CN115482093A - Transaction identification method and device, electronic equipment and computer storage medium - Google Patents

Transaction identification method and device, electronic equipment and computer storage medium Download PDF

Info

Publication number
CN115482093A
CN115482093A CN202211201677.2A CN202211201677A CN115482093A CN 115482093 A CN115482093 A CN 115482093A CN 202211201677 A CN202211201677 A CN 202211201677A CN 115482093 A CN115482093 A CN 115482093A
Authority
CN
China
Prior art keywords
transaction
data
transaction data
credit card
recognition model
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.)
Pending
Application number
CN202211201677.2A
Other languages
Chinese (zh)
Inventor
胡露露
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Bank of China Ltd
Original Assignee
Bank of China Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Bank of China Ltd filed Critical Bank of China Ltd
Priority to CN202211201677.2A priority Critical patent/CN115482093A/en
Publication of CN115482093A publication Critical patent/CN115482093A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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
    • G06Q40/08—Insurance

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Technology Law (AREA)
  • General Business, Economics & Management (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

The application discloses a transaction identification method, a transaction identification device, electronic equipment and a computer storage medium, which can be applied to the field of big data or the field of finance. The method comprises the following steps: acquiring transaction data of ongoing transaction of a user bank credit card; processing the transaction data to obtain processed transaction data; inputting the processed transaction data into a recognition model, processing the processed transaction data based on the recognition model, and outputting a recognition result, wherein the recognition model is obtained by training based on historical data. In the embodiment of the invention, after receiving a new transaction generated by the bank credit card, transaction data of the transaction is processed, and the processed transaction data is input into a pre-constructed identification model to determine whether the transaction generated by the bank credit card has fraud behavior. By the method, whether the credit card has fraud transaction can be effectively, quickly and accurately identified.

