CN114331153A - Risk control system and method - Google Patents
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- CN114331153A CN114331153A CN202111655171.4A CN202111655171A CN114331153A CN 114331153 A CN114331153 A CN 114331153A CN 202111655171 A CN202111655171 A CN 202111655171A CN 114331153 A CN114331153 A CN 114331153A
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Abstract
The invention discloses a risk control system and a method, wherein the risk control system comprises a risk control model construction module, a user information acquisition module, a user classification module, a risk analysis module and a backtracking module; the risk control model building module is used for building a plurality of risk control models; each risk control model has corresponding accuracy for each category of users; the user information acquisition module is used for acquiring user information; the user classification module is used for classifying corresponding users; the risk analysis module is used for distributing a corresponding risk control model to the user classification data according to the user classification data so as to analyze the risk of the corresponding user; the backtracking module is used for feeding back a risk result of the user to the risk control model building module, and the risk control model building module adjusts the corresponding risk control model according to the risk result. The risk control system and the risk control method provided by the invention can improve the accuracy and efficiency of risk control.
Description
Technical Field
The invention belongs to the technical field of risk control, relates to a risk control system, and particularly relates to a risk control system and method based on user categories.
Background
With the development of the times, the informatization, modeling and intelligence degrees of the wind control field technology are higher and higher. Big data can be effectively utilized through AI decision, so that the efficiency and the wind control capability in the aspect of client credit rating are improved.
The existing risk control mode is generally to utilize a continuously perfect risk control model to carry out risk assessment on all users; since the user classification is not performed, the calculation process is complex, the consumed time is long, and the accuracy is still to be improved.
In view of the above, there is an urgent need to design a new risk control method to overcome at least some of the above-mentioned disadvantages of the existing risk control methods.
Disclosure of Invention
The invention provides a risk control system and method, which can improve the accuracy and efficiency of risk control.
In order to solve the technical problem, according to one aspect of the present invention, the following technical solutions are adopted:
a risk control system, the risk control system comprising:
the risk control model building module is used for building a plurality of risk control models; each risk control model has corresponding accuracy for each category of users;
the user information acquisition module is used for acquiring user information;
the user classification module is used for classifying corresponding users according to the user information acquired by the user information acquisition module;
the risk analysis module is used for distributing a corresponding risk control model to the user according to the classification data of the user classification module on the user, so as to analyze the risk of the corresponding user;
the backtracking module is used for feeding back a risk result of the user to the risk control model building module, and the risk control model building module adjusts one of the following conditions according to the risk result: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; adjusting each risk control model to have corresponding accuracy for each category of users;
the risk control model building module comprises: a training unit and a testing unit;
the training unit is used for extracting the characteristics of the data in the training data set and learning by using a machine learning algorithm according to the labels and the training data subjected to characteristic extraction to obtain a risk control model;
and the testing unit is used for extracting the characteristics of the data in the testing data set and testing the testing data subjected to the characteristic extraction by using the risk control model obtained by training of the training unit to obtain the prediction label.
The user classification module classifies users into a plurality of classifications according to user information;
the risk analysis module distributes a plurality of corresponding risk control models for the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
According to another aspect of the invention, the following technical scheme is adopted: a risk control system, the risk control system comprising:
the risk control model building module is used for building a plurality of risk control models; each risk control model has corresponding accuracy for each category of users;
the user information acquisition module is used for acquiring user information;
the user classification module is used for classifying corresponding users according to the user information acquired by the user information acquisition module;
the risk analysis module is used for distributing a corresponding risk control model to the user according to the classification data of the user classification module on the user, so as to analyze the risk of the corresponding user;
the backtracking module is used for feeding back a risk result of the user to the risk control model building module, and the risk control model building module adjusts one of the following conditions according to the risk result: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; and adjusting each risk control model to have corresponding accuracy for each category of users.
As an embodiment of the present invention, the risk control model building module includes:
the training unit is used for extracting the characteristics of the data in the training data set and learning by using a machine learning algorithm according to the labels and the training data subjected to characteristic extraction to obtain a risk control model;
and the testing unit is used for extracting the characteristics of the data in the testing data set and testing the testing data subjected to the characteristic extraction by using the risk control model obtained by training of the training unit to obtain the prediction label.
As an embodiment of the present invention, the user classification module classifies users into a plurality of classifications according to user information;
the risk analysis module distributes a plurality of corresponding risk control models for the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
According to another aspect of the invention, the following technical scheme is adopted: a risk control method, the risk control method comprising:
a risk control model construction step, namely constructing a plurality of risk control models through a risk control model construction module; each risk control model has corresponding accuracy for each category of users;
a user information acquisition step of acquiring user information;
a user classification step of classifying corresponding users according to the user information acquired in the user information acquisition step;
a risk analysis step, wherein a corresponding risk control model is distributed to the user according to the classification data of the user in the user classification step, so that the risk of the corresponding user is analyzed;
and a backtracking step, wherein the risk result of the user is fed back to the risk control model building module, and the risk control model building module is adjusted by one of the following steps: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; adjusting each risk control model to have corresponding accuracy for each category of users;
the risk control model building step comprises:
a training step, wherein the data in the training data set are subjected to feature extraction, and learning is performed by using a machine learning algorithm according to the labels and the training data subjected to feature extraction, so that a risk control model is obtained;
a testing step, wherein the data in the test data set is subjected to feature extraction, and the test data subjected to the feature extraction is tested by using a risk control model obtained by training of the training unit to obtain a prediction label;
in the user classification step, the users are classified into a plurality of classifications according to user information;
in the risk analysis step, a plurality of corresponding risk control models are distributed to the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
According to another aspect of the invention, the following technical scheme is adopted: a risk control method, the risk control method comprising:
a risk control model construction step, namely constructing a plurality of risk control models through a risk control model construction module; each risk control model has corresponding accuracy for each category of users;
a user information acquisition step of acquiring user information;
a user classification step of classifying corresponding users according to the user information acquired in the user information acquisition step;
a risk analysis step, wherein a corresponding risk control model is distributed to the user according to the classification data of the user in the user classification step, so that the risk of the corresponding user is analyzed;
and a backtracking step, wherein the risk result of the user is fed back to the risk control model building module, and the risk control model building module is adjusted by one of the following steps: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; and adjusting each risk control model to have corresponding accuracy for each category of users.
As an embodiment of the present invention, the risk control model constructing step includes:
a training step, wherein the data in the training data set are subjected to feature extraction, and learning is performed by using a machine learning algorithm according to the labels and the training data subjected to feature extraction, so that a risk control model is obtained;
and a testing step, namely performing characteristic extraction on the data in the test data set, and testing the test data subjected to the characteristic extraction by using a risk control model obtained by training of the training unit to obtain a prediction label.
As an embodiment of the present invention, in the user classifying step, the users are classified into a plurality of classes according to the user information;
in the risk analysis step, a plurality of corresponding risk control models are distributed to the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
The invention has the beneficial effects that: the risk control system and the risk control method provided by the invention can improve the accuracy and efficiency of risk control.
Drawings
Fig. 1 is a schematic diagram of a risk control system according to an embodiment of the present invention.
Fig. 2 is a flowchart of a risk control method according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
For a further understanding of the invention, reference will now be made to the preferred embodiments of the invention by way of example, and it is to be understood that the description is intended to further illustrate features and advantages of the invention, and not to limit the scope of the claims.
The description in this section is for several exemplary embodiments only, and the present invention is not limited only to the scope of the embodiments described. It is within the scope of the present disclosure and protection that the same or similar prior art means and some features of the embodiments may be interchanged.
The steps in the embodiments in the specification are only expressed for convenience of description, and the implementation manner of the present application is not limited by the order of implementation of the steps. The term "connected" in the specification includes both direct connection and indirect connection.
Fig. 1 is a schematic diagram illustrating a risk control system according to an embodiment of the present invention; referring to fig. 1, the risk control system includes: the risk control model building module 1, the user information obtaining module 2, the user classifying module 3, the risk analyzing module 4 and the backtracking module 5.
The risk control model building module 1 is used for building a plurality of risk control models; each risk control model has corresponding accuracy for each category of users;
the user information acquisition module 2 is used for acquiring user information;
the user classification module 3 is used for classifying the corresponding users according to the user information acquired by the user information acquisition module 2;
the risk analysis module 4 is used for allocating a corresponding risk control model to the user according to the classification data of the user on the user classification module 3, so as to analyze the risk of the corresponding user;
the backtracking module 5 is configured to feed back a risk result of the user to the risk control model building module 1, and the risk control model building module 1 adjusts one of the following conditions in this way: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; and adjusting each risk control model to have corresponding accuracy for each category of users.
In an embodiment of the present invention, the risk control model building module includes: training unit and test unit.
The training unit 11 is configured to perform feature extraction on data in a training data set, and learn by using a machine learning algorithm according to the label and the training data subjected to feature extraction to obtain a risk control model;
the test unit 12 is configured to perform feature extraction on data in the test data set, and test the test data subjected to feature extraction by using a risk control model obtained by training of the training unit to obtain a prediction label.
In an embodiment of the present invention, the user classification module 3 classifies users into a plurality of classifications according to user information.
The risk analysis module 4 allocates a plurality of corresponding risk control models to the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
Fig. 2 is a flowchart of a risk control method according to an embodiment of the present invention; referring to fig. 2, the risk control method includes:
step S1, constructing a plurality of risk control models through a risk control model construction module; each risk control model has corresponding accuracy for each category of users;
in an embodiment of the present invention, the risk control model constructing step includes:
a training step, wherein the data in the training data set are subjected to feature extraction, and learning is performed by using a machine learning algorithm according to the labels and the training data subjected to feature extraction, so that a risk control model is obtained;
and a testing step, namely performing characteristic extraction on the data in the test data set, and testing the test data subjected to the characteristic extraction by using a risk control model obtained by training of the training unit to obtain a prediction label.
Step S2, acquiring user information;
step S3, a user classification step of classifying corresponding users according to the user information acquired in the user information acquisition step;
step S4, a risk analysis step, wherein a corresponding risk control model is distributed to the classification data of the user according to the user classification step, so that the risk of the corresponding user is analyzed;
step S5, feeding back the risk result of the user to the risk control model building module, so that the risk control model building module adjusts one of the following: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; and adjusting each risk control model to have corresponding accuracy for each category of users.
In an embodiment of the present invention, in the user classifying step, the users are classified into a plurality of classes according to the user information.
In the risk analysis step, a plurality of corresponding risk control models are distributed to the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
In summary, the risk control system and method provided by the invention can improve accuracy and efficiency of risk control.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware; for example, it may be implemented using Application Specific Integrated Circuits (ASICs), general purpose computers, or any other similar hardware devices. In some embodiments, the software programs of the present application may be executed by a processor to implement the above steps or functions. As such, the software programs (including associated data structures) of the present application can be stored in a computer-readable recording medium; such as RAM memory, magnetic or optical drives or diskettes, and the like. In addition, some steps or functions of the present application may be implemented using hardware; for example, as circuitry that cooperates with the processor to perform various steps or functions.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The description and applications of the invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. Effects or advantages referred to in the embodiments may not be reflected in the embodiments due to interference of various factors, and the description of the effects or advantages is not intended to limit the embodiments. Variations and modifications of the embodiments disclosed herein are possible, and alternative and equivalent various components of the embodiments will be apparent to those skilled in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other components, materials, and parts, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.
Claims (8)
1. A risk control system, characterized in that the risk control system comprises:
the risk control model building module is used for building a plurality of risk control models; each risk control model has corresponding accuracy for each category of users;
the user information acquisition module is used for acquiring user information;
the user classification module is used for classifying corresponding users according to the user information acquired by the user information acquisition module;
the risk analysis module is used for distributing a corresponding risk control model to the user according to the classification data of the user classification module on the user, so as to analyze the risk of the corresponding user;
the backtracking module is used for feeding back a risk result of the user to the risk control model building module, and the risk control model building module adjusts one of the following conditions according to the risk result: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; adjusting each risk control model to have corresponding accuracy for each category of users;
the risk control model building module comprises: a training unit and a testing unit;
the training unit is used for extracting the characteristics of the data in the training data set and learning by using a machine learning algorithm according to the labels and the training data subjected to characteristic extraction to obtain a risk control model;
and the testing unit is used for extracting the characteristics of the data in the testing data set and testing the testing data subjected to the characteristic extraction by using the risk control model obtained by training of the training unit to obtain the prediction label.
The user classification module classifies users into a plurality of classifications according to user information;
the risk analysis module distributes a plurality of corresponding risk control models for the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
2. A risk control system, characterized in that the risk control system comprises:
the risk control model building module is used for building a plurality of risk control models; each risk control model has corresponding accuracy for each category of users;
the user information acquisition module is used for acquiring user information;
the user classification module is used for classifying corresponding users according to the user information acquired by the user information acquisition module;
the risk analysis module is used for distributing a corresponding risk control model to the user according to the classification data of the user classification module on the user, so as to analyze the risk of the corresponding user;
the backtracking module is used for feeding back a risk result of the user to the risk control model building module, and the risk control model building module adjusts one of the following conditions according to the risk result: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; and adjusting each risk control model to have corresponding accuracy for each category of users.
3. The risk control system of claim 2, wherein:
the risk control model building module comprises:
the training unit is used for extracting the characteristics of the data in the training data set and learning by using a machine learning algorithm according to the labels and the training data subjected to characteristic extraction to obtain a risk control model;
and the testing unit is used for extracting the characteristics of the data in the testing data set and testing the testing data subjected to the characteristic extraction by using the risk control model obtained by training of the training unit to obtain the prediction label.
4. The risk control system of claim 2, wherein:
the user classification module classifies users into a plurality of classifications according to user information;
the risk analysis module distributes a plurality of corresponding risk control models for the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
5. A risk control method, characterized in that the risk control method comprises:
a risk control model construction step, namely constructing a plurality of risk control models through a risk control model construction module; each risk control model has corresponding accuracy for each category of users;
a user information acquisition step of acquiring user information;
a user classification step of classifying corresponding users according to the user information acquired in the user information acquisition step;
a risk analysis step, wherein a corresponding risk control model is distributed to the user according to the classification data of the user in the user classification step, so that the risk of the corresponding user is analyzed;
and a backtracking step, wherein the risk result of the user is fed back to the risk control model building module, and the risk control model building module is adjusted by one of the following steps: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; adjusting each risk control model to have corresponding accuracy for each category of users;
the risk control model building step comprises:
a training step, wherein the data in the training data set are subjected to feature extraction, and learning is performed by using a machine learning algorithm according to the labels and the training data subjected to feature extraction, so that a risk control model is obtained;
a testing step, wherein the data in the test data set is subjected to feature extraction, and the test data subjected to the feature extraction is tested by using a risk control model obtained by training of the training unit to obtain a prediction label;
in the user classification step, the users are classified into a plurality of classifications according to user information;
in the risk analysis step, a plurality of corresponding risk control models are distributed to the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
6. A risk control method, characterized in that the risk control method comprises:
a risk control model construction step, namely constructing a plurality of risk control models through a risk control model construction module; each risk control model has corresponding accuracy for each category of users;
a user information acquisition step of acquiring user information;
a user classification step of classifying corresponding users according to the user information acquired in the user information acquisition step;
a risk analysis step, wherein a corresponding risk control model is distributed to the user according to the classification data of the user in the user classification step, so that the risk of the corresponding user is analyzed;
and a backtracking step, wherein the risk result of the user is fed back to the risk control model building module, and the risk control model building module is adjusted by one of the following steps: firstly, adjusting a corresponding risk control model; adjusting a user classification mode; and adjusting each risk control model to have corresponding accuracy for each category of users.
7. The risk control method of claim 6, wherein:
the risk control model building step comprises:
a training step, wherein the data in the training data set are subjected to feature extraction, and learning is performed by using a machine learning algorithm according to the labels and the training data subjected to feature extraction, so that a risk control model is obtained;
and a testing step, namely performing characteristic extraction on the data in the test data set, and testing the test data subjected to the characteristic extraction by using a risk control model obtained by training of the training unit to obtain a prediction label.
8. The risk control method of claim 6, wherein:
in the user classification step, the users are classified into a plurality of classifications according to user information;
in the risk analysis step, a plurality of corresponding risk control models are distributed to the users according to a plurality of classifications of the users to obtain a plurality of risk data; and finally evaluating the user according to the obtained risk data: if the risk data obtained according to the risk control model with the highest accuracy corresponding to the user accords with a first risk threshold range, assessing the existence of risks; and if the risk data meeting a second risk threshold range exists in the risk data obtained according to the plurality of risk control models corresponding to the user, evaluating the existence of the risk.
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