CN111984840A - Online asset safety display locking method and device - Google Patents
Online asset safety display locking method and device Download PDFInfo
- Publication number
- CN111984840A CN111984840A CN202010927188.XA CN202010927188A CN111984840A CN 111984840 A CN111984840 A CN 111984840A CN 202010927188 A CN202010927188 A CN 202010927188A CN 111984840 A CN111984840 A CN 111984840A
- Authority
- CN
- China
- Prior art keywords
- client
- locking
- asset
- old
- new
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/904—Browsing; Visualisation therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
- G06F3/04817—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4014—Identity check for transactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
Abstract
The invention provides a method and a device for safely showing and locking an online asset, wherein the method comprises the following steps: acquiring client set data and determining a plurality of client subsets; analyzing each client subset, acquiring the proportion of active locking in the corresponding asset locking setting condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability; training a machine learning model according to the client set data and the corresponding asset locking setting condition information; acquiring new client information, inputting the trained machine learning model, determining the locking probability estimation value set by the new client, and sending a locking setting message to the new client; acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
Description
Technical Field
The invention relates to the technical field of computer information processing, in particular to a method and a device for safely showing and locking online assets.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
At present, in online banking applications, asset number information of a client is displayed, some applications are made into symbol display, which is similar to a star mark display, and a number can be displayed by clicking a relevant icon, such as an icon similar to eyes.
With the content of the application on the bank line becoming richer and richer, the frequency of using the application becomes higher and higher, and particularly, the probability of using the application under the condition that someone else participates in the application becomes higher and higher.
At present, the method for protecting the number of assets and other conditions by applying the online bank generally uses symbols such as a star mark (asterisk) to replace the symbols, and information such as specific numbers and the like can be checked by clicking related icons.
When other people and users see the application together, the users hope that other people can not see the asset condition of the users certainly, or other people can see the asset condition slightly, so that the user experience is poor, the mind safety is not high, the privacy of the users is not well protected, and certain risks are brought to the assets of the users.
Therefore, how to provide a new solution, which can solve the above technical problems, is a technical problem to be solved in the art.
Disclosure of Invention
The embodiment of the invention provides an online asset safety display locking method, which realizes accurate recommendation of an asset locking setting function, is beneficial to a new client to know and use the asset locking function, improves the viscosity and the safety experience of the client and improves the asset safety coefficient of the user, and comprises the following steps:
acquiring customer set data;
determining a plurality of customer subsets according to the customer set data; each client subset comprises locking setting condition information of corresponding assets;
analyzing each client subset, acquiring the proportion of active locking in the corresponding asset locking setting condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability;
training a machine learning model according to the client set data and the corresponding asset locking setting condition information;
acquiring new customer information, inputting the trained machine learning model, and determining a locking probability estimation value set by the new customer;
according to the locking probability estimation value set by the new client, sending a locking setting message to the new client;
acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
An embodiment of the present invention further provides an online asset security display locking device, including:
the data acquisition module is used for acquiring the client set data;
the client subset determining module is used for determining a plurality of client subsets according to the client set data; each client subset comprises locking setting condition information of corresponding assets;
the old client pushing and locking setting message module is used for analyzing each client subset, acquiring the proportion of active locking in the corresponding asset locking setting condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking setting pushing message, and sending the locking setting message to the old client which is not actively locked according to the probability;
the machine learning model training module is used for training a machine learning model according to the client set data and the corresponding asset locking setting condition information;
the new client setting locking probability estimation value determining module is used for acquiring new client information, inputting the new client information into the trained machine learning model and determining a new client setting locking probability estimation value;
the new client locking setting message sending module is used for sending a locking setting message to the new client according to the locking probability estimation value set by the new client;
the online asset locking module is used for acquiring an identity verification mode in the historical transaction of the old client and locking the online asset by combining locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the computer program, the online asset security display locking method is realized.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing the above-mentioned method for locking an online asset security display is stored in the computer-readable storage medium.
The embodiment of the invention provides a method and a device for safely showing and locking online assets, which comprises the steps of firstly obtaining client set data; then determining a plurality of customer subsets according to the customer set data; each client subset comprises locking setting condition information of corresponding assets; analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the active locking probability of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability; next, training a machine learning model according to the client set data and the corresponding asset locking setting condition information; then, new customer information is obtained, the trained machine learning model is input, and a locking probability estimation value set by the new customer is determined; further according to the locking probability estimation value set by the new client, sending a locking setting message to the new client; finally, acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked. The embodiment of the invention analyzes the client set based on the existing client set data to obtain a plurality of client sub-sets; according to the asset locking setting condition corresponding to each client in the client subset, the locking proportion is obtained, and the locking setting message is recommended to the old clients in the same subset, so that the locking setting can be accurately pushed, and the asset security of the old clients is facilitated; for a new client, the client set data and the corresponding asset locking setting condition information are utilized to train the machine learning model, the new client is identified to set the locking probability estimation value, and the locking setting information is sent to the new client, so that the accurate recommendation of the asset locking setting function is realized, the new client can know and use the asset locking function, the client viscosity and the safety experience are improved, and the asset safety coefficient of the user is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a schematic diagram of an online asset security display locking method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a machine learning model training process of an online asset security display locking method according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a locking process of an online asset in the method for locking the online asset in the security display of the online asset according to the embodiment of the present invention.
FIG. 4 is a schematic diagram of a computer device for implementing a locking method for an online asset security display according to an embodiment of the present invention.
Fig. 5 is a schematic view of an online asset security display locking device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Fig. 1 is a schematic diagram of an online asset security display locking method according to an embodiment of the present invention, and as shown in fig. 1, an embodiment of the present invention provides an online asset security display locking method, so as to implement accurate recommendation of an asset locking setting function, facilitate a new customer to know and use the asset locking function, improve the viscosity and the security experience of the customer, and improve the security coefficient of the asset of the user, where the method includes:
step 101: acquiring customer set data;
step 102: determining a plurality of customer subsets according to the customer set data; each client subset comprises locking setting condition information of corresponding assets;
step 103: analyzing each client subset, acquiring the proportion of active locking in the corresponding asset locking setting condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability;
step 104: training a machine learning model according to the client set data and the corresponding asset locking setting condition information;
step 105: acquiring new customer information, inputting the trained machine learning model, and determining a locking probability estimation value set by the new customer;
step 106: according to the locking probability estimation value set by the new client, sending a locking setting message to the new client;
step 107: acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
The embodiment of the invention provides an online asset safety display locking method, which comprises the steps of firstly obtaining client set data; then determining a plurality of customer subsets according to the customer set data; each client subset comprises locking setting condition information of corresponding assets; analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the active locking probability of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability; next, training a machine learning model according to the client set data and the corresponding asset locking setting condition information; then, new customer information is obtained, the trained machine learning model is input, and a locking probability estimation value set by the new customer is determined; further according to the locking probability estimation value set by the new client, sending a locking setting message to the new client; finally, acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked. The embodiment of the invention analyzes the client set based on the existing client set data to obtain a plurality of client sub-sets; according to the asset locking setting condition corresponding to each client in the client subset, the locking proportion is obtained, and the locking setting message is recommended to the old clients in the same subset, so that the locking setting can be accurately pushed, and the asset security of the old clients is facilitated; for a new client, the client set data and the corresponding asset locking setting condition information are utilized to train the machine learning model, the new client is identified to set the locking probability estimation value, and the locking setting information is sent to the new client, so that the accurate recommendation of the asset locking setting function is realized, the new client can know and use the asset locking function, the client viscosity and the safety experience are improved, and the asset safety coefficient of the user is improved.
When the method for locking and displaying online assets in the embodiment of the invention is implemented, the method specifically comprises the following steps:
acquiring customer set data; determining a plurality of customer subsets according to the customer set data; each client subset comprises locking setting condition information of corresponding assets; analyzing each client subset, acquiring the proportion of active locking in the corresponding asset locking setting condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability; training a machine learning model according to the client set data and the corresponding asset locking setting condition information; acquiring new customer information, inputting the trained machine learning model, and determining a locking probability estimation value set by the new customer; according to the locking probability estimation value set by the new client, sending a locking setting message to the new client; acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
In an embodiment of the invention, when the online asset security display locking method provided by the embodiment of the present invention is implemented specifically, the acquiring of the customer set data includes:
and connecting the banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
In the embodiment, a banking system is connected, asset information, app login data, transaction amount, transaction type and risk preference are obtained from a database of the banking system, and customer set data are formed; and simultaneously acquiring asset locking setting condition information corresponding to the client set data, wherein locking corresponds to 1, unlocking corresponds to 0, and the client is not set and is NULL.
In an embodiment of the method for locking and displaying online assets in security, determining a plurality of client subsets according to client set data includes:
and performing cluster analysis on the customer set data to determine a plurality of customer subsets.
In an embodiment, determining a plurality of customer subsets from the customer set data comprises: and performing cluster analysis on the customer set data to establish a plurality of customer subsets. Through cluster analysis, the clients of the same type can be divided into the same client subset, so that similar processing is facilitated, for example, locking setting messages are pushed to the clients in the same client subset; meanwhile, through cluster analysis, the locking setting condition information of the assets corresponding to each client subset can be obtained.
Fig. 2 is a schematic diagram of a machine learning model training process of an online asset security display locking method according to an embodiment of the present invention, and as shown in fig. 2, when the online asset security display locking method according to the embodiment of the present invention is specifically implemented, in an embodiment, the training of the machine learning model according to client set data and corresponding asset locking setting condition information includes:
step 201: setting asset locking setting condition information corresponding to the client set data as a target value, training a machine learning model by using the client set data as a characteristic attribute, and monitoring the convergence condition of the machine learning model;
step 202: and stopping training after the machine learning model converges to obtain the trained machine learning model.
In an embodiment, training the machine learning model according to the client set data and the corresponding asset locking setting condition information may include: and setting the asset locking setting condition information corresponding to the client set data as a target value, training the machine learning model by using the client set data as a characteristic attribute, continuously detecting the convergence condition of the machine learning model in the training process, and stopping training after the machine learning model converges to obtain the trained machine learning model.
And corresponding to a new customer, obtaining the customer information of the new customer, wherein the customer information comprises assets, app login data, transaction amount, transaction type and risk preference. And inputting the client information into the trained machine learning model, calculating to obtain a probability estimation value of the new client for setting locking, and recommending the new client to set locking information according to the obtained probability estimation value.
Fig. 3 is a schematic diagram of a process of locking an online asset in an online asset security display locking method according to an embodiment of the present invention, and as shown in fig. 3, when the online asset security display locking method according to the embodiment of the present invention is specifically implemented, in an embodiment, the obtaining of an identity verification manner in a customer history transaction of an old customer is performed, and the online asset is locked in combination with a locking setting message sent to the old customer and a new customer, including:
step 301: when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client historical transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client historical transaction of the old client to perform initial locking verification, and locking the on-line asset after the verification is successful;
step 302: when a new client receives the pushed locking setting message, according to the authentication mode in the historical transaction of the old client, the authentication modes are sorted from high to low according to the use frequency, the authentication modes are displayed to the new client, after the new client selects the authentication modes and inputs personal authentication information, the new client jumps to an asset display page, an asset locking icon is clicked on the asset display page, and the online asset is locked.
In an embodiment, obtaining an identity verification mode in a historical transaction of an old client, and locking an online asset in combination with a locking setting message pushed to the old client and a new client may include:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in the historical transaction of the old client, wherein the identity verification mode in the historical transaction can comprise the following steps: various modes such as passwords, fingerprints, face recognition and the like; then clicking an asset locking icon on an asset display page, calling an identity verification mode in the historical transaction of the old customer, calculating the similarity of multiple verification modes, performing initial locking verification by adopting the verification mode when the similarity of one verification mode exceeds a similarity threshold, and locking the on-line asset after the verification is successful; for example, setting the similarity threshold to be 0.9, and calculating the similarities of the multiple verification methods as follows: and if the password is 0.3, the fingerprint is 0.5, and the face recognition is 0.95, performing initial locking verification by adopting a face recognition mode.
When a new client receives the pushed locking setting message, according to the authentication mode in the historical transaction of the old client, the authentication modes are sorted from high to low according to the use frequency, the authentication modes are displayed to the new client, after the new client selects the authentication modes and inputs personal authentication information, the new client jumps to an asset display page, an asset locking icon is clicked on the asset display page, and the online asset is locked.
The embodiment of the invention also provides a process for locking the online assets by using the online assets safety display locking method, which mainly comprises the following steps:
1. a user logs in an online banking application such as a mobile phone bank;
2. when a user needs to check asset data, clicking to the position of my asset;
3. an asset locking icon can be arranged at the position edge of my asset;
4. the user may set the lock of the asset lock icon to be unlocked, that is, the asset data does not need to be locked, and the user can directly view the data or data of the star (in this embodiment, the star is set, and other symbols may be used). If the user sets the lock of the asset lock icon to be closed, step 6;
5. if the user wants to further view the asset data, clicking the icon which displays the star number as a number before;
6. when the user sets the lock of the asset lock icon, the user needs to verify the asset data to check the asset data, for example, inputting a password or a fingerprint or swiping a face or a short message verification code, and the asset data can be displayed after the verification. At this time, each unit can be designed by self without adding a mark;
7. the initial state of the lock can be set according to the specific situation of a client, the lock state setting can be pushed to the client, and the lock state setting can be pushed in the following way;
8. no verification may be set, i.e. the lock is open, for new customers who have not used the functionality. The push client settings are subsequently locked according to 10 and subsequent steps. The old client is not set with locking, the default key can be unlocked, and the client can be pushed to set with locking according to the step 10 and the following steps;
9. the application is provided with a verification switch function, so that a client can adjust whether verification is needed according to own preference.
Can be handled in an intelligent machine or a counter;
10. collecting customer set data including assets, app login data, transaction amount, transaction types, risk preferences, corresponding locking icon setting conditions (locking corresponds to 1, unlocking corresponds to 0, and the customer is not set and is NULL) which are actively set by the customer, and the like;
11. and clustering and analyzing the customer set according to the customer set data. Acquiring a corresponding customer subset;
12. and for each client subset, calculating the probability of setting locking corresponding to the old client without setting locking according to the proportion of the locking icons actively set by the clients in the subset. Pushing a locking setting message to the old client according to the probability;
13. setting the locking setting condition of the client set as a target value, and training a machine learning model by taking other data as characteristic attributes;
14. corresponding to a new customer, obtaining his customer information, including assets, app login data, transaction amount, transaction type, risk preferences. The information of the client is input into the machine learning model obtained in the step 13, and the probability estimation value of the locking of the client is calculated and obtained. And then recommending to set the locking information according to the obtained probability estimation value.
15. When the client sets locking, the corresponding authentication mode is set according to the means of authentication in the historical transaction of the client. For example, if the human face is verified before, and the similarity threshold is 0.9, the locking correspondence may also be set to human face verification, and the threshold is also 0.9.
The default state of the lock can be designed according to other schemes, whether the setting of the lock is on or off or not needs to be authorized, and the default state of the lock is also designed according to specific conditions.
The method for checking the assets of the online banking application is simple in setting, if the user uses the authorization, the checking mode is the same as that of the prior art after the authorization step, and if the user does not set the authorization, the method is the same as that of the prior art, so that the method is convenient for the user to use. For developers, the one-layer adding authorization is an existing mechanism and is easy to implement. However, the user does not need to worry about other functions or please teach the user in front of other people, which brings the user improved safety experience and also improves the safety factor of the user assets.
Fig. 4 is a schematic diagram of a computer device for executing an online asset security display locking method implemented by the present invention, and as shown in fig. 4, an embodiment of the present invention further provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the online asset security display locking method.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for implementing the above-mentioned method for locking and displaying an online asset is stored in the computer-readable storage medium.
