CN111127178A - Data processing method and device, storage medium and electronic equipment - Google Patents
Data processing method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN111127178A CN111127178A CN201911170009.6A CN201911170009A CN111127178A CN 111127178 A CN111127178 A CN 111127178A CN 201911170009 A CN201911170009 A CN 201911170009A CN 111127178 A CN111127178 A CN 111127178A
- Authority
- CN
- China
- Prior art keywords
- data
- verified
- verification
- rule
- machine learning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 29
- 238000003860 storage Methods 0.000 title claims abstract description 25
- 238000012795 verification Methods 0.000 claims abstract description 141
- 238000010801 machine learning Methods 0.000 claims abstract description 62
- 238000000034 method Methods 0.000 claims abstract description 49
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000004422 calculation algorithm Methods 0.000 claims description 22
- 238000012549 training Methods 0.000 claims description 12
- 238000000605 extraction Methods 0.000 claims description 6
- 238000013527 convolutional neural network Methods 0.000 claims description 4
- 238000007637 random forest analysis Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 13
- 238000010586 diagram Methods 0.000 description 7
- 238000009826 distribution Methods 0.000 description 6
- 238000007726 management method Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G06Q40/12—Accounting
- G06Q40/125—Finance or payroll
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Abstract
The disclosure belongs to the technical field of computers, and relates to a data processing method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring financial business data, and comparing the financial business data with other financial business data; if other financial business data identical to the financial business data exist, combining the financial business data with the other financial business data to generate data to be verified; inputting data to be verified into a machine learning model trained in advance so that the machine learning model outputs a target verification rule; verifying the data to be verified according to the target verification rule to obtain a verification result of the data to be verified; and if the verification result is that the verification is passed, generating target data corresponding to the data to be verified. The method and the device can reduce the error probability of the target data, improve the reliability of the target data, reduce the manual workload, strengthen the data processing flow and reduce the occurrence of safety accidents.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a data processing method, a data processing apparatus, a computer-readable storage medium, and an electronic device.
Background
The financial management system can be an overall operation system for combining all relevant factors in the financial management according to a certain rule and realizing the financial goal under various conditions and limited conditions provided by the external environment. The financial management system can provide the basis for paying and collecting money for enterprises and users, thereby improving the enterprise management level, inhibiting the production cost, standardizing the management flow and enhancing the strain capacity to the market change.
However, in the conventional financial receipt and payment flow, the function of verifying the receipt and payment amount waiting verification data is not provided, the error calculation cannot be controlled, the error payment or the collection is easy to occur, and huge losses of users and enterprises are caused.
In view of the above, there is a need in the art to develop a new data processing method and apparatus.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to a data processing method, a data processing apparatus, a computer-readable storage medium, and an electronic device, which overcome, at least to some extent, the problem that a payment amount cannot be verified to wait for verification of data due to limitations of related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of embodiments of the present invention, there is provided a data processing method, the method including: acquiring financial service data, and comparing the financial service data with other financial service data; if the other financial business data identical to the financial business data exist, combining the financial business data and the other financial business data to generate data to be verified; inputting the data to be verified into a machine learning model trained in advance so that the machine learning model outputs a target verification rule; verifying the data to be verified according to the target verification rule to obtain a verification result of the data to be verified; and if the verification result is that the verification is passed, generating target data corresponding to the data to be verified.
In an exemplary embodiment of the invention, the method further comprises: if the verification result is that the verification is not passed, acquiring the early warning priority of the data to be verified; and comparing the early warning priorities, and sending early warning information corresponding to the data to be verified according to a comparison result.
In an exemplary embodiment of the present invention, after the sending, according to the comparison result, the warning information corresponding to the data to be verified, the attribute feature further includes: acquiring a notification log corresponding to the verification result; and recording the data to be verified, the verification result and the early warning information in the notification log.
In an exemplary embodiment of the present invention, the inputting the data to be verified into a machine learning model trained in advance so that the machine learning model outputs a target verification rule includes: inputting the data to be verified to a pre-trained feature extraction model, and performing feature extraction on the data to be verified to obtain corresponding attribute features; and inputting the attribute characteristics into a pre-trained rule determination model so that the rule determination model outputs a target verification rule.
In an exemplary embodiment of the present invention, the attribute feature includes: product attributes, receipt and payment attributes, and business attributes.
In an exemplary embodiment of the invention, before the inputting the data to be verified into the pre-trained machine learning model, the method further includes: acquiring a verification data sample and a verification rule sample corresponding to the verification data sample; wherein the machine learning model is formed based on the verification data sample and the verification rule sample training; inputting the verification data sample into a machine learning model to be trained, and acquiring a verification rule which is output by the machine learning model to be trained and corresponds to the verification data sample; and if the check rule is not matched with the check rule sample, adjusting the parameters of the machine learning model to be trained so as to enable the check rule to be the same as the check rule sample.
In an exemplary embodiment of the present invention, the machine learning algorithm for training the machine learning model to be trained includes a random forest algorithm, a support vector machine algorithm, a rogerster regression algorithm, or a convolutional neural network algorithm.
According to a second aspect of the embodiments of the present invention, there is provided a data processing apparatus including: the data acquisition module is configured to acquire financial business data and compare the financial business data with other financial business data; the data processing module is configured to combine the financial business data with other financial business data to generate data to be verified if other financial business data identical to the financial business data exist; the rule determining module is configured to input the data to be verified into a machine learning model trained in advance so that the machine learning model outputs a target verification rule; the result determining module is configured to perform verification processing on the data to be verified according to the target verification rule so as to obtain a verification result of the data to be verified; and the data generation module is configured to generate target data corresponding to the data to be verified if the verification result is that the verification is passed.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus including: a processor and a memory; wherein the memory has stored thereon computer readable instructions which, when executed by the processor, implement the data processing method of any of the above exemplary embodiments.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a data processing method in any of the above-described exemplary embodiments.
As can be seen from the foregoing technical solutions, the data processing method, the data processing apparatus, the computer storage medium and the electronic device in the exemplary embodiments of the present invention have at least the following advantages and positive effects:
in the method and the device provided by the exemplary embodiment of the disclosure, the target verification rule is matched according to the machine learning model, and the data to be verified is verified. On one hand, the error probability of the target data can be reduced, the reliability of the target data is improved, and the manual workload of development, operation and maintenance is reduced; on the other hand, the error occurrence frequency of the time corresponding to the target data can be reduced, the data processing flow is robust, and the occurrence of safety accidents is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 schematically illustrates a flow chart of a data processing method in an exemplary embodiment of the disclosure;
FIG. 2 schematically illustrates a flow chart of a method of training a machine learning model to be trained in an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a method of determining a target validation rule in an exemplary embodiment of the disclosure;
fig. 4 schematically illustrates a flow chart of a method of sending early warning system information in an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of a method of logging notifications in an exemplary embodiment of the disclosure;
FIG. 6 is a schematic flow chart illustrating financial receipt and payment in the prior art;
FIG. 7 is a flow chart diagram schematically illustrating a data processing method in an application scenario in an exemplary embodiment of the present disclosure;
fig. 8 schematically shows a schematic structural diagram of a data processing apparatus in an exemplary embodiment of the present disclosure;
FIG. 9 schematically illustrates an electronic device for implementing a data processing method in an exemplary embodiment of the disclosure;
fig. 10 schematically illustrates a computer-readable storage medium for implementing a data processing method in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/parts/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.; the terms "first" and "second", etc. are used merely as labels, and are not limiting on the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In view of the problems in the related art, the present disclosure provides a data processing method. Fig. 1 shows a flow chart of a data processing method, which, as shown in fig. 1, comprises at least the following steps:
and S110, acquiring financial business data, and comparing the financial business data with other financial business data.
And S120, if other financial business data identical to the financial business data exist, combining the financial business data with the other financial business data to generate data to be verified.
And S130, inputting the data to be verified into a pre-trained machine learning model so that the machine learning model outputs a target verification rule.
And S140, verifying the data to be verified according to the target verification rule to obtain a verification result of the data to be verified.
And S150, if the verification result is that the verification is passed, generating target data corresponding to the data to be verified.
In an exemplary embodiment of the present disclosure, a target verification rule is matched according to a machine learning model, and data to be verified is verified. On one hand, the error probability of the target data can be reduced, the reliability of the target data is improved, and the manual workload of development, operation and maintenance is reduced; on the other hand, the error occurrence frequency of the time corresponding to the target data can be reduced, the data processing flow is robust, and the occurrence of safety accidents is reduced.
The respective steps of the data processing method will be described in detail below.
In step S110, the financial transaction data is obtained, and the financial transaction data is compared with other financial transaction data.
In an exemplary embodiment of the present disclosure, different service data may be acquired for different services. In the financial service, the obtained financial service data may include a policy number, a deduction account, a username, identity information, and the like.
And comparing the financial business data with other financial business data, and determining whether other financial business data same as the financial business data exist according to a comparison result. Before generating the data to be verified, in order to reduce the workload brought by verifying the data for multiple times, a judgment action can be performed after the financial data service is extracted. The judging method may be to compare the current financial transaction data with other extracted financial transaction data to determine a comparison result.
In step S120, if there are other financial transaction data that are the same as the financial transaction data, the financial transaction data and the other financial transaction data are merged to generate data to be verified.
In an exemplary embodiment of the present disclosure, if there is other financial transaction data that is the same as the financial transaction data, the financial transaction data and the other financial transaction data are merged to generate data to be verified. After the financial transaction data is compared with other financial transaction data, other financial transaction data identical to the financial transaction data can be obtained, and other financial transaction data identical to the financial transaction data may not exist. For example, when the user names of two or more financial services are the same, it may be determined that there are other financial service data that are the same as the financial service data, and further, the financial service data of the two financial services are merged to generate data to be checked to be sent. The data to be verified may be generated in real time or at equal time intervals, which is not particularly limited in this exemplary embodiment. It should be noted that, in the financial receipt and payment process, the data to be verified may be a temporary issue table, and the structure of the temporary issue table is substantially consistent with the structure of the issue table, for example, the structure of a field to which a verification result is added in the issue table may be used. The issuing is that one party of the transaction puts forward certain conditions to the other party, and the issuing is willing to achieve the transaction according to the conditions and establish an expression of a contract. The distribution may be a reply to the other party's invitation distribution, or may be directly distributed without an invitation. In short, the issuing may be a process of sending financial transaction data to the funding platform, and then transferring the data from the funding platform to the bank for a corresponding deduction or payment.
In the exemplary embodiment, the same financial service data is merged, so that the workload of the subsequent financial verification process can be reduced, and the operation cost of verification can be reduced.
In step S130, the data to be verified is input into the machine learning model trained in advance, so that the machine learning model outputs the target verification rule.
In an exemplary embodiment of the present disclosure, at the time of machine learning, the sample may be generally divided into three separate parts, a training set, a validation set, and a test set. The training set is used for estimating a model, the verification set is used for determining a network structure or parameters for controlling the complexity of the model, and the test set is used for testing how to finally select the optimal model. The machine learning model can be obtained by training through various machine learning algorithms and is used for obtaining a model of a target verification rule according to data to be verified. In alternative embodiments, the machine learning algorithm used to train the machine learning model to be trained may be a random forest algorithm, a support vector machine algorithm, a rogers regression algorithm, or a convolutional neural network algorithm. In addition, other machine algorithms are also possible, and the present exemplary embodiment is not particularly limited thereto. The effect of machine learning modeling can be represented by a true class rate and a negative positive class rate. The True Positive Rate (TPR) is calculated as: TPR is TP/(TP + FN) to represent the proportion of positive instances identified by the classifier to all positive instances; the calculation formula of the negative and positive class rate (FPR) is as follows: FPR ═ FP/(FP + TN), used to characterize the proportion of negative instances where the classifier mistakenly considered a positive class. In general, the greater the Kolmogorov-Smirnov value (abbreviated KS value), the greater the degree to which a characterization model can separate positive and negative classes. For example, considering the performance of KS value and TPR in four machine learning models, a convolutional neural network algorithm may be selected as the machine learning model for determining the target check rule. In addition, other machine learning algorithms may be selected according to other evaluation criteria, which is not particularly limited in the present exemplary embodiment. And evaluating the data to be verified through the machine learning model to obtain a target verification rule corresponding to the data to be verified.
Before determining the target verification rules using the machine learning model, the machine learning model to be trained may be trained. Fig. 2 shows a flow chart of a method for training a machine learning model to be trained, which, as shown in fig. 2, comprises at least the following steps: in step S210, a calibration data sample and a calibration rule sample corresponding to the calibration data sample are obtained; the machine learning model is formed based on the training of the verification data samples and the verification rule samples. The verification data sample and the verification rule sample may be selected from a set of target verification data and target verification rules of known corresponding rules, which is not particularly limited in this exemplary embodiment.
In step S220, the verification data sample is input into a machine learning model to be trained, and a verification rule output by the machine learning model to be trained and corresponding to the verification data sample is obtained.
In step S230, if the calibration rule does not match the calibration rule sample, the parameters of the machine learning model to be trained are adjusted so that the calibration rule is the same as the calibration rule sample. After the machine learning model to be trained outputs the check rule, the check rule can be matched with the check rule sample, whether the output check rule is the same as the check rule sample or not is judged, and whether the machine learning model to be trained is trained or not is judged according to the matching result.
If the check rule is not matched with the check rule sample, it is indicated that the machine learning model to be trained is not well trained, and therefore, the parameters of the machine learning model to be trained need to be adjusted, so that the check rule is the same as the corresponding check rule sample, and training of the machine learning model to be trained is completed.
In the exemplary embodiment, the accuracy of the target verification rule is ensured for the complete training of the machine learning model, and further, the correctness of the data to be verified is ensured.
