CN111127191A - Risk assessment method and device - Google Patents
Risk assessment method and device Download PDFInfo
- Publication number
- CN111127191A CN111127191A CN201911363866.8A CN201911363866A CN111127191A CN 111127191 A CN111127191 A CN 111127191A CN 201911363866 A CN201911363866 A CN 201911363866A CN 111127191 A CN111127191 A CN 111127191A
- Authority
- CN
- China
- Prior art keywords
- overdue
- user
- risk
- model
- overdue risk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application discloses a risk assessment method and device, and relates to the technical field of wind control. The specific implementation scheme is as follows: acquiring a behavior text of a user to be evaluated, inputting the behavior text into a preset overdue risk model, and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is a model obtained through Chinese corpus pre-training and risk scene data fine adjustment; the risk decision is carried out on the user by combining the overdue risk value, the method adopts a model with corresponding coefficients capable of reflecting semantic information and containing knowledge, and the overdue risk of the user is determined by combining the behavior text of the user, so that the accuracy of overdue risk calculation is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to the technical field of wind control, and particularly relates to a risk assessment method and device.
Background
Currently, in the auditing process of education credit and education cash credit, some automatic user wind control methods need to be introduced to evaluate the risks of users. In the related technology, the method for controlling the user's wind mainly comprises the steps of vectorizing the behavior text information of the user by a method based on a dictionary and a word vector, and then determining the overdue risk of the user by calculation based on the vector and a model.
However, in the above method, the model calculates the overdue risk based on the behavior vector of the user, and the behavior vector is determined by combining the behavior text of the user, the dictionary, and the like, so that it is difficult to reflect semantic information and implicit knowledge in the behavior text, and the calculated overdue risk is not accurate.
Disclosure of Invention
According to the risk assessment method and device, the model with the corresponding coefficients capable of reflecting semantic information and containing knowledge is adopted, the overdue risk of the user is determined by combining the behavior text of the user, and the accuracy of overdue risk calculation is improved.
An embodiment of one aspect of the present application provides a risk assessment method, including: acquiring a behavior text of a user to be evaluated; inputting the behavior text into a preset overdue risk model, and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is obtained through Chinese corpus pre-training and risk scene data fine adjustment; and combining the overdue risk value to carry out risk decision on the user.
In an embodiment of the present application, before the inputting the behavior text into a preset overdue risk model and acquiring an overdue risk value output by the overdue risk model, the method further includes: acquiring an initial overdue risk model and a Chinese corpus; the Chinese corpus comprises: chinese corpora larger than a first preset number; pre-training the initial overdue risk model by adopting the Chinese corpus to obtain a pre-trained overdue risk model; acquiring training data of a risk scenario, the training data comprising: the user behavior texts are larger than a second preset number of user behavior texts and corresponding overdue risk values; and training the pre-trained overdue risk model by adopting the training data to obtain the preset overdue risk model.
In an embodiment of the present application, the obtaining a behavior text of a user to be evaluated includes: obtaining feedback data of a user to be evaluated, wherein the feedback data comprises: a plurality of search texts; and sampling the feedback data and splicing separators to obtain the behavior text of the user.
In one embodiment of the present application, the overdue risk model is a knowledge-enhanced semantic representation model.
According to the risk assessment method, the behavior text of a user to be assessed is obtained; inputting the behavior text into a preset overdue risk model, and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is obtained through Chinese corpus pre-training and risk scene data fine adjustment; and determining the overdue risk value of the user by adopting a model with corresponding coefficients capable of reflecting semantic information and containing knowledge and combining the behavior text of the user, thereby improving the accuracy of overdue risk calculation.
Another embodiment of the present application provides a risk assessment apparatus, including: the acquisition module is used for acquiring a behavior text of a user to be evaluated; the input module is used for inputting the behavior text into a preset overdue risk model and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is obtained through Chinese corpus pre-training and risk scene data fine adjustment; and the decision module is used for carrying out risk decision on the user by combining the overdue risk value.
In one embodiment of the present application, the risk assessment apparatus further comprises: a pre-training module and a training module; the acquisition module is also used for acquiring an initial overdue risk model and a Chinese corpus; the Chinese corpus comprises: chinese corpora larger than a first preset number; the pre-training module is used for pre-training the initial overdue risk model by adopting the Chinese corpus to obtain a pre-trained overdue risk model; the obtaining module is further configured to obtain training data of a risk scenario, where the training data includes: the user behavior texts are larger than a second preset number of user behavior texts and corresponding overdue risk values; the training module is used for training the pre-trained overdue risk model by adopting the training data to obtain the preset overdue risk model.
In an embodiment of the present application, the obtaining module is specifically configured to obtain feedback data of a user to be evaluated, where the feedback data includes: a plurality of search texts; and sampling the feedback data and splicing separators to obtain the behavior text of the user.
In one embodiment of the present application, the overdue risk model is a knowledge-enhanced semantic representation model.
The risk assessment device of the embodiment of the application acquires the behavior text of a user to be assessed; inputting the behavior text into a preset overdue risk model, and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is obtained through Chinese corpus pre-training and risk scene data fine adjustment; the device can determine the overdue risk of the user by adopting a model with corresponding coefficients capable of reflecting semantic information and containing knowledge and combining the behavior text of the user, and improves the accuracy of overdue risk calculation.
An embodiment of another aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the risk assessment method of the embodiments of the present application.
Another embodiment of the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the risk assessment method of the embodiment of the present application.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present application;
fig. 2 is a schematic diagram according to a second embodiment of the present application.
FIG. 3 is a schematic diagram of an embodiment of a model framework for overdue risk;
FIG. 4 is a schematic illustration according to a third embodiment of the present application;
FIG. 5 is a schematic illustration according to a fourth embodiment of the present application;
fig. 6 is a block diagram of an electronic device for implementing a risk assessment method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The risk assessment method and apparatus according to the embodiments of the present application are described below with reference to the drawings.
Fig. 1 is a schematic diagram according to a first embodiment of the present application. It should be noted that an execution subject of the risk assessment method provided in this embodiment is a risk assessment device, which may be implemented in a software and/or hardware manner, and the device may be configured in a terminal device or a server, which is not limited in this embodiment.
As shown in fig. 1, the risk assessment method may include:
In the embodiment of the present application, the behavior text of the user to be evaluated may be feedback data of the user to be evaluated. Optionally, feedback data of a user to be evaluated is obtained, where the feedback data includes: a plurality of search texts; and sampling the feedback data and splicing the separators to obtain the behavior text of the user.
As an example, a user may input text in a browser search bar, and extracting according to the text input by the user may obtain feedback data of the user. In order to save related computing resources, the feedback data are sampled, and the adopted feedback data are spliced by separators to obtain a behavior text of the user.
In the embodiment of the application, after the behavior text of the user to be evaluated is acquired, the behavior text can be input into a preset overdue risk model, and an overdue risk value output by the overdue risk model is acquired. It should be noted that the overdue risk model may be a model obtained through pre-training of a chinese corpus and fine-tuning of risk scenario data. As an example, the overdue risk model may be, but is not limited to, a knowledge-enhanced semantic representation model.
It can be understood that the preset overdue risk model needs to be acquired before the behavior text is input into the preset overdue risk model and the overdue risk value output by the overdue risk model is acquired. Alternatively, as shown in fig. 2, fig. 2 is a schematic diagram according to a second embodiment of the present application. The specific implementation mode is as follows:
In an embodiment of the application, the initial overdue risk model may be a non-pre-trained knowledge-enhanced semantic representation model, and the chinese corpus may include any chinese corpus greater than a first preset number.
As an example, an initial overdue risk model is unsupervised trained by using a Chinese corpus, and the pretrained overdue risk model is obtained.
In the embodiment of the application, the feedback data of the user and the financial credit sample can be paired, the feedback data is sampled to obtain a sampled user behavior text and a corresponding overdue label, the sampled user behavior text and the corresponding overdue label are used as training data of a risk scene, and a pre-trained overdue risk model is trained. In order to improve the accuracy of the overdue risk model, the number of training data is greater than a second preset number. It should be noted that the overdue label may include, but is not limited to, overdue and non-overdue.
And 204, training the pre-trained overdue risk model by adopting the training data to obtain a preset overdue risk model.
Further, training the pre-trained overdue risk model by adopting training data to obtain a preset overdue risk model. In the embodiment of the present application, the preset overdue risk model may be built into a two-class model, wherein the class labels may correspond to overdue and non-overdue.
And 103, carrying out risk decision on the user by combining the overdue risk value.
In the embodiment of the application, the behavior text of the sampled user is input into the preset overdue risk model, the overdue risk value of the user is output, and the overdue risk value is integrated into the education loan and education cash loan model to obtain the final risk decision.
In conclusion, the model with the corresponding coefficients capable of reflecting semantic information and containing knowledge is adopted, large-scale Chinese corpora are adopted for pre-training the model, then the user behavior text and overdue records of the wind control scene are adopted for further training the model, so that the model can analyze the semantic information and the knowledge in the user behavior text, the overdue risk value of the user is determined by combining the semantic information and the knowledge, and the accuracy of overdue risk calculation is improved.
To better illustrate the above embodiments, fig. 3 is a schematic diagram of an overdue risk model framework according to an embodiment of the present disclosure, and as shown in fig. 3, the risk assessment method according to the embodiment of the present disclosure may be divided into an overdue risk model training phase and a risk value prediction phase. In the overdue risk model training stage, firstly, before a preset overdue risk model is obtained, an initial overdue risk model can be pre-trained by adopting a large-scale Chinese text to obtain the pre-trained overdue risk model, then, user feedback data and a financial credit sample are paired to obtain a sampled user behavior text and a corresponding overdue label, the sampled user behavior text and the corresponding overdue label are used as training data of a risk scene, and the pre-trained overdue risk model is trained to obtain the preset overdue risk model. And splicing the sampled user behavior texts by using separators, and inputting the spliced user behavior texts into a preset overdue risk model, wherein the model can output an overdue risk value. And finally, integrating the risk value into an education credit and education cash credit model to obtain a final risk decision.
According to the risk assessment method, the behavior text of a user to be assessed is obtained; inputting the behavior text into a preset overdue risk model, and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is a model obtained through Chinese corpus pre-training and risk scene data fine adjustment; the method includes the steps that a model with corresponding coefficients capable of reflecting semantic information and knowledge is adopted, large-scale Chinese linguistic data is adopted for pre-training the model, then a user behavior text and an overdue record pair of a wind control scene are adopted, the model is further trained, the model can analyze the semantic information and the knowledge in a user's text, the overdue risk of a user is determined by combining the semantic information and the knowledge, and the accuracy of overdue risk calculation is improved.
In order to implement the above embodiments, an embodiment of the present application further provides a risk assessment device.
Fig. 4 is a schematic diagram according to a third embodiment of the present application. As shown in fig. 4, the risk assessment apparatus 400 includes: an acquisition module 410, an input module 420, and a decision module 430.
The obtaining module 410 is configured to obtain a behavior text of a user to be evaluated; the input module 420 is configured to input the behavior text into a preset overdue risk model, and acquire an overdue risk value output by the overdue risk model; the overdue risk model is a model obtained through Chinese corpus pre-training and risk scene data fine adjustment; and the decision module 430 is configured to perform a risk decision on the user in combination with the overdue risk value.
As a possible implementation manner of the embodiment of the present application, as shown in fig. 5, fig. 5 is a schematic diagram according to a fourth embodiment of the present application, and on the basis of fig. 4, the risk assessment apparatus 400 further includes a pre-training module 440 and a training module 450.
The obtaining module 410 is further configured to obtain an initial overdue risk model and a chinese corpus; the Chinese corpus comprises: chinese corpora larger than a first preset number; a pre-training module 440, configured to pre-train the initial overdue risk model by using a chinese corpus, to obtain a pre-trained overdue risk model; the obtaining module 410 is further configured to obtain training data of the risk scenario, where the training data includes: the user behavior texts are larger than a second preset number of user behavior texts and corresponding overdue risk values; the training module 450 is configured to train the pre-trained overdue risk model by using the training data to obtain a preset overdue risk model.
As a possible implementation manner of the embodiment of the present application, the obtaining module 410 is specifically configured to obtain feedback data of a user to be evaluated, where the feedback data includes: a plurality of search texts; and sampling the feedback data and splicing the separators to obtain the behavior text of the user.
As a possible implementation manner of the embodiment of the present application, the overdue risk model is a knowledge-enhanced semantic representation model.
The risk assessment device of the embodiment of the application acquires the behavior text of a user to be assessed; inputting the behavior text into a preset overdue risk model, and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is a model obtained through Chinese corpus pre-training and risk scene data fine adjustment; the device can realize that the model which can reflect semantic information and contains knowledge by adopting corresponding coefficients is pre-trained by adopting large-scale Chinese linguistic data, and then the model is further trained by adopting a user behavior text and an overdue record pair of a pneumatic control scene, so that the model can analyze the semantic information and the knowledge in the user's text, the overdue risk of the user is determined by combining the semantic information and the knowledge, and the accuracy of overdue risk calculation is improved.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 6 is a block diagram of an electronic device according to the risk assessment method of the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the risk assessment methods provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the risk assessment method provided herein.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the stored data area may store data created from use of the trained electronic device of the text label model, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 602 optionally includes memory located remotely from processor 601, which may be connected to a risk assessment electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method of risk assessment may further comprise: an input device 303 and an output device 304. The processor 301, the memory 302, the input device 303 and the output device 304 may be connected by a bus or other means, and fig. 6 illustrates an example of a connection by a bus.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic equipment for risk assessment, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or other input device. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of risk assessment, comprising:
acquiring a behavior text of a user to be evaluated;
inputting the behavior text into a preset overdue risk model, and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is obtained through Chinese corpus pre-training and risk scene data fine adjustment;
and combining the overdue risk value to carry out risk decision on the user.
2. The method of claim 1, wherein before entering the behavior text into a preset overdue risk model and obtaining an overdue risk value output by the overdue risk model, the method further comprises:
acquiring an initial overdue risk model and a Chinese corpus; the Chinese corpus comprises: chinese corpora larger than a first preset number;
pre-training the initial overdue risk model by adopting the Chinese corpus to obtain a pre-trained overdue risk model;
acquiring training data of a risk scenario, the training data comprising: the user behavior texts are larger than a second preset number of user behavior texts and corresponding overdue risk values;
and training the pre-trained overdue risk model by adopting the training data to obtain the preset overdue risk model.
3. The method of claim 1, wherein obtaining the behavior text of the user to be evaluated comprises:
obtaining feedback data of a user to be evaluated, wherein the feedback data comprises: a plurality of search texts;
and sampling the feedback data and splicing separators to obtain the behavior text of the user.
4. The method according to any of claims 1-3, wherein the overdue risk model is a knowledge-enhanced semantic representation model.
5. A risk assessment device, comprising:
the acquisition module is used for acquiring a behavior text of a user to be evaluated;
the input module is used for inputting the behavior text into a preset overdue risk model and acquiring an overdue risk value output by the overdue risk model; the overdue risk model is obtained through Chinese corpus pre-training and risk scene data fine adjustment;
and the decision module is used for carrying out risk decision on the user by combining the overdue risk value.
6. The apparatus of claim 5, further comprising: a pre-training module and a training module;
the acquisition module is also used for acquiring an initial overdue risk model and a Chinese corpus; the Chinese corpus comprises: chinese corpora larger than a first preset number;
the pre-training module is used for pre-training the initial overdue risk model by adopting the Chinese corpus to obtain a pre-trained overdue risk model;
the obtaining module is further configured to obtain training data of a risk scenario, where the training data includes: the user behavior texts are larger than a second preset number of user behavior texts and corresponding overdue risk values;
the training module is used for training the pre-trained overdue risk model by adopting the training data to obtain the preset overdue risk model.
7. The apparatus of claim 5, wherein the obtaining module is specifically configured to,
obtaining feedback data of a user to be evaluated, wherein the feedback data comprises: a plurality of search texts;
and sampling the feedback data and splicing separators to obtain the behavior text of the user.
8. The apparatus of any of claims 5-7, wherein the overdue risk model is a knowledge-enhanced semantic representation model.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911363866.8A CN111127191B (en) | 2019-12-25 | 2019-12-25 | Risk assessment method and risk assessment device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911363866.8A CN111127191B (en) | 2019-12-25 | 2019-12-25 | Risk assessment method and risk assessment device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111127191A true CN111127191A (en) | 2020-05-08 |
| CN111127191B CN111127191B (en) | 2023-07-14 |
Family
ID=70502795
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911363866.8A Active CN111127191B (en) | 2019-12-25 | 2019-12-25 | Risk assessment method and risk assessment device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111127191B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112149179A (en) * | 2020-09-18 | 2020-12-29 | 支付宝(杭州)信息技术有限公司 | Risk identification method and device based on privacy protection |
| CN112749924A (en) * | 2021-02-01 | 2021-05-04 | 深圳无域科技技术有限公司 | Wind control model training method, system, equipment and computer readable medium |
| CN116629456A (en) * | 2023-07-20 | 2023-08-22 | 杭银消费金融股份有限公司 | Method, system and storage medium for predicting overdue risk of service |
| CN117333290A (en) * | 2023-12-01 | 2024-01-02 | 杭银消费金融股份有限公司 | Integrated multi-scale wind control model construction method |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160086111A1 (en) * | 2014-09-23 | 2016-03-24 | International Business Machines Corporation | Assessing project risks |
| CN106296391A (en) * | 2016-08-08 | 2017-01-04 | 联动优势科技有限公司 | A kind of assessment exceeds the time limit the method and apparatus of probability |
| WO2017125936A1 (en) * | 2016-01-19 | 2017-07-27 | Minacs Private Limited | System and method for holistically predicting risk associated with trade transactions in real-time |
| CN108389125A (en) * | 2018-02-27 | 2018-08-10 | 挖财网络技术有限公司 | The overdue Risk Forecast Method and device of credit applications |
| CN108985489A (en) * | 2018-06-08 | 2018-12-11 | 阿里巴巴集团控股有限公司 | A kind of Risk Forecast Method, risk profile device and terminal device |
| CN110070430A (en) * | 2019-03-12 | 2019-07-30 | 平安科技（深圳）有限公司 | Assess method and device, the storage medium, electronic equipment of refund risk |
| CN110134948A (en) * | 2019-04-23 | 2019-08-16 | 北京淇瑀信息科技有限公司 | A kind of Financial Risk Control method, apparatus and electronic equipment based on text data |
| CN110348008A (en) * | 2019-06-17 | 2019-10-18 | 五邑大学 | Medical text based on pre-training model and fine tuning technology names entity recognition method |
| CN110377905A (en) * | 2019-06-28 | 2019-10-25 | 北京百度网讯科技有限公司 | Semantic expressiveness processing method and processing device, computer equipment and the readable medium of sentence |
| CN110415102A (en) * | 2019-06-28 | 2019-11-05 | 上海上湖信息技术有限公司 | User credit methods of risk assessment and device, computer readable storage medium |
| CN110427627A (en) * | 2019-08-02 | 2019-11-08 | 北京百度网讯科技有限公司 | Task processing method and device based on semantic expressiveness model |
| CN110502753A (en) * | 2019-08-23 | 2019-11-26 | 昆明理工大学 | A kind of deep learning sentiment analysis model and its analysis method based on semantically enhancement |
-
2019
- 2019-12-25 CN CN201911363866.8A patent/CN111127191B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160086111A1 (en) * | 2014-09-23 | 2016-03-24 | International Business Machines Corporation | Assessing project risks |
| WO2017125936A1 (en) * | 2016-01-19 | 2017-07-27 | Minacs Private Limited | System and method for holistically predicting risk associated with trade transactions in real-time |
| CN106296391A (en) * | 2016-08-08 | 2017-01-04 | 联动优势科技有限公司 | A kind of assessment exceeds the time limit the method and apparatus of probability |
| CN108389125A (en) * | 2018-02-27 | 2018-08-10 | 挖财网络技术有限公司 | The overdue Risk Forecast Method and device of credit applications |
| CN108985489A (en) * | 2018-06-08 | 2018-12-11 | 阿里巴巴集团控股有限公司 | A kind of Risk Forecast Method, risk profile device and terminal device |
| CN110070430A (en) * | 2019-03-12 | 2019-07-30 | 平安科技（深圳）有限公司 | Assess method and device, the storage medium, electronic equipment of refund risk |
| CN110134948A (en) * | 2019-04-23 | 2019-08-16 | 北京淇瑀信息科技有限公司 | A kind of Financial Risk Control method, apparatus and electronic equipment based on text data |
| CN110348008A (en) * | 2019-06-17 | 2019-10-18 | 五邑大学 | Medical text based on pre-training model and fine tuning technology names entity recognition method |
| CN110377905A (en) * | 2019-06-28 | 2019-10-25 | 北京百度网讯科技有限公司 | Semantic expressiveness processing method and processing device, computer equipment and the readable medium of sentence |
| CN110415102A (en) * | 2019-06-28 | 2019-11-05 | 上海上湖信息技术有限公司 | User credit methods of risk assessment and device, computer readable storage medium |
| CN110427627A (en) * | 2019-08-02 | 2019-11-08 | 北京百度网讯科技有限公司 | Task processing method and device based on semantic expressiveness model |
| CN110502753A (en) * | 2019-08-23 | 2019-11-26 | 昆明理工大学 | A kind of deep learning sentiment analysis model and its analysis method based on semantically enhancement |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112149179A (en) * | 2020-09-18 | 2020-12-29 | 支付宝(杭州)信息技术有限公司 | Risk identification method and device based on privacy protection |
| CN112149179B (en) * | 2020-09-18 | 2022-09-02 | 支付宝(杭州)信息技术有限公司 | Risk identification method and device based on privacy protection |
| CN112749924A (en) * | 2021-02-01 | 2021-05-04 | 深圳无域科技技术有限公司 | Wind control model training method, system, equipment and computer readable medium |
| CN116629456A (en) * | 2023-07-20 | 2023-08-22 | 杭银消费金融股份有限公司 | Method, system and storage medium for predicting overdue risk of service |
| CN116629456B (en) * | 2023-07-20 | 2023-10-13 | 杭银消费金融股份有限公司 | Method, system and storage medium for predicting overdue risk of service |
| CN117333290A (en) * | 2023-12-01 | 2024-01-02 | 杭银消费金融股份有限公司 | Integrated multi-scale wind control model construction method |
| CN117333290B (en) * | 2023-12-01 | 2024-03-26 | 杭银消费金融股份有限公司 | Integrated multi-scale wind control model construction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111127191B (en) | 2023-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111428008B (en) | Method, apparatus, device and storage medium for training a model | |
| CN111325020B (en) | Event argument extraction method and device and electronic equipment | |
| CN110674314B (en) | Sentence recognition method and device | |
| CN111221984A (en) | Multimodal content processing method, device, equipment and storage medium | |
| CN111639710A (en) | Image recognition model training method, device, equipment and storage medium | |
| CN111221983A (en) | Time sequence knowledge graph generation method, device, equipment and medium | |
| CN111414482A (en) | Event argument extraction method and device and electronic equipment | |
| CN111241819B (en) | Word vector generation method and device and electronic equipment | |
| CN111127191A (en) | Risk assessment method and device | |
| CN111709247A (en) | Data set processing method and device, electronic equipment and storage medium | |
| CN111144115A (en) | Pre-training language model obtaining method and device, electronic equipment and storage medium | |
| CN112036509A (en) | Method and apparatus for training image recognition models | |
| CN111104514A (en) | Method and device for training document label model | |
| CN112000792A (en) | Extraction method, device, equipment and storage medium of natural disaster event | |
| CN111079945B (en) | End-to-end model training method and device | |
| CN111709252B (en) | Model improvement method and device based on pre-trained semantic model | |
| CN111259671A (en) | Semantic description processing method, device and equipment for text entity | |
| CN111950291A (en) | Semantic representation model generation method and device, electronic equipment and storage medium | |
| KR20210157342A (en) | Language model training method, device, electronic equipment and readable storage medium | |
| US20220027575A1 (en) | Method of predicting emotional style of dialogue, electronic device, and storage medium | |
| CN111522944A (en) | Method, apparatus, device and storage medium for outputting information | |
| CN111078878A (en) | Text processing method, device and equipment and computer readable storage medium | |
| CN111831814A (en) | Pre-training method and device of abstract generation model, electronic equipment and storage medium | |
| CN111539224A (en) | Pruning method and device of semantic understanding model, electronic equipment and storage medium | |
| CN112560499A (en) | Pre-training method and device of semantic representation model, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |