CN115239347A - Transaction limit determining method and device - Google Patents
Transaction limit determining method and device Download PDFInfo
- Publication number
- CN115239347A CN115239347A CN202210927276.9A CN202210927276A CN115239347A CN 115239347 A CN115239347 A CN 115239347A CN 202210927276 A CN202210927276 A CN 202210927276A CN 115239347 A CN115239347 A CN 115239347A
- Authority
- CN
- China
- Prior art keywords
- user
- transaction
- similarity
- determining
- abnormal
- 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
Links
Images
Classifications
-
- 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
- G06Q20/405—Establishing or using transaction specific rules
-
- 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
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- 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
- G06N3/086—Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention provides a transaction quota determining method and device, which can be used in the financial field or other fields. The method comprises the following steps: acquiring quota transaction data authorized by a user, determining abnormal transaction data in the quota transaction data according to a preset abnormal transaction judgment rule, and determining user information corresponding to the abnormal transaction data; clustering the abnormal transaction data and the corresponding user information to obtain a user clustering result; determining the user similarity corresponding to the user clustering result by using a cosine similarity algorithm; and inputting the user similarity corresponding to the user clustering result into a pre-established transaction limit model for processing to obtain the user transaction limit. According to the invention, the transaction data with abnormal occurrence is determined by collecting the limit transaction data of the user, clustering analysis and similarity calculation are carried out on the abnormal user, and the preset transaction limit model is combined, so that the transaction limit is accurately and reasonably set, and the transaction safety is improved.
Description
Technical Field
The present invention relates to the field of transaction processing, and in particular, to a method and an apparatus for determining a transaction quota.
Background
At present, banks such as bank-crossing transfer of mobile phone banks, ATM (automatic teller machine) non-card withdrawal, ATM transfer, mobile phone bank transaction limit and the like have certain limit on transactions. In the maximum limit, the user can set his limit by himself, and the user's limit is usually set according to his experience or by himself. Therefore, the problems of unreasonable transaction limit setting, non-compliance with the safety principle and the like exist.
Disclosure of Invention
In view of the problems in the prior art, the embodiments of the present invention mainly aim to provide a method and an apparatus for determining a transaction limit, so as to accurately and reasonably set the transaction limit and improve the transaction security.
In order to achieve the above object, an embodiment of the present invention provides a transaction quota determining method, including:
acquiring limit transaction data authorized by a user, determining abnormal transaction data in the limit transaction data according to a preset abnormal transaction judgment rule, and determining user information corresponding to the abnormal transaction data;
clustering abnormal transaction data and corresponding user information to obtain a user clustering result;
determining the user similarity corresponding to the user clustering result by using a cosine similarity algorithm;
and inputting the user similarity corresponding to the user clustering result into a pre-established transaction limit model for processing to obtain the user transaction limit.
Optionally, in an embodiment of the present invention, performing clustering processing on the abnormal transaction data and the user information corresponding to the abnormal transaction data, and obtaining a user clustering result includes:
and clustering the abnormal transaction data and the corresponding user information to obtain user age clustering, user occupation clustering and transaction amount clustering, and taking the user age clustering, the user occupation clustering and the transaction amount clustering as user clustering results.
Optionally, in an embodiment of the present invention, determining the user similarity corresponding to the user clustering result by using a cosine similarity algorithm includes:
determining feature vector data corresponding to the user clustering result according to the user clustering result;
and determining the similarity corresponding to the age cluster of the user, the similarity corresponding to the occupation cluster of the user and the similarity corresponding to the transaction amount cluster by utilizing a cosine similarity algorithm according to the feature vector data, and taking the similarity corresponding to the age cluster of the user, the similarity corresponding to the occupation cluster of the user and the similarity corresponding to the transaction amount cluster as the user similarity corresponding to the user clustering result.
Optionally, in an embodiment of the present invention, the method further includes: and generating risk prompt information by using a preset transaction threshold value and a user transaction limit, and sending the risk prompt information to the user.
