CN112150139B - Data analysis method and device - Google Patents

Data analysis method and device Download PDF

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Publication number
CN112150139B
CN112150139B CN202011062343.2A CN202011062343A CN112150139B CN 112150139 B CN112150139 B CN 112150139B CN 202011062343 A CN202011062343 A CN 202011062343A CN 112150139 B CN112150139 B CN 112150139B
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user
mobile phone
security function
phone bank
fraud
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CN112150139A (en
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季蕴青
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Bank of China Ltd
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    • 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
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • G06Q20/32—Payment architectures, schemes or protocols characterised by the use of specific devices or networks using wireless devices
    • G06Q20/322—Aspects of commerce using mobile devices [M-devices]
    • G06Q20/3223—Realising banking transactions through M-devices
    • 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
    • G06Q20/382—Payment protocols; Details thereof insuring higher security of transaction
    • 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
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • 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

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  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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  • Computing Systems (AREA)
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Abstract

The application discloses a data analysis method and a data analysis device. When a first user uses a mobile phone bank security function, a server firstly acquires behavior data of the first user using the mobile phone bank security function. After the behavior data are acquired, the behavior data are input into a pre-trained machine learning model, and the possibility that the first user uses the mobile banking security function to conduct fraud is obtained through the machine learning model. The machine learning model is used for determining the possibility of fraud by the first user by utilizing the mobile phone bank security function according to the behavior data of the first user by utilizing the mobile phone bank security function.

Description

Data analysis method and device
Technical Field
The present application relates to the field of data processing, and in particular, to a data analysis method and apparatus.
Background
At present, the mobile phone bank can display the account balance of the user in real time, and the function enables the user to view the account balance in real time, but other amount information is also easily known by other people except the user, so that a certain degree of risk is brought to the user.
To solve this problem, the bank system develops a mobile phone bank security function, and the user can preset a false value to be displayed, and when the mobile phone bank security function is started, the mobile phone bank displays false account balance information according to the preset false value. Through the mobile banking safety function, when the user is unwilling to disclose the real balance information of the user, such as the situation of being stressed to transfer accounts, the risk of the user can be reduced to the greatest extent by starting the mobile banking safety function.
However, since the mobile banking security function displays false account balance information, the user can use the false information to perform fraudulent activities such as fraud. Therefore, a solution is urgently needed to solve the above-mentioned problems.
Disclosure of Invention
The application aims to solve the technical problem of providing a data analysis method to solve the problem that when a user uses a mobile phone bank security function to avoid that other people know real account balance information, the mobile phone bank security function displays false account balance information, so that the user can use the false information to perform illegal activities such as fraud.
The embodiment of the application provides a data analysis method, when a first user uses a mobile phone banking security function, a server firstly acquires behavior data of the first user using the mobile phone banking security function. After the behavior data are acquired, the behavior data are input into a pre-trained machine learning model, and the possibility that the first user uses the mobile banking security function to conduct fraud is obtained through the machine learning model. The machine learning model is used for determining the possibility of fraud by the first user by utilizing the mobile phone bank security function according to the behavior data of the first user by utilizing the mobile phone bank security function. By adopting the scheme, when the user uses the mobile phone bank safety function, the server can know the possibility of fraud by using the mobile phone bank safety function according to the behavior data of the user using the mobile phone bank safety function, so that illegal activities such as fraud by using the mobile phone bank safety function are avoided to the greatest extent.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a flow chart of a data analysis method according to an embodiment of the application;
fig. 2 is a schematic structural diagram of a data analysis device according to an embodiment of the application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The inventor of the application discovers through research that at present, a user can use the mobile phone bank security function to avoid that other people acquire the real account balance information, but because the mobile phone bank security function displays false account balance information, the user can use the false information to perform illegal activities such as fraud.
In order to solve the above-mentioned problem, in the embodiment of the present application, when a first user uses a mobile banking security function, a server first obtains behavior data of the first user using the mobile banking security function. After the behavior data are acquired, the behavior data are input into a pre-trained machine learning model, and the possibility that the first user uses the mobile banking security function to conduct fraud is obtained through the machine learning model. The machine learning model is used for determining the possibility of fraud by the first user by utilizing the mobile phone bank security function according to the behavior data of the first user by utilizing the mobile phone bank security function. By adopting the scheme, when the user uses the mobile phone bank safety function, the server can know the possibility of fraud by using the mobile phone bank safety function according to the behavior data of the user using the mobile phone bank safety function, so that illegal activities such as fraud by using the mobile phone bank safety function are avoided to the greatest extent.
Various non-limiting embodiments of the present application are described in detail below with reference to the attached drawing figures.
Exemplary method
Referring to fig. 1, a flow chart of a data analysis method according to an embodiment of the application is shown. The method shown in fig. 1 may be performed, for example, by a server in one implementation.
In this embodiment, the method shown in fig. 1 can be implemented by, for example, the following steps S101 to S102.
S101: and when the first user uses the mobile phone bank security function, acquiring behavior data of the first user using the mobile phone bank security function.
In this embodiment, the first user may preset a virtual value to be displayed in the mobile phone bank security function, and when the mobile phone bank security function is started, the mobile phone bank displays virtual account balance information according to the preset virtual value. Through the mobile phone bank safety function, when the user is unwilling to disclose the real balance information of the user, such as the situation of being stressed to transfer accounts, the risk of the user can be reduced to the greatest extent by starting the mobile phone bank safety function.
In order to enable the server to acquire the behavior data of the first user using the mobile banking security function, the server records the behavior data of the first user using the mobile banking security function, and when the first user uses the mobile banking security function, the server acquires the behavior data of the first user using the mobile banking security function so as to further analyze the behavior data. The behavioral data is used to determine a likelihood of fraud by the first user with the mobile banking security function.
In some embodiments, it is contemplated that the frequency with which the first user uses the mobile banking security function may be used to determine the likelihood of fraud by the first user with the mobile banking security function. For example: if the frequency of the first user using the mobile banking security function is too high, the first user may use the mobile banking security function abnormally, for example, may be fraudulent by using the mobile banking security function. Because the mobile banking security function is to avoid that the real account balance information of the user is known by other people, a certain risk is brought to the user, however, situations such as being duress to transfer and the like do not always occur in daily life. Thus, the behavioral data may include: the first user uses the frequency of the mobile banking security function.
In some embodiments, considering that the time when the first user uses the mobile banking security function may be used to determine the likelihood of fraud by the first user with the mobile banking security function, the time may be a time sequence when the first user uses the mobile banking security function for the next time, such as: time of this time, last time. For example: if the time interval for the first user to use the mobile banking security function is too short, the user may use the mobile banking security function abnormally, for example, may use the mobile banking security function to make fraud. Thus, the behavioral data may include: and the time when the first user uses the mobile banking security function.
In some embodiments, it is contemplated that the amount set by the first user for the mobile banking secure functionality may be used to determine a likelihood of fraud by the first user with the mobile banking secure functionality. The amount set by the first user for the mobile banking security function refers to a false amount set by the first user that is not willing to disclose the true balance information of the first user, for example, in one possible implementation, the possibility of fraud may be determined by comparing the amount set by the first user for the mobile banking security function with the true account balance of the first user. If the real balance of the first user account is 5000 yuan and the amount set by the first user account aiming at the mobile phone banking safety function is 100 ten thousand yuan, the first user is considered to be possibly fraudled by utilizing the mobile phone banking safety function. Thus, the behavioral data may include: and the first user sets the amount for the mobile phone bank security function.
Of course, the behavior data may also include any one or more of the above frequency of using the mobile banking security function by the first user, time of using the mobile banking security function by the first user, and amount set by the first user for the mobile banking security function.
S102, inputting the behavior data into a pre-trained machine learning model to obtain the possibility of fraud by the first user through the mobile banking security function.
After the behavior data of the first user using the mobile banking security function is obtained, the behavior data is further analyzed, so that the possibility of fraud by the first user using the mobile banking security function is obtained, in this embodiment, the behavior data may be input into a pre-trained machine learning model, where the machine learning model is configured to determine, according to the behavior data of the first user using the mobile banking security function, the possibility of fraud by the first user using the mobile banking security function.
It can be appreciated that the pre-trained machine learning model can be trained based on training samples and labels corresponding to the training samples. The training samples comprise behavior data of the mobile phone banking function used by the network user, the labels corresponding to the training samples are used for indicating that the network user is in fraud by utilizing the mobile phone banking safety function, or the labels corresponding to the training samples are used for indicating that the network user is not in fraud by utilizing the mobile phone banking safety function. That is, in order to analyze the behavior data of the first user using the mobile banking security function, a pre-trained machine learning model is first established, where the pre-trained machine learning model is obtained according to the input of the training sample and the output of the label corresponding to the training sample. After the pre-trained machine learning model is established, the possibility of fraud by the first user by utilizing the mobile banking security function is determined according to the machine learning model by inputting behavior data of the first user using the mobile banking security function.
It may be appreciated that, after the probability of fraud by the first user using the mobile banking security function is obtained according to the machine learning model, if the probability of fraud by the first user using the mobile banking security function is greater than or equal to a first threshold value, it is indicated that the probability of abnormal use by the first user using the mobile banking security function is high, so, in order to prevent fraud by the first user using the mobile banking security function, in one possible implementation, the first user may be prohibited from using the mobile banking security function. The first threshold is a reference value preset by the server, and the value of the first threshold can be determined according to historical data, for example.
In addition, after determining that the possibility of fraud by the first user using the mobile banking security function is greater than or equal to a first threshold, for example, an account logged in by the first user in a mobile banking application program including the mobile banking security function may be frozen, so as to ensure that the first user cannot exchange the account to perform fraud, and ensure the fund security of the mobile banking user to the greatest extent.
Exemplary apparatus
Based on the method provided by the embodiment, the embodiment of the application also provides a device, and the device is described below with reference to the accompanying drawings.
Referring to fig. 2, a schematic structural diagram of a data analysis device according to an embodiment of the present application is shown. The device may, for example, specifically comprise:
the acquisition module 201: the method comprises the steps that when a first user uses a mobile phone banking safety function, behavior data of the first user using the mobile phone banking safety function is obtained;
the input module 202: the mobile phone bank security function is used for inputting the behavior data into a pre-trained machine learning model to obtain the possibility of fraud by the first user through the mobile phone bank security function;
wherein:
the machine learning model is used for determining the possibility of fraud by the first user by utilizing the mobile phone bank security function according to the behavior data of the first user by utilizing the mobile phone bank security function.
In one implementation manner, the machine learning model is obtained according to training samples and labels corresponding to the training samples; the training samples comprise behavior data of the mobile phone banking function used by the network user, the labels corresponding to the training samples are used for indicating that the network user is in fraud by utilizing the mobile phone banking safety function, or the labels corresponding to the training samples are used for indicating that the network user is not in fraud by utilizing the mobile phone banking safety function.
In one implementation, the behavior data of the first user using the mobile banking security function includes any one or more of the following:
the frequency of using the mobile phone bank security function by the first user, the amount of last using the mobile phone bank security function by the first user and the amount set by the first user for the mobile phone bank security function.
In one implementation, the apparatus further includes a disabling module configured to:
and if the possibility of fraud by the first user by utilizing the mobile banking security function is greater than or equal to a first threshold value, prohibiting the first user from using the mobile banking security function.
In one implementation, the apparatus further comprises a freezing module for:
and if the possibility of fraud by the first user by utilizing the mobile banking safety function is greater than or equal to a first threshold value, freezing an account logged in by the first user in a mobile banking application program comprising the mobile banking safety function.
Since the apparatus 200 is an apparatus corresponding to the method provided in the above method embodiment, the specific implementation of each unit of the apparatus 200 is the same as the above method embodiment, and therefore, with respect to the specific implementation of each unit of the apparatus 200, reference may be made to the description part of the above method embodiment, and details are not repeated herein.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.

Claims (4)

1. A method of data analysis, the method comprising:
when a first user uses a mobile phone bank security function, acquiring behavior data of the first user using the mobile phone bank security function;
inputting the behavior data into a pre-trained machine learning model to obtain the possibility of fraud by the first user by utilizing the mobile banking security function;
wherein:
the machine learning model is used for determining the possibility of fraud by the first user by utilizing the mobile phone bank security function according to the behavior data of the first user by utilizing the mobile phone bank security function;
the behavior data of the first user using the mobile banking security function includes any one or more of the following:
the frequency of using the mobile phone bank security function by the first user, the amount of last using the mobile phone bank security function by the first user and the amount set by the first user for the mobile phone bank security function;
if the possibility of fraud by the first user by utilizing the mobile banking security function is greater than or equal to a first threshold value, prohibiting the first user from using the mobile banking security function;
if the possibility of fraud by the first user by utilizing the mobile banking safety function is greater than or equal to a first threshold value, freezing an account logged in by the first user in a mobile banking application program comprising the mobile banking safety function;
when the mobile phone bank safety function is in a starting state, the mobile phone bank safety function is used for controlling the mobile phone bank to display false account balance information corresponding to preset false values, wherein the false values are preset values for display in the mobile phone bank safety function by a first user.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the machine learning model is obtained through training according to the training sample and the label corresponding to the training sample; the training samples comprise behavior data of the mobile phone bank safety function used by the network user, the labels corresponding to the training samples are used for indicating that the network user is in fraud by utilizing the mobile phone bank safety function, or the labels corresponding to the training samples are used for indicating that the network user is not in fraud by utilizing the mobile phone bank safety function.
3. A data analysis device, the device comprising:
the acquisition module is used for: the method comprises the steps that when a first user uses a mobile phone banking safety function, behavior data of the first user using the mobile phone banking safety function is obtained;
an input module: the mobile phone bank security function is used for inputting the behavior data into a pre-trained machine learning model to obtain the possibility of fraud by the first user through the mobile phone bank security function;
wherein:
the machine learning model is used for determining the possibility of fraud by the first user by utilizing the mobile phone bank security function according to the behavior data of the first user by utilizing the mobile phone bank security function;
the behavior data of the first user using the mobile banking security function includes any one or more of the following: the frequency of using the mobile phone bank security function by the first user, the amount of last using the mobile phone bank security function by the first user and the amount set by the first user for the mobile phone bank security function;
the apparatus further comprises a disabling module for: if the possibility of fraud by the first user by utilizing the mobile banking security function is greater than or equal to a first threshold value, prohibiting the first user from using the mobile banking security function;
the apparatus further comprises a freezing module for: if the possibility of fraud by the first user by utilizing the mobile banking safety function is greater than or equal to a first threshold value, freezing an account logged in by the first user in a mobile banking application program comprising the mobile banking safety function;
when the mobile phone bank safety function is in a starting state, the mobile phone bank safety function is used for controlling the mobile phone bank to display false account balance information corresponding to preset false values, wherein the false values are preset values for display in the mobile phone bank safety function by a first user.
4. The apparatus of claim 3, wherein the device comprises a plurality of sensors,
the machine learning model is obtained through training according to the training sample and the label corresponding to the training sample; the training samples comprise behavior data of the mobile phone bank safety function used by the network user, the labels corresponding to the training samples are used for indicating that the network user is in fraud by utilizing the mobile phone bank safety function, or the labels corresponding to the training samples are used for indicating that the network user is not in fraud by utilizing the mobile phone bank safety function.
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Citations (6)

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Publication number Priority date Publication date Assignee Title
JP2014106653A (en) * 2012-11-26 2014-06-09 Fujitsu Ltd Processing server and dispensing processing control program
CN106504086A (en) * 2016-10-20 2017-03-15 方琳 A kind of method and bank account system for protecting bank account safety
CN206312231U (en) * 2016-10-20 2017-07-07 方琳 A kind of bank account system
CN109409896A (en) * 2018-10-17 2019-03-01 北京芯盾时代科技有限公司 Identification model training method, bank's fraud recognition methods and device are cheated by bank
CN111160745A (en) * 2019-12-23 2020-05-15 中国建设银行股份有限公司 User account data processing method and device
CN111383027A (en) * 2020-03-10 2020-07-07 中国建设银行股份有限公司 Account case-involved detection method, device, equipment and storage medium

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014106653A (en) * 2012-11-26 2014-06-09 Fujitsu Ltd Processing server and dispensing processing control program
CN106504086A (en) * 2016-10-20 2017-03-15 方琳 A kind of method and bank account system for protecting bank account safety
CN206312231U (en) * 2016-10-20 2017-07-07 方琳 A kind of bank account system
CN109409896A (en) * 2018-10-17 2019-03-01 北京芯盾时代科技有限公司 Identification model training method, bank's fraud recognition methods and device are cheated by bank
CN111160745A (en) * 2019-12-23 2020-05-15 中国建设银行股份有限公司 User account data processing method and device
CN111383027A (en) * 2020-03-10 2020-07-07 中国建设银行股份有限公司 Account case-involved detection method, device, equipment and storage medium

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