CN115345627A

CN115345627A - Data processing method, device, equipment, medium and product

Info

Publication number: CN115345627A
Application number: CN202210992244.7A
Authority: CN
Inventors: 郎钊
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2022-08-18
Filing date: 2022-08-18
Publication date: 2022-11-15

Abstract

An embodiment of the application provides a data processing method, device, equipment, medium and product, including: acquiring user risk data of a target user, wherein the user risk data comprises risk data of an unnatural client and an affinity person of the unnatural client; calculating an index score corresponding to each index data based on a preset calculation rule and a plurality of index data included in the user risk data; and performing weighted summation calculation through the index score corresponding to each index data and the preset weight to obtain a user risk score, wherein the user risk score is used for evaluating the reliability of the target user. According to the embodiment of the application, the accuracy of evaluating the reliability of the user is improved.

Description

Data processing method, device, equipment, medium and product

Technical Field

The present application relates to the field of data processing, and in particular, to a data processing method, apparatus, device, medium, and product.

Background

In real life, in order to avoid abnormal transaction situations, the reliability of both transaction parties needs to be evaluated, so as to avoid abnormal transaction situations caused by excessive transaction risks of users. However, in the prior art, the reliability of the user is evaluated with low accuracy.

Disclosure of Invention

The embodiment of the application provides a data processing method, a device, equipment, a medium and a product thereof, and improves the accuracy of evaluating the reliability of a user.

In a first aspect, an embodiment of the present application provides a data processing method, where the method includes:

acquiring user risk data of a target user, wherein the user risk data comprises risk data of an unnatural client and an affinity person of the unnatural client;

calculating an index score corresponding to each index data based on a preset calculation rule and a plurality of index data included in the user risk data;

and performing weighted summation calculation through the index score corresponding to each index data and the preset weight to obtain a user risk score, wherein the user risk score is used for evaluating the reliability of the target user.

In an optional implementation of the first aspect, the method further comprises:

and updating the user risk score based on the ratio of the user risk score to the preset risk grade number.

and determining the user risk level of the target user based on the user risk score and the corresponding relation between the preset user risk score and the user risk level, wherein the user risk level is used for evaluating the reliability of the target user.

In an optional implementation manner of the first aspect, the user risk data further comprises at least one of user characteristic risk data, regional risk data, business risk data, and industry risk data.

In an alternative embodiment of the first aspect, wherein,

under the condition that the target user is a newly added user, the user characteristic risk data comprises at least two data of enterprise industry data, license validity period, user information integrity, first list hit data and second list hit data;

under the condition that the target user is an inventory user, the user risk characteristic data comprises at least two data of enterprise industry data, license validity period, user information integrity, first list hit data, second list hit data and information updating frequency.

In an alternative embodiment of the first aspect, the regional risk data includes at least two of a business registration address, a business administration address, and a business associate.

In an alternative embodiment of the first aspect, wherein,

under the condition that the target user is a newly added investment user, the business risk data comprises at least two data of an investment receiving mode, an investment account receiving correlation degree, a first-day transaction number and a first-day transaction amount;

under the condition that the target user is a newly added fund raising user, the business risk data comprises at least two data of fund transfer mode, fund transfer account association degree, first-day transaction number and first-day transaction amount;

under the condition that the target user is an inventory investment user, the business risk data comprises a transaction type and a transaction magnitude;

in the case where the target user is an inventory funding user, the business risk data includes a risk level of selling a product, a number of transactions during the period, an amount of transactions during the period, and an amount of industry/professional reference revenue.

In an alternative embodiment of the first aspect, the risk data of the unnatural customer and its affinity includes at least two of age of the legal/beneficial owner, type of legal/beneficial owner document, number of legal/beneficial owner identity document, validity period of legal/beneficial owner identity document.

In a second aspect, an embodiment of the present application provides a data processing apparatus, including:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring user risk data of a target user, and the user risk data comprises risk data of an unnatural client and an affinity person thereof;

the calculation module is used for calculating index scores corresponding to each index data based on a preset calculation rule and a plurality of index data included in the user risk data;

the calculation module is further used for performing weighted summation calculation through the index scores corresponding to each index data and the preset weight to obtain user risk scores, and the user risk scores are used for evaluating the reliability of the target user.

In a third aspect, an electronic device is provided, including: a memory for storing computer program instructions; and the processor is used for reading and executing the computer program instructions stored in the memory so as to execute the data processing method provided by any optional implementation manner of the first aspect or the second aspect.

In a fourth aspect, a computer storage medium is provided, on which computer program instructions are stored, and the computer program instructions, when executed by a processor, implement the data processing method provided in any optional implementation manner of the first aspect or the second aspect.

In a fifth aspect, a computer program product is provided, and when executed by a processor of an electronic device, instructions in the computer program product cause the electronic device to execute a data processing method provided by an implementation manner of any one of the first aspect or the second aspect.

In the embodiment of the application, the index score corresponding to each index data can be calculated based on a preset calculation rule and a plurality of index data included in the user risk data by acquiring the user risk data of the target user, and based on the index score corresponding to each index data and the preset weight, the weighted sum calculation can be performed to obtain the user risk score. Since the user risk score is used for evaluating the reliability of the target user, the accuracy of evaluating the reliability of the user can be improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings may be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flowchart of a data processing method provided in an embodiment of the present application;

fig. 2 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof. In addition, in the technical scheme of the application, the acquisition, storage, use, processing and the like of the data all conform to relevant regulations of national laws and regulations.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.

In order to solve the problem of low reliability of an evaluation user in the prior art, embodiments of the present application provide a data processing method, an apparatus, a device, and a medium, which may calculate an index score corresponding to each index data based on a preset calculation rule and a plurality of index data included in the user risk data by obtaining user risk data of a target user, and based on this, may perform weighted summation calculation by the index score corresponding to each index data and a preset weight to obtain a user risk score. Since the user risk score is used for evaluating the reliability of the target user, the accuracy of evaluating the reliability of the user can be improved.

In the data processing method provided by the embodiment of the application, the execution main body may be a data processing device or a control module used for executing the data processing method in the data processing device. In the embodiment of the present application, a data processing method performed by a data processing apparatus is taken as an example, and the data method provided in the embodiment of the present application is described as an example.

The data processing method provided by the embodiment of the present application is described in detail below with reference to the accompanying drawings by specific embodiments.

Fig. 1 is a schematic flowchart of a data processing method according to an embodiment of the present application.

As shown in fig. 1, an execution subject of the data processing method may be a data processing apparatus, and the method may specifically include the following steps:

and S110, acquiring user risk data of the target user.

When the data processing apparatus evaluates the reliability of the target user, the data processing apparatus may first obtain user risk data of the target user, so as to subsequently perform risk evaluation on the reliability of the target user based on the user risk data. User risk data may include, among other things, risk data for non-natural customers and their affinities.

And S120, calculating index scores corresponding to each index data based on a preset calculation rule and a plurality of index data included in the user risk data.

The preset calculation rule is a calculation rule preset based on actual experience or conditions, and the preset calculation rule may be a weighted sum calculation rule or a rule calculated by using a formula, which is not specifically limited herein.

Specifically, after acquiring the user risk data, the data processing apparatus may calculate a plurality of index data included in the user risk data based on a preset calculation rule to obtain an index score corresponding to each index data. The index score can be used to evaluate the reliability of the target user in a certain aspect, and the specific requirement depends on the actual index data, which is not limited herein.

And S130, carrying out weighted summation calculation through the index score corresponding to each index data and the preset weight to obtain the user risk score.

Wherein the user risk score is used to assess the reliability of the target user.

Specifically, the data processing apparatus may perform, after calculating the index score corresponding to each index data, weighted sum calculation by performing weighted sum calculation on the index score corresponding to each index data and a preset weight to obtain the user risk score of the target user.

The specific formula for calculating the user risk score is specifically shown in formula (1):

where A may represent a user risk score, a _i Can represent the ith index score, p _i The weight corresponding to the ith index score may be represented, and n may represent the number of index scores.

In an embodiment, in order to more accurately evaluate the reliability of the target user, the data processing method may further include:

The preset risk level number may be a preset risk level number based on actual experience or situation, and is not specifically limited herein.

The specific calculation formula can be shown as formula (2):

where B may represent updated user risk and m may be a preset number of risk levels.

In this embodiment, for convenience of statistics, the data processing device limits the user risk score within a certain range, and based on this, after the data processing device calculates the user risk score, the data processing device updates the user risk score based on a ratio of the user risk score to a preset risk level number, that is, determines that the ratio of the user risk score to the preset risk level number is a new user risk score. Thus, subsequent statistics are facilitated.

In one embodiment, in order to accurately determine the user risk level of the user, the data processing method may further include:

and determining the user risk level of the target user based on the user risk score and the corresponding relation between the preset user risk score and the user risk level.

