CN112017059A - Hierarchical optimization risk control method and device and electronic equipment - Google Patents
Hierarchical optimization risk control method and device and electronic equipment Download PDFInfo
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Abstract
The invention discloses a risk control method for hierarchical optimization, which comprises the following steps: acquiring user information of the current transaction, extracting and acquiring primary user information according to the user information, and substituting the primary user information into the real-time wind control model to acquire real-time wind control scores of the user; formulating a real-time wind control strategy of the user based on the primary user information and the real-time wind control score of the user to be applied to the current transaction of the user; substituting the user information into the risk estimation model group to obtain the estimated risk score of the user; and formulating a wind control strategy according to the estimated risk score for the post-transaction wind control management of the user. The invention can optimize transaction strategies in grades in the loan, ensures that the transaction takes millisecond response, generates scenes through two-layer risk estimation, executes the client non-sensible background wind control strategy and controls risk clients, achieves full omnibearing coverage of transaction risks in the loan, ensures quick response of real-time transaction, and ensures the overall transaction and the wind control quality after the transaction.
Description
Technical Field
The invention relates to the field of computer information processing, in particular to a risk control method and device for hierarchical optimization, electronic equipment and a computer readable medium.
Background
With continuous progress of big data and artificial intelligence technology, emerging technical means inject strong development power for the traditional industry, and the financial field firstly and maturely applies the artificial intelligence means to explore more values from mass data. As the core of the financial field, the level of risk control is also changed from the evaluation of a small number of strong features to the comprehensive evaluation of strong features and a large number of false features with the use of new technologies, and good results are obtained.
In the prior art, all dimensional characteristics related to a user can be obtained by constructing and analyzing a knowledge graph, and a risk control model is formed based on the characteristics in a training mode and used for risk scoring of a new user or an inventory user during and after transaction, so that an accurate risk strategy is formed and applied. However, the above method has problems of long response time and poor user experience when the user actually performs the transaction. The reason is mainly two aspects: firstly, in order to ensure the wind control quality, all dimensional characteristics of a user need to be acquired, all data related to the user, even data related to a second-degree or third-degree contact person are acquired from a knowledge graph, and the data are stored in hundreds of millions of pieces of information, so that time and calculation power are required for retrieval and extraction; secondly, the comprehensiveness of the wind control strategy requires that wind control scoring of each dimension needs to be carried out, and long-time calculation is often needed, so that the current transaction cannot be applied with a proper wind control strategy in time, and the waiting time of a user is prolonged.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present disclosure provides a risk control method, an apparatus, an electronic device, and a computer readable medium for hierarchical optimization of a transaction policy in a loan, wherein a user performs a transaction real-time wind control policy through a first layer of real-time indicators and a simple hard indicator when initiating a transaction, so as to ensure a millisecond response consumed by the transaction, and generates a scene through two layers of risk estimation, executes a non-sensitive background wind control policy for the client and controls a risk client, so as to achieve full omnibearing coverage of transaction risks in the loan, thereby ensuring both fast response of the real-time transaction and overall transaction and post-transaction wind control quality.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a risk control method for hierarchical optimization is provided, including:
acquiring user information of current transaction, and extracting and acquiring primary user information according to the user information, wherein the primary user information comprises basic user information;
substituting the primary user information into a real-time wind control model to obtain a real-time wind control score of the user;
formulating a real-time wind control strategy of the user based on the primary user information and the real-time wind control score of the user to be applied to the current transaction of the user;
substituting the user information into a risk estimation model group to obtain an estimated risk score of the user;
and formulating a wind control strategy according to the estimated risk score for the post-transaction wind control management of the user.
Optionally, the user-level information further includes user key service node information and current transaction information.
Optionally, the user key service node information includes state information of the user at a key service node.
Optionally, the key service nodes include a move node, a payment node, a overdue node, and a overdue and clear node.
Optionally, the step of obtaining the user-level information according to the user information extraction further includes: and acquiring the primary user information from a primary user information base according to the identity information of the user, wherein the primary user information base is a database only used for storing the primary user information.
Optionally, the step of formulating the real-time wind control strategy of the user based on the primary user information and the real-time wind control score further includes: generating pre-rule result information of the user according to the primary user information and a preset rule; formulating a real-time wind control strategy of the user based on the primary information of the user, the pre-rule result information and the real-time wind control score; and performing risk control on the current transaction of the user according to the real-time wind control strategy.
Optionally, the step of substituting the user information into a risk prediction model group to obtain the predicted risk score of the user further includes: acquiring the user full information according to the user information; and substituting the total information of the user into a risk estimation model group to obtain the estimated risk score of the user.
Optionally, the risk prediction model includes one or more of a blacklist probability model, a relationship-based risk assessment model, a relationship-product-based risk assessment model.
Optionally, training the acquisition model using the historical user data as a sample.
According to an aspect of the present disclosure, a risk control device for hierarchical optimization is provided, including: the information module is used for acquiring user information of the current transaction and extracting and acquiring primary user information according to the user information, wherein the primary user information comprises basic user information; the first scoring module is used for substituting the primary user information into a real-time wind control model to obtain a real-time wind control score of the user; the first wind control module is used for making a real-time wind control strategy of the user based on the primary user information and the real-time wind control score of the user so as to be applied to the current transaction of the user; the second scoring module is used for substituting the user information into a risk estimation model group to obtain estimated risk scoring of the user; and the second wind control module is used for making a wind control strategy according to the estimated risk score so as to be used for wind control management after the user deals with.
Optionally, the user-level information further includes user key service node information and current transaction information.
Optionally, the user key service node information includes state information of the user at a key service node.
Optionally, the key service nodes include a move node, a payment node, a overdue node, and a overdue and clear node.
Optionally, the information module further comprises: and the extraction unit is used for acquiring the primary user information from a primary user information base according to the user identity information, wherein the primary user information base is a database only used for storing the primary user information.
Optionally, the first wind control module further comprises: the pre-rule unit is used for generating pre-rule result information of the user according to the primary user information and a preset rule; the strategy unit is used for making a real-time wind control strategy of the user based on the primary information of the user, the pre-rule result information and the real-time wind control score; and the execution unit is used for carrying out risk control on the current transaction of the user according to the real-time wind control strategy.
Optionally, the second scoring module further comprises: a total information unit, configured to obtain the user total information according to the user information; and the scoring unit is used for substituting the total information of the user into a risk estimation model group to obtain the estimated risk score of the user.
Optionally, the risk prediction model set includes one or more of a blacklist probability model, a relationship-based risk assessment model, and a relationship-product-based risk assessment model.
Optionally, the method further comprises: and the model training module is used for training and acquiring the model by using the historical user data as a sample.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the risk control method, the risk control device, the electronic equipment and the computer readable medium for hierarchical optimization disclosed by the invention, user information of current transaction is obtained, and user primary information is extracted and obtained according to the user information, wherein the user primary information comprises user basic information; substituting the primary user information into a real-time wind control model to obtain a real-time wind control score of the user; formulating a real-time wind control strategy of the user based on the primary user information and the real-time wind control score of the user to be applied to the current transaction of the user; substituting the user information into a risk estimation model group to obtain an estimated risk score of the user; and a wind control strategy is formulated according to the estimated risk score to be used for the post-transaction wind control management of the user, the transaction strategy can be optimized in stages in the credit, when the user initiates a transaction, the transaction real-time wind control strategy is made through a layer of real-time index and a simple rigid index, the time-consuming millisecond response of the transaction is ensured, and meanwhile, a scene is generated through two layers of risk estimation, the non-sensible background wind control strategy of the client is executed and the risk client is controlled, so that the comprehensive coverage of the transaction risk in the credit is achieved, the quick response of the real-time transaction is ensured, and the overall transaction and the post-transaction wind control quality are also ensured.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive faculty.
Fig. 1 is a system block diagram illustrating a method and apparatus for risk control for hierarchical optimization according to an exemplary embodiment.
FIG. 2 is a flow diagram illustrating a hierarchical optimization risk control method according to an exemplary embodiment.
FIG. 3 is a flow diagram illustrating a user-level information base construction method in accordance with an exemplary embodiment.
FIG. 4 is a block diagram illustrating a hierarchical optimization risk control apparatus according to an exemplary embodiment.
FIG. 5 is a block diagram illustrating a user-level information base building apparatus in accordance with an example embodiment
FIG. 6 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 7 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a system block diagram illustrating a method and apparatus for risk control for hierarchical optimization according to an exemplary embodiment.
As shown in fig. 1, the system architecture 10 may include terminal devices 101, 102, 103, a network 104, and servers 105, 106. The network 104 is a medium used to provide communication links between the terminal devices 101, 102, 103 and the servers 105, 106, and may also be used to provide communication links between the servers 105 and 106. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
Users may use terminal devices 101, 102, 103 to interact with servers 105, 106 via network 104 to receive or transmit information, etc. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a financial services application, a shopping application, a web browser application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The servers 105, 106 may be servers that provide various services, such as a background management server that supports financial services websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received user data, and feed back a processing result (e.g., a trained user risk model or a risk score performed on the user through the user risk model) to an administrator of the financial service website.
The servers 105 and 106 may, for example, obtain user information of a current transaction, and extract and obtain user primary information according to the user information, where the user primary information includes user basic information; the servers 105, 106 may, for example, substitute the user-level information into a real-time wind control model to obtain a user real-time wind control score; servers 105, 106 may formulate a real-time wind control policy for the user to apply to the user's current transaction, e.g., based on the user-level information and user real-time wind control scores; the servers 105, 106 may, for example, substitute the user information into a risk prediction model set to obtain a predicted risk score for the user; the servers 105, 106 may develop a wind control strategy for post-transaction wind control management of the user, for example, according to the estimated risk score.
The servers 105, 106 may be servers of one entity, and may also be composed of a plurality of servers, for example.
It should be noted that the risk control method for hierarchical optimization provided by the embodiments of the present disclosure may be executed by the servers 105 and 106, and accordingly, the risk control device for hierarchical optimization may be disposed in the servers 105 and 106. And the web page end provided for the user to browse the service platform is generally located in the terminal equipment 101, 102, 103.
FIG. 2 is a flow diagram illustrating a hierarchical optimization risk control method according to an exemplary embodiment. As shown in fig. 2, the hierarchical optimization risk control method 20 includes at least steps S201 to S209.
In step S201, user information is acquired
And acquiring user information of the current transaction, wherein the transaction can be a transaction of entity goods or a transaction of service. Preferably, it may be a transaction of a financial product. The user information is user information that must be filled in to complete the transaction, including but not limited to the user's name, account, phone number, address, etc.
In step S202, user-level information is extracted
Based on the user information obtained in step S201, primary information of the user is obtained from the primary user information base. The user level information comprises basic information of a user, key service node information and current transaction information.
The key service node information of the user refers to the state information of the user at the key service node, and the key service node includes but is not limited to a mobile node, a payment node, an overdue and a clearing node, etc.
The user primary information base is a database only used for storing the user primary information. And searching and extracting primary information stored in a primary user information base by the user based on the identity information of the user.
In step S203, real-time wind control scoring
And substituting the acquired primary information of the user into a real-time wind control model to acquire a real-time wind control score. The real-time wind control model is a model which is trained in advance by using historical user data as sample data and adopting a machine learning method. More specifically, primary information of historical users and financial performance data are acquired, and a machine learning method is adopted to train a model to obtain a real-time wind control model, which can be used for inputting the primary information of a new user to predict and output the financial performance data of the new user, namely real-time wind control scoring.
In step S204, a real-time wind control strategy is formulated
Generating pre-rule result information of the user according to the primary user information and a preset rule; and formulating a real-time wind control strategy of the user based on the primary information of the user, the pre-rule result information and the real-time wind control score.
More specifically, an evaluation rule is preset, and the preset evaluation rule is an acquisition rule which can be directly obtained or directly corresponds to an attribute value according to user information, rather than feature acquisition which is mined and extracted through a mathematical method.
Preferably, the real-time wind control strategy applied to the current transaction is obtained based on the real-time transaction processing calculation result, the overdue payment settlement information, the customer basic information, the control information, the pre-rule result and the model result through comprehensive analysis.
In step S205, the real-time transaction application
And applying the real-time wind control strategy acquired in the step S204 to the current transaction of the user so as to ensure the safety and stability of the current transaction.
In step S206, user full-volume information is acquired
According to the user information obtained in step S201, the corresponding user information is searched in the relationship map, and the total information is extracted and obtained.
The relationship map is a map for describing individuals and relationships among individuals, and is widely applied to various industries. The node types in the relationship graph may include IP addresses, devices, payment accounts, account contacts, and the like, and different relationships may exist between nodes, such as IP login behavior, device login behavior, contact registration behavior, and the like.
And searching and acquiring all information of the user in the relation map, the first-degree second-degree contact information related to the user and other user transaction information similar to transaction products.
In step S207, a risk score is estimated
And substituting the total information of the user into a risk estimation model group to obtain the estimated risk score of the user. More specifically, the risk prediction model includes one or more of a blacklist probability model, a relationship person-based risk assessment model, and a relationship product-based risk assessment model. More specifically, the total information of the historical users and the financial performance data are obtained, and the model is trained by adopting a machine learning method to obtain a risk estimation model which can be used for inputting the total information of the new users to predict and output the financial performance data of the new users, namely the blacklist probability, the risk score and the like.
In step S208, a post-transaction wind control strategy is formulated
And (5) making a post-transaction wind control strategy according to the estimated risk score obtained in the step (S207). More specifically, the model is used for carrying out blacklist related calculation, associated information related calculation, other product post-loan information calculation, other product loan related information calculation and credit worthiness updating information calculation, and a risk control strategy applied to the user after transaction is formulated based on the calculation result.
In step S209, the post-transaction wind control application
And (4) applying the risk strategy formulated in the step (S208) to risk control after the user transaction.
FIG. 3 is a flow diagram illustrating a user-level information base construction method in accordance with an exemplary embodiment. As shown in fig. 3, the user-level information base construction method includes steps S301 to S304.
In step S301, a user status change is monitored
Specifically, the server detects whether an event causing the user state change exists during operation, and records the event information causing the user state change when the event causing the user state change is detected.
In step S302, an event is transmitted
Specifically, the recorded event information causing the user state change is written into the message queue in a character string form according to the format of the queue, and the event information is written into the message queue in the character string form according to the format of the queue, so that the communication of data information and the processing of queue failure are facilitated.
In step S303, a tag is extracted
Specifically, the event queue information of the user state change is obtained in the message queue, and then the event queue information of the user state change is indexed to generate a new user tag.
In step S304, the associative memory
Specifically, the generated new user tag is associated with user identity information and stored in a primary information base for convenience of searching.
By the method, the key state information of the user is monitored and indexed in real time and is stored in the primary information base of the user, and when the real-time wind control requirement exists, the relevant label is directly obtained from the key state information, so that the searching from mass data can be avoided, and the information obtaining time is greatly shortened.
In addition, mapping between the user primary information base and the user information total base (full-scale base/relational graph) can be established, so that full-scale information of the user can be conveniently and quickly extracted, and characteristic extraction of the relational person is carried out.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Embodiments of the apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
FIG. 4 is a block diagram illustrating a hierarchical optimization risk control apparatus according to an exemplary embodiment. As shown in fig. 4, the hierarchical optimization risk control apparatus 40 includes: the system comprises an information module 401, a first scoring module 402, a first wind control module 403, a second scoring module 404, a second wind control module 405 and a model training module 406.
The information module 401 is configured to obtain user information of a current transaction, and extract and obtain primary user information according to the user information, where the primary user information includes basic user information.
More specifically, the information module 401 includes an acquisition unit and an extraction unit.
The acquisition unit is used for acquiring user information in transactions, wherein the transactions can be transactions of entity commodities or services. Preferably, it may be a transaction of a financial product. The user information is user information that must be filled in to complete the transaction, including but not limited to the user's name, account, phone number, address, etc.
The extraction unit is used for extracting the primary user information from the primary user information base according to the user information acquired by the acquisition unit, wherein the primary user information comprises the basic information of the user, the key service node information and the current transaction information.
The key service node information of the user refers to the state information of the user at the key service node, and the key service node includes but is not limited to a mobile node, a payment node, an overdue and a clearing node, etc.
The user primary information base is a database only used for storing the user primary information. And searching and extracting primary information stored in a primary user information base by the user based on the identity information of the user.
And a first scoring module 402, configured to substitute the user primary information into the real-time wind control model to obtain a user real-time wind control score.
A first wind control module 403, configured to formulate a real-time wind control policy of the user based on the user primary information and the user real-time wind control score to be applied to the current transaction of the user.
More specifically, the first wind control module 403 further includes a pre-rule unit, a policy unit, and an enforcement unit.
And the pre-rule unit is used for generating pre-rule result information of the user according to the primary information of the user and a preset rule. The pre-rule unit is used for presetting an evaluation rule, wherein the preset evaluation rule is an acquisition rule which can be directly obtained or directly corresponds to the attribute value according to the user information, and is not required to be subjected to characteristic acquisition of mining extraction through a mathematical method.
And the strategy unit is used for making a real-time wind control strategy of the user based on the primary information of the user, the pre-rule result information and the real-time wind control score. Preferably, the strategy unit comprehensively analyzes and acquires the real-time wind control rule applied to the current transaction based on the real-time transaction processing calculation result, the overdue payment settlement information, the client basic information, the control information, the pre-rule result and the model result
And the execution unit is used for carrying out risk control on the current transaction of the user according to the real-time wind control strategy.
A second scoring module 404, configured to substitute the user information into a risk prediction model group to obtain a prediction risk score of the user.
More specifically, the second scoring unit 404 includes a full-size information unit and a scoring unit.
And the full information unit is used for acquiring the user full information according to the user information. Preferably, the full information unit searches the corresponding user information in the relationship graph, and extracts and obtains the full information. The relationship map is a map for describing individuals and relationships among individuals, and is widely applied to various industries. The node types in the relationship graph may include IP addresses, devices, payment accounts, account contacts, and the like, and different relationships may exist between nodes, such as IP login behavior, device login behavior, contact registration behavior, and the like. The full information unit searches and acquires all information of the user, first-degree second-degree contact information related to the user and other user transaction information similar to transaction products through searching in a relation map.
And the scoring unit is used for substituting the total information of the user into a risk estimation model group to obtain the estimated risk score of the user.
And a second wind control module 405, configured to formulate a wind control strategy according to the estimated risk score, so as to be used for post-transaction wind control management of the user.
More specifically, the second wind control module 405 formulates a risk control policy applied to the user after transaction by using the result of the blacklist related calculation, the related information related calculation, the information calculation after the loan of other products, and the information calculation result of credit worthiness update performed by the second scoring unit 404.
And the model training module 406 is used for training the models required by the other modules by using the historical user data as samples.
Fig. 5 is a block diagram illustrating a user-level information base construction apparatus according to an example embodiment. As shown in fig. 5, the user-level information base building apparatus 50 at least includes a detection module 501, an information sending module 502, a label module 503 and a storage module 504.
The detection module 501 detects an event that causes a user state.
Specifically, when the server runs, the detection module 501 detects whether an event causing a change in the user state exists, and when the event causing the change in the user state is detected, the detection module 501 records the event information causing the change in the user state.
An information sending module 502, configured to send event information of the state change to a message queue;
specifically, the information sending module 502 writes the recorded event information causing the user state change into the message queue in the form of a character string according to the format of the queue, and writes the event information into the message queue in the form of the character string according to the format of the queue, so that the communication of the data information and the processing of queue failure are facilitated.
A label module 503, configured to obtain queue information from the message queue, and generate a label for the event information index of the state change;
specifically, the tag module 503 obtains the event queue information of the user state change from the message queue, and then indexes the event queue information of the user state change to generate a new user tag.
A storage module 504, which stores the tag in a tag database;
specifically, the tag storage module 504 stores the generated new user tag in the user information and stores the user tag in the tag database for searching convenience.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
FIG. 6 is a block diagram of an electronic device architecture for client testing based on simulation of server responses in accordance with the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 610 may perform the steps as shown in fig. 2.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to carry out the above-described methods of the invention.
The computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (10)
1. A hierarchical optimized risk control method, comprising:
acquiring user information of current transaction, and extracting and acquiring primary user information according to the user information, wherein the primary user information comprises basic user information;
substituting the primary user information into a real-time wind control model to obtain a real-time wind control score of the user;
formulating a real-time wind control strategy of the user based on the primary user information and the real-time wind control score of the user to be applied to the current transaction of the user;
substituting the user information into a risk estimation model group to obtain an estimated risk score of the user;
and formulating a wind control strategy according to the estimated risk score for the post-transaction wind control management of the user.
2. The method of claim 1, wherein the user-level information further comprises user key service node information and current transaction information.
3. A method according to any of claims 1-2, wherein the user key service node information comprises status information of the user at a key service node.
4. A method according to any of claims 1-3, wherein the key service nodes comprise a move support node, a repayment node, a overdue node, and a overdue return node.
5. The method according to any of claims 1-4, wherein the step of obtaining user-level information based on the user information extraction further comprises:
and acquiring the primary user information from a primary user information base according to the identity information of the user, wherein the primary user information base is a database only used for storing the primary user information.
6. The method according to any one of claims 1-5, wherein the step of formulating a real-time wind control strategy for the user based on the user-level information and a user real-time wind control score further comprises:
generating pre-rule result information of the user according to the primary user information and a preset rule;
formulating a real-time wind control strategy of the user based on the primary information of the user, the pre-rule result information and the real-time wind control score;
and performing risk control on the current transaction of the user according to the real-time wind control strategy.
7. The method according to any one of claims 1-6, wherein the step of substituting the user information into a risk prediction model set to obtain the predicted risk score of the user further comprises:
acquiring the user full information according to the user information;
and substituting the total information of the user into a risk estimation model group to obtain the estimated risk score of the user.
8. A hierarchically optimized risk control device, comprising:
the information module is used for acquiring user information of the current transaction and extracting and acquiring primary user information according to the user information, wherein the primary user information comprises basic user information;
the first scoring module is used for substituting the primary user information into a real-time wind control model to obtain a real-time wind control score of the user;
the first wind control module is used for making a real-time wind control strategy of the user based on the primary user information and the real-time wind control score of the user so as to be applied to the current transaction of the user;
the second scoring module is used for substituting the user information into a risk estimation model group to obtain estimated risk scoring of the user;
and the second wind control module is used for making a wind control strategy according to the estimated risk score so as to be used for wind control management after the user deals with.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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