Embodiments of the present invention further provide an online asset security display locking apparatus, as described in the following embodiments. Because the principle of solving the problems of the device is similar to that of the on-line asset safety display locking method, the implementation of the device can refer to the implementation of the on-line asset safety display locking method, and repeated parts are not described again.
Fig. 5 is a schematic view of an online asset security display locking device according to an embodiment of the present invention, and as shown in fig. 5, an embodiment of the present invention further provides an online asset security display locking device, including:
a data obtaining module 501, configured to obtain client set data;
a client subset determining module 502, configured to determine a plurality of client subsets according to the client set data; each client subset comprises locking setting condition information of corresponding assets;
the old client push-lock setting message module 503 is configured to analyze each client subset, obtain the proportion of active locking in the corresponding asset lock setting condition information, calculate the probability of active locking after the old client that is not actively locked receives the lock setting push message, and send the lock setting message to the old client that is not actively locked according to the probability;
a machine learning model training module 504, configured to train a machine learning model according to the client set data and the corresponding asset locking setting condition information;
a new client setting locking probability estimation value determination module 505, configured to obtain new client information, input the trained machine learning model, and determine a new client setting locking probability estimation value;
a new client lock setting message sending module 506, configured to send a lock setting message to the new client according to the estimated value of the new client lock probability;
the online asset locking module 507 is used for acquiring an identity verification mode in the historical transaction of the old client and locking the online asset by combining locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
In an embodiment of the invention, when the on-line asset security display locking device provided in the embodiment of the present invention is implemented, the data obtaining module is specifically configured to:
and connecting the banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
In an embodiment of the invention, when the online asset security display locking device provided in the embodiment of the present invention is implemented, the client subset determining module is specifically configured to:
and performing cluster analysis on the customer set data to determine a plurality of customer subsets.
In an embodiment of the invention, when the on-line asset security display locking device provided in the embodiment of the present invention is implemented, the machine learning model training module is specifically configured to:
setting asset locking setting condition information corresponding to the client set data as a target value, training a machine learning model by using the client set data as a characteristic attribute, and monitoring the convergence condition of the machine learning model;
and stopping training after the machine learning model converges to obtain the trained machine learning model.
In an embodiment of the invention, when the online asset security display locking device provided in the embodiment of the present invention is implemented, the online asset locking module is specifically configured to:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client historical transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client historical transaction of the old client to perform initial locking verification, and locking the on-line asset after the verification is successful;
when a new client receives the pushed locking setting message, according to the authentication mode in the historical transaction of the old client, the authentication modes are sorted from high to low according to the use frequency, the authentication modes are displayed to the new client, after the new client selects the authentication modes and inputs personal authentication information, the new client jumps to an asset display page, an asset locking icon is clicked on the asset display page, and the online asset is locked.
To sum up, the online asset security display locking method and device provided by the embodiment of the invention first obtain client set data and corresponding asset locking setting condition information; then determining a plurality of client sub-collections according to the client collection data and the corresponding asset locking setting condition information; analyzing each client subset, acquiring the active locking proportion in the corresponding asset locking setting condition information, calculating the active locking probability of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability; next, training a machine learning model according to the client set data and the corresponding asset locking setting condition information; then, new customer information is obtained, the trained machine learning model is input, and a locking probability estimation value set by the new customer is determined; further according to the locking probability estimation value set by the new client, sending a locking setting message to the new client; finally, acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked. The embodiment of the invention analyzes the client set based on the existing client set data to obtain a plurality of client sub-sets; according to the asset locking setting condition corresponding to each client in the client subset, the locking proportion is obtained, and the locking setting message is recommended to the old clients in the same subset, so that the locking setting can be accurately pushed, and the asset security of the old clients is facilitated; for a new client, the client set data and the corresponding asset locking setting condition information are utilized to train the machine learning model, the new client is identified to set the locking probability estimation value, and the locking setting information is sent to the new client, so that the accurate recommendation of the asset locking setting function is realized, the new client can know and use the asset locking function, the client viscosity and the safety experience are improved, and the asset safety coefficient of the user is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. An online asset safety display locking method is characterized by comprising the following steps:
acquiring customer set data;
determining a plurality of customer subsets according to the customer set data; each client subset comprises locking setting condition information of corresponding assets;
analyzing each client subset, acquiring the proportion of active locking in the corresponding asset locking setting condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking setting push message, and sending the locking setting message to the old client which is not actively locked according to the probability;
training a machine learning model according to the client set data and the corresponding asset locking setting condition information;
acquiring new customer information, inputting the trained machine learning model, and determining a locking probability estimation value set by the new customer;
according to the locking probability estimation value set by the new client, sending a locking setting message to the new client;
acquiring an identity verification mode in the historical transaction of the old client, and locking the on-line assets in combination with locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
2. The method of claim 1, wherein obtaining customer set data comprises:
and connecting the banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
3. The method of claim 1, wherein determining a plurality of customer subsets from customer set data comprises:
and performing cluster analysis on the customer set data to determine a plurality of customer subsets.
4. The method of claim 1, wherein training a machine learning model based on customer aggregate data and corresponding asset lock setting information comprises:
setting asset locking setting condition information corresponding to the client set data as a target value, training a machine learning model by using the client set data as a characteristic attribute, and monitoring the convergence condition of the machine learning model;
and stopping training after the machine learning model converges to obtain the trained machine learning model.
5. The method of claim 1, wherein obtaining the identity verification mode in the historical transaction of the old client and combining the locking setting message pushed to the old client and the new client to lock the online asset comprises:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client historical transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client historical transaction of the old client to perform initial locking verification, and locking the on-line asset after the verification is successful;
when a new client receives the pushed locking setting message, according to the authentication mode in the historical transaction of the old client, the authentication modes are sorted from high to low according to the use frequency, the authentication modes are displayed to the new client, after the new client selects the authentication modes and inputs personal authentication information, the new client jumps to an asset display page, an asset locking icon is clicked on the asset display page, and the online asset is locked.
6. An online asset safety shows locking device which characterized in that includes:
the data acquisition module is used for acquiring the client set data;
the client subset determining module is used for determining a plurality of client subsets according to the client set data; each client subset comprises locking setting condition information of corresponding assets;
the old client pushing and locking setting message module is used for analyzing each client subset, acquiring the proportion of active locking in the corresponding asset locking setting condition information, calculating the probability of active locking of the old client which is not actively locked after receiving the locking setting pushing message, and sending the locking setting message to the old client which is not actively locked according to the probability;
the machine learning model training module is used for training a machine learning model according to the client set data and the corresponding asset locking setting condition information;
the new client setting locking probability estimation value determining module is used for acquiring new client information, inputting the new client information into the trained machine learning model and determining a new client setting locking probability estimation value;
the new client locking setting message sending module is used for sending a locking setting message to the new client according to the locking probability estimation value set by the new client;
the online asset locking module is used for acquiring an identity verification mode in the historical transaction of the old client and locking the online asset by combining locking setting messages pushed to the old client and the new client; after the online assets are locked, identity verification is needed when the online assets are checked.
7. The apparatus of claim 6, wherein the data acquisition module is specifically configured to:
and connecting the banking system, and acquiring asset information, app login data, transaction amount, transaction type and risk preference from the banking system to form customer set data.
8. The apparatus of claim 6, wherein the client subset determination module is specifically configured to:
and performing cluster analysis on the customer set data to determine a plurality of customer subsets.
9. The apparatus of claim 6, wherein the machine learning model training module is specifically configured to:
setting asset locking setting condition information corresponding to the client set data as a target value, training a machine learning model by using the client set data as a characteristic attribute, and monitoring the convergence condition of the machine learning model;
and stopping training after the machine learning model converges to obtain the trained machine learning model.