Specifically, after the data processing device calculates the user risk score, the data processing device may accurately determine the user risk level of the target user based on the user risk score and the preset corresponding relationship between the user risk score and the user risk level.

Wherein the user risk level may be used to assess the reliability of the target user. The preset corresponding relationship between the user risk score and the user risk level may be a mapping relationship preset based on actual experience or actual conditions.

The specific preset corresponding relationship between the user risk score and the user risk level may be as shown in table 1:

TABLE 1 corresponding relationship between preset user risk scores and user risk levels

User risk scoring	User risk rating
		T ₁ ≤t<T ₂	R ₁
T ₂ ≤t<T ₃	R ₂
		T ₃ ≤t<T ₄	R ₃
……	……
		T _i-1 ≤t<T _i	R _i

In this embodiment, after the data processing device calculates the user risk score, the data processing device may accurately determine the user risk level of the target user based on the user risk score and the preset corresponding relationship between the user risk score and the user risk level.

In some embodiments, the user risk data referred to above may further include at least one of user characteristic risk data, regional risk data, business risk data, and industry risk data.

Based on this, in this embodiment, the data processing apparatus may calculate, based on a preset calculation rule, an index score corresponding to risk data of an unnatural customer and an affinity thereof included in the user risk data, and an index score corresponding to at least one of user characteristic risk data, geographic risk data, business risk data, and industry risk data included in the user risk data, which are calculated based on the preset calculation rule, and may further obtain at least two index scores based on the calculation, and perform weighted summation on preset weights corresponding to the at least two index scores, thereby obtaining a more accurate user risk score.

In some embodiments, in the case that the target user is a new user, the user characteristic risk data may include at least two data of enterprise industry data, license validity period, user information integrity, first list hit data, and second list hit data;

in the case that the target user is an inventory user, the user risk characteristic data may include at least two data of enterprise industry data, license validity period, user information integrity, first list hit data, second list hit data, and information update frequency.

Based on this, the preset calculation rule may be a weighted sum calculation rule.

Specifically, in the case that the target user is a newly added user, corresponding weights may be assigned to at least two of the enterprise industry data, the license validity period, the user information integrity, the first list hit data, and the second list hit data included in the user characteristic risk data in advance, and then, a weighted sum may be performed based on at least two of the enterprise industry data, the license validity period, the user information integrity, the first list hit data, and the second list hit data included in the user characteristic risk data, and the weights corresponding to the at least two data, so as to obtain an index score corresponding to the user characteristic risk data.

Or under the condition that the target user is an inventory user, corresponding weights can be preset to be allocated to at least two data in enterprise industry data, license validity period, user information integrity, first list hit data, second list hit data and information updating frequency included in the user characteristic risk data, and further, weighted sum calculation can be performed on the basis of at least two data in enterprise industry data, license validity period, user information integrity, first list hit data, second list hit data and information updating frequency included in the user characteristic risk data and the weights corresponding to the at least two data to obtain index scores corresponding to the user characteristic risk data.

It should be noted that whether the target user is a new user or an inventory user may be determined based on the account transaction time of the target user, and is not specifically limited herein.

It should be further noted that the above mentioned enterprise industry data is to consider whether the user can provide the certificate or document issued by the government authority and capable of proving the legal and real identity of the user, and whether the relationship can establish a business relationship with the user. In addition, the scores of different industry categories can be specified according to the external consultation result.

The license validity period is related to the fact that when the user transacts the service for the first time, the corresponding score is higher when the user license is in the critical validity period or the license is over the validity period. Specifically, the correspondence between the time of the license of the user from the validity period and the score may be preset to obtain data on the validity period of the license.

Since the user information includes the elements of the natural person 9 and the unnatural person 29, the above-mentioned relation to the integrity of the user information is to consider whether the user information meets the requirements, and to register the user identification element, which is related to whether the user can be identified effectively. Different scores can be given according to the importance degree of each item of information, and a rating rule for directly giving the highest score to the user characteristic primary index when the information exceeding the preset proportion cannot be obtained is set.

The second list hit data mentioned above is for whether the user and legal person trigger the second list. If the user hits the list, the highest score of the user characteristic risk data is directly given.

The information updating frequency is to give different score values considering the number of times of changing the natural human 9 element or the unnatural human 29 element (the number of times of updating the multiple elements once according to the number of the elements) by the user in a preset period. The preset period may be one year, and the specific description is not particularly limited herein.

In some embodiments, the regional risk data may include at least two of a business registration address, a business administration address, and a business associate.

Based on this, the preset calculation rule may be a weighted sum rule.

Specifically, the data processing apparatus may assign corresponding weights to at least two data of the enterprise registration address, the enterprise operation address, and the enterprise associator in advance, and further may calculate an index score corresponding to the region risk data based on the at least two data and their respective corresponding weights.

It should be noted that the regional risk data can measure the relevance between the user and its actual beneficiary, actual controller, legal representative, registered place, residence, business location and non-compliance activity, and the regional risk transmission between the user's main counter-party and the overseas participating financial institution. It should be further noted that, in the embodiment of the present application, three assigning items of a high risk area, an overseas area (including the hong kong and australian), and an inbound area may also be set based on actual situations, and specific details are not described herein.

In some embodiments, in the case that the target user is a newly added investment user, the business risk data may include at least two data of an investment accepting mode, an investment account association degree, a first-day transaction number, and a first-day transaction amount;

under the condition that the target user is a newly added fund raising user, the business risk data can comprise at least two data of fund transfer mode, fund transfer account association degree, first-day transaction number and first-day transaction amount;

in the case where the target user is an investor in inventory, the business risk data may include a transaction type and a transaction magnitude;

in the case where the target user is an inventory of funding users, the business risk data may include a risk level of selling a product, a number of transactions in the period, an amount of transactions in the period, and an amount of industry/professional reference revenue.

Based on this, it should be noted that, in the case that the target user is a newly added investment user, or in the case that the target user is a newly added investment user, the data processing device may assign corresponding weights to the data of each dimension included in the business risk data in advance, and further, in the case that the target user is a newly added investment user, or in the case that the target user is a newly added investment user, perform weighted summation based on the data of each dimension corresponding to the business risk data and the respective corresponding weights of the data of each dimension, so as to obtain index scores corresponding to the business risk data in the case that the target user is a newly added investment user, or in the case that the target user is a newly added investment user.

It should be noted that, in the case where the target user is an inventory of investment users, the preset calculation rule may be a formula calculation rule.

Specifically, it can be shown as formula (3):

C＝(1+α)×(A1+A2) (3)

wherein, C may be an index score corresponding to the service risk data, α is a service natural risk value, and an initial value may be assigned to the service natural risk value in advance, if the regulatory agency penalizes the type of service within the first preset duration, the first preset value is raised, and the subsequent rating is determined based on the first preset value. If no punishment occurs, maintaining the initial value;

a1 is a transaction type (an active investment transaction takes a first coefficient, a passive investment transaction takes a second coefficient, wherein the first coefficient is smaller than the second coefficient), the active investment can be an actively initiated equity transaction, the rest are passive investment transactions, and the first coefficient and the second coefficient can be coefficient values preset based on actual experience;

a2 is a transaction magnitude, (the transaction amount is less than or equal to a preset stock right, namely a second preset value is taken, when the preset stock right is doubled, the second preset value is taken to be multiplied by 2, and the highest value is taken to be 100.

It should be further noted that, in the case that the target user is a stock amount of fund-raising users, the preset calculation rule may be a formula calculation rule, and a specific formula may be as shown in formula (4):

D＝(1+α)×(B1+B2+B3+B4) (4)

wherein, D may be an index score corresponding to the business risk data when the target user is an inventory fund raising user, α is a business natural risk value, an initial value may be allocated to the business natural risk value in advance, if the regulatory agency penalizes the type of business within a first preset time, a first preset value is raised, and the subsequent rating is determined based on the first preset value. If no punishment occurs, maintaining the initial value;

b1 is the risk level (R1-R5 level) of selling products, if the user purchases products with different risk levels, the product with the largest sum of the purchased products is taken as the reference (or the risk level of purchasing the products is taken as the highest according to the circumstances);

b2 is the number of transactions in the period, specifically, the median average value of the number of transactions of all users of the same type can be taken as a third preset value, for example, 10 points can be used, and when a double value is increased, the third preset value is multiplied by a preset multiple, the highest point is taken as 100 points, and the point is taken as 10 points when the median is lower than the median;

b3 is the transaction amount in the period, the median average value of the total transaction amounts of all users of the same type is 1, the third preset value is taken, for example, 10 minutes, when the numerical value is doubled, the third preset value is multiplied by the preset multiple, the highest 100 minutes is taken, and the median is 10 minutes lower than the median;

and B4, determining a parameter industry amount standard by taking the average income amount of the fund-raising user in the past natural year as a reference, wherein the average income amount is the industry/occupation reference income amount. This amount is risk-free downwards (i.e. B4 is taken to be 0) and increases upwards by 10 points for every 10% increase.

In some embodiments, the risk data for the unnatural customer and their affinities includes at least two data of the legal/beneficial owner age, legal/beneficial owner document type, legal/beneficial owner identity document number, legal/beneficial owner identity document validity period.

Based on this, the preset calculation rule may be a weighted sum rule. Risk data of unnatural customers and their close relations mainly evaluate the identity of the legal and beneficial owners of the unnatural users.

Specifically, the data processing apparatus may assign corresponding weights to at least two data in the valid period of the legal/beneficial owner age, the legal/beneficial owner certificate type, the legal/beneficial owner identity certificate number, and the legal/beneficial owner identity certificate included in the risk data of the unnatural client and the affinity thereof in advance, and may further perform weighted sum calculation based on the at least two data included in the risk data of the unnatural client and the affinity thereof and the corresponding preset weights to obtain an index score corresponding to the risk data of the unnatural client and the affinity thereof.

The ages of the legal persons/beneficial persons involved in the above can be set to be scores based on the ages of the users and the corresponding difference of performance capability, job performance capability and income capability. For example, when a legal, beneficial owner is too young or old, then the risk score is relatively higher.

The types of the certificates of the legal persons/beneficial persons involved in the method can be checked with different difficulties based on different certificate types, and different types of certificates are given scores according to the difficulty of checking.

The identity document number of the legal person/beneficial owner related to the identity document number can be given corresponding scores based on the conditions of the normalization of the format of the identity document number, the conformity of the validity to the requirements, the existence of deletion, embezzlement, misuse and the like. When the identity document number provided by the user is inconsistent with the related personnel and has no reasonable reason explanation, the highest score of the index of the risk data of the unnatural client and the close relation person thereof is directly given

The validity period of the identity document of the legal person/beneficial owner related to the method can be endowed with corresponding scores based on the condition that the user license has a critical validity period or a valid period over the license when the business is transacted for the first time. When the user license has a critical validity period or the license has a valid period, the corresponding score is higher.

In addition, the highest scoring judgment rule of the index of risk data directly given to the unnatural client and the close relation thereof when the identity document of the legal person is lost or the identity document of all persons is benefited can be set.

In addition, it should be further noted that the index score corresponding to the industry risk data may be assigned with a corresponding score based on a preset rule in an actual operation process, and may also be specifically classified into results of risk industry/occupation, medium-high risk industry/occupation, medium-low risk industry/occupation, low-risk industry/occupation, and the like, which is not specifically limited herein.

It should be further noted that, in consideration of situations that user background information updating exists in initial identification and continuous identification, and the like, which may cause the user risk level to change during the service life, the data processing method provided in the embodiment of the present application may classify target users into two categories, namely newly added customers and stock customers, so as to more accurately evaluate the user risk levels in different life cycles.

Based on the same inventive concept, the embodiment of the application also provides a data processing device. The data processing apparatus provided in the embodiment of the present application is specifically described with reference to fig. 2.

Fig. 2 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

As shown in fig. 2, the data processing apparatus 200 may include: an acquisition module 210 and a calculation module 220.

An obtaining module 210, configured to obtain user risk data of a target user, where the user risk data includes risk data of an unnatural client and an affinity thereof;

a calculating module 220, configured to calculate an index score corresponding to each index data based on a preset calculation rule and a plurality of index data included in the user risk data;

the calculating module 230 is further configured to perform weighted summation calculation through the index score corresponding to each index data and the preset weight to obtain a user risk score, where the user risk score is used to evaluate the reliability of the target user.

In one embodiment, the data processing module may further include an update module.

In one embodiment, the determining module is further configured to determine a user risk level of the target user based on the user risk score and a preset corresponding relationship between the user risk score and the user risk level, where the user risk level is used to evaluate reliability of the target user.

In one embodiment, the user risk data further comprises at least one of user characteristic risk data, regional risk data, business risk data, and industry risk data.

In one embodiment, in the case that the target user is a newly added user, the user characteristic risk data includes at least two data of enterprise industry data, license validity period, user information integrity, first list hit data and second list hit data;

In one embodiment, the regional risk data includes at least two of a business registration address, a business administration address, and a business associate.

In one embodiment, in the case that the target user is a newly added investment user, the business risk data includes at least two data of an investment accepting mode, an investment account receiving association degree, a first-day transaction number and a first-day transaction amount;

under the condition that the target user is a newly added fund raising user, the business risk data comprises at least two data of a fund transfer mode, a fund transfer account association degree, a first-day transaction number and a first-day transaction amount;

under the condition that the target user is an investment user of the stock, the business risk data comprises a transaction type and a transaction magnitude;

In one embodiment, the risk data for the unnatural customer and their affinity includes at least two data of legal/beneficial owner age, legal/beneficial owner document type, legal/beneficial owner identity document number, legal/beneficial owner identity document validity period.

Each module in the data processing apparatus provided in the embodiment of the present application may implement the method steps in the embodiment shown in fig. 1, and may achieve the corresponding technical effects, and for brevity, no further description is given here.

Fig. 3 shows a hardware structure diagram of an electronic device according to an embodiment of the present application.

The electronic device may comprise a processor 301 and a memory 302 in which computer program instructions are stored.

Specifically, the processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 302 may include mass storage for data or instructions. By way of example, and not limitation, memory 302 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 302 may include removable or non-removable (or fixed) media, where appropriate. The memory 302 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 302 is a non-volatile solid-state memory.

The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform operations described with reference to the method according to an aspect of the disclosure.

The processor 301 realizes any one of the data processing methods in the above-described embodiments by reading and executing computer program instructions stored in the memory 302.

In one example, the electronic device may also include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 to complete communication therebetween.

The communication interface 303 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiment of the present application.

Bus 310 includes hardware, software, or both to couple the components of the online data traffic billing device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. Bus 310 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

In addition, in combination with the data processing method in the foregoing embodiments, the embodiments of the present application may provide a computer storage medium to implement. The computer storage medium having computer program instructions stored thereon; the computer program instructions realize the data processing method provided by the embodiment of the application when being executed by the processor.

The embodiment of the present application further provides a computer program product, and when an instruction in the computer program product is executed by a processor of an electronic device, the electronic device executes the scientific and technological innovation achievement evaluation method provided in the embodiment of the present application.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

The functional blocks shown in the above structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.

Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.

As will be apparent to those skilled in the art, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered within the scope of the present application.

Claims

1. A method of data processing, the method comprising:

and performing weighted summation calculation through the index score corresponding to each index data and a preset weight to obtain a user risk score, wherein the user risk score is used for evaluating the reliability of the target user.

2. The method of claim 1, further comprising:

3. The method according to claim 1 or 2, characterized in that the method further comprises:

4. The method of claim 1, wherein the user risk data further comprises at least one of user characteristic risk data, regional risk data, business risk data, and industry risk data.

5. The method of claim 4,

under the condition that the target user is a newly added user, the user characteristic risk data comprise at least two data of enterprise industry data, license validity, user information integrity, first list hit data and second list hit data;

and under the condition that the target user is an inventory user, the user risk characteristic data comprises at least two data of enterprise industry data, license validity period, user information integrity, first list hit data, second list hit data and information updating frequency.

6. The method of claim 4, wherein the regional risk data comprises at least two of a business registration address, a business administration address, and a business associate.

7. The method of claim 4,

under the condition that the target user is a newly added investment user, the business risk data comprises at least two data of an investment receiving mode, an investment account receiving degree of association, a first-day transaction number and a first-day transaction amount;

in the case where the target user is an inventory of funding users, the business risk data includes a risk level of selling a product, a number of interim transactions, an interim transaction amount, and an industry/occupation benchmark revenue amount.

8. The method of claim 1, wherein the risk data for the unnatural customer and its affinity includes at least two of age of the legal/beneficial owner, type of legal/beneficial owner document, number of legal/beneficial owner identity document, validity period of legal/beneficial owner identity document.

9. A data processing apparatus, characterized in that the apparatus comprises:

the calculation module is further configured to perform weighted summation calculation through the index score corresponding to each index data and a preset weight to obtain a user risk score, where the user risk score is used to evaluate the reliability of a target user.

10. An electronic device, characterized in that the device comprises: a processor and a memory storing computer program instructions;

the processor reads and executes the computer program instructions to implement the data processing method of any one of claims 1 to 8.

11. A computer storage medium, characterized in that it has stored thereon computer program instructions which, when executed by a processor, implement a data processing method according to any one of claims 1 to 8.

12. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the data processing method according to any of claims 1-8.