An embodiment of the present invention further provides a transaction quota determining apparatus, including:
the abnormal transaction module is used for acquiring quota transaction data authorized by a user, determining abnormal transaction data in the quota transaction data according to a preset abnormal transaction judgment rule, and determining user information corresponding to the abnormal transaction data;
the clustering result module is used for clustering the abnormal transaction data and the corresponding user information to obtain a user clustering result;
the similarity module is used for determining the user similarity corresponding to the user clustering result by utilizing a cosine similarity algorithm;
and the transaction limit module is used for inputting the user similarity corresponding to the user clustering result into a pre-established transaction limit model for processing to obtain the user transaction limit.
Optionally, in an embodiment of the present invention, the clustering result module is further configured to perform clustering processing on the abnormal transaction data and the corresponding user information to obtain user age clusters, user occupation clusters, and transaction amount clusters, and use the user age clusters, the user occupation clusters, and the transaction amount clusters as user clustering results.
Optionally, in an embodiment of the present invention, the similarity module includes:
the characteristic vector unit is used for determining characteristic vector data corresponding to the user clustering result according to the user clustering result;
and the similarity unit is used for determining the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster and the similarity corresponding to the transaction amount cluster by utilizing a cosine similarity algorithm according to the feature vector data, and taking the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster and the similarity corresponding to the transaction amount cluster as the user similarity corresponding to the user clustering result.
Optionally, in an embodiment of the present invention, the apparatus further includes: and the risk prompt module is used for generating risk prompt information by utilizing a preset transaction threshold and a user transaction limit and sending the risk prompt information to the user.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method when executing the program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program for executing the above method.
The invention also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above method.
According to the invention, the transaction data with abnormal occurrence is determined by collecting the limit transaction data of the user, clustering analysis and similarity calculation are carried out on the abnormal user, and the preset transaction limit model is combined, so that the transaction limit is accurately and reasonably set, and the transaction safety 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 will be briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a transaction limit determination method according to an embodiment of the invention;
FIG. 2 is a flowchart of determining user similarity according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of user age clustering according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a transaction quota determining apparatus according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a similarity module according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of the structure of a transaction limit determining apparatus according to another embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a method and a device for determining transaction limits, which can be used in the financial field and other fields.
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 obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Fig. 1 is a flowchart illustrating a transaction limit determination method according to an embodiment of the present invention, where an execution subject of the transaction limit determination method provided by the embodiment of the present invention includes, but is not limited to, a computer. According to the invention, the transaction data with abnormal occurrence is determined by collecting the limit transaction data of the user, clustering analysis and similarity calculation are carried out on the abnormal user, and the preset transaction limit model is combined, so that the transaction limit is accurately and reasonably set, and the transaction safety is improved. The method shown in fig. 1 comprises:
s1, acquiring quota transaction data authorized by a user, determining abnormal transaction data in the quota transaction data according to a preset abnormal transaction judgment rule, and determining user information corresponding to the abnormal transaction data;
s2, clustering abnormal transaction data and corresponding user information to obtain a user clustering result;
s3, determining the user similarity corresponding to the user clustering result by using a cosine similarity algorithm;
and S4, inputting the user similarity corresponding to the user clustering result into a pre-established transaction limit model for processing to obtain the user transaction limit.
In which, collect the limit transaction data of all users in the transaction with transaction limit, such as channels of mobile phone, ATM, POS, etc. The method comprises the steps of presetting abnormal transaction judgment rules, wherein the abnormal transaction judgment rules can comprise sensitive words, abnormal accounts or abnormal mobile phone numbers and the like, determining abnormal transaction data by using the abnormal transaction judgment rules, and determining user information corresponding to the abnormal transaction data, namely information of users with abnormal conditions, namely information of users with cheating conditions.
As an embodiment of the present invention, clustering abnormal transaction data and user information corresponding to the abnormal transaction data, and obtaining a user clustering result includes: and clustering abnormal transaction data and user information corresponding to the abnormal transaction data to obtain user age clusters, user occupation clusters and transaction amount clusters, and taking the user age clusters, the user occupation clusters and the transaction amount clusters as user clustering results.
The abnormal transaction data and the corresponding user information are clustered, the abnormal users are specifically clustered, and the same factors in the abnormal users, such as age range, occupation clustering, proportion of abnormal transaction amount to total deposit, and the like, are found. The obtained user clustering result can comprise user age clustering, user occupation clustering and amount clustering (the transfer amount accounts for the total deposit ratio).