After the training of the machine learning model to be trained is completed, the trained machine learning model can be used to determine the target verification rule. Fig. 3 shows a flow diagram of a method of determining a target verification rule, as shown in fig. 3, the method at least comprising the steps of: in step S310, the data to be verified is input to a pre-trained feature extraction model, and feature extraction is performed on the data to be verified to obtain corresponding attribute features. In an alternative embodiment, the attribute characteristics include product attributes, receipt and payment attributes, and business attributes. The product attribute can be a category attribute of the product, and can include health risk, serious risk, continuous risk, endowment risk and the like, different risk categories can have different business attributes, for example, account type risk categories have interest, insurance fee cannot be seen when refunding, and account life condition is also considered. The pay attribute may include both collection and payment, e.g., the contract is collection and the claim is payment. The business attribute can be a business module, for example, a contract module, a security module and a claim settlement module can be included; further, the service module may be further subdivided into different service types, for example, the security module may be subdivided into security items such as a person-reduction type, a security-returned type, a company-release type, and a one-time-picking type.
In step S320, the attribute features are input into a rule determination model trained in advance, so that the rule determination model outputs a target verification rule. The learning method of the rule-determining model can be various, and preferably, the rule-determining model can be a logistic regression algorithm. In addition, the present exemplary embodiment may also be a neural network, and may also be other algorithms, which are not particularly limited in this respect.
The target check rule is stored in the check rule set of the data to be checked. The check rule set comprises check rules used for checking data to be checked. Different verification rules can be set for different data to be verified, and the verification rules are stored in a preset verification rule set in advance. When the data to be verified needs to be verified, a target verification rule matched with the data to be verified is extracted from the verification rule set. The design of the check rule can respectively comprise a rule check table, a rule classification table, a check notifier table, a check result track table and a check result notification record table. Specifically, the rule checking table is shown in table 1 below:
TABLE 1
The check rule table may be specific information defining a certain rule, such as rule content, rule description, whether the rule is applicable, and the like. The rule classification table is shown in table 2 below:
TABLE 2
According to the table 1 and the table 2, the verification rule matched with the data to be verified can be inquired to obtain a matching result. The check notifier table is shown in table 3 below:
TABLE 3
In table 3, the relevant person may be notified according to the verification result, and information such as the time of the verification notification may be recorded. The verification result trajectory table is shown in table 4 below:
TABLE 4
In table 4, information such as the track of the check query and the query time may be recorded. The verification result notification record table is shown in table 5 below:
TABLE 5
In table 5, various notification information of the verification result can be recorded for query.
When the verification rule in the verification rule set is output according to the rule determination model, a target verification rule corresponding to the data to be verified can be determined. For example, for data to be verified of a certain refund service, the target verification rule may be refund amount < account 1.05. In addition, other target verification rules may be used, and this exemplary embodiment is not particularly limited in this respect.
In the exemplary embodiment, the target verification rule corresponding to the data to be verified can be obtained through the extracted attribute characteristics, so that the verification mode is more accurate, and the verification result is ensured to have a better effect.
In step S140, the data to be verified is verified according to the target verification rule, so as to obtain a verification result of the data to be verified.
For example, in the fee refunding service, the fee refunding is based on the premium paid to the customer, and other situations need to be considered for different products. For example, the account type dangerous seed has not only the traditional guarantee function, but also the value-added characteristic, namely, the accumulated information. In the refund business of such products, in addition to refunding the premium paid, interest generated by the account during the guarantee period is also calculated. However, since interest is a value that cannot be accurately defined, the general interest rate in this case can be made 0.05 according to the experience of the manager and the situation in the actual operation. Thus, the preset transaction amount in this case may be the account amount x 1.05. In addition, other preset transaction amounts may be determined according to other situations, and the exemplary embodiment is not particularly limited thereto.
And determining that the preset transaction amount is equivalent to an early warning line for determining the transaction amount of the current time, and determining the amount range lower than or higher than the early warning line as the transaction amount range according to different service conditions. In the refund service of the account type risky variety, it may be determined that the transaction amount range is less than 1.05 times the account amount. And in the financial receipt and payment process, the target transaction amount of the transaction is determined.
Further, the determined target transaction amount can be compared with the transaction amount range, namely 1.05 times of the account amount, and the verification result of the transaction is determined according to the comparison result. And through the comparison result of the transaction amount range and the determined target transaction amount, the corresponding verification result can be determined, the determination mode is simple, the probability of mistakenly collecting the premium is reduced, and the occurrence of safety accidents is reduced.
In step S150, if the verification result is that the verification is passed, target data corresponding to the data to be verified is generated.
In an exemplary embodiment of the present disclosure, for example, if the target transaction amount is within the transaction amount range, the verification result is determined to be verification pass. In the account type refund service of the dangerous type, if the account amount is 1000 yuan, the transaction amount range can be determined to be less than 1050 yuan, and the target transaction amount determined in the financial receipt and payment process is 1020 yuan, the target transaction amount 1020 yuan can be determined to be within the range of less than 1050 yuan, and the verification is passed.
After the verification is passed, target data can be generated according to the target transaction amount and the data to be verified, and the data to be verified with one less verification result can be determined as the target data. In addition, the target data may be generated according to other methods, which is not particularly limited in the present exemplary embodiment.
In the exemplary embodiment, a processing method when the verification passes is provided, so that the continuous execution of the subsequent processes, such as payment and deduction, is ensured, the occurrence probability of cases of over-paying or double-paying is avoided and reduced, and the safety of the business process is ensured.
In an alternative embodiment, fig. 4 shows a flowchart of a method for sending warning information, and as shown in fig. 4, the method at least includes the following steps: in step S410, if the verification result is that the verification fails, the early warning priority of the data to be verified is obtained. The early warning priority is set preferentially, or may be generated according to different data to be checked, which is not particularly limited in this exemplary embodiment. The early warning priority can be three, namely high, medium and low, and the higher the level is, the earlier the sequence of sending the corresponding early warning information is.
In step S420, the pre-warning priorities are compared, and pre-warning information corresponding to the data to be verified is sent according to the comparison result. When a plurality of data to be verified pass the verification, the corresponding early warning priorities can be compared, and early warning information with high priority is sent preferentially. For example, the warning information may be a warning notification initiated to the manager through a short message, an email, an audible and visual alarm, and a mobile terminal message. In addition, the warning information may be other warning manners that attract attention of the administrator, and this is not particularly limited in this exemplary embodiment. Moreover, since the verification result is a result for batch data, the warning information may further include the number of suspicious data verified this time, the suspicious degree, and the like. And, the time to send the notification to the manager may be preset according to the suspicious degree. For example, if the suspicious degree is higher, the early warning information can be sent in real time; when the degree of suspicion is low, it may be sent after a certain time interval. In addition, the notification time may also be set according to the number of suspicious data, and the like, and may also be set according to other information, which is not particularly limited in this exemplary embodiment.
In the exemplary embodiment, a method for determining early warning information is provided, the comparison mode is simple, the determination mode is accurate, and the practicability is strong.
In addition, in order to facilitate developers to check programs of related systems, data which are not verified can be corrected in time and recorded. In an alternative embodiment, fig. 5 shows a flow diagram of a method of logging notifications, as shown in fig. 5, the method at least comprising the steps of: in step S510, a notification log corresponding to the verification result is acquired. The notification log can record the data to be verified and the verification result of the data to be verified, so that a developer can conveniently check the current result of the data to be verified and the historical result of the data to be verified.
In step S520, the data to be verified, the verification result, and the warning information are recorded in the notification log. When the verification result of the data to be verified is that the data to be verified passes the verification, corresponding early warning information can be added to the corresponding position where the data to be verified and the data to be verified pass the verification. In addition, the early warning priority of the early warning information can be recorded in the notification log, and developers can conveniently perform troubleshooting and processing according to the priority sequence.
In the exemplary embodiment, information such as a corresponding result of the data to be verified can be recorded through the notification log, so that developers can conveniently check and process the data to be verified, the processing speed of the data to be verified is increased, and production accidents are reduced.
The following describes the data processing method in the embodiment of the present disclosure in detail with reference to an application scenario.
Fig. 6 is a schematic diagram illustrating a financial receipt and payment process in the prior art, and as shown in fig. 6, in the financial receipt and payment process, a core system extracts business data from a receipt and payment list and performs a merging process to generate distribution data. And the fund and cashier platform scans the distribution data at regular time and pushes the data to the bank. In this process, the receipt and payment amount is not verified. Even if a check flow exists in the system, the check is recalculated through the service data by using the same algorithm, and the effect of error handling cannot be achieved at all.
Fig. 7 is a flowchart illustrating a data processing method in an application scenario, and as shown in fig. 7, a core system may extract and combine business data to generate data to be verified. The financial verification system can capture data to be verified in real time or at regular time and match verification rules. And if the verification rule is not matched, sending corresponding early warning information, informing a manager of processing abnormity, and storing the notification log. If the verification rule is matched, the transaction amount range established according to the target verification rule is compared with the preset transaction amount range to obtain a verification result. If the verification result is that the verification is not passed, corresponding early warning information is sent; and if the verification result is that the verification is passed, generating distribution data according to the data to be verified, and sending the distribution data to a bank and the like.
In an exemplary embodiment of the present disclosure, the present disclosure checks according to the matched target check rule in the process of transmitting the target data. On one hand, the error probability of the target data can be reduced, the reliability of the target data is improved, and the manual workload of development, operation and maintenance is reduced; on the other hand, the probability of error occurrence of over-claim and double-claim cases can be reduced and avoided, the occurrence of insurance fee error collection events is reduced, the data processing flow is robust, and the occurrence of safety accidents is reduced.