Description

Transaction identification method and device, electronic equipment and computer storage medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a transaction identification method and apparatus, an electronic device, and a computer storage medium.
Background
With the prevalence of fraud in the financial industry, the rights and interests of bank users are compromised.
At present, users often find transaction anomalies in credit cards between queries. The credit card fraudulent transaction cannot be effectively, quickly and accurately identified through the mode.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a transaction identification method, an apparatus, an electronic device, and a computer storage medium, so as to solve the problem in the prior art that a credit card fraudulent transaction cannot be identified efficiently, quickly, and accurately.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
a first aspect of an embodiment of the present invention shows a transaction identification method, including:
acquiring transaction data of ongoing transaction of a user bank credit card;
processing the transaction data to obtain processed transaction data;
inputting the processed transaction data into a recognition model, processing the processed transaction data based on the recognition model, and outputting a recognition result, wherein the recognition model is obtained by training based on historical data.
Optionally, the processing the transaction data to obtain processed transaction data includes:
performing feature extraction on the transaction data to obtain target features;
and converting based on the target characteristics to obtain processed transaction data.
Optionally, the process of training based on the historical data to obtain the recognition model includes:
acquiring historical data, and taking the historical data as a sample set;
extracting the characteristics of the sample set to obtain sample characteristic data;
converting the sample characteristic data into sample characteristic data in a preset format;
and training the CNN network model by using the sample characteristic data until the obtained recognition result representing the violation is the same as the preset result, and determining the CNN network model obtained by current training as the recognition model.
Optionally, the method further includes:
and if the identification result shows that the transaction is illegal, performing freezing operation on the bank credit card of the user.
A second aspect of an embodiment of the present invention shows a transaction identification apparatus, including:
the acquisition unit is used for acquiring transaction data of transactions which are carried out by a user bank credit card;
the first processing unit is used for processing the transaction data to obtain processed transaction data;
and the recognition model is used for inputting the processed transaction data into the recognition model, processing the processed transaction data based on the recognition model and outputting a recognition result, and the recognition model is constructed based on the construction unit.
Optionally, the first processing unit is specifically configured to: performing feature extraction on the transaction data to obtain target features;
and converting based on the target characteristics to obtain processed transaction data.
Optionally, the constructing unit is configured to:
acquiring historical data, and taking the historical data as a sample set;
extracting the characteristics of the sample set to obtain sample characteristic data;
converting the sample characteristic data into sample characteristic data in a preset format;
and training the CNN network model by using the sample characteristic data until the obtained recognition results representing the violation are the same in the preset result, and determining the CNN network model obtained by current training as the recognition model.
Optionally, the method further includes:
and the second processing unit is used for freezing the bank credit card of the user if the identification result shows that the transaction is illegal.
A third aspect of the embodiment of the present invention shows an electronic device, where the electronic device is configured to execute a program, where the program executes the transaction identification method shown in the first aspect of the embodiment of the present invention.
A fourth aspect of the embodiments of the present invention shows a computer storage medium, where the storage medium includes a storage program, and when the program runs, a device on which the storage medium is located is controlled to execute the transaction identification method shown in the first aspect of the embodiments of the present invention.
Based on the above transaction identification method, apparatus, electronic device and computer storage medium provided by the embodiments of the present invention, the method includes: acquiring transaction data of ongoing transaction of a user bank credit card; processing the transaction data to obtain processed transaction data; inputting the processed transaction data into a recognition model, processing the processed transaction data based on the recognition model, and outputting a recognition result, wherein the recognition model is obtained by training based on historical data. In the embodiment of the invention, after receiving a new transaction generated by the bank credit card, transaction data of the transaction is processed, and the processed transaction data is input into a pre-constructed identification model to determine whether the transaction generated by the bank credit card has fraud behavior. By the method, whether the credit card has fraud transaction can be effectively, quickly and accurately identified.
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 embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating interaction between a user terminal and a bank server according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a transaction identification method according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a process of training a recognition model according to an embodiment of the present invention;
FIG. 4 is a block diagram illustrating an architecture for implementing transaction identification according to an embodiment of the present invention;
FIG. 5 is a flow diagram illustrating another method of transaction identification according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a transaction identification device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of another transaction identification device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the transaction identification method, the transaction identification device, the electronic device and the computer storage medium provided by the invention can be used in the field of big data or the field of finance. The above description is only an example, and does not limit the application fields of the transaction identification method, the transaction identification apparatus, the electronic device, and the computer storage medium provided in the present invention.
In the embodiment of the invention, after receiving a new transaction generated by a bank credit card, transaction data of the transaction is processed, the processed transaction data is input into a pre-constructed identification model, and an identification result is output to determine whether the transaction generated by the bank credit card has fraud behaviors. By the method, whether the credit card has fraudulent transactions can be effectively, quickly and accurately identified.
Referring to fig. 1, a schematic diagram of interaction between a user terminal and a bank server according to an embodiment of the present invention is shown;
the user terminal 10 is connected to the bank server 20.
The user terminal 10 is bound with a corresponding bank credit card, and when the user performs transaction payment through the bank credit card in the user terminal 10, the user terminal sends transaction data of the transaction to the bank server 20 based on account information of the bank credit card.
The process of implementing transaction identification based on the bank server 20 includes:
the bank server 20 receives transaction data of a transaction being performed by a user's bank credit card; processing the transaction data to obtain processed transaction data; inputting the processed transaction data into a recognition model, processing the processed transaction data based on the recognition model, and outputting a recognition result, wherein the recognition model is obtained by training based on historical data.
In the embodiment of the invention, after receiving a new transaction generated by a bank credit card, transaction data of the transaction is processed, the processed transaction data is input into a pre-constructed identification model, and an identification result is output to determine whether the transaction generated by the bank credit card has fraud behaviors. By the method, whether the credit card has fraudulent transactions can be effectively, quickly and accurately identified.
Referring to fig. 2, a flow chart of a transaction identification method according to an embodiment of the present invention is shown, where the method includes:
step S201: transaction data for a transaction being conducted by a user's bank credit card is obtained.
Optionally, a corresponding bank credit card is bound in the user terminal, and when the user performs transaction payment through the bank credit card in the user terminal, the user terminal sends transaction data of the transaction to the bank server based on account information of the bank credit card.
In the process of implementing step S201, the bank server receives transaction data corresponding to a transaction occurring on a bank credit card.
The transaction data includes transaction time, transaction amount, transaction event, and the like.
Step S202: and processing the transaction data to obtain processed transaction data.
It should be noted that the process of processing the transaction data in the step S202 to obtain processed transaction data includes the following steps:
step S11: and performing feature extraction on the transaction data to obtain target features.
In the process of implementing step S11 specifically, the characteristics of the transaction data are extracted to obtain target characteristics.
It should be noted that the target feature includes not only the transaction amount feature but also the transaction entropy feature, etc. to simulate more complicated consumption behavior.
Step S12: and converting based on the target characteristics to obtain processed transaction data.
In the process of implementing step S12 specifically, the format of the target feature is converted into data in a preset format, that is, the processed transaction data is obtained.
It should be noted that the preset format is set by a technician according to experience in advance, for example, the preset format is set as data in a numerical format.
Step S203: inputting the processed transaction data into an identification model, processing the processed transaction data based on the identification model, and outputting an identification result.
In step S203, the recognition model is trained based on historical data.
It should be noted that, as shown in fig. 3, the process of training to obtain a recognition model based on historical data includes the following steps:
step S301: historical data is obtained and used as a sample set.
The historical data refers to data of transactions conducted by the user through the bank credit card in a historical time period.
In the process of implementing step S301 specifically, data of transactions made by the user through the bank credit card in the historical time period is obtained and used as a sample set.
It should be noted that the sample set includes a plurality of samples, and each sample corresponds to historical transaction data of one historical transaction.
The historical time period is set by the technician based on experience or multiple experiments, and may be set to the past 1 year, for example.
Step S302: and extracting the characteristics of the sample set to obtain sample characteristic data.
In the process of implementing step S302 specifically, extracting the features of the sample set corresponding to each transaction to obtain the features of each transaction; and integrated into sample characterization data.
It should be noted that the characteristics of the transaction include not only the transaction amount characteristics but also the transaction entropy characteristics, so as to simulate more complicated consumption behaviors.
Step S303: and converting the sample characteristic data into sample characteristic data in a preset format.
In the process of implementing step S303 specifically, the format of the feature of each transaction in the sample feature data is converted to obtain sample feature data in a preset format.
It should be noted that the preset format is set by a technician according to experience in advance, for example, the preset format is set to be data in a numerical format.
Therefore, for sample feature data of a text type, text data conversion needs to be performed on the sample feature data, that is, the text data needs to be converted into numerical data, so as to obtain sample feature data of a preset format.
Step S304: and training the CNN network model by using the sample characteristic data until the obtained recognition results are the same in a preset result, and determining the CNN network model obtained by current training as the recognition model.
In the process of implementing step S304 specifically, some samples consistent with the true fraud data are selected from the sample set by using a preset sampling method, and are used as fraud samples, and a preset result is set based on the fraud samples; and dividing the sample characteristic data to obtain a training set and a testing set.
Determining model parameters for constructing a general CNN network model; training the model parameters by using the training set so as to optimize the model parameters, and constructing a CNN network model based on the optimized network parameters; establishing a CNN network model based on the optimized network parameters to perform classified identification on each sample in the test set to obtain an identification result of each sample; if the recognition result indicates that no illegal samples exist or any illegal sample does not exist in the preset result, continuing training the optimized CNN network model based on the training set and the test set; and if the test identification result indicates that all illegal samples exist in the preset result, determining the current optimized CNN network model as the identification model.
It should be noted that the identification result of each sample is used to indicate that the sample is fraudulent, i.e. illegal, or legal.
In the process of implementing step S203, the processed transaction data is identified by using the established identification model, and it is determined whether the transaction is a fraudulent transaction or a legal transaction, so as to obtain an identification result.
It should be noted that the identification result is used to indicate that the transaction is a fraudulent transaction or a legitimate transaction.
Accordingly, a specific process based on the above-described construction of the transaction identification and identification model can be illustrated by fig. 4.
In the embodiment of the invention, after receiving a new transaction generated by the bank credit card, the transaction data of the transaction is processed, the processed transaction data is input into the pre-constructed recognition model, and the recognition result is output to determine whether the transaction generated by the bank credit card has a fraud behavior. By the method, whether the credit card has fraud transaction can be effectively, quickly and accurately identified.
Corresponding to the transaction identification method shown in the above embodiment of the present invention, with reference to fig. 2 and fig. 5, the method further includes:
step S204: and judging whether the identification result is a legal transaction, if so, enabling the bank credit card to continue the transaction, and if not, executing the step S205.
In the process of implementing step S204 specifically, it is determined whether the identification result indicates that the transaction is a legal transaction, if so, the bank credit card is enabled to continue the transaction, if not, it is determined that the identification result indicates that the transaction is illegal, and step S205 is executed.
Step S205: and performing freezing operation on the bank credit card of the user.
In the process of implementing step S205 specifically, the bank credit card of the user is frozen, so that the user cannot use the bank credit card to perform a transaction currently.
In the embodiment of the invention, after receiving a new transaction generated by a bank credit card, transaction data of the transaction is processed, the processed transaction data is input into a pre-constructed identification model, and an identification result is output to determine whether the transaction generated by the bank credit card has fraud behaviors. And performing a freezing operation on the bank credit card of the user when the fraud is existed. Thereby protecting the rights and interests of the user.
Based on the transaction identification method shown in the above embodiment of the present invention, correspondingly, the embodiment of the present invention further discloses a schematic structural diagram of a transaction identification device, as shown in fig. 6, the device includes:
an acquisition unit 601, configured to acquire transaction data of a transaction being performed by a user's bank credit card;
a first processing unit 602, configured to process the transaction data to obtain processed transaction data;
the recognition model 603 is configured to input the processed transaction data into a recognition model, process the processed transaction data based on the recognition model, and output a recognition result, where the recognition model is constructed based on the construction unit 604.
It should be noted that, the specific principle and the implementation process of each unit in the transaction identification device disclosed in the embodiment of the present application are the same as those of the transaction identification method described in the embodiment of the present application, and reference may be made to corresponding parts in the transaction identification method disclosed in the embodiment of the present application, and details are not repeated here.
In the embodiment of the invention, after receiving a new transaction generated by a bank credit card, transaction data of the transaction is processed, the processed transaction data is input into a pre-constructed identification model, and an identification result is output to determine whether the transaction generated by the bank credit card has fraud behaviors. By the method, whether the credit card has fraud transaction can be effectively, quickly and accurately identified.
Optionally, based on the transaction identification apparatus shown in the foregoing embodiment of the present invention, the first processing unit 602 is specifically configured to: performing feature extraction on the transaction data to obtain target features; and converting based on the target characteristics to obtain processed transaction data.
Optionally, based on the transaction identification apparatus shown in the above embodiment of the present invention, the constructing unit 604 is configured to;
acquiring historical data, and taking the historical data as a sample set;
extracting the characteristics of the sample set to obtain sample characteristic data;
converting the sample characteristic data into sample characteristic data in a preset format;
and training the CNN network model by using the sample characteristic data until the obtained recognition results representing the violation are the same in the preset result, and determining the CNN network model obtained by current training as the recognition model.
Optionally, based on the transaction identification apparatus shown in the above-mentioned embodiment of the present invention, referring to fig. 7 in conjunction with fig. 6, the apparatus is further provided with a second processing unit 605.
And the second processing unit 605 is configured to perform a freezing operation on the bank credit card of the user if the identification result indicates that the transaction is illegal.
In the embodiment of the invention, after receiving a new transaction generated by a bank credit card, transaction data of the transaction is processed, the processed transaction data is input into a pre-constructed identification model, and an identification result is output to determine whether the transaction generated by the bank credit card has fraud behaviors. And performing a freezing operation on the bank credit card of the user when the fraud is existed. Thereby protecting the rights and interests of the user.
Based on the transaction identification device disclosed in the embodiment of the present disclosure, the above modules may be implemented by a hardware device composed of a processor and a memory. Specifically, the modules are stored in the memory as program units, and the processor executes the program units stored in the memory to realize transaction identification.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more, and the transaction identification is realized by adjusting the kernel parameters.
The embodiment of the present disclosure provides a computer storage medium, which includes a stored text processing program, wherein the program implements the transaction identification method described in fig. 2 to 5 when executed by a processor.
The disclosed embodiment provides a processor for executing a program, wherein the program executes the transaction identification method described in fig. 2 to 5.
The embodiment of the disclosure provides an electronic device, which may be a server, a PC, a PAD, a mobile phone, or the like.
The electronic device includes at least one processor, and at least one memory coupled to the processor, and a bus.
The processor and the memory complete mutual communication through the bus. A processor for executing the program stored in the memory.
A memory for storing a program for at least: acquiring transaction data of ongoing transaction of a user bank credit card; processing the transaction data to obtain processed transaction data; inputting the processed transaction data into a recognition model, processing the processed transaction data based on the recognition model, and outputting a recognition result, wherein the recognition model is obtained by training based on historical data.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on an electronic device: acquiring transaction data of ongoing transaction of a user bank credit card; processing the transaction data to obtain processed transaction data; inputting the processed transaction data into a recognition model, processing the processed transaction data based on the recognition model, and outputting a recognition result, wherein the recognition model is obtained by training based on historical data.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, the system or system embodiments, which are substantially similar to the method embodiments, are described in a relatively simple manner, and reference may be made to some descriptions of the method embodiments for relevant points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A transaction identification method, the method comprising:
acquiring transaction data of ongoing transaction of a user bank credit card;
processing the transaction data to obtain processed transaction data;
inputting the processed transaction data into a recognition model, processing the processed transaction data based on the recognition model, and outputting a recognition result, wherein the recognition model is obtained by training based on historical data.
2. The method of claim 1, wherein said processing the transaction data to obtain processed transaction data comprises:
performing feature extraction on the transaction data to obtain target features;
and converting based on the target characteristics to obtain processed transaction data.
3. The method of claim 1, wherein the training based on historical data to obtain a recognition model comprises:
acquiring historical data, and taking the historical data as a sample set;
extracting the characteristics of the sample set to obtain sample characteristic data;
converting the sample characteristic data into sample characteristic data in a preset format;
and training the CNN network model by using the sample characteristic data until the obtained recognition result representing the violation is the same as the preset result, and determining the CNN network model obtained by current training as the recognition model.
4. The method of claim 1, further comprising:
and if the identification result shows that the transaction is illegal, freezing the bank credit card of the user.
5. A transaction identification device, the device comprising:
the acquisition unit is used for acquiring transaction data of transactions which are carried out by the user bank credit card;
the first processing unit is used for processing the transaction data to obtain processed transaction data;
and the recognition model is used for inputting the processed transaction data into the recognition model, processing the processed transaction data based on the recognition model and outputting a recognition result, and the recognition model is constructed based on the construction unit.
6. The apparatus according to claim 5, wherein the first processing unit is specifically configured to: performing feature extraction on the transaction data to obtain target features;
and converting based on the target characteristics to obtain processed transaction data.
7. The apparatus of claim 5, wherein the construction unit is configured to:
acquiring historical data, and taking the historical data as a sample set;
extracting the characteristics of the sample set to obtain sample characteristic data;
converting the sample characteristic data into sample characteristic data in a preset format;
and training the CNN network model by using the sample characteristic data until the obtained recognition result representing the violation is the same as the preset result, and determining the CNN network model obtained by current training as the recognition model.
8. The apparatus of claim 5, further comprising:
and the second processing unit is used for freezing the bank credit card of the user if the identification result shows that the transaction is illegal.
9. An electronic device, characterized in that the electronic device is adapted to run a program, wherein the program when running performs the transaction identification method according to any of claims 1-4.
10. A computer storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when executed, controls a device on which the storage medium is located to execute the transaction identification method according to any one of claims 1-4.
CN202211201677.2A 2022-09-29 2022-09-29 Transaction identification method and device, electronic equipment and computer storage medium Pending CN115482093A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211201677.2A CN115482093A (en) 2022-09-29 2022-09-29 Transaction identification method and device, electronic equipment and computer storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211201677.2A CN115482093A (en) 2022-09-29 2022-09-29 Transaction identification method and device, electronic equipment and computer storage medium

Publications (1)

Publication Number Publication Date
CN115482093A true CN115482093A (en) 2022-12-16

Family

ID=84393875

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211201677.2A Pending CN115482093A (en) 2022-09-29 2022-09-29 Transaction identification method and device, electronic equipment and computer storage medium

Country Status (1)

Country Link
CN (1) CN115482093A (en)

Similar Documents

Publication Publication Date Title
CN109063920B (en) Transaction risk identification method and device and computer equipment
CN105337928B (en) Method for identifying ID, safety protection problem generation method and device
CN110490721B (en) Financial voucher generating method and related product
US20180357643A1 (en) Apparatus and method of detecting abnormal financial transaction
CN110442712B (en) Risk determination method, risk determination device, server and text examination system
CN105303442A (en) Online bank account number detection method and apparatus
CN114116802A (en) Data processing method, device, equipment and storage medium of Flink computing framework
CN112016931A (en) Online transaction method and device, electronic equipment and computer storage medium
CN110020196B (en) User analysis method and device based on different data sources and computing equipment
CN111445258A (en) Risk prevention and control information processing method, device and equipment
CN114553813A (en) Message pushing method and device based on banking business, processor and electronic equipment
WO2017032056A1 (en) Point-of-sale-based cash-out determining method and apparatus
CN111047146B (en) Risk identification method, device and equipment for enterprise users
CN115482093A (en) Transaction identification method and device, electronic equipment and computer storage medium
US11973756B2 (en) Systems and methods for improving computer identification
CN115601044A (en) Fraud detection model training method, fraud detection device and electronic equipment
CN110442375B (en) Mobile payment product channel integration method, device, equipment and storage medium
CN108234729B (en) Method for adjusting verification model, verification method, server and storage medium
CN112019642A (en) Audio uploading method, device, equipment and storage medium
CN110929153A (en) Personalized start page pushing method, device and system
CN111324732A (en) Model training method, text processing device and electronic equipment
CN112101952B (en) Bank suspicious transaction evaluation and data processing method and device
CN110971759A (en) Processing method and device for unsubscribed short message and server
CN115001778B (en) Page authentication method and device, storage medium and electronic equipment
CN112150139B (en) Data analysis method and device

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