1. A bank user credit investigation evaluation method based on big data comprises the following steps:

processing initial data to determine target data, wherein the initial data is express delivery data, and the target data comprises address data, sender identity information and date data;

determining user types according to the target data, wherein the user types comprise individual users and enterprise users;

determining characteristic information of a receiving and sending region according to the user type and the target data;

determining a user credit investigation score according to the characteristic information of the receiving and sending region and the characteristic model of the express receiving and sending region;

the express receiving and sending region characteristic model is pre-established and used for determining credit investigation scores of the users according to the receiving and sending region characteristic information of the users.

2. The method of claim 1, wherein the express receiving area feature model is trained in advance based on:

acquiring sample data, wherein the sample data comprises user express data and user credit investigation data;

and training the characteristic model of the express receiving and sending region according to the sample data.

3. The method of claim 2, wherein training the express recipient region feature model based on the sample data comprises:

calculating credit investigation scores of users according to the sample data and the express receiving and sending region characteristic model;

determining accuracy according to the user credit investigation score and the user credit investigation data;

and updating the characteristic model of the express receiving and sending region based on the accuracy.

4. The method of any one of claims 1 to 3, wherein the processing the initial data to determine the target data comprises:

acquiring initial data;

and cleaning the initial data according to preset indexes to determine target data, wherein the preset indexes comprise an addressee address, a sender address, addressee information, sender information, an addressee date and a sender date.

5. The method of claim 4, wherein determining the user type based on the target data comprises:

determining identity information of the user according to the target data;

and determining the user type according to the identity information of the user.

6. The method of claim 5, wherein said determining recipient zone characteristics information based on said user type and said target data comprises:

if the user type is a personal user, determining first receiving and sending area characteristic information according to the target data;

if the user type is an enterprise user, determining second receiving and sending region characteristic information according to the target data;

the first receiving and forwarding region characteristic information comprises receiving and forwarding address information within preset time, and the second receiving and forwarding region characteristic information comprises receiving and forwarding address information and digital quantity change information within preset time.

7. The method of claim 6 wherein said determining a user credit assessment score based on said recipient area characteristic information and a courier recipient area characteristic model comprises:

determining credit investigation scores of the personal users according to the first receiving and sending region characteristic information and the express receiving and sending region characteristic model;

and determining credit investigation scores of the enterprise users according to the second receiving and sending region characteristic information and the express receiving and sending region characteristic model.

8. The method of claim 7 wherein determining a personal user credit assessment score based on the first consignee characteristic information and the express consignee characteristic model comprises:

determining the receiving and sending address information within the preset time according to the first receiving and sending area characteristic information;

determining address information of the sensitive area according to the characteristic model of the express receiving and sending area;

comparing the address information of the receiving and sending areas in the preset time with the address information of the sensitive areas;

and determining credit assessment scores of the individual users according to the comparison results.

9. The method of claim 8, wherein determining a credit assessment score for the individual user based on the comparison comprises:

if the address information of the sensitive area in the preset time contains the address information of the sensitive area, determining a first calculated value;

if the address information of the sensitive area is not contained in the address information of the receiving and sending area within the preset time, determining a second calculated value;

and determining the credit investigation score of the personal user according to the first calculation value and the second calculation value.

10. The method of claim 7 wherein determining an enterprise user credit assessment score based on the second consignee characteristic information and the express consignee characteristic model comprises:

determining the receiving and sending address information and the quantity change information within the preset time according to the second receiving and sending area characteristic information;

and determining credit investigation scores of enterprise users according to the receiving and sending address information and the quantity change information in the preset time.

11. The method of claim 10, wherein determining a credit assessment score for an enterprise user based on the recipient address information and the quantity variation information within the predetermined time comprises:

if the quantity of the address information is kept unchanged within the preset time, determining a third calculation value;

if the number of the address receiving and sending information in the preset time keeps increasing, determining a fourth calculated value;

if the number of the address receiving and sending information in the preset time is reduced, determining a fifth calculated value;

if the address information of the receiving and sending in the preset time is coincident with the address information of the sensitive area, determining a sixth calculated value;

and determining the credit investigation score of the enterprise user according to the third calculated value, the fourth calculated value, the fifth calculated value and the sixth calculated value.

12. A bank user credit investigation evaluation device based on big data comprises:

the processing module is used for processing initial data to determine target data, wherein the initial data is express delivery data, and the target data comprises receiving and sending address data, receiving and sending person identity information and receiving and sending date data;

a first determining module, configured to determine a user type according to the target data, where the user type includes an individual user and an enterprise user;

the second determining module is used for determining the characteristic information of the receiving and sending region according to the user type and the target data; and

the third determining module is used for determining credit investigation scores of users according to the characteristic information of the receiving and sending areas and the characteristic model of the express receiving and sending areas;

the express receiving and sending region characteristic model is pre-established and used for determining credit investigation scores of the users according to the receiving and sending region characteristic information of the users.

13. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-11.

14. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 11.

15. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 11.

Background

In one example, the credit investigation assessment of the bank mostly adopts the tracking and assessment of traditional user economic behaviors such as fixed assets owned by the user, large consumption and the like, however, due to the fact that the data are relatively low in frequency and not fresh enough, the assessment mode cannot dynamically reflect the real credit investigation condition of the client.

Disclosure of Invention

In view of the above, the present disclosure provides a big data-based bank user credit assessment method, apparatus, device, medium, and program product.

According to a first aspect of the present disclosure, there is provided a big data-based bank user credit assessment method, including:

processing initial data to determine target data, wherein the initial data is express delivery data, and the target data comprises address data, sender identity information and date data;

determining user types according to the target data, wherein the user types comprise individual users and enterprise users;

determining characteristic information of a receiving and sending region according to the user type and the target data;

determining a user credit investigation score according to the characteristic information of the receiving and sending region and the characteristic model of the express receiving and sending region;

the express receiving and sending region characteristic model is pre-established and used for determining credit investigation scores of the users according to the receiving and sending region characteristic information of the users.

According to the embodiment of the disclosure, the characteristic model of the express receiving and sending region is trained in advance based on the following modes:

acquiring sample data, wherein the sample data comprises user express data and user credit investigation data;

and training the characteristic model of the express receiving and sending region according to the sample data.

According to the embodiment of the present disclosure, the training the characteristic model of the express receiving and sending region according to the sample data includes:

calculating credit investigation scores of users according to the sample data and the express receiving and sending region characteristic model;

determining accuracy according to the user credit investigation score and the user credit investigation data;

and updating the characteristic model of the express receiving and sending region based on the accuracy.

According to an embodiment of the present disclosure, the processing the initial data to determine the target data includes:

acquiring initial data;

and cleaning the initial data according to preset indexes to determine target data, wherein the preset indexes comprise an addressee address, a sender address, addressee information, sender information, an addressee date and a sender date.

According to an embodiment of the present disclosure, the determining a user type according to the target data includes:

determining identity information of the user according to the target data;

and determining the user type according to the identity information of the user.

According to an embodiment of the present disclosure, the determining of the characteristics information of the forwarding area according to the user type and the target data includes:

if the user type is a personal user, determining first receiving and sending area characteristic information according to the target data;

if the user type is an enterprise user, determining second receiving and sending region characteristic information according to the target data;

the first receiving and forwarding region characteristic information comprises receiving and forwarding address information within preset time, and the second receiving and forwarding region characteristic information comprises receiving and forwarding address information and digital quantity change information within preset time.

According to the embodiment of the present disclosure, the determining a credit investigation score of a user according to the characteristic information of the receiving and sending region and the characteristic model of the express receiving and sending region includes:

determining credit investigation scores of the personal users according to the first receiving and sending region characteristic information and the express receiving and sending region characteristic model;

and determining credit investigation scores of the enterprise users according to the second receiving and sending region characteristic information and the express receiving and sending region characteristic model.

According to the embodiment of the present disclosure, the determining the credit investigation score of the personal user according to the first receiving and forwarding region characteristic information and the express receiving and forwarding region characteristic model includes:

determining the receiving and sending address information within the preset time according to the first receiving and sending area characteristic information;

determining address information of the sensitive area according to the characteristic model of the express receiving and sending area;

comparing the address information of the receiving and sending areas in the preset time with the address information of the sensitive areas;

and determining credit assessment scores of the individual users according to the comparison results.

According to the embodiment of the disclosure, the determining the credit investigation score of the individual user according to the comparison result comprises the following steps:

if the address information of the sensitive area in the preset time contains the address information of the sensitive area, determining a first calculated value;

if the address information of the sensitive area is not contained in the address information of the receiving and sending area within the preset time, determining a second calculated value;

and determining the credit investigation score of the personal user according to the first calculation value and the second calculation value.

According to the embodiment of the present disclosure, the determining the credit investigation score of the enterprise user according to the second receiving and sending region characteristic information and the express receiving and sending region characteristic model includes:

determining the receiving and sending address information and the quantity change information within the preset time according to the second receiving and sending area characteristic information;

and determining credit investigation scores of enterprise users according to the receiving and sending address information and the quantity change information in the preset time.

According to the embodiment of the disclosure, determining the credit investigation score of the enterprise user according to the receiving and sending address information and the quantity change information within the preset time comprises the following steps:

if the quantity of the address information is kept unchanged within the preset time, determining a third calculation value;

if the number of the address receiving and sending information in the preset time keeps increasing, determining a fourth calculated value;

if the number of the address receiving and sending information in the preset time is reduced, determining a fifth calculated value;

if the address information of the receiving and sending in the preset time is coincident with the address information of the sensitive area, determining a sixth calculated value;

and determining the credit investigation score of the enterprise user according to the third calculated value, the fourth calculated value, the fifth calculated value and the sixth calculated value.

A second aspect of the present disclosure provides a bank user credit assessment device based on big data, including: the processing module is used for processing initial data to determine target data, wherein the initial data is express delivery data, and the target data comprises receiving and sending address data, receiving and sending person identity information and receiving and sending date data;

a first determining module, configured to determine a user type according to the target data, where the user type includes an individual user and an enterprise user;

the second determining module is used for determining the characteristic information of the receiving and sending region according to the user type and the target data; and

the third determining module is used for determining credit investigation scores of users according to the characteristic information of the receiving and sending areas and the characteristic model of the express receiving and sending areas;

the express receiving and sending region characteristic model is pre-established and used for determining credit investigation scores of the users according to the receiving and sending region characteristic information of the users.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the big data based bank user credit assessment method described above.

The fourth aspect of the present disclosure also provides a computer-readable storage medium, on which executable instructions are stored, and when executed by a processor, the instructions cause the processor to execute the above big data-based bank user credit assessment method.

The fifth aspect of the present disclosure also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the method for assessing credit of a bank user based on big data is implemented.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:

fig. 1 schematically shows a flow chart of a big data based bank user credit assessment method according to an embodiment of the present disclosure;

fig. 2 schematically shows a flow chart of another big data-based bank user credit assessment method according to an embodiment of the disclosure;

fig. 3 schematically illustrates a flow chart of credit investigation by an individual user based on first recipient region characteristic information in an embodiment of the disclosure;

figure 4 schematically illustrates a flow chart of credit assessment by an enterprise user based on second recipient region characteristic information in an embodiment of the present disclosure;

fig. 5 is a block diagram schematically illustrating the structure of a big data-based bank user credit assessment device according to an embodiment of the present disclosure; and

fig. 6 schematically shows a block diagram of an electronic device suitable for implementing a big data based bank user credit assessment method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

It should be noted that the method and apparatus provided by the embodiments of the present disclosure may be used in the financial field, and may also be used in any field other than the financial field.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.

The embodiment of the disclosure provides a bank user credit investigation evaluation method based on big data, which comprises the following steps: processing initial data to determine target data, wherein the initial data is express delivery data, and the target data comprises address data, sender identity information and date data; determining user types according to the target data, wherein the user types comprise individual users and enterprise users; determining characteristic information of a receiving and sending region according to the user type and the target data; determining a user credit investigation score according to the characteristic information of the receiving and sending region and the characteristic model of the express receiving and sending region; the express receiving and sending region characteristic model is pre-established and used for determining credit investigation scores of the users according to the receiving and sending region characteristic information of the users.

In one example, the credit investigation assessment of the bank mostly adopts the tracking and assessment of traditional user economic behaviors such as fixed assets owned by the user, large consumption and the like, however, due to the fact that the data are relatively low in frequency and not fresh enough, the assessment mode cannot dynamically reflect the real credit investigation condition of the client. With the development of information digitization, online shopping becomes the mainstream of shopping consumption, and as the commodities purchased online need to be distributed through express logistics, massive express data are generated, and the frequency of the express data generation is high, so that the consumption behavior and the operation behavior of the user can be reflected to a certain extent, and the credit investigation state of the user can be reflected.

Based on the technical problems, the embodiment of the disclosure provides a bank user credit investigation evaluation method based on big data.

The bank user credit investigation evaluation method based on big data according to the embodiment of the present disclosure will be described in detail below with reference to fig. 1 to 4.

Fig. 1 schematically shows a flow chart of a big data-based bank user credit assessment method according to an embodiment of the disclosure.

As shown in fig. 1, the embodiment includes operations S110 to S140, where an execution subject of the method may be a server, a computing cluster, or a background system, or may be a device and an apparatus for executing the method of the present disclosure, and the present disclosure is described with the background system as the execution subject.

In operation S110, the initial data is processed to determine target data.

According to the embodiment of the disclosure, the initial data is express delivery data, and the target data comprises receiving and sending address data, receiving and sending person identity information and receiving and sending date data.

In one example, the initial data is obtained by a legal means, and before the initial data is used, the initial data needs to be cleaned according to preset indexes to remove junk data or irrelevant data, wherein the preset indexes include but are not limited to an addressee, a mailing address, addressee information, sender information, an addressee date and a mailing date, and the target data include addressee data, addressee identity information and mailing date data.

In operation S120, a user type is determined according to the target data.

Determining user identity information according to the target data obtained in operation S110, and further determining a user type according to the user identity information, where the user type includes an individual user and an enterprise user; specifically, the user identity information can be identity card information, and because the individual user needs to provide the identity card information when sending the mail, the enterprise user has the identity card information, does not need the identity card information, and can judge the user type according to the identity card information.

In operation S130, recipient zone characteristic information is determined according to the user type and the target data.

The characteristic information of the receiving and sending region of the user can be determined according to the user type and the target data, and the characteristic information of the receiving and sending region can represent the consumption behavior and the operation condition of the user to a certain extent.

In one example, if the user type is a personal user, address information of a receiving and sending article in a preset time is counted according to target data and is used as characteristic information of a receiving and sending region of the personal user; and if the user type is an enterprise user, counting the address quantity change information of the receiving and sending articles in the preset time according to the target data as the characteristic information of the receiving and sending regions of the enterprise user. The address information of the receiving and sending articles in the preset time can reflect the consumption behaviors of individual users and whether the illegal criminal risk exists, and the address quantity change information of the receiving and sending articles in the preset time can reflect the operation condition of enterprise users.

In operation S140, a credit assessment score of the user is determined according to the characteristics information of the receiving and dispatching area and the characteristics model of the express receiving and dispatching area.

According to the embodiment of the disclosure, the characteristic model of the express receiving and sending area is pre-established according to expert experience and used for determining credit investigation scores of users according to characteristic information of the receiving and sending areas of the users, the characteristic model comprises preset indexes and score value intervals and preset weight values corresponding to the preset indexes, the characteristic model of the express receiving and sending area can be corrected by comparing output results with actual credit investigation conditions of the users, and the score value intervals and the preset weight values corresponding to the preset indexes are adjusted.

In one example, inputting the characteristics of the express receiving region into the characteristics model of the receiving region according to the characteristics information of the receiving region obtained in operation S130 may output a credit investigation score of the user, which is one of the reference weights of the credit investigation, to supplement the credit investigation.

According to the method and the device, the target data are obtained through the express data, the user type is determined according to the target data, the characteristic information of the receiving and sending region of the user is further determined, the credit investigation score of the user is determined according to the characteristic model and the characteristic information of the receiving and sending region of the express, the real-time credit investigation of the user is determined through mining and analyzing the express data, and the problem of a short board of a dynamic evaluation mode for credit investigation of the client in the prior art is solved.

Fig. 2 schematically shows a flowchart of another big data-based bank user credit assessment method according to an embodiment of the disclosure.

As shown in fig. 2, the embodiment includes operations S210 to S280, where an execution subject of the method may be a server or a background system, or may be an apparatus and a device for executing the method of the present disclosure, and the present disclosure is described with the background system as the execution subject.

Before credit assessment is performed on bank users based on express delivery data, the characteristic model of the express delivery receiving and sending region needs to be trained in advance through operation S210 and operation S220 based on the following modes:

in operation S210, sample data is acquired, where the sample data includes user express data and user credit data.

First sample data is obtained, which may be a plurality of groups, e.g., 100 groups. The sample data is user express data, namely target data and user credit investigation data, and the user actual credit investigation data reflects the current actual credit investigation condition of the user.

In operation S220, the characteristic model of the express delivery receiving and dispatching area is trained according to the sample data.

Operation S220 specifically includes the following steps:

in the first step, user credit investigation scoring is calculated according to the sample data and the express receiving and sending region characteristic model.

And in the second step, determining the accuracy according to the credit investigation score of the user and credit investigation data of the user.

And in the third step, updating the characteristic model of the express receiving and sending region based on the accuracy rate.

In one example, first, receiving and sending region feature information of each group of users is determined according to user express delivery data in sample data in operation S210, each group of user credit investigation score is determined according to the receiving and sending region feature information and an initial express receiving and sending region feature model, the user score is compared with actual credit investigation data of the users to determine accuracy, if the accuracy is less than or equal to a preset threshold, the current express receiving and sending region feature model is represented to be inaccurate, a score value interval and a preset weight value corresponding to a preset index need to be adjusted, and the user credit investigation score is recalculated until the accuracy is greater than the preset threshold, preferably, the preset threshold is 90%.

In operation S230, the initial data is processed to determine target data

According to the embodiment of the disclosure, initial data is acquired; and cleaning the initial data according to preset indexes to determine target data, wherein the preset indexes comprise an addressee address, a mailing address, addressee information, sender information, an addressee date and a mailing date.

The operation is the same as the technical solution and principle of operation S110 shown in fig. 1, and is not described again.

In operation S240, a user type is determined according to the target data.

According to the embodiment of the disclosure, identity information of a user is determined according to the target data; and determining the user type according to the identity information of the user.

The technical solution and principle of the operation are the same as those of operation S120 shown in fig. 1, and are not described again.

In operation S250, if the user type is a personal user, first forwarding and forwarding region feature information is determined according to the target data, where the first forwarding and forwarding region feature information includes forwarding and forwarding address information within a preset time.

In one example, if the user type is an individual user, the consumption capacity of the individual user can be indirectly reflected through the address receiving and sending information of the user, the target data is counted, and the high-frequency express receiving and sending addresses are merged to form first receiving and sending area characteristic information.

In operation S260, a credit assessment score of the personal user is determined according to the first shipping region characteristic information and the express shipping region characteristic model.

Fig. 3 schematically illustrates a flow chart of credit investigation by an individual user based on first recipient region characteristic information in an embodiment of the disclosure.

As shown in fig. 3, operation S260 includes operations S261 to S264.

In operation S261, forwarding address information within a preset time is determined according to the first forwarding region characteristic information.

In one example, the express delivery behavior of the individual user within the preset time is determined according to the first receiving and sending region characteristic information, and the first receiving and sending region characteristic information mainly comprises receiving and sending address information within the preset time.

In operation S262, sensitive region address information is determined according to the express delivery receiving and forwarding region feature model.

The express receiving and sending area characteristic model comprises sensitive area address information, the sensitive area refers to an area with high frequency of illegal criminal activities, and the sensitive area address information can be acquired by relevant departments and updated regularly.

In operation S263, the recipient address information and the sensitive area address information within the preset time are compared.

In one example, the address information of the sensitive area and the address information of the receiving and sending address within the preset time are compared, so that whether the individual user has the receiving and sending to and from the sensitive area within the preset time can be determined.

In operation S264, a credit assessment score of the individual user is determined according to the comparison result.

According to the embodiment of the disclosure, if the address information of the receiving and sending area in the preset time contains the address information of the sensitive area, the first calculated value is determined. If the address information of the sensitive area is not contained in the address information of the receiving and sending in the preset time, determining a second calculated value; and determining the credit investigation score of the personal user according to the first calculation value and the second calculation value.

The first calculation value is a negative number, the second calculation value is a positive number, and if the address information of the sensitive area in the preset time contains address information of the sensitive area, namely the user and the sensitive area have the address receiving and sending in the preset time, the user is represented to have a certain legal risk, and credit investigation and scoring are carried out; if the address information of the sensitive area does not contain address information of the sensitive area within the preset time, namely the user and the sensitive area do not receive and send the mail within the preset time, the express behavior of the user is represented to be normal, and the credit investigation score is added properly.

In operation S270, if the user type is an enterprise user, second recipient area characteristic information is determined according to the target data, where the second recipient area characteristic information includes recipient address information and quantity variation information within a preset time.

In one example, the target data is counted, the high-frequency express receiving and sending addresses are merged, the receiving number, the sending number and the express number generated in a period of time of each area address are respectively counted, and second receiving and sending area characteristic information is formed. The receiving and sending address information and the quantity change information of the enterprise users can indirectly reflect the operation behaviors of the enterprise users.

In operation S280, determining a credit investigation score of the enterprise user according to the second receiving and forwarding region characteristic information and the express receiving and forwarding region characteristic model;

fig. 4 schematically illustrates a flowchart of credit investigation by an enterprise user based on second recipient region characteristic information in an embodiment of the disclosure.

As shown in fig. 4, operations S281 to S282 are included.

In operation S281, the recipient address information and the number variation information within a preset time are determined according to the second recipient area characteristic information.

In operation S282, a credit assessment score of the enterprise user is determined according to the recipient address information and the quantity variation information within the preset time.

According to the embodiment of the disclosure, if the number of the receiving and sending address information in the preset time is kept unchanged, determining a third calculation value; if the number of the address receiving and sending information in the preset time keeps increasing, determining a fourth calculated value; if the number of the address receiving and sending information in the preset time is reduced, determining a fifth calculated value; if the address information of the receiving and sending in the preset time is coincident with the address of the sensitive area, determining a sixth calculated value; and determining the credit investigation score of the enterprise user according to the third calculated value, the fourth calculated value, the fifth calculated value and the sixth calculated value.

In one example, the quantity change information includes address quantity change information and express quantity change information, and the business situation of the enterprise may be determined according to the address information, the address quantity change information and the express quantity change information of the enterprise user.

For example, if there are several stable address express delivery transactions in the express delivery behavior of the enterprise user and the number is relatively stable, it is proved that the business behavior of the enterprise user is relatively healthy, a third calculation value can be determined, and the credit investigation score can be added appropriately; if a plurality of address express delivery transactions which are continuously and newly added exist in the express delivery behaviors of the enterprise user and the number of the address express delivery transactions is stably increased, the operation behaviors of the enterprise user are in an expansion type, a fourth calculated value can be determined, and an additional value can be obtained by credit investigation scoring; if the number of express delivery transactions of the existing consignment address in the express delivery behaviors of the enterprise user is sharply reduced and the operation behaviors of the enterprise user may fluctuate, determining a fifth calculated value and deducting the credit investigation score of the fifth calculated value; and if the express delivery behaviors of the enterprise users show frequent traffic with sensitive areas and the business behaviors of the enterprise users have legal risks, determining a sixth calculated value and deducting the credit investigation score.

According to the method provided by the embodiment of the disclosure, target data is obtained through express data, the user type is determined according to the target data, then the first receiving and sending area characteristic information and the second receiving and sending area characteristic information are determined, the credit investigation score of an individual user is determined according to the express receiving and sending area characteristic model and the first receiving and sending area characteristic information, the credit investigation score of an enterprise user is determined according to the express receiving and sending area characteristic model and the second receiving and sending area characteristic information, and the real-time credit investigation of the user is determined through mining and analyzing express data, so that the problem of a short board of a dynamic evaluation mode for client credit investigation in the prior art is solved.

Based on the bank user credit investigation evaluation method based on the big data, the disclosure also provides a bank user credit investigation evaluation device based on the big data. The apparatus will be described in detail below with reference to fig. 5.

Fig. 5 is a block diagram schematically illustrating the structure of a big data-based bank user credit assessment device according to an embodiment of the present disclosure.

As shown in fig. 5, the big-data-based bank user credit assessment apparatus 500 of this embodiment includes a processing module 510, a first determining module 520, a second determining module 530, and a third determining module 540.

The processing module 510 is configured to process initial data to determine target data, where the initial data is express delivery data, and the target data includes address data, identity information of a sender and a date of the sender and the address data. In an embodiment, the processing module 510 may be configured to perform the operation S110 described above, which is not described herein again.

The first determination module 520 is used to determine user types including individual users and enterprise users based on the target data. In an embodiment, the first determining module 520 may be configured to perform the operation S120 described above, which is not described herein again.

The second determining module 530 is configured to determine the recipient region characteristic information according to the user type and the target data. In an embodiment, the second determining module 530 may be configured to perform the operation S130 described above, and is not described herein again.

The third determining module 540 is configured to determine a credit investigation score of the user according to the characteristic information of the receiving and dispatching area and the characteristic model of the express receiving and dispatching area. In an embodiment, the third determining module 540 may be configured to perform the operation S140 described above, and is not described herein again.

According to an embodiment of the present disclosure, any plurality of the processing module 510, the first determining module 520, the second determining module 530, and the third determining module 540 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the processing module 510, the first determining module 520, the second determining module 530, and the third determining module 540 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the processing module 510, the first determining module 520, the second determining module 530 and the third determining module 540 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.

Fig. 6 schematically shows a block diagram of an electronic device suitable for implementing a big data based bank user credit assessment method according to an embodiment of the present disclosure.

As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.

In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

Electronic device 600 may also include input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604, according to an embodiment of the disclosure. The electronic device 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer 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. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than the ROM 602 and RAM 603.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the item recommendation method provided by the embodiment of the disclosure.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 601. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, downloaded and installed through the communication section 609, and/or installed from the removable medium 611. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, 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).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.