CN112053051A - Due diligence application system and information processing method thereof - Google Patents
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Abstract
A due diligence application system and an information processing method thereof are disclosed. In the embodiment of the application, the bank can be helped to standardize the due diligence investigation flow of the bank enterprise client, the risk that due diligence investigation is not blamed and artificial counterfeiting is avoided, and the case-related risk of the bank enterprise client is greatly reduced.
Description
Technical Field
The application relates to the technical field of computers, in particular to a due diligence application system and an information processing method thereof.
Background
At present, due diligence investigation before enterprise customers open accounts is completed manually by commercial banks, including customer managers visiting on the spot, taking photos in office areas and the like, and risks that the customer managers cannot perform due diligence investigation or help the case-related enterprises to make fake exist in the period, so that a plurality of case-related enterprises cannot be identified in the account opening link, and finally, smooth account opening is realized, and conditions are provided for similar crimes such as telecom fraud, illegal fund collection and the like. Therefore, how to standardize the due diligence investigation flow by the information processing technology is a technical problem to be solved urgently.
Disclosure of Invention
In order to solve the above technical problems, it is desirable to provide a due diligence application system and an information processing method thereof.
According to an aspect of the present application, there is provided a due diligence application system including:
the system comprises an information collection module, an information processing module and a display module, wherein the information collection module is deployed at a client and configured to collect due diligence information of an object to be evaluated and upload the due diligence information to the information processing module, and the due diligence information comprises field diligence information reflecting the field diligence condition of the object to be evaluated;
and the information processing module is deployed at a server and configured to receive the due diligence information from the information collection module, identify the reliability of the due diligence information, and determine a risk evaluation result of the object to be evaluated by using a pre-trained risk evaluation model according to the reliability of the due diligence information, the due diligence information of the object to be evaluated and pre-acquired registration information of a registration authority of the object to be evaluated.
In some examples, the due diligence application further includes: and the information query module is deployed at the server and configured to acquire registration information of the object to be evaluated from a server of a registration authority by using a crawler technology or a data interface provided by the registration authority after the information processing module receives the due-employment survey information.
In some examples, the due diligence application further includes: and the information storage module is configured to store information of the object to be evaluated, wherein the information comprises the due diligence survey information and registration information of a registration institution.
In some examples, the due diligence application further includes: and the result display module is deployed at the client and configured to acquire and display the risk evaluation result of the object to be evaluated from the information processing module.
In some examples, the due diligence application further includes: and the model training module is deployed in the server and configured to train the risk assessment model by using historical data, wherein the historical data comprises real risk information and due diligence survey information of the assessed object.
According to an aspect of the present application, there is provided an information processing method of a due diligence application system, including:
acquiring due diligence survey information of an object to be evaluated, wherein the due diligence survey information comprises field survey information reflecting the field survey condition of the object to be evaluated;
identifying the credibility of the due diligence survey information;
and determining a risk evaluation result of the object to be evaluated by utilizing a pre-trained risk evaluation model according to the credibility of the due diligence survey information, the due diligence survey information of the object to be evaluated and pre-acquired registration information of a registration authority of the object to be evaluated.
In some examples, the identifying the trustworthiness of the due diligence information includes: determining authenticity of the field survey information using a pre-trained deep learning model.
In some examples, the field survey information comprises images acquired while the subject to be evaluated is being field surveyed; the determining authenticity of the field survey information using a pre-trained deep learning model includes: and determining whether the image is tampered by using a pre-trained image tampering monitoring model, wherein if the image is tampered, the image indicates that the due diligence information is not credible, and if the image is not tampered, the image indicates that the due diligence information is credible.
In some examples, determining a risk assessment result of the object to be assessed by using a pre-trained risk assessment model according to the credibility of the due diligence survey information, the due diligence survey information of the object to be assessed, and pre-acquired registration information of a registration authority of the object to be assessed includes: generating feature data of the object to be evaluated by using due diligence information of the object to be evaluated, registration information of a registration authority, and due diligence supplementary information further acquired when the due diligence information is not credible, and processing the feature data of the object to be evaluated through the risk evaluation model, thereby obtaining a risk evaluation result of the object to be evaluated.
In some examples, determining a risk assessment result of the object to be assessed by using a pre-trained risk assessment model according to the credibility of the due diligence survey information, the due diligence survey information of the object to be assessed, and pre-acquired registration information of a registration authority of the object to be assessed includes: and when the due diligence information is credible, generating feature data of the object to be evaluated by directly utilizing the due diligence information of the object to be evaluated and registration information of a registration institution, and processing the feature data of the object to be evaluated through the risk evaluation model so as to obtain a risk evaluation result of the object to be evaluated.
According to the due diligence application system and the information processing method thereof in the embodiment of the application, the collected due diligence information contains field diligence information, the credibility of the due diligence information is firstly identified, then the due diligence information and a pre-trained risk assessment model are utilized to carry out risk assessment by combining the credibility, the bank is helped to assess the risk level of the object to be assessed, the due diligence investigation flow of bank enterprise customers is effectively standardized, the risk that due diligence investigation is not responsible for the most and artificial counterfeiting is avoided, and the risk related to a case of the bank enterprise customers is greatly reduced.
Drawings
Fig. 1 is a schematic diagram of an exemplary system architecture of a due diligence application system in an embodiment of the present application;
fig. 2 is a flowchart of an information processing method of the due diligence application system in the embodiment of the present application.
Detailed Description
Hereinafter, embodiments of the present application will be described in detail with reference to the accompanying drawings. It should be noted that, in the present application, the embodiments and the features thereof may be arbitrarily combined with each other without conflict.
As described above, currently, due diligence before an enterprise customer makes an account is completed manually by a commercial bank, and there is a risk that a customer manager cannot perform due diligence or help an involved enterprise to make a fake product, so that many involved enterprises cannot be identified in the account making process. The application aims to help and standardize a customer manager to complete due-time investigation under the scenes of enterprise customer account opening and the like through an application system.
It should be noted that the embodiment of the present application is applicable to various application scenarios requiring due diligence. In some examples, the embodiments of the present application are particularly applicable to the due diligence of banks on public accounts. Of course, the embodiment of the application can also be applied to due diligence scenes such as investment and the like. When different scenes are applied, the related differences mainly refer to the specific contents of survey materials, and the processing method and the system architecture are similar.
The object to be evaluated in the embodiment of the present application refers to an object for which no risk evaluation has been performed, and the evaluated object refers to an object for which a risk evaluation has been performed, and these objects may be, but are not limited to, bank customers such as enterprises, institutions, organizations, and the like.
In the embodiment of the present application, the risk assessment result may be, but is not limited to, a score that may reflect a risk degree of the subject to be assessed, where a higher score indicates a higher risk degree of opening an account for the subject to be assessed, and a lower score indicates a lower risk degree of opening an account for the subject to be assessed. In practical application, a client manager or a server side can determine whether to open an account for the object to be evaluated according to preset account opening conditions and the risk evaluation result.
Exemplary System
Fig. 1 illustrates an exemplary architecture of a due diligence application system provided by an embodiment of the present application. As shown in fig. 1, the due diligence application system in the embodiment of the present application may include:
the system comprises an information collection module 11, a data processing module and a data processing module, wherein the information collection module is deployed at a client and configured to collect due diligence survey information of an object to be evaluated and upload the due diligence survey information to the information processing module, and the due diligence survey information comprises field survey information reflecting the field survey condition of the object to be evaluated;
the information processing module 12 is deployed at the server, and is configured to receive the due diligence survey information from the information collecting module, identify the reliability of the due diligence survey information, and determine a risk assessment result of the object to be assessed by using a pre-trained risk assessment model according to the reliability of the due diligence survey information, the due diligence survey information of the object to be assessed, and pre-acquired registration information of a registration authority of the object to be assessed.
According to the due diligence investigation application system, the collected due diligence investigation information contains on-site investigation information, the credibility of the due diligence investigation information is identified firstly, then the due diligence investigation information and a risk assessment model trained in advance are utilized to carry out risk assessment by combining the credibility, the bank is helped to assess the risk level of the object to be assessed, the due diligence investigation process of bank enterprise customers is effectively standardized, the risk that due diligence is not blamed and artificial counterfeiting is avoided, and the risk related to a case of the bank enterprise customers is greatly reduced.
As shown in fig. 1, the due diligence application system provided in the embodiment of the present application may further include: and the information query module 13 is deployed at the server and configured to acquire registration information of the registration authority of the object to be evaluated from a server of the registration authority by using a crawler technology or a data interface provided by the registration authority after the information processing module receives the due-time investigation information. Specifically, the information query module 13 may generate a query condition by using the identification information of the name, the uniform social credit code, the legal person, and the like of the target to be evaluated in the due diligence information after the information processing module receives the due diligence information of the target to be evaluated, and then query registration information of a registration authority such as a business authority (the registration information of the registration authority may include, but is not limited to, business authority registration information, and the like) of the target to be evaluated from a dedicated server or a database of the registration authority based on the query condition.
As shown in fig. 1, the due diligence application system provided in the embodiment of the present application may further include: and the information storage module 14 is configured to store information of the object to be evaluated, wherein the information comprises the due diligence survey information and registration information of a registration institution. Specifically, the information of the object to be evaluated stored by the information storage module 14 may include, but is not limited to, due diligence information of the object to be evaluated submitted by a customer manager, information of a related person of the object to be evaluated (e.g., information in a pedestrian related to an account opening enterprise), account opening application information of the object to be evaluated, registration information of a registration authority of the object to be evaluated, and the like.
As shown in fig. 1, the due diligence application system provided in the embodiment of the present application may further include: the result display module 15 is deployed at the client, and is configured to obtain and display the risk assessment result of the object to be assessed from the information processing module 12. Therefore, the final account opening enterprise risk assessment result can be displayed to a client manager or a teller to judge whether the account opening can be performed on the enterprise.
As shown in fig. 1, the due diligence application system provided in the embodiment of the present application may further include: and the model training module 16 is deployed at the server and configured to train the risk assessment model by using historical data, wherein the historical data comprises real risk information and due diligence information of the assessed object. In practice, the risk assessment model library may be, but is not limited to, a deep learning model such as a neural network, binary classification, or the like. Furthermore, the model training module 16 may be further configured to train an image tampering monitoring model, described below, using historical image data, including raw pictures that have not been tampered with, tampered pictures, and the like.
In practical applications, the client and the server can be implemented by software, hardware or a combination of the two. For example, the client may be, but is not limited to, an internal application for an employee inside a bank, a client application for an object to be evaluated. The internal application program may be an application program on an electronic device such as a mobile phone, and an internal employee in a bank may complete a process such as due diligence by logging in the internal application program. The client application may be used by the customer and submit bank-related applications such as an account opening. For example, the server may include, but is not limited to, a dedicated server of a financial institution such as a bank or a program on the dedicated server that is responsible for data processing, data storage, and the like.
In practical application, in the due diligence application system of the embodiment of the application, a customer manager can submit due diligence investigation information of an enterprise customer through an application program such as a mobile phone APP developed by a bank, and a server can further complete risk assessment such as an enterprise waiting for an assessment object through the condition of performing credibility identification on the due diligence investigation information, so that the process of the bank enterprise customer account opening due diligence investigation is standardized, the risk of artificial counterfeiting and incapability of investigation in the account opening process is avoided, and the risk of the bank enterprise customer involved in a case is greatly reduced.
Specific technical details of information processing of the due diligence application system in the embodiment of the present application may refer to the following "exemplary method" section, and are not described again.
Exemplary method
Fig. 2 shows a flow of an exemplary information processing method of the due diligence application system in the embodiment of the present application. As shown in fig. 2, the process may include:
step S210, acquiring due diligence survey information of an object to be evaluated, wherein the due diligence survey information comprises field survey information reflecting the field survey condition of the object to be evaluated;
step S220, identifying the credibility of the due diligence information;
step S230, determining a risk assessment result of the object to be assessed by using a pre-trained risk assessment model according to the reliability of the due diligence survey information, the due diligence survey information of the object to be assessed, and pre-acquired registration information of a registration authority of the object to be assessed.
The information processing method of the embodiment of the application can be realized through the information processing model 12 of the service end of the above due diligence application system. The information handling model library 12 may be software, hardware, or a combination of both.
In the embodiment of the present application, due diligence information may include, but is not limited to, field survey information collected by a customer manager during field survey and information collected by other means and used for reflecting the background of a subject to be evaluated. Here, the field survey information may include, but is not limited to, location information during a process of a customer conducting a field survey, inputted identity information (e.g., a face, a fingerprint, an iris, voice, etc., which can be used to identify a personal identity of a customer manager), a photograph of an office area and a photograph of a surrounding environment, etc.
The deep learning technology is mature at present and is applied to the field of image tampering monitoring, the image tampering monitoring accuracy is greatly improved, and the mature algorithms include CNN, false R-CNN and the like. In some embodiments, a deep learning model may be utilized to identify the accuracy and authenticity of due diligence material. In some embodiments, the step of identifying the credibility of the due diligence information in step S220 may include, but is not limited to: determining authenticity of the field survey information using a pre-trained deep learning model. Specifically, the field investigation information may include an image acquired when the object to be evaluated is subjected to field investigation; the step of determining the authenticity of the field survey information using a pre-trained deep learning model may comprise: and determining whether the image is tampered by using a pre-trained image tampering monitoring model, wherein if the image is tampered, the image indicates that the due diligence information is not credible, and if the image is not tampered, the image indicates that the due diligence information is credible. Therefore, whether the picture in the on-site investigation information is artificially modified or not is identified through the image tampering monitoring model, so that whether the on-site investigation is real or effective can be efficiently and accurately confirmed, and whether the corresponding due investigation information is credible or not can be confirmed, so that the on-site investigation process of a customer manager is efficiently and accurately standardized, the authenticity and accuracy of the on-site investigation material are ensured, and the enterprise submits the counterfeit material, thereby avoiding the risks of incomplete investigation and artificial counterfeiting in the account opening process, and greatly reducing the risk of case involvement of the bank enterprise customer.
In practical applications, the image tampering monitoring model trained in advance is used in step S220 to determine whether the image is tampered, which may include, but is not limited to, image tampering monitoring for a part of key images (e.g., facial images of customer managers, office scene photographs of objects to be evaluated, etc.) in due diligence information.
In practical application, the image tampering monitoring model can be trained by using pre-collected historical picture data. The image tampering monitoring model may be, but is not limited to, a convolutional neural network, a fast convolutional neural network, or the like. In addition, the image tampering monitoring model can also be realized by adopting other two-classification deep learning models. The embodiments of the present application are not limited thereto.
In other embodiments, the step S220 of identifying the credibility of the due diligence information may further include: and judging the consistency of the account opening application information pre-submitted by the object to be evaluated and the registration information of the registration authority of the object to be evaluated. And if the account opening application information pre-submitted by the object to be evaluated is consistent with the registration information of the registration authority, the due diligence information is credible. And if the account opening application information pre-submitted by the object to be evaluated is inconsistent with the registration information of the registration authority, the due diligence information is not credible. Thus, the authenticity of the due diligence information can be preliminarily determined by comparing the information submitted by the enterprise itself with its actual registration information.
In other embodiments, the image tampering monitoring in the field investigation information and the information matching program of the object to be evaluated can be combined to comprehensively identify the credibility of the due-employment investigation information. In practical application, the specific means for identifying the credibility of the due-employment survey information can be selected according to the requirements of application scenarios.
In some embodiments, step S230 may include: generating feature data of the object to be evaluated by using due diligence information of the object to be evaluated, registration information of a registration authority, and due diligence supplementary information further acquired when the due diligence information is not credible, and processing the feature data of the object to be evaluated through the risk evaluation model, thereby obtaining a risk evaluation result of the object to be evaluated. Here, due diligence supplementary information may be submitted by a client after providing the client with an authentication result that the due diligence information is not authentic. Therefore, under the condition that the reliability of the due diligence information is low, a client manager can be allowed to resubmit relevant supplementary information (for example, text descriptions of pictures modified based on what reasons) through the client to continue subsequent risk assessment, the termination of the due diligence process caused by some problems of the application system or modification of parameters such as picture pixels and definition can be avoided, and the complex situation during the field investigation can be better adapted to the due diligence application system and the information processing method thereof, and the method is more humanized.
In some embodiments, step S230 may include: determining a risk evaluation result of the object to be evaluated by using a pre-trained risk evaluation model according to the credibility of the due diligence survey information, the due diligence survey information of the object to be evaluated and pre-acquired registration information of a registration authority of the object to be evaluated, wherein the method comprises the following steps: and when the due diligence information is credible, generating feature data of the object to be evaluated by directly utilizing the due diligence information of the object to be evaluated and registration information of a registration institution, and processing the feature data of the object to be evaluated through the risk evaluation model so as to obtain a risk evaluation result of the object to be evaluated. Therefore, the subsequent risk assessment can be completed quickly and efficiently under the condition that the due diligence information is credible, the due diligence process is accelerated, and the processing efficiency and the user experience of the application system can be improved as much as possible on the premise of ensuring higher accuracy of the risk assessment.
The information processing method according to the embodiment of the present application may further include: and after the due diligence information is received, acquiring registration information of the registration institution of the object to be evaluated from a server of the registration institution by calling an information inquiry module. Specifically, the information query module may use a crawler technology or a data interface provided by a registration authority to acquire registration authority registration information of the object to be evaluated from a server of the registration authority. The information query module generates a query condition by using the identification information of the name, the unified social credit code, the legal person and the like of the object to be evaluated in the due diligence survey information, and queries registration information of a registration authority of the object to be evaluated (the registration information of the registration authority may include, but is not limited to, the registration information of the business authority and the like) from a special server or a database of the registration authority of the business authority and the like based on the query condition.
In practical applications, the registration information of the registration authority of the object to be evaluated may be, but is not limited to, the business registration information of the enterprise. Specifically, the information may include: the social credit code, the name of the business, the registration number, the type, the legal representative, the establishment date, the approval date, the registered capital, the business term, the registration organization, the business scope, the business place, the registration status, the shareholder and the financing information, the information of the main staff, the information of the branch organization, the information of the unified multiple certificates, the clearing information, the change information, etc. are unified.
Exemplary electronic device
The embodiment of the application also provides the electronic equipment. The electronic device may include one or more processors and memory.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
The memory may include one or more computer programs, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer programs may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the information processing methods of the various embodiments described above and/or other desired functions.
In addition, the electronic device may include any other suitable components, such as a bus, input/output interfaces, and the like, depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps of the information processing method according to various embodiments of the present application described in the above-mentioned "exemplary methods" section of this specification.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in an information processing method according to various embodiments of the present application described in the "exemplary methods" section above of the present specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A due diligence application system comprising:
the system comprises an information collection module, an information processing module and a display module, wherein the information collection module is deployed at a client and configured to collect due diligence information of an object to be evaluated and upload the due diligence information to the information processing module, and the due diligence information comprises field diligence information reflecting the field diligence condition of the object to be evaluated;
and the information processing module is deployed at a server and configured to receive the due diligence information from the information collection module, identify the reliability of the due diligence information, and determine a risk evaluation result of the object to be evaluated by using a pre-trained risk evaluation model according to the reliability of the due diligence information, the due diligence information of the object to be evaluated and pre-acquired registration information of a registration authority of the object to be evaluated.
2. The due diligence application system of claim 1, further comprising:
and the information query module is deployed at the server and configured to acquire registration information of the object to be evaluated from a server of a registration authority by using a crawler technology or a data interface provided by the registration authority after the information processing module receives the due-employment survey information.
3. The due diligence application system of claim 1, further comprising:
and the information storage module is configured to store information of the object to be evaluated, wherein the information comprises the due diligence survey information and registration information of a registration institution.
4. The due diligence application system of claim 1, further comprising:
and the result display module is deployed at the client and configured to acquire and display the risk evaluation result of the object to be evaluated from the information processing module.
5. The due diligence application system of claim 1, further comprising:
and the model training module is deployed in the server and configured to train the risk assessment model by using historical data, wherein the historical data comprises real risk information and due diligence survey information of the assessed object.
6. An information processing method of a due diligence application system includes:
acquiring due diligence survey information of an object to be evaluated, wherein the due diligence survey information comprises field survey information reflecting the field survey condition of the object to be evaluated;
identifying the credibility of the due diligence survey information;
and determining a risk evaluation result of the object to be evaluated by utilizing a pre-trained risk evaluation model according to the credibility of the due diligence survey information, the due diligence survey information of the object to be evaluated and pre-acquired registration information of a registration authority of the object to be evaluated.
7. The information processing method of the due diligence application system according to claim 6, wherein the authenticating the credibility of the due diligence information includes: determining authenticity of the field survey information using a pre-trained deep learning model.
8. The information processing method of the due diligence application system according to claim 7, wherein the field survey information includes an image acquired when the subject to be evaluated is field surveyed;
the determining authenticity of the field survey information using a pre-trained deep learning model includes: and determining whether the image is tampered by using a pre-trained image tampering monitoring model, wherein if the image is tampered, the image indicates that the due diligence information is not credible, and if the image is not tampered, the image indicates that the due diligence information is credible.
9. The information processing method of the due diligence application system according to claim 6, wherein determining a risk assessment result of the subject to be assessed using a pre-trained risk assessment model based on the credibility of the due diligence information, the due diligence information of the subject to be assessed, and pre-acquired registration information of a registration authority of the subject to be assessed, comprises:
generating feature data of the object to be evaluated by using due diligence information of the object to be evaluated, registration information of a registration authority, and due diligence supplementary information further acquired when the due diligence information is not credible, and processing the feature data of the object to be evaluated through the risk evaluation model, thereby obtaining a risk evaluation result of the object to be evaluated.
10. The information processing method of the due diligence application system according to claim 6, wherein determining a risk assessment result of the subject to be assessed using a pre-trained risk assessment model based on the credibility of the due diligence information, the due diligence information of the subject to be assessed, and pre-acquired registration information of a registration authority of the subject to be assessed, comprises: and when the due diligence information is credible, generating feature data of the object to be evaluated by directly utilizing the due diligence information of the object to be evaluated and registration information of a registration institution, and processing the feature data of the object to be evaluated through the risk evaluation model so as to obtain a risk evaluation result of the object to be evaluated.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105405054A (en) * | 2015-12-11 | 2016-03-16 | 平安科技（深圳）有限公司 | Insurance claim antifraud implementation method based on claim photo deep learning and server |
| CN108734569A (en) * | 2018-04-17 | 2018-11-02 | 天逸财金科技服务（武汉）有限公司 | A kind of factoring information investigation system and method |
| CN109711967A (en) * | 2017-10-25 | 2019-05-03 | 嘉兴市友贷金融信息服务有限公司 | Real estate debt-credit long-range air control system and method before borrowing |
| CN109711965A (en) * | 2017-10-25 | 2019-05-03 | 嘉兴市友贷金融信息服务有限公司 | A kind of intelligence antifraud network loan remotely reconciles to the greatest extent risk control method |
| CN110147998A (en) * | 2019-04-16 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Client's networking processing method, device, computer equipment and storage medium |
| CN110264222A (en) * | 2019-05-21 | 2019-09-20 | 平安银行股份有限公司 | Responsible investigation method, apparatus and terminal device based on data acquisition |
| CN110322116A (en) * | 2019-05-24 | 2019-10-11 | 平安银行股份有限公司 | Task processing method, device, electronic equipment and storage medium |
-
2020
- 2020-08-27 CN CN202010878768.4A patent/CN112053051A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105405054A (en) * | 2015-12-11 | 2016-03-16 | 平安科技（深圳）有限公司 | Insurance claim antifraud implementation method based on claim photo deep learning and server |
| CN109711967A (en) * | 2017-10-25 | 2019-05-03 | 嘉兴市友贷金融信息服务有限公司 | Real estate debt-credit long-range air control system and method before borrowing |
| CN109711965A (en) * | 2017-10-25 | 2019-05-03 | 嘉兴市友贷金融信息服务有限公司 | A kind of intelligence antifraud network loan remotely reconciles to the greatest extent risk control method |
| CN108734569A (en) * | 2018-04-17 | 2018-11-02 | 天逸财金科技服务（武汉）有限公司 | A kind of factoring information investigation system and method |
| CN110147998A (en) * | 2019-04-16 | 2019-08-20 | 深圳壹账通智能科技有限公司 | Client's networking processing method, device, computer equipment and storage medium |
| CN110264222A (en) * | 2019-05-21 | 2019-09-20 | 平安银行股份有限公司 | Responsible investigation method, apparatus and terminal device based on data acquisition |
| CN110322116A (en) * | 2019-05-24 | 2019-10-11 | 平安银行股份有限公司 | Task processing method, device, electronic equipment and storage medium |
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