Further, as shown in fig. 3, the user identity clustering module may further continue to partition, for example: [40-50],[70-80].
In this embodiment, as shown in fig. 2, determining the user similarity corresponding to the user clustering result by using a cosine similarity algorithm includes:
step S21, determining feature vector data corresponding to the user clustering result according to the user clustering result;
and S22, determining the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster and the similarity corresponding to the transaction amount cluster by utilizing a cosine similarity algorithm according to the feature vector data, and taking the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster and the similarity corresponding to the transaction amount cluster as the user similarity corresponding to the user clustering result.
And determining feature vector data corresponding to the user clustering result according to the user clustering result by using a conventional feature vector extraction method. Specifically, the feature vector data includes the same feature vector of a certain user, a certain specific feature vector, and the like.
Further, the user similarity includes similarity corresponding to user age clustering, similarity corresponding to user occupation clustering, and similarity corresponding to transaction amount clustering.
Furthermore, the similarity is calculated by using a cosine similarity algorithm, namely, the cosine similarity is used for screening users similar to the cosine similarity, and the cosine similarity formula is shown as formula (1).
Wherein, T i Representing the same feature vector, T, screened out in the abnormal user set it Abnormal user T i The T-th vector of (2), T j Are other users.
In this embodiment, the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster, and the similarity corresponding to the transaction amount cluster are input into a transaction quota model, which is an artificial intelligence model, established in advance. The transaction limit model outputs the specific amount of the user's transaction limit (the specific amount is calculated as a percentage of the maximum limit, e.g., the high level is 100% of the maximum amount in the banking system, the medium level is 80% and the low level is 60%).
The establishment process of the transaction quota model is specifically described as follows: the transaction quota model can adopt a BP network neural model, and the input is as follows: and outputting the three similarity degrees as a transaction limit. The trade quota model is established by adopting a BP neural network and a genetic algorithm, and the genetic algorithm is introduced in the aspect of optimizing the weight and the threshold of the BP neural network to construct a GA-BP neural network model. And determining a GA-BP neural network structure, determining the BP neural network structure according to the number of input and output of the network, and further determining the number of parameters needing to be optimized in a genetic algorithm. According to the kolmogorov principle, a three-layer BP neural network can sufficiently complete any mapping from n dimensions to m dimensions, only one hidden layer is needed, and the number of nodes of the hidden layer is determined by a trial and error method, so that the GA-BP neural network structure is determined.
Further, the optimal individuals output by the genetic algorithm are used as initial weights and thresholds of the BP neural network to train and learn the BP neural network. And taking the collected client information as historical data, dividing the historical data into a training set and a testing set, training the GA-BP neural network model based on historical data analysis, and verifying the prediction accuracy of the model by using a testing sample. The model is continuously and automatically optimized through a machine learning method in the using process, and the effectiveness of the model is improved.
As an embodiment of the invention, the method further comprises: and generating risk prompt information by using a preset transaction threshold and a user transaction limit, and sending the risk prompt information to the user.
When the transaction limit calculated by the transaction limit model is exceeded or a preset transaction threshold (which may be 90% of the transaction limit) is exceeded in a certain channel, the user can be reminded, specifically, for example, you have certain risk in current operation and ask you to handle carefully. Meanwhile, the air control processing is started, so that the user fills in the information of the transferred person of the transfer service, including an identity card, occupation and the like, and meanwhile, the information of the transferred person is examined, and after the examination is passed, the transaction can be carried out.
According to the invention, the transaction data with abnormal occurrence is determined by collecting the limit transaction data of the user, clustering analysis and similarity calculation are carried out on the abnormal user, and the preset transaction limit model is combined, so that the transaction limit is accurately and reasonably set, and the transaction safety is improved.
Fig. 4 is a schematic structural diagram of a transaction quota determining apparatus according to an embodiment of the present invention, where the apparatus includes:
the abnormal transaction module 10 is configured to obtain quota transaction data authorized by a user, determine abnormal transaction data in the quota transaction data according to a preset abnormal transaction determination rule, and determine user information corresponding to the abnormal transaction data;
the clustering result module 20 is used for clustering the abnormal transaction data and the corresponding user information to obtain a user clustering result;
the similarity module 30 is configured to determine a user similarity corresponding to a user clustering result by using a cosine similarity algorithm;
and the transaction limit module 40 is used for inputting the user similarity corresponding to the user clustering result into a pre-established transaction limit model for processing to obtain the user transaction limit.
As an embodiment of the present invention, the clustering result module 20 is further configured to perform clustering processing on the abnormal transaction data and the corresponding user information thereof to obtain user age clusters, user occupation clusters, and transaction amount clusters, and use the user age clusters, the user occupation clusters, and the transaction amount clusters as user clustering results.
In the present embodiment, as shown in fig. 5, the similarity module 30 includes:
the feature vector unit 31 is configured to determine feature vector data corresponding to a user clustering result according to the user clustering result;
and the similarity unit 32 is configured to determine, according to the feature vector data and by using a cosine similarity algorithm, a similarity corresponding to the user age cluster, a similarity corresponding to the user occupation cluster, and a similarity corresponding to the transaction amount cluster, and use the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster, and the similarity corresponding to the transaction amount cluster as the user similarity corresponding to the user clustering result.
As an embodiment of the present invention, as shown in fig. 6, the apparatus further includes: and the risk prompting module 50 is used for generating risk prompting information by using a preset transaction threshold value and a user transaction limit, and sending the risk prompting information to a user.
The invention also provides the transaction limit determining device based on the same application concept as the transaction limit determining method. Since the principle of solving the problem of the transaction limit determining device is similar to that of a transaction limit determining method, the implementation of the transaction limit determining device can refer to the implementation of the transaction limit determining method, and repeated details are omitted.
According to the method, the transaction data with abnormal data are determined by collecting the limit transaction data of the user, the abnormal user is subjected to cluster analysis and similarity calculation, and the transaction limit is accurately and reasonably set by combining a preset transaction limit model, so that the transaction safety is improved.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the method when executing the program.
The invention also provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the above method.
The present invention also provides a computer-readable storage medium having stored thereon a computer program for executing the above method.
As shown in fig. 7, the electronic device 600 may further include: communication module 110, input unit 120, audio processor 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in fig. 7; in addition, the electronic device 600 may also include components not shown in fig. 7, which may be referred to in the prior art.
As shown in fig. 7, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
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 principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A method of transaction limit determination, the method comprising:
acquiring quota transaction data authorized by a user, determining abnormal transaction data in the quota transaction data according to a preset abnormal transaction judgment rule, and determining user information corresponding to the abnormal transaction data;
clustering the abnormal transaction data and the corresponding user information to obtain a user clustering result;
determining the user similarity corresponding to the user clustering result by using a cosine similarity algorithm;
and inputting the user similarity corresponding to the user clustering result into a pre-established transaction limit model for processing to obtain the user transaction limit.
2. The method according to claim 1, wherein the clustering the abnormal transaction data and the corresponding user information to obtain a user clustering result comprises:
and clustering the abnormal transaction data and the corresponding user information to obtain user age clusters, user occupation clusters and transaction amount clusters, and taking the user age clusters, the user occupation clusters and the transaction amount clusters as user clustering results.
3. The method according to claim 2, wherein the determining the user similarity corresponding to the user clustering result by using a cosine similarity algorithm comprises:
determining feature vector data corresponding to the user clustering result according to the user clustering result;
and according to the feature vector data, determining the similarity corresponding to the age cluster of the user, the similarity corresponding to the occupation cluster of the user and the similarity corresponding to the transaction amount cluster by utilizing a cosine similarity algorithm, and taking the similarity corresponding to the age cluster of the user, the similarity corresponding to the occupation cluster of the user and the similarity corresponding to the transaction amount cluster as the user similarity corresponding to the user clustering result.
4. The method of claim 1, further comprising: and generating risk prompt information by using a preset transaction threshold value and the user transaction limit, and sending the risk prompt information to the user.
5. A transaction limit determination device, the device comprising:
the abnormal transaction module is used for acquiring quota transaction data authorized by a user, determining abnormal transaction data in the quota transaction data according to a preset abnormal transaction judgment rule, and determining user information corresponding to the abnormal transaction data;
the clustering result module is used for clustering the abnormal transaction data and the corresponding user information to obtain a user clustering result;
the similarity module is used for determining the user similarity corresponding to the user clustering result by utilizing a cosine similarity algorithm;
and the transaction quota module is used for inputting the user similarity corresponding to the user clustering result into a pre-established transaction quota model for processing to obtain the user transaction quota.
6. The apparatus of claim 5, wherein the clustering result module is further configured to perform clustering processing on the abnormal transaction data and the corresponding user information thereof to obtain user age clusters, user occupation clusters, and transaction amount clusters, and use the user age clusters, the user occupation clusters, and the transaction amount clusters as user clustering results.
7. The apparatus of claim 6, wherein the similarity module comprises:
the feature vector unit is used for determining feature vector data corresponding to the user clustering result according to the user clustering result;
and the similarity unit is used for determining the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster and the similarity corresponding to the transaction amount cluster by utilizing a cosine similarity algorithm according to the feature vector data, and taking the similarity corresponding to the user age cluster, the similarity corresponding to the user occupation cluster and the similarity corresponding to the transaction amount cluster as the user similarity corresponding to the user clustering result.
8. The apparatus of claim 5, further comprising: and the risk prompt module is used for generating risk prompt information by utilizing a preset transaction threshold value and the user transaction quota and sending the risk prompt information to the user.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
11. A computer program product comprising computer program/instructions, characterized in that said computer program/instructions, when executed by a processor, implement the steps of the method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210927276.9A CN115239347A (en) | 2022-08-03 | 2022-08-03 | Transaction limit determining method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210927276.9A CN115239347A (en) | 2022-08-03 | 2022-08-03 | Transaction limit determining method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115239347A true CN115239347A (en) | 2022-10-25 |
Family
ID=83677740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210927276.9A Pending CN115239347A (en) | 2022-08-03 | 2022-08-03 | Transaction limit determining method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115239347A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117455497A (en) * | 2023-11-12 | 2024-01-26 | 北京营加品牌管理有限公司 | Transaction risk detection method and device |
-
2022
- 2022-08-03 CN CN202210927276.9A patent/CN115239347A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117455497A (en) * | 2023-11-12 | 2024-01-26 | 北京营加品牌管理有限公司 | Transaction risk detection method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102239129B1 (en) | End-to-end speaker recognition using deep neural network | |
| CN110166438B (en) | Account information login method and device, computer equipment and computer storage medium | |
| CN111275546B (en) | Financial customer fraud risk identification method and device | |
| CN111932269B (en) | Equipment information processing method and device | |
| CN111582341B (en) | User abnormal operation prediction method and device | |
| CN108009058A (en) | Warping apparatus recognition methods and device and electronic equipment | |
| CN111401906A (en) | Transfer risk detection method and system | |
| CN111524526A (en) | Voiceprint recognition method and device | |
| CN111428217A (en) | Method and device for identifying cheat group, electronic equipment and computer readable storage medium | |
| CN106357411A (en) | Identity verification method and device | |
| CN112801775A (en) | Client credit evaluation method and device | |
| CN115239347A (en) | Transaction limit determining method and device | |
| CN115204901B (en) | Credit card fraud detection method, device and medium based on multi-feature fusion | |
| CN114066620A (en) | Client information processing method and device based on client portrait | |
| CN112734565B (en) | Fluidity coverage prediction method and device | |
| CN112950357B (en) | Transaction abnormal group identification method and device | |
| CN107516112A (en) | Object type recognition methods, device, equipment and storage medium | |
| CN113593579B (en) | Voiceprint recognition method and device and electronic equipment | |
| CN112766824B (en) | Data processing method, device, electronic equipment and storage medium | |
| CN115965456A (en) | Data change analysis method and device | |
| CN112820298B (en) | Voiceprint recognition method and device | |
| CN115511670A (en) | Online education method and online education platform | |
| CN114254078A (en) | Information screening method and device | |
| CN112101952B (en) | Bank suspicious transaction evaluation and data processing method and device | |
| CN112116478A (en) | Method and device for processing suspicious bank anti-money-laundering report |
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 |