It should be noted that although the above exemplary embodiment implementations describe the various steps of the method in the present disclosure in a particular order, this does not require or imply that these steps must be performed in that particular order, or that all of the steps must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Further, in an exemplary embodiment of the present disclosure, a data processing apparatus is also provided. Fig. 8 shows a schematic structural diagram of a data processing apparatus, and as shown in fig. 8, the data processing apparatus 800 may include: a data acquisition module 810, a data processing module 820, a rule determination module 830, a result determination module 840, and a data generation module 850. Wherein:
a data acquisition module 810 configured to acquire financial transaction data and compare the financial transaction data with other financial transaction data; the data processing module 820 is configured to, if other financial business data identical to the financial business data exists, combine the financial business data with the other financial business data to generate data to be verified; a rule determining module 830, configured to input data to be verified into a machine learning model trained in advance, so that the machine learning model outputs a target verification rule; the result determining module 840 is configured to perform verification processing on the data to be verified according to the target verification rule to obtain a verification result of the data to be verified; and the data generation module 850 is configured to generate target data corresponding to the data to be verified if the verification result is that the verification is passed.
The specific details of the data processing apparatus have been described in detail in the corresponding data processing method, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the data processing device 800 are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
An electronic device 900 according to such an embodiment of the invention is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.
Wherein the storage unit stores program code that is executable by the processing unit 910 to cause the processing unit 910 to perform steps according to various exemplary embodiments of the present invention described in the above section "exemplary methods" of the present specification.
The storage unit 920 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)921 and/or a cache memory unit 922, and may further include a read only memory unit (ROM) 923.
The electronic device 900 may also communicate with one or more external devices 1100 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 900, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interface 950. Also, the electronic device 900 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 960. As shown, the network adapter 940 communicates with the other modules of the electronic device 900 over the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
Referring to fig. 10, a program product 1000 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method of data processing, the method comprising:
acquiring financial service data, and comparing the financial service data with other financial service data;
if the other financial business data identical to the financial business data exist, combining the financial business data and the other financial business data to generate data to be verified;
inputting the data to be verified into a machine learning model trained in advance so that the machine learning model outputs a target verification rule;
verifying the data to be verified according to the target verification rule to obtain a verification result of the data to be verified;
and if the verification result is that the verification is passed, generating target data corresponding to the data to be verified.
2. The data processing method of claim 1, wherein the method further comprises:
if the verification result is that the verification is not passed, acquiring the early warning priority of the data to be verified;
and comparing the early warning priorities, and sending early warning information corresponding to the data to be verified according to a comparison result.
3. The data processing method according to claim 2, wherein after the sending of the warning information corresponding to the data to be verified according to the comparison result, the method further comprises:
acquiring a notification log corresponding to the verification result;
and recording the data to be verified, the verification result and the early warning information in the notification log.
4. The data processing method of claim 1, wherein the inputting the data to be verified into a machine learning model trained in advance to make the machine learning model output a target verification rule comprises:
inputting the data to be verified to a pre-trained feature extraction model, and performing feature extraction on the data to be verified to obtain corresponding attribute features;
and inputting the attribute characteristics into a pre-trained rule determination model so that the rule determination model outputs a target verification rule.
5. The data processing method of claim 4, wherein the attribute feature comprises: product attributes, receipt and payment attributes, and business attributes.
6. The data processing method of claim 1, wherein prior to the inputting the data to be verified into a pre-trained machine learning model, the method further comprises:
acquiring a verification data sample and a verification rule sample corresponding to the verification data sample; wherein the machine learning model is formed based on the verification data sample and the verification rule sample training;
inputting the verification data sample into a machine learning model to be trained, and acquiring a verification rule which is output by the machine learning model to be trained and corresponds to the verification data sample;
and if the check rule is not matched with the check rule sample, adjusting the parameters of the machine learning model to be trained so as to enable the check rule to be the same as the check rule sample.
7. The data processing method of claim 6, wherein the machine learning algorithm used to train the machine learning model to be trained comprises a random forest algorithm, a support vector machine algorithm, a Rogerster regression algorithm, or a convolutional neural network algorithm.
8. A data processing apparatus, comprising:
the data acquisition module is configured to acquire financial business data and compare the financial business data with other financial business data;
the data processing module is configured to combine the financial business data with other financial business data to generate data to be verified if other financial business data identical to the financial business data exist;
the rule determining module is configured to input the data to be verified into a machine learning model trained in advance so that the machine learning model outputs a target verification rule;
the result determining module is configured to perform verification processing on the data to be verified according to the target verification rule so as to obtain a verification result of the data to be verified;
and the data generation module is configured to generate target data corresponding to the data to be verified if the verification result is that the verification is passed.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the data processing method of any one of claims 1 to 7.
10. An electronic device, comprising:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the data processing method of any one of claims 1-7 via execution of the executable instructions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911170009.6A CN111127178A (en) | 2019-11-26 | 2019-11-26 | Data processing method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911170009.6A CN111127178A (en) | 2019-11-26 | 2019-11-26 | Data processing method and device, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111127178A true CN111127178A (en) | 2020-05-08 |
Family
ID=70496711
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911170009.6A Pending CN111127178A (en) | 2019-11-26 | 2019-11-26 | Data processing method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111127178A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626865A (en) * | 2020-05-22 | 2020-09-04 | 泰康保险集团股份有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN111813399A (en) * | 2020-07-23 | 2020-10-23 | 平安医疗健康管理股份有限公司 | Machine learning-based auditing rule processing method and device and computer equipment |
| CN112084183A (en) * | 2020-09-11 | 2020-12-15 | 北京有竹居网络技术有限公司 | Data verification method and device, electronic equipment and computer readable medium |
| CN112184474A (en) * | 2020-09-25 | 2021-01-05 | 江苏中利集团股份有限公司 | Intelligent material manufacturing method and device based on block chain |
| CN112506747A (en) * | 2021-02-03 | 2021-03-16 | 腾讯科技（深圳）有限公司 | Business process monitoring method and device, electronic equipment and storage medium |
| CN113033807A (en) * | 2021-05-26 | 2021-06-25 | 新东方教育科技集团有限公司 | Online data collection method, neural network training method, related device and storage medium |
| CN113220777A (en) * | 2021-03-12 | 2021-08-06 | 中国平安财产保险股份有限公司 | Service data processing method and device, computer equipment and storage medium |
| CN113837584A (en) * | 2021-09-17 | 2021-12-24 | 平安养老保险股份有限公司 | Service processing system and abnormal data processing method based on service processing system |
| CN114694420A (en) * | 2022-03-23 | 2022-07-01 | 合肥置顶信息技术有限公司 | System and method for making and publishing civil aviation weather forecast capable of intelligently correcting errors |
| CN116109382A (en) * | 2023-01-13 | 2023-05-12 | 上海峰沄网络科技有限公司 | Data verification method, device, equipment and storage medium |
| CN117435630A (en) * | 2023-12-21 | 2024-01-23 | 杭银消费金融股份有限公司 | Rule preposition-based data verification method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577571A (en) * | 2013-10-31 | 2014-02-12 | 北京奇虎科技有限公司 | Data processing method and device |
| CN105206005A (en) * | 2015-10-15 | 2015-12-30 | 成都信息工程大学 | Integrated early warning device |
| CN108874665A (en) * | 2018-05-29 | 2018-11-23 | 百度在线网络技术（北京）有限公司 | A kind of test result method of calibration, device, equipment and medium |
| CN110245049A (en) * | 2019-06-06 | 2019-09-17 | 深圳前海微众银行股份有限公司 | Monitoring method, device, equipment and the storage medium of product configuration data |
| CN110427277A (en) * | 2019-07-30 | 2019-11-08 | 腾讯科技（深圳）有限公司 | Data verification method, device, equipment and storage medium |
-
2019
- 2019-11-26 CN CN201911170009.6A patent/CN111127178A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577571A (en) * | 2013-10-31 | 2014-02-12 | 北京奇虎科技有限公司 | Data processing method and device |
| CN105206005A (en) * | 2015-10-15 | 2015-12-30 | 成都信息工程大学 | Integrated early warning device |
| CN108874665A (en) * | 2018-05-29 | 2018-11-23 | 百度在线网络技术（北京）有限公司 | A kind of test result method of calibration, device, equipment and medium |
| CN110245049A (en) * | 2019-06-06 | 2019-09-17 | 深圳前海微众银行股份有限公司 | Monitoring method, device, equipment and the storage medium of product configuration data |
| CN110427277A (en) * | 2019-07-30 | 2019-11-08 | 腾讯科技（深圳）有限公司 | Data verification method, device, equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 寇乃天: "《赢在风控》", 经济日报出版社, pages: 381 - 382 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626865B (en) * | 2020-05-22 | 2023-10-03 | 泰康保险集团股份有限公司 | Data processing method, device, electronic equipment and storage medium |
| CN111626865A (en) * | 2020-05-22 | 2020-09-04 | 泰康保险集团股份有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN111813399B (en) * | 2020-07-23 | 2022-05-31 | 平安医疗健康管理股份有限公司 | Machine learning-based auditing rule processing method and device and computer equipment |
| CN111813399A (en) * | 2020-07-23 | 2020-10-23 | 平安医疗健康管理股份有限公司 | Machine learning-based auditing rule processing method and device and computer equipment |
| CN112084183A (en) * | 2020-09-11 | 2020-12-15 | 北京有竹居网络技术有限公司 | Data verification method and device, electronic equipment and computer readable medium |
