transaction flow data therefrom; according to the first transaction flow data and the second transaction The flow data obtains the transaction flow data of the target user.
  • Figure 2 describes the internal functions and structure of the business data monitoring device.
  • the structure of the business data monitoring device can be implemented as a computer device, which can be a processing device in a banking system.
  • Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present application.
  • the computer device of this embodiment includes: a processor 21, a memory 22, and a computer program 23 stored in the memory 22 and running on the processor 21.
  • the computer program 23 is implemented when the processor 21 is executed. To avoid repetition, the service data monitoring method in the embodiment will not be repeated here.
  • the computer program is executed by the processor 21, the function of each model/unit in the business data monitoring device in the embodiment is realized. To avoid repetition, it will not be repeated here.
  • Computer equipment can be computing equipment such as desktop computers, notebooks, palmtop computers, and cloud servers.
  • the computer equipment may include, but is not limited to, a processor 21 and a memory 22.
  • FIG. 3 is only an example of a computer device, and does not constitute a limitation on the computer device. It may include more or less components than those shown in the figure, or combine certain components, or different components, for example Computer equipment may also include input and output devices, network access devices, buses, and so on.
  • the so-called processor 21 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the memory 22 may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device.
  • the memory 22 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a Secure Digital (SD) card, and a flash card (Flash Card) equipped on the computer device. Wait.
  • the memory 22 may also include both an internal storage unit of a computer device and an external storage device.
  • the memory 22 is used to store computer programs and other programs and data required by the computer equipment.
  • the memory 22 can also be used to temporarily store data that has been output or will be output.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined Or it can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • each unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional units.
  • the above-mentioned integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium.
  • the above-mentioned software functional unit is stored in a storage medium and includes several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (Processor) execute the method described in each embodiment of the present application Part of the steps.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program code .

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Abstract

一种业务数据监控方法和装置。该业务数据监控方法包括：接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据（101）；从业务流水数据中提取N个目标参数，基于N个目标参数计算所述待批准业务的风险值，所述N≥1（102）；判断所述待批准业务的风险值是否超过相应的风险阈值（103）；如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施（104）。该方法在现有业务数据评价中增加用户流水数据的评价因素，可以增强对业务风险的控制。

Description

业务数据监控方法和装置
本申请要求于2019年6月17日提交中国专利局，申请号为201910521007.0、发明名称为“业务数据监控方法和装置”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
本申请涉及大数据技术领域，尤其涉及一种业务数据监控方法和装置。
背景技术
银行、信贷公司等金融机构在批准用户的如贷款等信贷业务之前，需要对用户的信用、收入和资产等信息进行审核，由此以确定合理的授信额度以及控制业务风险。
目前，各类金融机构大多基于用户在提出业务申请时提交的各项资料收集关于用户信用、收入和资产等情况的信息，并在后续程序中鉴别各项资料的真实程度，发明人发现，这种方式难以避免的会出现虚假资料蒙混过关的情况，由此可能会增大各类金融机构的业务。
如何在现有的业务审批流程中进一步增强业务风险的控制成为亟待解决的问题。
申请内容
根据本申请公开的各种实施例，提供一种业务数据监控方法和装置，用以增强对业务风险的风险控制。
一种业务数据监控方法，所述方法包括：
接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
判断所述待批准业务的风险值是否超过相应的风险阈值；
如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
一种业务数据监控装置，包括：
数据爬取模块，用于接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
风险计算模块，用于从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
判断模块，用于判断所述待批准业务的风险值是否超过相应的风险阈值；
措施实施模块，用于如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现以下方法的步骤：
接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
判断所述待批准业务的风险值是否超过相应的风险阈值；
如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
一种计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现以下方法的步骤：
接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
判断所述待批准业务的风险值是否超过相应的风险阈值；
如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
本申请的一个或多个实施例的细节在下面的附图和描述中提出。本申请的其它特征和优点将从说明书、附图以及权利要求书变得明显。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申 请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图。
图1是本申请实施例提供的一种可选的业务数据监控方法的流程图；
图2是本申请实施例提供的一种可选的业务数据监控装置的示意图；
图3是是本申请实施例提供的一种可选的计算机设备的示意图。
具体实施方式
为了更好的理解本申请的技术方案，下面结合附图对本申请实施例进行详细描述。
应当明确，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本申请保护的范围。
在本申请实施例中使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本申请。在本申请实施例和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。
应当理解，本文中使用的术语“和/或”仅仅是一种描述关联对象的相同的字段，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
应当理解，尽管在本申请实施例中可能采用术语第一、第二、第三等来描述预设范围等，但这些预设范围不应限于这些术语。这些术语仅用来将预设范围彼此区分开。例如，在不脱离本申请实施例范围的情况下，第一预设范围也可以被称为第二预设范围，类似地，第二预设范围也可以被称为第一预设范围。
取决于语境，如在此所使用的词语“如果”可以被解释成为“在……时”或“当……时”或“响应于确定”或“响应于检测”。类似地，取决于语境，短语“如果确定”或“如果检测(陈述的条件或事件)”可以被解释成为“当确定时”或“响应于确定”或“当检测(陈述的条件或事件)时”或“响应于检测(陈述的条件或事件)”。
图1是本申请实施例提供的一种可选的业务数据监控方法的流程图。如图1所示，该方法包括：
步骤S101：接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据。
其中，采用预设的方式从业务网络中抓取目标用户的业务流水数据包括：采用预先设置好的网络爬虫从业务网络中抓取目标用户的业务流水数据。
网络爬虫，又称为网页蜘蛛、网络机器人，其能够自动从网络中抓取信息。区别于传统的关键字搜索，网络爬虫从一个或若干初始网页的URL开始，不断从关联页面上抽取新的URL放入队列，直至满足停止条件。基于上述过程不断抽取的网络页面获取目标信息。
本申请实施例方案中，当接收到请求对目标用户所申请的待批准业务批准通过的请求时，可以采用预先设置好的网络爬虫从业务网络中抓取目标用户的业务流水数据。可选的，基于目标用户授权的登录信息采用预先设置好的网络爬虫登录业务网络，登录成功后从业务网络中抓取目标用户的业务流水数据。其中，可以采用selenium+scrapy爬虫技术框架模拟浏览器进行业务网络的登录操作。在登录成功后从业务网络中抓取的业务流水数据包括目标用户的用户资料数据以及交易流水数据。
在一种具体实现方式中，目标用户所申请的待批准业务为贷款业务，从银行网络系统中抓取的目标用户业务流水数据包括用户资料数据以及交易流水数据。所述用户资料数据包括：基本账户信息，如账户名、币种、卡号、卡类型(借记卡或信用卡)、开卡地址、账户余额；交易流水数据包括网银流水，如还款总金额、最低还款额、到期还款日、账单日、本期还款金额、本期最低还款额、信用额度、预借现金额度、币种简码、消费类别等。
上述的业务网络可以是目标用户所申请的待批准业务对应的业务网络，还可以包括与待批准业务关联的第三方业务网络。例如，目标用户所申请的待批准业务为贷款业务，所述业务网络包括所申请贷款业务的银行网络系统，还包括与目标用户有所关联的其它银行的银行网络系统、第三方支付平台等。此时，从业务网络中抓取目标用户的业务流水数据包括：基于所述目标用户的特征数据在预设业务系统中抓取交易页面；从所述交易页面中提取所述目标用户的第一交易流水数据以及与所述目标用户关联的第三方平台的特征数据；基于所述第三方平台的特征数据从第三方平台系统中抓取所述目标用户的交易页面并从中提取所述目标用户的第二交易流水数据；根据所述第一交易流水数据和所述第二交易流水数据得到所述目标用户的交易流水数据。所述第三方平台指第三方银行或者第三方支付平台。鉴于当前支付环境的多样性以及各支付平台之间的关联性，可以从用户所申请贷款业 务的银行系统抓取与目标用户关联的其他支付平台的交易数据，比如，可以基于用户的身份证号码、人脸头像、银行卡账户、转账记录等作为特征数据搜索第三方平台的交易页面。
步骤S102：从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1。
从业务网络中获取目标用户的业务流水数据后，将所述业务流水数据应用在对目标用户所申请的待批准业务的风险评价中，以确定目标用户所申请的待批准业务的风险值。可选的，从目标用户的业务流水数据中提取N个目标参数，赋予所述N个目标参数或者赋予基于所述N个参数计算得到的至少一个评价值相应的分数权重，基于N个目标参数或者基于所述N个参数计算得到的至少一个评价值的分数权重计算待批准业务的风险值。
步骤S103：判断所述待批准业务的风险值是否超过相应的风险阈值。
步骤S104：如果确定待批准业务的风险值超过其相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
本申请实施例中，对应于目标用户所申请的待批准业务设置有相对应的风险阈值，当基于目标用户的业务流水数据计算出的风险值超过设置的风险阈值后可以对目标用户所申请的待批准业务采取预定措施，所述预定措施可以是拒绝审核通过待批准业务、要求目标用户追加评价资料等，由此以在现有控制业务风险的基础上增加业务风险评价因素，增强业务风险控制。
在本申请实施例中，从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，包括：确定所述待批准业务的当前处理阶段；从与待批准业务的当前处理阶段关联的预设时间段内的业务流水数据中提取N个目标参数，并基于该N个目标参数计算所述待批准业务当前处理阶段的风险值。
在一种可能的设计中，所述待批准业务的处理阶段包括：批准前的准入阶段、审中查验阶段以及批准后的监控阶段，对于每个处理阶段基于目标流水数据计算待批准业务当前处理阶段的风险值包括：
当待批准业务处于批准前的准入阶段时，计算所述待批准业务在准入阶段的风险值包括：从目标用户提出待批准业务的批准申请时的前M个月的业务流水数据中提取交易流水数据以及违约记录数据，M＞1；根据所述交易流水数据及所述违约记录数据计算所述待批准业务在准入阶段的风险值。其中，预先存储交易流水数据和收入评分的关联关系以及存储 违约记录数据和违约评分的关联关系；根据从所述M个月的业务流水数据中提取的交易流水数据及违约记录数据分别计算目标用户的收入评分x以及违约评分y，基于收入评分x和违约评分y所具有的权重计算目标用户的待批准业务在准入阶段的风险值，风险值＝f(x，y)。
上述计算待批准业务在准入阶段的风险值，包括：
(1)计算违约评分y包括：确定M个月中的总违约次数x 1；若x 1大于X 1，y取值为y 1；若x 1在(0，X 1]范围内则按照预设步长a 1，x 1每减少一个步长a 1，y的取值在y 1的基础上减去一个b 1，即y＝y 1-n 1b 1，其中n 1的取值为(X 1-x 1)/a 1向上取整，即若(X 1-x 1)/a 1余数不为零则计算结果的整数部分加1并取整，上述x 1、X 1、y 1、a 1及b 1均为正数，并可以根据实际需要确定。
(2)确定y取值是否大于或等于约定阈值，若大于或等于约定阈值，则确定待批准业务在准入阶段的风险值高于风险阈值，并采取预定措施。
(3)如果y小于约定阈值，则计算收入评分x；其中计算收入评分x，包括：确定M个月的收入均值x 2，若x 2大于X 2，则x取值为x 3；若x 2在(X 1，X 2]范围内则按照预设步长a 2，每减少一个步长a 2，x的取值在x 3基础上减去一个b 2，即x＝x 3-n 2b 2，其中n 2的取值为(X 2-x 2)/a 2向上取整，即(X 2-x 2)/a 2余数不为零则计算结果的整数部分加1并取整，上述X 2、X 1、a 2、b 2均为正数，X 1小于X 2，并可以根据实际需要确定；若x 2小于或者等于X 1，则确定待批准业务在准入阶段的风险值高于风险阈值，并采取预定措施。
(4)基于计算的违约评分y和收入评分x计算待批准业务在准入阶段的风险值＝y+k(m-x)，其中m，k均为常数，m为大于x的正整数，k(m-x)为收入评分对应的风险值，y为违约评分对应的风险值。
在一种具体实现方式中，目标用户所申请的待批准业务为贷款业务，计算目标用户贷款业务准入阶段风险值的包括：基于目标用户提出贷款申请时的前六个月流水数据的违约记录数据计算目标用户的违约评分，如前六个月违约记录次数大于三次，违约评分为5，直接拒绝目标用户的贷款申请业务；如前六个月违约记录次数为2次，违约评分为3；如前六个月违约记录次数为1次，违约评分为1；如果没有违约记录则违约评分为0；如果违约评分小于或者等于3，则基于目标用户提出贷款申请时的前六个月交易流水数据计算目标用户的收入评分，不同的收入数据对应不同的收入评分，如六个月收入平均值低于3000收入评分为0，收入风险值为5-0＝5；3000-5000收入评分为1，收入风险值为5-1＝4；5000-7000收入评分为2，收入风险值为5-2＝3；以此类推，收入评分最高为5，对应的风险系数最 低为0；收入风险值加上违约评分得到贷款业务准入阶段风险值，若大于6，则拒绝贷款或者追加质押物。
当待批准业务处于审中查验阶段时，计算所述待批准业务在审中查验阶段的风险值包括：从所述目标用户的业务流水数据中提取用户资料数据，基于提取的所述用户资料数据验证目标用户所提交资料数据的真实评价值。如果真实评价值低于预设参考值，则审中查验失败；如果所述真实评价值大于或者等于所述预设参考值，则根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值，K＞1；根据所述信用评价值确定对所述待批准业务批准通过的业务额度。
所述用户资料数据包括：身份信息、收入信息、借贷信息；用户所提交的资料数据包括：身份验证文件、收入证明文件及借贷证明文件；从所述身份验证文件、收入证明文件及借贷证明文件分别提取用户的身份验证信息、收入验证信息及借贷验证信息，将所述身份验证信息、收入验证信息及借贷验证信息分别与抓取的身份信息、收入信息、借贷信息进行比对，根据比对的结果确定真实评价值。
在一种具体实现方式中：如果身份信息验证不一致，则真实评价值为0，收入信息及借贷信息不再比对；如果身份信息验证一致，收入信息及借贷信息中一项验证不通过，则真实评价值为3；如果身份信息验证一致，收入信息及借贷信息中两项验证均不通过，则真实评价值为1；如果三项信息验证均通过则真实评价值为5，如果真实评价值低于3，则审中查验失败；如果所述真实评价值大于或者等于3，则根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值。
在本申请实施例方案中，根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值包括：从所述前K个月的业务流水数据中提取目标用户已经拥有的信用额度、消费总金额、前K个月包括的A个履约周期中每个履约周期的履约信息，A＞1；根据信用额度、消费总金额、A个履约周期中每个履约周期的履约信息分别对应的信用评分计算目标用户的信用评价值。
其中，计算目标用户的信用评价值包括：
(1)基于信用额度得到目标用户信用评分的增加值z 1。其中，实现步骤包括：判断所述信用额度是否大于预设数值B 1，如果大于B 1，则确定z 1的取值为k 1；上述B 1、k 1均为正数，B 1、k 1可根据实际需要设定。
(2)基于消费总金额得到目标用户信用评分的增加值z 2。其中，实现步骤包括：判断所述消费总金额是否大于B 2，如果大于B 2，则确定z 2的取值为k 2；上述B 2、k 2均为正数，B 2、k 2可根据实际需要设定。
(3)基于A个履约周期中每个履约周期的履约信息计算目标用户信用评分的增加值z 3。实现步骤包括：基于每个履约周期的履约信息中所包括的消费金额、约定币种消费信息、分期消费信息、履约状况信息确定z 3，其中包括：若消费金额大于B 3的履约周期个数大于v 1，则z 3的取值加k 3；若单个履约周期中未出现违约情况，z 3的取值加k 4，若连续未出现违约情况的履约周期个数大于v 2则z 3的取值加k 5；若A个履约周期中出现约定币种消费的履约周期个数大于v 3则z 3的取值加k 6；若单个履约周期中出现违约情况，则z 3取值减k 7；若单个履约周期中出现分期履约情况，则z 3取值减k 8；根据计算的z 3的各个分值汇总z 3最终取值；B 3、v 1、k 3、k 4、v 2、k 5、v 3、k 6、k 7、k 8均为正数，并且可根据实际需要设定。
(4)将上述计算得到的z 1+z 2+z 3相加并且加上信用基础评分得到目标用户的信用评价值，所述信用基础评分可根据实际需要设定。
在一种具体实现方式中，获取目标用户前12个月的业务流水数据，该12个月的业务流水数据分为12个履约周期，从所述12个月的业务流水数据中提取目标用户的信用额度、消费总金额、12个履约周期中每个履约周期的履约信息。如在这12个月中，目标用户的信用额度大于60000，信用评分加2；消费总金额大于50000，则信用评分加5；若存在大于等于1个履约周期中，消费金额大于5000，信用评分加1；每个履约周期中成功履约未出现逾期的账单周期信用评分加2；出现大于等于1次外币消费信用评分加1；每个履约周期中出现逾期信用评分扣除2.5；每个履约周期中出现出现分期行为,信用评分扣除0.5；连续3次以上履约周期逾期信用评分额外扣除3。综上基于12个履约周期的业务流水数据计算目标用户最终的信用评价值，根据预先设置的信用评价值与贷款额度的对应关系确定贷款额度。
信用评价值与贷款额度的对应关系可以是：信用评价值<10，贷款额度为0，大于等于10小于20，贷款额度为5000，依次形成对应关系表。
当待批准业务处于批准后的监控阶段时，相应的，计算所述待批准业务在批准后的监控阶段的风险值包括：从所述目标用户当前业务的前L个履约周期的业务流水数据中提取违约次数以及违约金额，L≥1；根据所述违约次数和违约金额计算所述目标用户的预警评分值；若所述预警评分值大于约定值则生成警告信息。
根据违约次数和违约金额计算目标用户的预警评分值包括：不同的违约次数和违约金额分别对应不同的预警评分，根据从业务流水数据中提取的违约次数和违约金额对应的预警评分确定目标用户的预警评分值。
其中，计算目标用户的预警评分值包括：
(1)计算每个履约周期的预警评分增加值。具体实现步骤包括：确定当前履约周期中是否出现违约情况，若出现违约则预警评分增加l 1。确定当前履约周期的违约额度与所申请业务总额度的百分比，根据所述百分比确定预警评分增加值，可选的，若所述百分比大于B 3，则预警评分增加l 2；若所述百分比小于或等于B 3并且大于B 4，则预警评分增加l 3。确定当前履约周期中是否出现分期履约行为，若出现分期履约行为则根据当前分期履约行为的总额度确定预警评分增加值，可选的，若当前分期履约行为的总额度大于B 5，则预警评分增加l 4，若当前分期履约行为的总额度小于或等于B 5并且大于B 6，则预警评分增加l 5。上述l 1、B 3、l 2、B 4、l 3、B 5、l 4、B 6、l 5均为正数且可根据实际需要设定，B 3大于B 4，B 5大于B 6。
(2)基于计算得到的每个履约周期的预警评分增加值计算L个履约周期的预警评分增加值。
(3)计算额外预警评分增加值；其中，包括：若L个履约周期中有连续B 7个周期出现违约，则预约评分增加l 6，若L个履约周期均正常履约则预警评分减l 7。上述B 7、l 6、l 7均为正数且可根据实际需要设定。
(4)在基础预警评分的基础上增加L个履约周期的预警评分增加值以及额外预警评分增加值得到目标用户的预警评分值。
在一种具体实现方式中，获取目标用户当前履约周期的流水数据，若出现逾期，预警评分加1，若逾期金额大于当前用户贷款额度30％，预警评分加2，若大于50％，预警评分加3，若大于70％，预警评分加5；若出现分期行为，且当期账单消费总金额大于当前用户贷款总金额50％，预警评分加1，若大于70％，预警评分加3；连续2个履约周期显示逾期，预警评分加2，连续3个履约周期逾期预警评分加3，出现正常还款未逾期预警评分减0.5；根据所述规则计算目标用户的预警评分值，一旦大于等于5即触发告警。
在本申请实施例方案中，将用户业务流水数据增加到业务风险评价体系中，能够增加业务风险评价因素。具体而言，所述方案具有以下
有益效果：
1、采用网络爬虫技术从业务网络系统中抓取目标用户的业务流水数据，不仅可以提高获取用户业务数据的便捷性，而且从业务系统中抓取数据可以确保业务流水数据的可信性；
2、将抓取的业务流水数据补充到用户业务的批准前的准入、审中查验和批准后的监控等环节中，实现对用户业务的全程自动监控，为 业务系统评价用户的履约能力增加评价因素，使业务系统可以合理控制业务风险。
以下将详细描述本申请的一个或多个实施例的业务数据监控装置。本领域技术人员可以理解，这些业务数据监控装置均可使用市售的硬件组件通过本方案所教导的步骤进行配置来构成。
图2是本申请实施例提供的一种可选的业务数据监控装置的示意图。图2所示装置用于执行上述的业务数据监控方法。如图2所示，该装置包括：
数据爬取模块11，用于接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
风险计算模块12，用于从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
判断模块13，用于判断所述待批准业务的风险值是否超过相应的风险阈值；
措施实施模块14，用于如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
在一种可能的设计中，风险计算模块12具体用于：确定所述待批准业务的当前处理阶段；从与待批准业务的当前处理阶段关联的预设时间段内的业务流水数据中提取N个目标参数，并基于该N个目标参数计算所述待批准业务当前处理阶段的风险值。
在一种可能的设计中，所述待批准业务的处理阶段包括：批准前的准入阶段；计算所述待批准业务在准入阶段的风险值包括：从目标用户提出待批准业务的批准申请时的前M个月的业务流水数据中提取交易流水数据以及违约记录数据，M＞1；根据所述交易流水数据及所述违约记录数据计算所述待批准业务在准入阶段的风险值。
在一种可能的设计中，所述待批准业务的处理阶段包括：审中查验阶段；相应的，风险计算模块12计算所述待批准业务在审中查验阶段的风险值包括：从所述目标用户的业务流水数据中提取用户资料数据，基于提取的所述用户资料数据验证目标用户所提交资料数据的真实评价值；如果所述真实评价值低于预设参考值，审中查验失败；如果所述真实评价值大于或者等于所述预设参考值，则根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值，K＞1；根据所述信用评价值确定对所述待批准业务批准通过的业务额度。
在一种可能的设计中，所述待批准业务的处理阶段包括：批准后的监控阶段；相应的，风险计算模块12计算所述待批准业务在批准后的监控阶段的风险值包括：从所述目标用户当前业务的前L个履约周期的业务流水数据中提取违约次数以及违约金额，L≥1；根据所述违约次数和违约金额计算所述目标用户的预警评分值；若所述预警评分值大于约定值则生成警告信息。
在一种可能的设计中，所述业务流水数据为交易流水数据；数据爬取模块11采用预设的方式从业务网络中抓取目标用户的业务流水数据，包括：基于所述目标用户的特征数据在预设业务系统中抓取交易页面；从所述交易页面中提取所述目标用户的第一交易流水数据以及与所述目标用户关联的第三方平台的特征数据；基于所述第三方平台的特征数据从第三方平台系统中抓取所述目标用户的交易页面并从中提取所述目标用户的第二交易流水数据；根据所述第一交易流水数据和所述第二交易流水数据得到所述目标用户的交易流水数据。
本申请实施例的业务数据监控装置从业务网络系统中抓取目标用户的业务流水数据，不仅可以提高获取用户数据的便捷性，而且从业务系统中抓取数据可以确保业务流水数据的可信性；进一步，所述方案将抓取的业务流水数据补充到业务风险评价环节中，实现对用户业务的全程自动监控，为业务系统评价用户的履约能力增加评价因素，使业务系统可以合理控制业务风险。
图2所示装置可以执行图1所示实施例的方法，本实施例未详细描述的部分，可参考对图1所示实施例的相关说明。该技术方案的执行过程和技术效果参见图1所示实施例中的描述，在此不再赘述。
进一步，本申请实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述业务数据监控方法的以下步骤：
接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
判断所述待批准业务的风险值是否超过相应的风险阈值；
如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
可选地，在计算机程序被处理器执行时还实现以下步骤：从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准 业务的风险值，包括：确定所述待批准业务的当前处理阶段；从与待批准业务的当前处理阶段关联的预设时间段内的业务流水数据中提取N个目标参数，并基于该N个目标参数计算所述待批准业务当前处理阶段的风险值。
可选地，在计算机程序被处理器执行时还实现以下步骤：所述待批准业务的处理阶段包括：批准前的准入阶段；相应的，计算所述待批准业务在准入阶段的风险值包括：从目标用户提出待批准业务的批准申请时的前M个月的业务流水数据中提取交易流水数据以及违约记录数据，M＞1；根据所述交易流水数据及所述违约记录数据计算所述待批准业务在准入阶段的风险值。
可选地，在计算机程序被处理器执行时还实现以下步骤：所述待批准业务的处理阶段包括：审中查验阶段；相应的，计算所述待批准业务在审中查验阶段的风险值包括：从所述目标用户的业务流水数据中提取用户资料数据，基于提取的所述用户资料数据验证目标用户所提交资料数据的真实评价值；如果所述真实评价值低于预设参考值，审中查验失败；如果所述真实评价值大于或者等于所述预设参考值，则根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值，K＞1；根据所述信用评价值确定对所述待批准业务批准通过的业务额度。
可选地，在计算机程序被处理器执行时还实现以下步骤：所述待批准业务的处理阶段包括：批准后的监控阶段；相应的，计算所述待批准业务在批准后的监控阶段的风险值包括：从所述目标用户当前业务的前L个履约周期的业务流水数据中提取违约次数以及违约金额，L≥1；根据所述违约次数和违约金额计算所述目标用户的预警评分值；若所述预警评分值大于约定值则生成警告信息。
可选地，在计算机程序被处理器执行时还实现以下步骤：所述业务流水数据为交易流水数据；采用预设的方式从业务网络中抓取目标用户的业务流水数据，包括：基于所述目标用户的特征数据在预设业务系统中抓取交易页面；从所述交易页面中提取所述目标用户的第一交易流水数据以及与所述目标用户关联的第三方平台的特征数据；基于所述第三方平台的特征数据从第三方平台系统中抓取所述目标用户的交易页面并从中提取所述目标用户的第二交易流水数据；根据所述第一交易流水数据和所述第二交易流水数据得到所述目标用户的交易流水数据。
图2描述了业务数据监控装置的内部功能和结构，在一个可能的设计中，业务数据监控装置的结构可实现为一计算机设备，该计算机设备可以为银行系统中的处理设备。图3是本申请实施例提供的一种计 算机设备的示意图。如图3所示，该实施例的计算机设备包括：处理器21、存储器22以及存储在存储器22中并可在处理器21上运行的计算机程序23，该计算机程序23被处理器21执行时实现实施例中的业务数据监控方法，为避免重复，此处不一一赘述。或者，该计算机程序被处理器21执行时实现实施例中业务数据监控装置中各模型/单元的功能，为避免重复，此处不一一赘述。
计算机设备可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。计算机设备可包括，但不仅限于，处理器21、存储器22。本领域技术人员可以理解，图3仅仅是计算机设备的示例，并不构成对计算机设备的限定，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，例如计算机设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器21可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
存储器22可以是计算机设备的内部存储单元，例如计算机设备的硬盘或内存。存储器22也可以是计算机设备的外部存储设备，例如计算机设备上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，存储器22还可以既包括计算机设备的内部存储单元也包括外部存储设备。存储器22用于存储计算机程序以及计算机设备所需的其他程序和数据。存储器22还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如，多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以 是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述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，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
上述以软件功能单元的形式实现的集成的单元，可以存储在一个计算机可读取存储介质中。上述软件功能单元存储在一个存储介质中，包括若干指令用以使得一台计算机装置(可以是个人计算机，服务器，或者网络装置等)或处理器(Processor)执行本申请各个实施例所述方法的部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述仅为本申请的较佳实施例而已，并不用以限制本申请，凡在本申请的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本申请保护的范围之内。

Claims (20)

  1. 一种业务数据监控方法，所述方法包括：
    接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
    从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
    判断所述待批准业务的风险值是否超过相应的风险阈值；
    如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
  2. 根据权利要求1所述的方法，其中，从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，包括：
    确定所述待批准业务的当前处理阶段；
    从与待批准业务的当前处理阶段关联的预设时间段内的业务流水数据中提取N个目标参数，并基于该N个目标参数计算所述待批准业务当前处理阶段的风险值。
  3. 根据权利要求2所述的方法，其中，所述待批准业务的处理阶段包括：批准前的准入阶段；
    相应的，计算所述待批准业务在准入阶段的风险值包括：从目标用户提出待批准业务的批准申请时的前M个月的业务流水数据中提取交易流水数据以及违约记录数据，M＞1；根据所述交易流水数据及所述违约记录数据计算所述待批准业务在准入阶段的风险值。
  4. 根据权利要求2所述的方法，其中，所述待批准业务的处理阶段包括：审中查验阶段；
    相应的，计算所述待批准业务在审中查验阶段的风险值包括：
    从所述目标用户的业务流水数据中提取用户资料数据，基于提取的所述用户资料数据验证目标用户所提交资料数据的真实评价值；如果所述真实评价值低于预设参考值，审中查验失败；
    如果所述真实评价值大于或者等于所述预设参考值，则根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值，K＞1；根据所述信用评价值确定对所述待批准业务批准通过的业务额度。
  5. 根据权利要求2所述的方法，其中，所述待批准业务的处理阶段包括：批准后的监控阶段；
    相应的，计算所述待批准业务在批准后的监控阶段的风险值包括：从所述目标用户当前业务的前L个履约周期的业务流水数据中提取违约次数以及违约金额，L≥1；根据所述违约次数和违约金额计算所述目标用户的预警评分值；若所述预警评分值大于约定值则生成警告信息。
  6. 根据权利要求1所述的方法，其中，所述业务流水数据为交易流水数据；采用预设的方式从业务网络中抓取目标用户的业务流水数据，包括：
    基于所述目标用户的特征数据在预设业务系统中抓取交易页面；
    从所述交易页面中提取所述目标用户的第一交易流水数据以及与所述目标用户关联的第三方平台的特征数据；
    基于所述第三方平台的特征数据从第三方平台系统中抓取所述目标用户的交易页面并从中提取所述目标用户的第二交易流水数据；
    根据所述第一交易流水数据和所述第二交易流水数据得到所述目标用户的交易流水数据。
  7. 一种业务数据监控装置，包括：
    数据爬取模块，用于接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
    风险计算模块，用于从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
    判断模块，用于判断所述待批准业务的风险值是否超过相应的风险阈值；
    措施实施模块，用于如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
  8. 根据权利要求7所述的装置，其中，风险计算模块具体用于：
    确定所述待批准业务的当前处理阶段；
    从与待批准业务的当前处理阶段关联的预设时间段内的业务流水数据中提取N个目标参数，并基于该N个目标参数计算所述待批准业务当前处理阶段的风险值。
  9. 一种计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如下方法的步骤：
    接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
    从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
    判断所述待批准业务的风险值是否超过相应的风险阈值；
    如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
  10. 根据权利要求9所述的计算机设备，其中，从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，包括：
    确定所述待批准业务的当前处理阶段；
    从与待批准业务的当前处理阶段关联的预设时间段内的业务流水数据中提取N个目标参数，并基于该N个目标参数计算所述待批准业务当前处理阶段的风险值。
  11. 根据权利要求10所述的计算机设备，其中，所述待批准业务的处理阶段包括：批准前的准入阶段；
    相应的，计算所述待批准业务在准入阶段的风险值包括：从目标用户提出待批准业务的批准申请时的前M个月的业务流水数据中提取交易流水数据以及违约记录数据，M＞1；根据所述交易流水数据及所述违约记录数据计算所述待批准业务在准入阶段的风险值。
  12. 根据权利要求10所述的计算机设备，其中，所述待批准业务的处理阶段包括：审中查验阶段；
    相应的，计算所述待批准业务在审中查验阶段的风险值包括：
    从所述目标用户的业务流水数据中提取用户资料数据，基于提取的所述用户资料数据验证目标用户所提交资料数据的真实评价值；如果所述真实评价值低于预设参考值，审中查验失败；
    如果所述真实评价值大于或者等于所述预设参考值，则根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值，K＞1；根据所述信用评价值确定对所述待批准业务批准通过的业务额度。
  13. 根据权利要求10所述的计算机设备，其中，所述待批准业务的处理阶段包括：批准后的监控阶段；
    相应的，计算所述待批准业务在批准后的监控阶段的风险值包括：从所述目标用户当前业务的前L个履约周期的业务流水数据中提取违约次数以及违约金额，L≥1；根据所述违约次数和违约金额计算所述目标用户的预警评分值；若所述预警评分值大于约定值则生成警告信息。
  14. 根据权利要求9所述的计算机设备，其中，所述业务流水数据为交易流水数据；采用预设的方式从业务网络中抓取目标用户的业务流水数据，包括：
    基于所述目标用户的特征数据在预设业务系统中抓取交易页面；
    从所述交易页面中提取所述目标用户的第一交易流水数据以及与所述目标用户关联的第三方平台的特征数据；
    基于所述第三方平台的特征数据从第三方平台系统中抓取所述目标用户的交易页面并从中提取所述目标用户的第二交易流水数据；
    根据所述第一交易流水数据和所述第二交易流水数据得到所述目标用户的交易流水数据。
  15. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如下的方法的步骤：
    接收到请求对目标用户所申请的待批准业务批准通过的请求消息时，采用预设的方式从业务网络中抓取目标用户的业务流水数据；
    从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，所述N≥1；
    判断所述待批准业务的风险值是否超过相应的风险阈值；
    如果所述待批准业务的风险值超过相应的风险阈值，则对所述目标用户所申请的待批准业务采取预定措施。
  16. 根据权利要求15所述的存储介质，其中，从所述业务流水数据中提取N个目标参数，基于所述N个目标参数计算所述待批准业务的风险值，包括：
    确定所述待批准业务的当前处理阶段；
    从与待批准业务的当前处理阶段关联的预设时间段内的业务流水数据中提取N个目标参数，并基于该N个目标参数计算所述待批准业务当前处理阶段的风险值。
  17. 根据权利要求16所述的存储介质，其中，所述待批准业务的处理阶段包括：批准前的准入阶段；
    相应的，计算所述待批准业务在准入阶段的风险值包括：从目标用户提出待批准业务的批准申请时的前M个月的业务流水数据中提取交易流水数据以及违约记录数据，M＞1；根据所述交易流水数据及所述违约记录数据计算所述待批准业务在准入阶段的风险值。
  18. 根据权利要求2所述的存储介质，其中，所述待批准业务的处理阶段包括：审中查验阶段；
    相应的，计算所述待批准业务在审中查验阶段的风险值包括：
    从所述目标用户的业务流水数据中提取用户资料数据，基于提取的所述用户资料数据验证目标用户所提交资料数据的真实评价值；如果所述真实评价值低于预设参考值，审中查验失败；
    如果所述真实评价值大于或者等于所述预设参考值，则根据目标用户提出审中查验申请时的前K个月的业务流水数据计算所述目标用户的信用评价值，K＞1；根据所述信用评价值确定对所述待批准业务批准通过的业务额度。
  19. 根据权利要求16所述的存储介质，其中，所述待批准业务的处理阶段包括：批准后的监控阶段；
    相应的，计算所述待批准业务在批准后的监控阶段的风险值包括：从所述目标用户当前业务的前L个履约周期的业务流水数据中提取违约次数以及违约金额，L≥1；根据所述违约次数和违约金额计算所述目标用户的预警评分值；若所述预警评分值大于约定值则生成警告信息。
  20. 根据权利要求15所述的存储介质，其中，所述业务流水数据为交易流水数据；采用预设的方式从业务网络中抓取目标用户的业务流水数据，包括：
    基于所述目标用户的特征数据在预设业务系统中抓取交易页面；
    从所述交易页面中提取所述目标用户的第一交易流水数据以及与所述目标用户关联的第三方平台的特征数据；
    基于所述第三方平台的特征数据从第三方平台系统中抓取所述目标用户的交易页面并从中提取所述目标用户的第二交易流水数据；
    根据所述第一交易流水数据和所述第二交易流水数据得到所述目标用户的交易流水数据。
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Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112767162A (zh) * 2021-01-22 2021-05-07 中信银行股份有限公司 交易处理方法、装置及存储介质
CN112925663A (zh) * 2021-03-25 2021-06-08 支付宝(杭州)信息技术有限公司 业务数据的计算方法和装置
CN113159911A (zh) * 2021-02-25 2021-07-23 华夏方圆信用评估有限公司 基于大数据平台的智能招投标主体履约监控及系统
CN113610631A (zh) * 2021-08-02 2021-11-05 北京淇瑀信息科技有限公司 用户策略调整方法、装置及电子设备
CN113706300A (zh) * 2021-09-18 2021-11-26 中国银行股份有限公司 一种小微企业的贷款方法及装置
CN114219622A (zh) * 2021-12-15 2022-03-22 上海品顺信息科技有限公司 一种业务处理方法、装置、设备和可读存储介质
CN114240210A (zh) * 2021-12-22 2022-03-25 建信金融科技有限责任公司 信用评价方法、装置、设备及计算机可读存储介质
CN114386824A (zh) * 2022-01-05 2022-04-22 广东电网有限责任公司 电力业务平台的风险控制方法、装置、设备及存储介质
CN114629973A (zh) * 2022-03-29 2022-06-14 深圳市金蝶天燕云计算股份有限公司 报文处理方法、装置、计算机设备和存储介质
CN117076747A (zh) * 2023-08-16 2023-11-17 兴业银行股份有限公司 基于机器人的数据抓取方法、装置和计算机设备
CN119398503A (zh) * 2024-10-17 2025-02-07 广东拓威天海科技股份有限公司 一种客户风险控制系统

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110414914A (zh) * 2019-06-17 2019-11-05 深圳壹账通智能科技有限公司 业务数据监控方法和装置
CN111951011B (zh) * 2020-07-30 2023-09-29 中国工商银行股份有限公司 监控系统阈值确定方法及装置
CN113610534B (zh) * 2021-07-28 2024-06-25 浙江惠瀜网络科技有限公司 用于反欺诈的数据处理方法和装置
CN115082183B (zh) * 2022-06-15 2025-12-12 中国建设银行股份有限公司 业务对象的处理方法、装置、计算机设备、存储介质
CN115311077A (zh) * 2022-08-16 2022-11-08 中国工商银行股份有限公司 贷款信息处理方法、服务器、应用端和系统
CN115496576A (zh) * 2022-08-31 2022-12-20 重庆富民银行股份有限公司 一种业务风险监控及预警系统
CN115456758A (zh) * 2022-09-15 2022-12-09 中国工商银行股份有限公司 信用风险预警方法、装置、设备、存储介质和程序产品

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5802499A (en) * 1995-07-13 1998-09-01 Cedel Bank Method and system for providing credit support to parties associated with derivative and other financial transactions
CN107093101A (zh) * 2017-03-29 2017-08-25 广州汇融易互联网金融信息服务有限公司 基于pos流水数据的潜在贷款用户挖掘与风险评分方法
CN109461070A (zh) * 2018-10-25 2019-03-12 深圳壹账通智能科技有限公司 一种风险审批方法、装置、存储介质和服务器
CN109657894A (zh) * 2018-09-27 2019-04-19 深圳壹账通智能科技有限公司 企业信用风险预警方法、装置、设备及存储介质
CN109670936A (zh) * 2018-09-26 2019-04-23 深圳壹账通智能科技有限公司 贷款审批处理方法、平台、设备及计算机可读存储介质
CN110414914A (zh) * 2019-06-17 2019-11-05 深圳壹账通智能科技有限公司 业务数据监控方法和装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5802499A (en) * 1995-07-13 1998-09-01 Cedel Bank Method and system for providing credit support to parties associated with derivative and other financial transactions
CN107093101A (zh) * 2017-03-29 2017-08-25 广州汇融易互联网金融信息服务有限公司 基于pos流水数据的潜在贷款用户挖掘与风险评分方法
CN109670936A (zh) * 2018-09-26 2019-04-23 深圳壹账通智能科技有限公司 贷款审批处理方法、平台、设备及计算机可读存储介质
CN109657894A (zh) * 2018-09-27 2019-04-19 深圳壹账通智能科技有限公司 企业信用风险预警方法、装置、设备及存储介质
CN109461070A (zh) * 2018-10-25 2019-03-12 深圳壹账通智能科技有限公司 一种风险审批方法、装置、存储介质和服务器
CN110414914A (zh) * 2019-06-17 2019-11-05 深圳壹账通智能科技有限公司 业务数据监控方法和装置

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112767162A (zh) * 2021-01-22 2021-05-07 中信银行股份有限公司 交易处理方法、装置及存储介质
CN113159911A (zh) * 2021-02-25 2021-07-23 华夏方圆信用评估有限公司 基于大数据平台的智能招投标主体履约监控及系统
CN112925663A (zh) * 2021-03-25 2021-06-08 支付宝(杭州)信息技术有限公司 业务数据的计算方法和装置
CN113610631A (zh) * 2021-08-02 2021-11-05 北京淇瑀信息科技有限公司 用户策略调整方法、装置及电子设备
CN113706300A (zh) * 2021-09-18 2021-11-26 中国银行股份有限公司 一种小微企业的贷款方法及装置
CN114219622A (zh) * 2021-12-15 2022-03-22 上海品顺信息科技有限公司 一种业务处理方法、装置、设备和可读存储介质
CN114240210A (zh) * 2021-12-22 2022-03-25 建信金融科技有限责任公司 信用评价方法、装置、设备及计算机可读存储介质
CN114386824A (zh) * 2022-01-05 2022-04-22 广东电网有限责任公司 电力业务平台的风险控制方法、装置、设备及存储介质
CN114629973A (zh) * 2022-03-29 2022-06-14 深圳市金蝶天燕云计算股份有限公司 报文处理方法、装置、计算机设备和存储介质
CN117076747A (zh) * 2023-08-16 2023-11-17 兴业银行股份有限公司 基于机器人的数据抓取方法、装置和计算机设备
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