CN112465632A - New financial AI intelligent wind control decision method and system - Google Patents
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Abstract
The embodiment of the invention provides a new financial AI intelligent wind control decision method, which comprises the following steps: calling a wind control scene in response to a user operation for calling the wind control scene; matching a corresponding preset wind control decision model according to a wind control scene; responding to user operation for receiving a data access request, and acquiring basic data of a user; and analyzing the basic data and generating a decision result. The method comprises the steps of setting a sub-model with a data tag in a wind control decision model, obtaining the authority of accessing data for a user on line, automatically collecting basic data of the user, importing the basic data into the data tag, converting the basic data into data parameters capable of being subjected to mathematical calculation, calculating the data parameters according to dimensional combinations, obtaining an evaluation result and making a decision, and therefore intelligent wind control decision can be made for the credit of a loan user, loan user information with high authenticity can be automatically collected, and the intelligentization degree and decision accuracy of the wind control decision are high.
Description
Technical Field
The invention relates to the technical field of financial wind control, in particular to a new financial AI intelligent wind control decision method and a new financial AI intelligent wind control decision system.
Background
The financial nature of the internet is finance, the core of financial business is wind control, big data is the key of intelligent wind control, and the final presentation of the wind control capability can be reflected in the index of the loss rate. At present, the internet financial wind control intelligence is exposed, but the concept is not emphasized yet, but it is expected that the wind control intelligence can be a great trend of the internet financial wind control as the internet technical reserve and the capability reserve are continuously strengthened. Therefore, innovation and improvement of wind control capability by utilizing big data become important topics of attention and discussion of the whole financial industry.
At present, most domestic small and micro financial institutions are trapped in talents and insufficient funds, do not have on-line technical development capacity, and use the traditional off-line data collection mode, off-line personnel classify through the collected data, establish a wind control scoring card mechanism through a simple mathematical calculation formula, grade the credit level of a loan user through the off-line scoring card mechanism, and make a manual decision.
Therefore, the existing loan user information acquisition mode has low efficiency, low authenticity, strong subjectivity of wind control decision and low accuracy.
Disclosure of Invention
In view of the above problems, embodiments of the present invention are proposed to provide a new financial AI intelligent wind control decision method and a corresponding new financial AI intelligent wind control decision system that overcome or at least partially solve the above problems.
In order to solve the above problems, the embodiment of the present invention discloses a new financial AI intelligent wind control decision method for making a wind control decision on the loan credit granting aspect of a small and micro customer, including:
calling a wind control scene in response to a user operation for calling the wind control scene;
matching a corresponding preset wind control decision model according to the wind control scene;
responding to user operation for accepting a data access request, and acquiring basic data of a user, wherein the basic data comprises personal identity information data, personal financial information data and behavior information data;
and the wind control decision model analyzes the basic data based on a first preset rule and generates a decision result.
Further, the responding to the user operation for calling the wind control scene before calling the wind control scene comprises:
configuring one or more sub-models having one or more data tags based on the wind control scenario, wherein the data tags are associated with the base data;
and combining the wind control decision model according to a plurality of sub models.
Further, the wind control decision model analyzes the basic data and generates an evaluation result based on a first preset rule, including:
configuring the base data into the corresponding data tags based on the data tags associated with the base data;
converting the basic data into data parameters, wherein the data parameters are computable data parameters;
calculating the data parameters according to a second preset rule to generate more than one evaluation score, wherein one sub-model has one evaluation score;
calculating the evaluation score according to the first preset rule to generate an evaluation result;
and generating a decision result according to the evaluation result.
Further, said configuring said base data into respective said data tags based on said data tags associated with said base data comprises: each item of the base data is configured into the data tag of more than one of the submodels.
Further, the wind control scenes comprise pre-loan wind control scenes, mid-loan wind control scenes and post-loan wind control scenes.
The embodiment of the invention also discloses a new financial AI intelligent wind control decision system, which comprises:
the calling module is used for calling the wind control scenes in response to user operation for calling the wind control scenes, wherein the wind control scenes comprise pre-credit wind control scenes, mid-credit wind control scenes and post-credit wind control scenes;
the scene module is used for matching a corresponding preset wind control decision model according to the wind control scene;
the system comprises an acquisition module, a data access module and a data processing module, wherein the acquisition module is used for responding to user operation for receiving a data access request and acquiring basic data of a user, and the basic data comprises personal identity information data, personal financial information data and behavior information data;
and the wind control decision model is used for analyzing the basic data based on a first preset rule and generating a decision result.
Further, the new financial AI intelligent wind control decision system further includes:
a configuration module to configure one or more sub-models having one or more data tags based on the wind control scenario, wherein the data tags are associated with the base data;
and the combination module is used for combining the wind control decision model according to the plurality of sub models.
Further, the wind control decision model comprises:
a data configuration module for configuring the base data into the corresponding data tags based on the data tags associated with the base data;
the conversion module is used for converting the basic data into data parameters, and the data parameters are computable data parameters;
the score generation module is used for calculating the data parameters according to a second preset rule and generating more than one evaluation score, wherein one sub-model has one evaluation score;
the result generation module is used for calculating the evaluation score according to the first preset rule and generating an evaluation result;
and the decision generation module is used for generating a decision result according to the evaluation result.
The embodiment of the invention also discloses electronic equipment, which comprises a processor, a memory and a computer program which is stored on the memory and can run on the processor, wherein the computer program realizes the steps of the method for the new financial AI intelligent wind control decision when being executed by the processor.
The embodiment of the invention also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the method for the new financial AI intelligent wind control decision are realized.
The embodiment of the invention has the following advantages: the method comprises the steps that a submodel with a data label is arranged in a wind control decision model, the authority of accessing data is acquired from a user on line, basic data of the user is automatically collected, the basic data are imported into the data label and then converted into data parameters capable of being subjected to mathematical calculation, the submodel calculates the data parameters according to dimension combination, an evaluation result is obtained and decision is made, so that intelligent wind control decision can be made for credit of a loan user, loan user information with high authenticity is automatically acquired, and the intelligentization degree and decision accuracy of the wind control decision are high.
Drawings
FIG. 1 is a flow chart illustrating steps of an embodiment of a new financial AI intelligent wind control decision method of the present invention;
FIG. 2 is a block diagram of an embodiment of a new financial AI intelligent wind control decision making system according to the invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
One of the core ideas of the embodiment of the invention is that a sub-model with a data tag is arranged in a wind control decision model, the authority of accessing data is acquired from a user on line, basic data of the user is automatically collected, the basic data is imported into the data tag and then converted into data parameters capable of being subjected to mathematical computation, the sub-model computes the data parameters according to dimension combination to obtain an evaluation result and make a decision, so that the loan user can be subjected to intelligent wind control decision for crediting, loan user information with high authenticity is automatically acquired, and the intelligentization degree and decision accuracy of the wind control decision are high.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a new financial AI intelligent wind control decision method according to the present invention is shown, which may specifically include the following steps:
s10, responding to the user operation for calling the wind control scene, and calling the wind control scene;
s20, matching a corresponding preset wind control decision model according to the wind control scene;
s30, responding to the user operation for receiving the data access request, and acquiring basic data of the user, wherein the basic data comprises personal identity information data, personal financial information data and behavior information data;
and S40, analyzing the basic data and generating a decision result by the wind control decision model based on a first preset rule.
The invention relates to a new financial AI (Artificial Intelligence) intelligent wind control decision method, which is used for carrying out wind control decision on the loan credit granting aspect of a small and micro client, and comprises the following specific processes:
s10, responding to the user operation for calling the wind control scene, and calling the wind control scene; when the user needs to loan, clicking to select a wind control scene, and calling the corresponding wind control scene after receiving the operation of the user; the wind control scenes comprise pre-credit wind control scenes, mid-credit wind control scenes and post-credit wind control scenes.
S20, matching a corresponding preset wind control decision model according to the wind control scene; after the corresponding wind control scene is called, matching a corresponding preset wind control decision model according to the wind control scene so as to adapt to the wind control scene under different conditions, for example: and when the wind control scene is a pre-loan wind control scene, matching a pre-loan wind control decision model according to the pre-loan wind control scene, and thus performing evaluation decision on a user waiting for loan.
S30, responding to the user operation for receiving the data access request, and acquiring basic data of the user, wherein the basic data comprises personal identity information data, personal financial information data and behavior information data; sending a data access request to a user who is carrying out loan business, automatically collecting credit internet credit investigation data of the user after the user receives the data access request, wherein the credit internet credit investigation data mainly comprises personal identity information (personal basic information, education and academic information and driving license information), personal consumption related data (asset information, interests and hobbies and E-commerce registration behaviors), bank cardholder data (personal debit and credit card bill information and offline payment data), internet user and behavior information (application software browsing data, network browsing data and geographic position information), judicial law enforcement information (referee document information, performance implemented information and distrust behavior information), debit and credit blacklist high-risk client list (traditional finance and internet finance), voyage information (trip frequency and ticket information), position information (real-time position, common address, credit account information and credit account information, Travel track), etc., thereby solving the problem that user data information is difficult to collect.
S40, analyzing the basic data and generating a decision result by the wind control decision model based on a first preset rule; the wind control decision model is combined into a sub-model according to labels such as personal identity information, bank cardholder data, personal consumption related data, internet user and behavior information, judicial law executed information, loan blacklist high-risk client list, position information and the like, is combined into a decision model according to scene requirements, and analyzes the basic data to obtain a final decision result (refusing, manually checking and passing).
S10, before the invoking of the wind control scene in response to the user operation for invoking the wind control scene, including:
s01, configuring more than one sub-model with more than one data label based on the wind control scene, wherein the data label is associated with the basic data;
and S02, combining the wind control decision model according to the plurality of sub models.
Configuring different sub-models according to different wind control scenes, and setting more than one data label in the different sub-models, wherein the data label is associated with the basic data, such as: data such as asset information, interests and hobbies, e-commerce registration behaviors and the like are subordinate to the personal consumption data tags, and data such as application software browsing data, network browsing data and geographic positions are subordinate to information internet users and behavior data tags.
S40, the wind control decision model analyzes the basic data and generates an evaluation result based on a first preset rule, and the evaluation result comprises the following steps:
based on the data tags associated with the base data, configuring the base data into the respective data tags, such as: data such as asset information, interests and hobbies, e-commerce registration behaviors and the like are imported into the personal consumption data tags, and data such as application software browsing data, network browsing data, geographic positions and the like are imported into the information internet users and behavior data tags.
Converting the basic data into data parameters, wherein the data parameters are computable data parameters; specifically, the basic data in each data tag is converted into data parameters which can be calculated, so that analysis and calculation can be carried out.
Calculating the data parameters according to a second preset rule to generate more than one evaluation score, wherein one sub-model has one evaluation score, and built-in function formulas of different sub-models are different; the second preset rule is a preset function formula, and research personnel set corresponding variables according to the importance and the relevance degree of different basic data in the data tag, so that the second preset rule is formed.
Calculating the evaluation score according to the first preset rule to generate an evaluation result; and calculating the evaluation score according to the first preset rule to generate an evaluation result, wherein the first preset rule sets different weights according to different importance and relevance degrees of different submodels, introduces the corresponding weights into a preset evaluation function formula to calculate, and outputs the evaluation result.
And generating a decision result according to the evaluation result, specifically, if the evaluation result is greater than a certain preset value, the decision result is credit granting, if the evaluation result is less than the certain preset value, the decision result is not credit granting, and if the evaluation result is within a certain preset interval value, the decision result is to be manually checked.
In this embodiment, the configuring the basic data into the corresponding data tag based on the data tag associated with the basic data includes: each item of the base data is configured into the data tag of more than one of the submodels.
In this embodiment, the wind control scenes include a pre-loan wind control scene, a mid-loan wind control scene, and a post-loan wind control scene.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Referring to fig. 2, a block diagram of a new financial AI intelligent wind control decision system according to an embodiment of the present invention is shown, which may specifically include the following modules:
the calling module 10 is configured to call a wind control scene in response to a user operation for calling the wind control scene, where the wind control scene includes a pre-credit wind control scene, and a post-credit wind control scene;
the scene module 20 is configured to match a corresponding preset wind control decision model according to the wind control scene;
the acquisition module 30 is configured to acquire basic data of a user in response to a user operation for accepting a data access request, where the basic data includes personal identity information data, personal financial information data, and behavior information data;
and the wind control decision model 40 is used for analyzing the basic data based on a first preset rule and generating a decision result.
The calling module 10 is configured to call a wind control scene in response to a user operation for calling the wind control scene; when the user needs to loan, clicking to select a wind control scene, and calling the corresponding wind control scene after receiving the operation of the user; the wind control scenes comprise pre-credit wind control scenes, mid-credit wind control scenes and post-credit wind control scenes.
The scene module 20 is configured to match a corresponding preset wind control decision model according to the wind control scene; after the corresponding wind control scene is called, matching a corresponding preset wind control decision model according to the wind control scene so as to adapt to the wind control scene under different conditions, for example: and when the wind control scene is a pre-loan wind control scene, matching a pre-loan wind control decision model according to the pre-loan wind control scene, and thus performing evaluation decision on a user waiting for loan.
The acquisition module 30 is configured to acquire basic data of a user in response to a user operation for accepting a data access request, where the basic data includes personal identity information data, personal financial information data, and behavior information data; sending a data access request to a user who is carrying out loan business, automatically collecting credit internet credit investigation data of the user after the user receives the data access request, wherein the credit internet credit investigation data mainly comprises personal identity information (personal basic information, education and academic information and driving license information), personal consumption related data (asset information, interests and hobbies and E-commerce registration behaviors), bank cardholder data (personal debit and credit card bill information and offline payment data), internet user and behavior information (application software browsing data, network browsing data and geographic position information), judicial law enforcement information (referee document information, performance implemented information and distrust behavior information), debit and credit blacklist high-risk client list (traditional finance and internet finance), voyage information (trip frequency and ticket information), position information (real-time position, common address, credit account information and credit account information, Travel track), etc., thereby solving the problem that user data information is difficult to collect.
The wind control decision model 40 is configured to analyze the basic data based on a first preset rule and generate a decision result; the wind control decision model is combined into a sub-model according to labels such as personal identity information, bank cardholder data, personal consumption related data, internet user and behavior information, judicial law executed information, loan blacklist high-risk client list, position information and the like, is combined into a decision model according to scene requirements, and analyzes the basic data to obtain a final decision result (refusing, manually checking and passing).
The new financial AI intelligent wind control decision system further comprises:
a configuration module to configure one or more sub-models having one or more data tags based on the wind control scenario, wherein the data tags are associated with the base data;
and the combination module is used for combining the wind control decision model according to the plurality of sub models.
Configuring different sub-models according to different wind control scenes, and setting more than one data label in the different sub-models, wherein the data label is associated with the basic data, such as: data such as asset information, interests and hobbies, e-commerce registration behaviors and the like are subordinate to the personal consumption data tags, and data such as application software browsing data, network browsing data and geographic positions are subordinate to information internet users and behavior data tags.
The wind control decision model 40 includes:
a data configuration module, configured to configure the basic data into the corresponding data tag based on the data tag associated with the basic data, for example: data such as asset information, interests and hobbies, e-commerce registration behaviors and the like are imported into the personal consumption data tags, and data such as application software browsing data, network browsing data, geographic positions and the like are imported into the information internet users and behavior data tags.
The conversion module is used for converting the basic data into data parameters, and the data parameters are computable data parameters; specifically, the basic data in each data tag is converted into data parameters which can be calculated, so that analysis and calculation can be carried out.
The score generation module is used for calculating the data parameters according to a second preset rule and generating more than one evaluation score, wherein one submodel has one evaluation score, and built-in function formulas of different submodels are different; the second preset rule is a preset function formula, and research personnel set corresponding variables according to the importance and the relevance degree of different basic data in the data tag, so that the second preset rule is formed.
The result generation module is used for calculating the evaluation score according to the first preset rule and generating an evaluation result; and calculating the evaluation score according to the first preset rule to generate an evaluation result, wherein the first preset rule sets different weights according to different importance and relevance degrees of different submodels, introduces the corresponding weights into a preset evaluation function formula to calculate, and outputs the evaluation result.
And the decision generation module is used for generating a decision result according to the evaluation result, specifically, if the evaluation result is greater than a certain preset value, the decision result is credit granting, if the evaluation result is less than the certain preset value, the decision result is not credit granting, and if the evaluation result is within a certain preset interval value, the decision result is to be manually checked.
The invention also provides an electronic device comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for new financial AI intelligent wind control decision.
The invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for new financial AI intelligent wind control decision-making.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The detailed description is given above to a new financial AI intelligent wind control decision method and a new financial AI intelligent wind control decision system provided by the present invention, and a specific example is applied in the present document to explain the principle and the implementation manner of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A new financial AI intelligent wind control decision method is used for making wind control decision on the aspect of loan credit granting of a small and micro client, and is characterized by comprising the following steps:
calling a wind control scene in response to a user operation for calling the wind control scene;
matching a corresponding preset wind control decision model according to the wind control scene;
responding to user operation for accepting a data access request, and acquiring basic data of a user, wherein the basic data comprises personal identity information data, personal financial information data and behavior information data;
and the wind control decision model analyzes the basic data based on a first preset rule and generates a decision result.
2. The method of claim 1, wherein invoking the wind-controlled scene in response to the user action for invoking the wind-controlled scene is preceded by:
configuring one or more sub-models having one or more data tags based on the wind control scenario, wherein the data tags are associated with the base data;
and combining the wind control decision model according to a plurality of sub models.
3. The method of claim 2, wherein the wind control decision model analyzes the base data based on a first preset rule and generates an evaluation result, comprising:
configuring the base data into the corresponding data tags based on the data tags associated with the base data;
converting the basic data into data parameters, wherein the data parameters are computable data parameters;
calculating the data parameters according to a second preset rule to generate more than one evaluation score, wherein one sub-model has one evaluation score;
calculating the evaluation score according to the first preset rule to generate an evaluation result;
and generating a decision result according to the evaluation result.
4. The method of claim 3, wherein configuring the base data into the corresponding data tag based on the data tag associated with the base data comprises: each item of the base data is configured into the data tag of more than one of the submodels.
5. The method of claim 1, wherein the wind controlled scenarios comprise pre-loan wind controlled scenarios, mid-loan wind controlled scenarios, and post-loan wind controlled scenarios.
6. A new financial AI intelligent wind-controlled decision system, comprising:
the calling module is used for calling the wind control scenes in response to user operation for calling the wind control scenes, wherein the wind control scenes comprise pre-credit wind control scenes, mid-credit wind control scenes and post-credit wind control scenes;
the scene module is used for matching a corresponding preset wind control decision model according to the wind control scene;
the system comprises an acquisition module, a data access module and a data processing module, wherein the acquisition module is used for responding to user operation for receiving a data access request and acquiring basic data of a user, and the basic data comprises personal identity information data, personal financial information data and behavior information data;
and the wind control decision model is used for analyzing the basic data based on a first preset rule and generating a decision result.
7. The new financial AI intelligent wind decision system according to claim 6, further comprising:
a configuration module to configure one or more sub-models having one or more data tags based on the wind control scenario, wherein the data tags are associated with the base data;
and the combination module is used for combining the wind control decision model according to the plurality of sub models.
8. The new financial AI intelligent wind decision system according to claim 7, wherein the wind decision model includes:
a data configuration module for configuring the base data into the corresponding data tags based on the data tags associated with the base data;
the conversion module is used for converting the basic data into data parameters, and the data parameters are computable data parameters;
the score generation module is used for calculating the data parameters according to a second preset rule and generating more than one evaluation score, wherein one sub-model has one evaluation score;
the result generation module is used for calculating the evaluation score according to the first preset rule and generating an evaluation result;
and the decision generation module is used for generating a decision result according to the evaluation result.
9. Electronic device, characterized in that it comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, which computer program, when executed by the processor, carries out the steps of the method of new financial AI intelligent wind control decision according to any of claims 1 to 5.
10. Computer-readable storage medium, characterized in that it stores thereon a computer program which, when being executed by a processor, carries out the steps of the method of new financial AI intelligent wind control decision according to any one of claims 1 to 5.
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