| CN112184474A (en) * | 2020-09-25 | 2021-01-05 | 江苏中利集团股份有限公司 | Intelligent material manufacturing method and device based on block chain |
| CN112506747A (en) * | 2021-02-03 | 2021-03-16 | 腾讯科技（深圳）有限公司 | Business process monitoring method and device, electronic equipment and storage medium |
| CN112506747B (en) * | 2021-02-03 | 2021-05-18 | 腾讯科技（深圳）有限公司 | Business process monitoring method and device, electronic equipment and storage medium |
| CN113220777B (en) * | 2021-03-12 | 2023-06-30 | 中国平安财产保险股份有限公司 | Service data processing method, device, computer equipment and storage medium |
| CN113220777A (en) * | 2021-03-12 | 2021-08-06 | 中国平安财产保险股份有限公司 | Service data processing method and device, computer equipment and storage medium |
| CN113033807A (en) * | 2021-05-26 | 2021-06-25 | 新东方教育科技集团有限公司 | Online data collection method, neural network training method, related device and storage medium |
| CN113837584A (en) * | 2021-09-17 | 2021-12-24 | 平安养老保险股份有限公司 | Service processing system and abnormal data processing method based on service processing system |
| CN113837584B (en) * | 2021-09-17 | 2024-04-12 | 平安养老保险股份有限公司 | Service processing system and abnormal data processing method based on service processing system |
| CN114694420A (en) * | 2022-03-23 | 2022-07-01 | 合肥置顶信息技术有限公司 | System and method for making and publishing civil aviation weather forecast capable of intelligently correcting errors |
| CN114694420B (en) * | 2022-03-23 | 2024-01-12 | 合肥置顶信息技术有限公司 | Civil aviation weather forecast making and publishing system and method capable of intelligently correcting errors |
| CN116109382A (en) * | 2023-01-13 | 2023-05-12 | 上海峰沄网络科技有限公司 | Data verification method, device, equipment and storage medium |
| CN117435630A (en) * | 2023-12-21 | 2024-01-23 | 杭银消费金融股份有限公司 | Rule preposition-based data verification method and system |
| CN117435630B (en) * | 2023-12-21 | 2024-03-29 | 杭银消费金融股份有限公司 | Rule preposition-based data verification method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111127178A (en) | Data processing method and device, storage medium and electronic equipment | |
| US20230099864A1 (en) | User profiling based on transaction data associated with a user | |
| CN111192131A (en) | Financial risk prediction method and device and electronic equipment | |
| CN111144697A (en) | Data processing method, data processing device, storage medium and electronic equipment | |
| CN109685645A (en) | User credit methods of risk assessment and device, storage medium | |
| CN111181757B (en) | Information security risk prediction method and device, computing equipment and storage medium | |
| CN111145009A (en) | Method and device for evaluating risk after user loan and electronic equipment | |
| US20210224810A1 (en) | Transaction tracking and fraud detection using voice and/or video data | |
| CN111179051A (en) | Financial target customer determination method and device and electronic equipment | |
| CN113610625A (en) | Overdue risk warning method and device and electronic equipment | |
| CN111598122B (en) | Data verification method and device, electronic equipment and storage medium | |
| CN116664306A (en) | Intelligent recommendation method and device for wind control rules, electronic equipment and medium | |
| CN115689571A (en) | Abnormal user behavior monitoring method, device, equipment and medium | |
| CN115482084A (en) | Method and device for generating wind control rule set | |
| CN111429257B (en) | Transaction monitoring method and device | |
| CN110362981B (en) | Method and system for judging abnormal behavior based on trusted device fingerprint | |
| CN112419025A (en) | User data processing method and device, storage medium and electronic equipment | |
| CN113298121A (en) | Message sending method and device based on multi-data source modeling and electronic equipment | |
| CN113516398A (en) | Risk equipment identification method and device based on hierarchical sampling and electronic equipment | |
| CN114880369A (en) | Risk credit granting method and system based on weak data technology | |
| CN111489101A (en) | Order auditing method, device, equipment and medium based on big data | |
| US11915313B2 (en) | Using email history to estimate creditworthiness for applicants having insufficient credit history | |
| CN110610320A (en) | Financial risk level prediction method, device, electronic equipment and storage medium | |
| CN112580971A (en) | Method and device for checking effectiveness of external institution rating | |
| CN117575595A (en) | Payment risk identification method, device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |