CN112541706A - Big data wind control model construction method - Google Patents
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Abstract
The invention discloses a big data wind control model construction method which comprises data collection and data classification, wherein the data classification is arranged on the lower layer of the data collection, the lower layer of the data classification is respectively connected with enterprise loan information collection and personal information collection, and the lower layers of the enterprise loan information collection and the personal information collection are respectively connected with data modeling, data portrayal and risk pricing. According to the big data wind control model building method, more detailed and various aspects of information of a user are collected, so that risks are reduced, more comprehensive collection of user information is facilitated, the possibility of occurrence of risk events is reduced, the information model is more accurately depicted, risk avoidance is facilitated better, development of the user and data collection are achieved through accumulation of big data, and the wind control model is more accurate in calculation through integration, supplement, calling, judgment and the like of the data.
Description
Technical Field
The invention relates to the technical field of big data wind control models, in particular to a big data wind control model construction method.
Background
The wind control model is a powerful high-frequency transaction and programmed transaction model which requires a faster transaction channel and a more efficient strategy; on the other hand, rapid trading leads to exponential growth of the risk faced by investments, so that the market and investors need a more comprehensive strategy combination and a more accurate wind control model for risk hedging. Reducing the various possibilities of occurrence of a risk event, or reducing the losses that a risk controller incurs when a risk event occurs, there are always things that cannot be controlled and a risk always exists. As a manager, various measures are taken to reduce the possibility of occurrence of the risk event, or possible loss is controlled within a certain range so as to avoid the loss which is hard to bear when the risk event occurs, but the wind control model still has defects.
The general method for constructing the data wind control model can not collect user information in more detailed aspects, so that risks are increased, more comprehensive collection of user information is inconvenient, the possibility of risk events is reduced, the information model is not accurate enough in portrayal during information collection, risk avoidance is inconvenient to achieve better, client development and data collection are achieved through accumulation of big data, and data evaluation is meaningless due to the fact that specific scenes reach different data parameters and are separated from specific scenes, so that the wind control model is inconvenient to operate more accurately through integration, supplement, calling, judgment and the like of the data, and therefore the method for constructing the wind control model based on the block chain is provided, and the problems are solved conveniently.
Disclosure of Invention
The invention aims to provide a big data wind control model construction method, and aims to solve the problems that the general data wind control model construction method provided in the background technology cannot collect user information in a more detailed and various aspects, so that the risk is improved, the user information is inconvenient to collect more comprehensively, the possibility of occurrence of risk events is reduced, the information model is not accurately carved and is inconvenient to avoid the risk better when the information is collected, the client development and data collection are realized through the accumulation of big data, and the specific scenes reach different data parameters and are separated from the specific scenes, so that the data of parameter evaluation has no meaning, and the wind control model operation is more accurate and inconvenient to integrate, supplement, call, judge and the like through the data.
In order to achieve the purpose, the invention provides the following technical scheme: the big data wind control model construction method comprises data collection and data classification, wherein the data classification is arranged on the lower layer of the data collection, the lower layer of the data classification is respectively connected with enterprise loan information collection and personal information collection, and the lower layers of the enterprise loan information collection and the personal information collection are respectively connected with data modeling, data portrayal and risk pricing.
Preferably, the data modeling comprises industry background records, identity verification and bank auditing systems, and the identity verification is divided into personal asset condition, basic information verification and stockholder industrial and commercial information.
Preferably, the authentication comprises the steps of:
step 1: : checking through the basic information of personal name, ID card and mobile phone number, and when the personal information data is matched with the system, performing the next data collection of the personal bank card bill details and the third-party user information of the house property vehicle;
step 2: when the personal information is not matched with the third-party system, rejecting the personal information;
and step 3: storing the collected information, re-checking the matching of the third-party user information input by the data again, and performing the next procedure check according with the requirements;
and 4, step 4: and inquiring personal academic records and personal industrial and commercial stockholder information through the approved data network and qualification certification.
Preferably, the data images are summed by a predicted latitude label, and the predicted latitude label includes setting a behavior characterization and an information inference.
Preferably, the behavior characterization and information inference comprises the steps of:
step 1: through the purchasing channel, the consumption frequency and the family condition, the selection of good user labels can enable the user to be more rich in description, so that the labels are described through behavior activities;
step 2: by presuming the user information with the depicted activity label through characters such as 'dormitory', 'school', 'university' and the like appearing in the delivery address, the user identity is presumed to be a student, a company name and a building appear, and the user can be judged to be a working group, so that the effective information of the user and the like can be mastered.
Preferably, the risk pricing is divided into institution pricing, customized risk and multi-dimensional pricing, and the institution pricing, customized risk and multi-dimensional pricing are the overall conclusions of the parameter scores.
Preferably, the institutional pricing comprises the steps of:
step 1: pricing assets for risks to the bank's third party itself;
step 2: realizing data acquisition developed for the client according to the internet data and the financial condition through self risks;
and step 3: and analyzing and positioning the data acquisition result.
Preferably, the customisation risk comprises the steps of:
step 1: specific services such as scoring, models and strategies are served through a certain intelligent angle, and the condition that the accuracy cannot be achieved due to the fact that the services are separated from a service scene is avoided;
step 2: analyzing parameters and evaluating through different data generated by different service scenes and rule ranges contained in the different data;
and step 3: and (4) performing risk-oriented sorting and collection on the data according to different parameters and evaluations, and performing risk-oriented scoring.
Preferably, the multidimensional pricing comprises the following steps:
step 1: due to the loss of personal credit data, the central row credit investigation can only cover 25% of people, but the data service function is larger, and a plurality of third-party data are accessed through a P2P platform for wind control;
step 2: the accuracy of the wind control model operation result is achieved through data integration, supplement, calling, evaluation and the like.
Preferably, the lower layer of the institution pricing, the customized risk and the multidimensional pricing is provided with parameter scores, and the parameter scores are the result of obtaining comments.
Compared with the prior art, the invention has the beneficial effects that: according to the big data wind control model construction method, information of a user is collected in a more detailed way, so that risks are reduced, the user information is conveniently and comprehensively collected, the possibility of occurrence of risk events is reduced, the information model is more accurately depicted, risk avoidance is conveniently and well achieved, the development of the user and the collection of data are completed through the accumulation of big data, and the calculation of the wind control model is more accurate through the integration, supplement, calling, judgment and the like of the data;
1. the personal information of the user is collected through a data modeling system comprising an industry background record, an identity verification system and a bank auditing system, and the identity verification system comprises a personal asset condition, a basic information verification system and shareholder business information, so that the user information is collected in detail, the user can be better known, and the risk is reduced;
2. the method comprises the steps that a behavior depiction and an information conjecture are set through a predicted latitude tag, the behavior depiction judges the purchase channel, the integral condition and the consumption evaluation rate of a person, and the information conjecture analyzes and conjectures the background according to the analysis of data and the address destination, so that the detailed conjecture judgment is carried out on the person, the prediction is better carried out, the relevance analysis is achieved, and the better risk avoidance is carried out;
3. the risk pricing is divided into mechanism pricing, customized risk and multi-dimensional pricing, asset pricing is carried out according to self preference to make benefits of assets, and through data accumulation, client development and data acquisition are completed, so that data are integrated, supplemented, called and judged, and results achieved by the wind control model are accurate.
Drawings
FIG. 1 is a general work flow diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: the big data wind control model construction method comprises data collection and data classification, wherein the data classification is arranged on the lower layer of the data collection, the lower layer of the data classification is respectively connected with enterprise loan information collection and personal information collection, and the lower layers of the enterprise loan information collection and the personal information collection are respectively connected with data modeling, data portrayal and risk pricing.
As shown in fig. 1, the data modeling includes an industry background record, an authentication and a bank audit system, and the authentication is divided into a personal asset status, a basic information check and shareholder industry and commerce information, and the authentication includes the following steps: step 1: : checking through the basic information of personal name, ID card and mobile phone number, and when the personal information data is matched with the system, performing the next data collection of the personal bank card bill details and the third-party user information of the house property vehicle; step 2: when the personal information is not matched with the third-party system, rejecting the personal information; and step 3: storing the collected information, re-checking the matching of the third-party user information input by the data again, and performing the next procedure check according with the requirements; and 4, step 4: and inquiring the personal academic calendar through the audited data network, and inquiring the information of the shareholder of the personal industry and commerce through qualification certification, thereby facilitating the detailed collection of the personal identity.
As shown in fig. 1, the data images are summed by a predicted latitude tag, and the predicted latitude tag includes a behavior characterization and an information inference, and the behavior characterization and the information inference include the following steps: step 1: through the purchasing channel, the consumption frequency and the family condition, the selection of good user labels can enable the user to be more rich in description, so that the labels are described through behavior activities; step 2: through presuming the user information after the activity label is carved out, through sending characters such as "dormitory", "school", "university" appearing in the cargo address, the user identity presumes as the student, appear company name and mansion, can judge as the working clan, thus grasp user's effective information, etc., presume the template carving of information collection of every channel of consumption behavior of the user to every angle through background and behavior, thus make the risk better avoid, reduce the risk rate;
as shown in FIG. 1, the risk pricing is divided into institution pricing, customized risk and multi-dimensional pricing, and the institution pricing, the customized risk and the multi-dimensional pricing are the overall conclusion of parameter scoring, and the risk parameter is analyzed and evaluated to conclude, wherein the institution pricing comprises the following steps: step 1: pricing assets for risks to the bank's third party itself; step 2: realizing data acquisition developed for the client according to the internet data and the financial condition through self risks; and step 3: the data acquisition result is analyzed and positioned, so that the risk of the data acquisition result is priced conveniently, and the data analysis and acquisition of data and finance integration are realized, so that the risk is reduced better, the loss is controlled, and the better accuracy is achieved;
as in fig. 1, customizing the risk comprises the following steps: step 1: specific services such as scoring, models and strategies are served through a certain intelligent angle, and the condition that the accuracy cannot be achieved due to the fact that the services are separated from a service scene is avoided; step 2: analyzing parameters and evaluating through different data generated by different service scenes and rule ranges contained in the different data; and step 3: according to different parameters and evaluations, risk sorting and collecting are carried out on the data, risk scoring is carried out, so that risk matching is carried out according to parameter analysis, and risk is reduced;
as in FIG. 1, multidimensional pricing comprises the following steps: step 1: due to the loss of personal credit data, the central row credit investigation can only cover 25% of people, but the data service function is larger, and a plurality of third-party data are accessed through a P2P platform for wind control; step 2: the accuracy of the calculation result of the wind control model is achieved by integrating, supplementing, calling, judging and the like data, the risk control is better performed through the data of the third party, the accuracy of the risk model is improved, the parameter scoring is arranged on the lower layer of mechanism pricing, customized risk and multi-dimensional pricing, the parameter scoring is the result of the comment, the pricing risk is better analyzed and the parameter is evaluated, and the conclusion is obtained.
The working principle is as follows: when the big data wind control model construction method is used, as shown in figure 1, personal and enterprise data are collected, market investigation is firstly carried out on the industry background of a user through data modeling, materials are collected according to testimonials or units provided by external job seekers, enterprises have self standard baseline collection bases for employee use, integrity and the like, then verification is carried out according to basic information, whether identity is real or not, whether a crime record exists or not, whether a mobile phone number is a common mobile phone number or not is judged according to bank card bills, bank grades, total amount of account posting and average consumption expenditure levels are verified according to identity authentication, whether the mobile phone number is a common mobile phone number or not is judged according to crime record or not, and whether credit card is suspected to be monitored and overdue record or not, whether collected information is in blacklists of industrial and commercial businesses and internet finance or not is collected and compared, whether the collected information is not matched or not is judged to enable the information to be rejected, when the information is matched, stockholder industrial and commercial business information is further verified, whether high-management information, stockholder information and investment information are matched or not is judged, bank system audit is carried out, data portrait judgment is carried out on audit materials to predict latitude labels, grouping accurate marketing is carried out through portrait data of users, user data are normalized and converted into feature vectors with the same dimensionality to be classified, for structural data, feature extraction work is usually the beginning of data behavior labels, such as purchasing channels, consumption frequency, age, family conditions and the like, so that behavior portrayals are carried out on the users, and according to calculation and statistics, participation degree of promotion activities of the users is calculated and counted, through presumption of characters such as 'dormitory', 'school' and 'university' appearing in the obtained delivery address, the user identity is presumed to be a student, the company name and the building appear, and the user identity can be judged to be a working clan, so that the information of the user can be mastered, the printed label is discretized according to different analysis scenes, or the label of the classification type is divided into a plurality of labels, and the classification prediction is carried out on the labels, and finally the prediction result is analyzed to generate vector dimensions;
and finally, combining the risk pricing shown in the figure 1 according to the risk preference of the user, wherein the high-risk assets are priced higher, the low-risk assets are priced lower, the data collection developed for the client is realized according to the internet data and the financial condition, the consumption service system proposed by the current data is consumed and used by the appointed service provider, the service function is realized by utilizing big data to a certain extent, and currently, the functions are used for wind control by accessing a plurality of third-party data according to some P2P platforms, the wind control model is more accurate in operation through integration, supplement, calling, evaluation and the like of data, the customized risk is that different data parameters are reached from a specific scene and are separated from the specific scene, the data of parameter evaluation has no significance, therefore, how to better and intelligently consider the wind control model to be different according to different people needs to be considered at the beginning of the design of the wind control model, and the method is the use method of the big data wind control model construction method.
The standard parts used in the invention can be purchased from the market, the special-shaped parts can be customized according to the description of the specification and the accompanying drawings, the specific connection mode of each part adopts conventional means such as bolts, rivets, welding and the like mature in the prior art, the machinery, parts and equipment adopt conventional models in the prior art, and the circuit connection adopts the conventional connection mode in the prior art, and the details are not described, and the content not described in detail in the specification belongs to the prior art known by persons skilled in the art.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes in the embodiments and/or modifications of the invention can be made, and equivalents and modifications of some features of the invention can be made without departing from the spirit and scope of the invention.
Claims (10)
1. A big data wind control model building method comprises data collection and data classification, and is characterized in that: the lower floor of data collection is provided with data classification, and data classification's lower floor is connected with enterprise's loan information acquisition and personal information acquisition respectively, and enterprise's loan information acquisition and personal information acquisition's lower floor all is connected with data modeling, data portrays and risk pricing.
2. The big data wind control model building method according to claim 1, characterized in that: the data modeling comprises industry background records, identity verification and a bank auditing system, and the identity verification is divided into personal asset condition, basic information verification and shareholder industry and commerce information.
3. The big data wind control model building method according to claim 1, characterized in that: the identity authentication comprises the following steps:
step 1: : checking through the basic information of personal name, ID card and mobile phone number, and when the personal information data is matched with the system, performing the next data collection of the personal bank card bill details and the third-party user information of the house property vehicle;
step 2: when the personal information is not matched with the third-party system, rejecting the personal information;
and step 3: storing the collected information, re-checking the matching of the third-party user information input by the data again, and performing the next procedure check according with the requirements;
and 4, step 4: and inquiring personal academic records and personal industrial and commercial stockholder information through the approved data network and qualification certification.
4. The big data wind control model building method according to claim 1, characterized in that: the data images are summed by a predicted latitude label, and the predicted latitude label comprises a behavior characterization and an information inference.
5. The big data wind control model building method according to claim 1, characterized in that: the behavior characterization and information inference includes the steps of:
step 1: through the purchasing channel, the consumption frequency and the family condition, the selection of good user labels can enable the user to be more rich in description, so that the labels are described through behavior activities;
step 2: by presuming the user information with the depicted activity label through characters such as 'dormitory', 'school', 'university' and the like appearing in the delivery address, the user identity is presumed to be a student, a company name and a building appear, and the user can be judged to be a working group, so that the effective information of the user and the like can be mastered.
6. The big data wind control model building method according to claim 1, characterized in that: the risk pricing is divided into mechanism pricing, customized risk and multi-dimensional pricing, and the mechanism pricing, the customized risk and the multi-dimensional pricing are the overall conclusion of parameter scoring.
7. The big data wind control model building method according to claim 1, characterized in that: the institutional pricing comprises the steps of:
step 1: pricing assets for risks to the bank's third party itself;
step 2: realizing data acquisition developed for the client according to the internet data and the financial condition through self risks;
and step 3: and analyzing and positioning the data acquisition result.
8. The big data wind control model building method according to claim 1, characterized in that: the customized risk comprises the steps of:
step 1: specific services such as scoring, models and strategies are served through a certain intelligent angle, and the condition that the accuracy cannot be achieved due to the fact that the services are separated from a service scene is avoided;
step 2: analyzing parameters and evaluating through different data generated by different service scenes and rule ranges contained in the different data;
and step 3: and (4) performing risk-oriented sorting and collection on the data according to different parameters and evaluations, and performing risk-oriented scoring.
9. The big data wind control model building method according to claim 1, characterized in that: the multidimensional pricing comprises the following steps:
step 1: due to the loss of personal credit data, the central row credit investigation can only cover 25% of people, but the data service function is larger, and a plurality of third-party data are accessed through a P2P platform for wind control;
step 2: the accuracy of the wind control model operation result is achieved through data integration, supplement, calling, evaluation and the like.
10. The big data wind control model building method according to claim 1, characterized in that: the lower layers of the mechanism pricing, the customized risk and the multi-dimensional pricing are provided with parameter scores, and the parameter scores are the results of the comments.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116468460A (en) * | 2023-04-27 | 2023-07-21 | 苏银凯基消费金融有限公司 | Consumer finance customer image recognition system and method based on artificial intelligence |
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| CN110009479A (en) * | 2019-03-01 | 2019-07-12 | 百融金融信息服务股份有限公司 | Credit assessment method and device, storage medium, computer equipment |
| CN111080440A (en) * | 2019-12-18 | 2020-04-28 | 上海良鑫网络科技有限公司 | Big data wind control management system |
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| CN108876134A (en) * | 2018-06-08 | 2018-11-23 | 山东汇贸电子口岸有限公司 | A kind of medium and small micro- enterprise's credit system |
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