10. The apparatus of claim 6, wherein the online asset locking module is specifically configured to:
when the old client receives the pushed locking setting message, acquiring an identity verification mode in the client historical transaction of the old client, clicking an asset locking icon on an asset display page, calling the identity verification mode in the client historical transaction of the old client to perform initial locking verification, and locking the on-line asset after the verification is successful;
when a new client receives the pushed locking setting message, according to the authentication mode in the historical transaction of the old client, the authentication modes are sorted from high to low according to the use frequency, the authentication modes are displayed to the new client, after the new client selects the authentication modes and inputs personal authentication information, the new client jumps to an asset display page, an asset locking icon is clicked on the asset display page, and the online asset is locked.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 5 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing a method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010927188.XA CN111984840B (en) | 2020-09-07 | 2020-09-07 | Online asset security display locking method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010927188.XA CN111984840B (en) | 2020-09-07 | 2020-09-07 | Online asset security display locking method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111984840A true CN111984840A (en) | 2020-11-24 |
| CN111984840B CN111984840B (en) | 2023-09-22 |
Family
ID=73447006
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010927188.XA Active CN111984840B (en) | 2020-09-07 | 2020-09-07 | Online asset security display locking method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111984840B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109726763A (en) * | 2018-12-29 | 2019-05-07 | 北京神州绿盟信息安全科技股份有限公司 | A kind of information assets recognition methods, device, equipment and medium |
| CN111159694A (en) * | 2019-12-17 | 2020-05-15 | 上海七印信息科技有限公司 | Private use authorization method of block chain digital assets based on zero knowledge proof |
| CN111507723A (en) * | 2020-06-18 | 2020-08-07 | 海南安迈云网络技术有限公司 | Digital asset management transaction encryption method |
-
2020
- 2020-09-07 CN CN202010927188.XA patent/CN111984840B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109726763A (en) * | 2018-12-29 | 2019-05-07 | 北京神州绿盟信息安全科技股份有限公司 | A kind of information assets recognition methods, device, equipment and medium |
| CN111159694A (en) * | 2019-12-17 | 2020-05-15 | 上海七印信息科技有限公司 | Private use authorization method of block chain digital assets based on zero knowledge proof |
| CN111507723A (en) * | 2020-06-18 | 2020-08-07 | 海南安迈云网络技术有限公司 | Digital asset management transaction encryption method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111984840B (en) | 2023-09-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11797657B1 (en) | Behavioral profiling method and system to authenticate a user | |
| US10313882B2 (en) | Dynamic unlock mechanisms for mobile devices | |
| CN108780475B (en) | Personalized inference authentication for virtual assistance | |
| Li et al. | Unobservable re-authentication for smartphones. | |
| US6076167A (en) | Method and system for improving security in network applications | |
| CN104580091B (en) | A kind of auth method, device, system and storage medium | |
| Sen et al. | Putting ‘pressure’on mobile authentication | |
| CN106650350B (en) | Identity authentication method and system | |
| WO2010057174A1 (en) | Method and apparatus for an end user identity protection suite | |
| US9202035B1 (en) | User authentication based on biometric handwriting aspects of a handwritten code | |
| CN111241517A (en) | Method and device for constructing biological characteristic verification question-answer library | |
| US8327420B2 (en) | Authentication system and method | |
| Mondal et al. | Continuous authentication and identification for mobile devices: Combining security and forensics | |
| Bhardwaj et al. | A novel behavioural biometric technique for robust user authentication | |
| CN107517180A (en) | Login method and device | |
| Mondal et al. | A continuous combination of security & forensics for mobile devices | |
| Marasco et al. | Biometric multi‐factor authentication: On the usability of the FingerPIN scheme | |
| US7478436B1 (en) | System and method for providing last log-in screen shots for security confirmation | |
| US20230046813A1 (en) | Selecting communication schemes based on machine learning model predictions | |
| CN107808082B (en) | Electronic device, data access verification method, and computer-readable storage medium | |
| US20190205513A1 (en) | User interface competence based authentication | |
| CN111984840B (en) | Online asset security display locking method and device | |
| US20220414193A1 (en) | Systems and methods for secure adaptive illustrations | |
| US10003464B1 (en) | Biometric identification system and associated methods | |
| CN111882425B (en) | Service data processing method, device and server |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |