CN110648217A - Wind control system based on big data and artificial intelligence - Google Patents
Wind control system based on big data and artificial intelligence Download PDFInfo
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Abstract
The invention discloses a wind control system based on big data and artificial intelligence, which belongs to the technical field of wind control systems and comprises a platform management layer, a distributed service layer, a plurality of groups of user service trusteeship layers, a service integration layer, a visualization layer, a general function architecture, a data acquisition mode, credit risk prevention big data requirements, a credit big data wind control model system and a wind control business rule system, wherein the platform management layer is connected with the plurality of groups of user service trusteeship layers through the distributed service layer, and the plurality of groups of user service trusteeship layers are connected with the visualization layer through a uniform service access interface in the service integration layer. The big data wind control system takes data as a basis, a model and a method as a gripper, a business rule as a core, differentiation customization is carried out according to business and product characteristics, and the process and the result are monitored and displayed by statistical indexes and reports.
Description
Technical Field
The invention belongs to the technical field of wind control systems, and particularly relates to a wind control system based on big data and artificial intelligence.
Background
Currently, the financial industry has achieved certain success by utilizing big data to perform wind control. The wind control by using big data becomes standard configuration of internet financial enterprises at home and abroad, and the application of big data wind control of traditional financial institutions is also in the process of groping and advancing.
The social-oriented credit service system sesame credit is provided in Ali, the sesame credit carries out credit evaluation on the user by analyzing a large amount of network transaction and behavior data, the credit evaluation can help Internet financial enterprises to draw conclusions about the repayment willingness and the repayment capacity of the user, and then related financial and economic services are provided for the user.
The core of the wind control of the 'particle credit' product introduced by Tencent 'micro-people's bank is that 5 dimensions of social circle, behavior characteristic, transaction, basic social characteristic and pedestrian credit are used for comprehensively rating a client by combining social big data and traditional bank credit data such as central bank credit, and a large number of indexes are used for constructing a multiple model so as to quickly identify the credit risk of the client.
The Hell consumption finance establishes the big data of 1.5 million real-name users of Hell, continuously combines authoritative credit investigation and wind control institutions to simplify the service flow and reduce the service threshold, thereby making it possible to provide financial service for users without credit investigation records or with few credit investigation records.
The big data is used as a grip, company business positioning of community finance, industry supply chain finance and accurate poverty relief financial service is provided, company business development is determined, particularly, large data of clients is needed for risk management as a basis, and big data wind control construction is needed to be the core of company business support system construction.
Disclosure of Invention
The invention aims to provide a wind control system based on big data and artificial intelligence, and aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
the wind control system based on big data and artificial intelligence comprises a platform management layer, a distributed service layer, a plurality of groups of user service trusteeship layers, a service integration layer, a visualization layer, a general function architecture, a data acquisition mode, credit risk prevention big data requirements, a credit big data wind control model system and a wind control business rule system, wherein the platform management layer is connected with the plurality of groups of user service trusteeship layers through the distributed service layer, and the plurality of groups of user service trusteeship layers are connected with the visualization layer through a uniform service access interface in the service integration layer.
As a further scheme of the invention: the data acquisition mode comprises internal platform precipitation and service provider provision, and the acquisition mode I comprises six types: user submission, system stationing collection, SDK, crawler, API, and data package.
As a further scheme of the invention: the credit risk containment big data requirements include credit risk containment, market risk containment, liquidity risk containment, operational risk containment, legal risk containment, and ethical risk containment.
As a further scheme of the invention: the credit big data wind control model system comprises risk quantification, risk pricing, business monitoring statistics, wind control rules and customer management, wherein the risk quantification comprises a credit evaluation model, an operation risk quantification model and a market risk quantification model, the risk pricing model is mainly the credit risk pricing model, the business monitoring statistics comprise a fraud monitoring model, an expected migration model, a Vintage risk outbreak model, a recovery rate model and a loss rate rolling model, the wind control rules comprise a Bayesian expert review model and an artificial intelligent deep learning model, and the customer management comprises a customer value management model and a customer segmentation model.
As a further scheme of the invention: the wind control business rule system comprises registration application, identity verification, crawler capture, credit investigation and evaluation, document inspection, manual electric checking, manual anti-fraud, manual credit review, post-loan management and financial management.
Compared with the prior art, the invention has the beneficial effects that: the big data wind control system takes data as a basis, a model and a method as a handle, a business rule as a core, differentiation customization is carried out according to business and product characteristics, and a process and a result are monitored and displayed by statistical indexes and reports, so that a user can visually obtain an evaluation result; the artificial intelligence data processing mode is adopted, the workload of people is reduced, the data processing result can be reported to a safety network in real time, the data can be sorted, cleaned and evaluated, and the artificial intelligence can be cultured with deep learning ability, so that the data can be subjected to complex transformation analysis according to the similarity and difference of the data, and the reliability of the data evaluation result is enhanced.
Drawings
FIG. 1 is a big data wind control technical architecture diagram in a wind control system based on big data and artificial intelligence.
FIG. 2 is a functional architecture diagram of a big data wind control platform in a wind control system based on big data and artificial intelligence.
FIG. 3 is a schematic diagram of a business operation mode in a wind control system based on big data and artificial intelligence.
Fig. 4 is a schematic diagram of a data acquisition mode in a wind control system based on big data and artificial intelligence.
FIG. 5 is a schematic diagram of credit risk prevention big data requirements in a wind control system based on big data and artificial intelligence.
FIG. 6 is a schematic diagram of a credit big data wind control model system in a wind control system based on big data and artificial intelligence.
FIG. 7 is a diagram of a wind control business rule system in a wind control system based on big data and artificial intelligence.
Detailed Description
The technical solution of the present patent will be described in further detail with reference to the following embodiments.
Referring to fig. 1 to 7, the wind control system based on big data and artificial intelligence includes a platform management layer, a distributed service layer, a plurality of groups of user service hosting layers, a service integration layer, a visualization layer, a general functional architecture, a data acquisition mode, a credit risk prevention big data requirement, a credit big data wind control model system and a wind control business rule system, wherein the platform management layer is connected with the plurality of groups of user service hosting layers through the distributed service layer, and the plurality of groups of user service hosting layers are connected with the visualization layer through a uniform service access interface in the service integration layer.
The platform management layer mainly utilizes a Docker and a System Center to realize the dynamic resource allocation management function of the data Center through the cloud platform dynamic data.
And the distributed service layer is built by utilizing a distributed computing framework and a NoSQL distributed storage framework.
The multiple user service hosting layers can be compatible with application services of various technical implementation modes, but the application services need to have the functions of multiple user management, multiple user isolation and application service load sharing so as to realize capacity dynamic expansion, and can be well integrated with a platform.
In the service integration layer, the service operation environment can provide various services including SaaS software service, PaaS platform service and IaaS infrastructure service to the end user by using the application service integration bus, and the service integration bus can collect the platform use condition and return the platform use condition to the operation management platform as a system charging basis besides providing functions of SSO, authentication and authorization and the like.
The visualization layer is mainly realized based on technologies such as html5, bootstrap, CSS3, Ajax, ECharts, face recognition and the like, the server side provides Web service through a tomcat cluster, and access of the mobile terminal can be supported according to the interface function of the application.
As shown in fig. 2, the functional architecture of the big data wind control platform is divided into four layers from bottom to top:
cloud computing platform infrastructure and services: the method comprises the steps of including software and hardware basic services required by an upper layer;
application integration services: the platform comprises a platform basic module, which is convenient for managing and expanding the platform;
multiple groups of user application architecture services: corresponding platform data services are given according to platform user group authorities (such as managers, data analysts, wind control personnel and the like);
"sky eye" big data wind accuse platform: the system comprises implementation logics of all the wind control models, a crawler module and the like.
As shown in fig. 3 and 4, the data collection method includes internal platform deposition and service provider supply, and the collection method includes six types: user submission, system stationing collection, SDK, crawler, API, and data package.
User submission: the platform user is provided in a form or file uploading mode, and the data types comprise texts, pictures, videos, audios and the like;
and (3) system distribution and collection: recording page access tracks of WEB, APP and the like of a user in a network probe mode;
and (3) SDK: acquiring user behavior track data in a mode that a data acquisition point is pre-embedded in a system software package or an APP;
the crawler comprises an online real-time crawler and an offline timing crawler, wherein the online real-time crawler captures user data after a user is triggered, and the offline timing crawler captures user data such as authorized login data acquisition; the latter periodically captures the incremental data of the public data source;
API: the method comprises the steps of acquiring or exchanging data from an external data source in real time in an application program interface mode;
data packet: the data is acquired and imported at one time or periodically in the form of a data file.
As shown in fig. 5, the credit risk prevention big data demand includes credit risk prevention, market risk prevention, liquidity risk prevention, operation risk prevention, law risk prevention, ethical risk prevention, the market risk prevention is acquired according to macro industry data and news public opinion data, and the credit risk prevention includes repayment willingness evaluation (quality), repayment ability evaluation (ability), capital wealth evaluation (capital), environment evaluation (condition), and constraint (mortgage).
The liquidity risk precaution comprises the evaluation of assets and liabilities, investment related data and the evaluation of market quotation transaction data of financial transactions, the operation risk precaution comprises the evaluation and precaution of internal regulation and operation specification data and the evaluation and precaution of risk events and operation behavior track data, the law risk precaution comprises the evaluation of related legal documents and media reports, and the moral risk precaution comprises the evaluation and precaution of fraud events, external blacklist negative list data, and business data and identity characteristic data of the same industry.
As shown in fig. 6, the credit big data wind control model system includes risk quantification, risk pricing, business monitoring statistics, wind control rules and customer management, wherein the risk quantification includes credit evaluation, an operational risk quantification model and a market risk quantification model, the risk pricing is mainly the credit risk pricing model, the business monitoring statistics includes a fraud monitoring model, an expected migration model, a Vintage risk outbreak model, a recovery rate model and a loss rate rolling model, the wind control rules include a bayes expert review model and an artificial intelligence deep learning model, and the customer management includes a customer value management model and a customer segmentation model.
As shown in fig. 7, the system of the pneumatic control business rules includes a registration application, an identity verification, a crawler capture, a credit investigation evaluation, a document check, an artificial electric core, an artificial anti-fraud, an artificial credit approval, a post-loan management and a financial management, wherein the registration application needs to comply with a client basic information rule, a repayment capability rule, a behavior filtering rule, a policy filtering rule and a contact information rule, the identity verification needs to comply with an identity verification rule, a bank card verification rule and an external blacklist rule, and the crawler capture needs to comply with a naught data rule, a jingdong data rule, a telecommunication data rule, a mobile data rule, a Unicom data rule, a social security data rule and a public deposit data rule.
The credit investigation evaluation needs to follow a PBDC data rule, the document detection needs to follow a document inspection rule, the human power supply core needs to follow a telephone verification rule, the artificial anti-fraud needs to follow an anti-fraud prompt rule, the artificial credit investigation needs to follow a final investigation rule, the post-loan management needs to follow an acceptance rule, an early warning intervention rule and a committed repayment rule, and the financial management needs to follow a liquidity early warning rule, a loan classification management rule and a preparation plan rule.
Although the preferred embodiments of the present patent have been described in detail, the present patent is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present patent within the knowledge of those skilled in the art.
Claims (5)
1. The wind control system based on big data and artificial intelligence comprises a platform management layer, a distributed service layer, a plurality of groups of user service trusteeship layers, a service integration layer, a visualization layer, a general function architecture, a data acquisition mode, credit risk prevention big data requirements, a credit big data wind control model system and a wind control business rule system.
2. The wind control system based on big data and artificial intelligence according to claim 1, wherein the data collection modes comprise internal platform deposition and service provider supply, and the collection modes include six types: user submission, system stationing collection, SDK, crawler, API, and data package.
3. The big-data and artificial-intelligence based wind control system according to claim 1, wherein the credit risk prevention big-data requirements include credit risk prevention, market risk prevention, liquidity risk prevention, operational risk prevention, legal risk prevention, and moral risk prevention.
4. The wind control system based on big data and artificial intelligence according to claim 1, wherein the credit big data wind control model system comprises risk quantification, risk pricing, business monitoring statistics, wind control rules and customer management, wherein the risk quantification comprises credit evaluation, an operational risk quantification model and a market risk quantification model, the risk pricing is mainly a credit risk pricing model, the business monitoring statistics comprises a fraud monitoring model, an expected migration model, a Vintage risk outbreak model, a recovery rate model and a loss rate rolling model, the wind control rules comprise a Bayesian expert review model and an artificial intelligence deep learning model, and the customer management comprises a customer value management model and a customer segmentation model.
5. The big data and artificial intelligence based wind control system according to claim 1, wherein the wind control business rule system comprises registration application, identity verification, crawler capture, credit assessment, document inspection, artificial electric checking, artificial anti-fraud, artificial credit review, post-loan management and financial management.
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| CN111311136A (en) * | 2020-05-14 | 2020-06-19 | 深圳索信达数据技术有限公司 | Wind control decision method, computer equipment and storage medium |
| CN112651619A (en) * | 2020-12-22 | 2021-04-13 | 上海哔哩哔哩科技有限公司 | Business-oriented wind control method and device |
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