CN113095722A - ATM (automatic Teller machine) banning determination method and device - Google Patents
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Abstract
The invention provides an ATM (automatic teller machine) banning determination method and device, which can be used in the technical field of artificial intelligence, and the method comprises the following steps: collecting personnel data and financial data of a plurality of ATMs; for each ATM, longitudinally aggregating personnel data and financial data of the ATM to obtain characteristic data of the ATM; for each ATM, determining tag data for the ATM based on financial data for the ATM; transversely aggregating the characteristic data of all ATMs to obtain training data; transversely aggregating the label data of all ATMs to obtain training labels; training an ATM (automatic teller machine) rejection model based on the training data and the training labels to obtain a trained ATM rejection model; and inputting the obtained personnel data and financial data of the new ATM into the trained ATM rejection model to obtain an ATM rejection analysis result. The invention can accurately judge whether the ATM should be banned.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an ATM (automatic Teller machine) banning determination method and device.
Background
With the development of mobile payment and the influence of epidemic situations, people have less and less demand for cash, and a more convenient online payment and zero-distance contact payment mode is used as a substitute. In the last years, in order to expand business and facilitate cash withdrawal of users, banks deploy a large number of ATMs in each street lane to build an ATM automatic withdrawal network. Due to the reduction of the required amount of cash, a large number of ATMs are in an idle state, not only rent, electric power and network cost are needed, but also extra personnel are needed for mechanical maintenance, and the mode greatly increases the operation cost of banks. To achieve cost reduction and efficiency enhancement, banks typically recover a portion of the ATMs based on experience. However, the accurate use condition of the ATM cannot be fully considered, and whether the ATM should be banned or not cannot be accurately evaluated, so that the banning effect is poor.
Disclosure of Invention
The embodiment of the invention provides an ATM (automatic Teller machine) banning determining method, which is used for accurately judging whether an ATM should be banned or not, and comprises the following steps:
collecting personnel data and financial data of a plurality of ATMs;
for each ATM, longitudinally aggregating personnel data and financial data of the ATM to obtain characteristic data of the ATM;
for each ATM, determining tag data for the ATM based on financial data for the ATM;
transversely aggregating the characteristic data of all ATMs to obtain training data;
transversely aggregating the label data of all ATMs to obtain training labels;
training an ATM (automatic teller machine) rejection model based on the training data and the training labels to obtain a trained ATM rejection model;
and inputting the obtained personnel data and financial data of the new ATM into the trained ATM rejection model to obtain an ATM rejection analysis result.
An embodiment of the present invention provides an ATM banning determination device, which accurately determines whether an ATM should be banned, including:
the data acquisition module is used for acquiring personnel data and financial data of a plurality of ATMs;
the characteristic data acquisition module is used for longitudinally aggregating personnel data and financial data of each ATM to acquire the characteristic data of the ATM;
a tag data determination module for determining, for each ATM, tag data for the ATM based on financial data for the ATM;
the training data acquisition module is used for transversely aggregating the characteristic data of all ATMs to acquire training data;
the training label obtaining module is used for transversely aggregating the label data of all ATMs to obtain training labels;
the training module is used for training the ATM revocation model based on the training data and the training labels to obtain a trained ATM revocation model;
and the rejection analysis module is used for inputting the personnel data and the financial data of the new ATM into the trained ATM rejection model after obtaining the personnel data and the financial data of the new ATM, and obtaining an ATM rejection analysis result.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the ATM revocation determination method is implemented.
An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program for executing the above-mentioned ATM revocation determination method.
In the embodiment of the invention, personnel data and financial data of a plurality of ATMs are collected; for each ATM, longitudinally aggregating personnel data and financial data of the ATM to obtain characteristic data of the ATM; for each ATM, determining tag data for the ATM based on financial data for the ATM; transversely aggregating the characteristic data of all ATMs to obtain training data; transversely aggregating the label data of all ATMs to obtain training labels; training an ATM (automatic teller machine) rejection model based on the training data and the training labels to obtain a trained ATM rejection model; and inputting the obtained personnel data and financial data of the new ATM into the trained ATM rejection model to obtain an ATM rejection analysis result. In the above embodiment, the accuracy of the obtained training data and training labels is higher through longitudinal aggregation and transverse aggregation, so that the accuracy of the trained ATM banning module obtained through final training is high, and whether the ATM of a certain network point should be banned can be accurately judged by using the model.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a method of ATM revocation determination in accordance with an embodiment of the present invention;
FIG. 2 is a detailed flow chart of an ATM revocation determination method in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of an ATM revocation determination apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a tag data determination module in an embodiment of the invention;
FIG. 5 is a diagram of a computer device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are used in an open-ended fashion, i.e., to mean including, but not limited to. Reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is for illustrative purposes to illustrate the implementation of the present application, and the sequence of steps is not limited and can be adjusted as needed.
Fig. 1 is a flowchart of an ATM banning determination method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
103, for each ATM, determining the label data of the ATM according to the financial data of the ATM;
and step 107, inputting the obtained personnel data and financial data of the new ATM into the trained ATM banning model to obtain an ATM banning analysis result.
In the embodiment of the invention, the accuracy of the obtained training data and the training labels is higher through longitudinal aggregation and transverse aggregation, so that the accuracy of the finally trained ATM banned module is high, and whether the ATM of a certain network point is banned or not can be accurately judged by using the model, thereby really reducing the operation cost.
In specific implementation, the ATM often exists in a cluster form at a network point, and an intelligent terminal of the ATM generally has a hardware camera, an infrared sensing device and other devices, and can sense the surrounding environment.
In one embodiment, the people data of the ATM comprises one or any combination of the number of people entering the shooting range of the ATM camera N, the number of people approaching the ATM H and the number of people operating the ATM U within a preset period.
The preset period may be determined according to actual conditions, for example, the preset period is daily. The number of people approaching the ATM may be obtained via an infrared sensing device and the number of people operating the ATM may be obtained via ATM system log transaction records.
In one embodiment, the financial data of the ATM includes one or any combination of a procedure fee earned daily, a daily average turnover P, ATM for cash deposit daily transaction amount D, a withdrawal daily transaction amount W, a transfer transaction amount E, a deposit peak value DP, a withdrawal peak value WP, a money replenishment duration, a loss fee and maintenance times.
The data may be obtained by extracting data stored in the ATM and in a banking system in communication with the ATM.
In one embodiment, for each ATM, determining tag data for the ATM based on financial data for the ATM comprises:
obtaining bank income and cost expenditure for conducting transactions through the ATM according to the financial data of the ATM;
determining that the tag data of the ATM is not banned when the bank income is greater than the cost expenditure; otherwise, determining the label data of the ATM as banned data.
In the above embodiment, the ATM has a bank income of procedure cost + counter cost earned daily.
The counter cost is the average daily turnover of the teller P × the actual operation time of the ATM (for example, the operation time is 4 hours in 24 hours, and the operation time is 4/8 days — 0.5 days).
The cost expenditure is the loss cost of the ATM every day, the operation and maintenance cost and the money supplementing cost.
Wherein, the loss cost is depreciation cost + communication cost + electricity cost + monitoring cost, and the loss cost is a fixed statistical average value;
the operation and maintenance cost is the operation and maintenance times multiplied by the fixed operation and maintenance working hour cost;
the money-replenishing expense is the money-replenishing time length multiplied by the fixed money-replenishing working hour expense.
And when the bank income is greater than the cost expenditure, determining that the label data of the ATM is in a profit state and is not banned, and otherwise, determining that the label data of the ATM is banned data in a loss state.
For each ATM, longitudinally aggregating personal data { N, H, U } and financial data { D, W, E, DP, WP } of the ATM to obtain feature data { N, H, U, D, W, E, DP, WP } of the ATM, and then transversely aggregating feature data of all ATMs to obtain training data T. And transversely aggregating the label data of all ATMs to obtain a training label L.
In the embodiment of the present invention, the ATM banning model may adopt an SVM (support vector machine, which is a supervised learning-based binary algorithm) algorithm, of course, a neural network model may also be adopted, and during specific training, 1/10 training data may be randomly selected from the training data and the label data as a test set, and the remaining training data may be used as a training set. And adjusting the ATM banned model according to the preset prediction precision, wherein the adjustment comprises the adjustment of model parameters, the pretreatment of characteristic data and the like, and the model with the maximum AUC value is selected as the finally trained ATM banned model. The AUC value is an index value for evaluating the two-classification effect, and the classification effect is better as the value is closer to 1.
After the trained ATM revocation model is obtained, inputting personnel data and financial data of any new ATM into the trained ATM revocation model to obtain an ATM revocation analysis result. And automatically initiating the internal approval process of the bank when the result of the ATM banning analysis is in need of banning, and determining the feasibility of the ATM banning after arbitrary on-site investigation and manual review by the bank.
In summary of the foregoing embodiments, fig. 2 is a detailed flowchart of an ATM revocation determination method according to an embodiment of the present invention, and as shown in fig. 2, the method includes:
and step 208, inputting the obtained personnel data and financial data of the new ATM into the trained ATM revocation model to obtain an ATM revocation analysis result.
Of course, it should be understood that other variations of the above detailed processes are possible and are intended to fall within the scope of the present invention.
In summary, in the method provided in the embodiment of the present invention, the personal data and the financial data of a plurality of ATMs are collected; for each ATM, longitudinally aggregating personnel data and financial data of the ATM to obtain characteristic data of the ATM; for each ATM, determining tag data for the ATM based on financial data for the ATM; transversely aggregating the characteristic data of all ATMs to obtain training data; transversely aggregating the label data of all ATMs to obtain training labels; training an ATM (automatic teller machine) rejection model based on the training data and the training labels to obtain a trained ATM rejection model; and inputting the obtained personnel data and financial data of the new ATM into the trained ATM rejection model to obtain an ATM rejection analysis result. In the above embodiment, the accuracy of the obtained training data and training labels is higher through longitudinal aggregation and transverse aggregation, so that the accuracy of the trained ATM banning module obtained through final training is high, and whether the ATM of a certain network point should be banned can be accurately judged by using the model.
The embodiment of the invention also provides an ATM revocation determination apparatus, the principle of which is similar to that of the ATM revocation determination method, and details are not repeated here.
Fig. 3 is a schematic diagram of an ATM revocation determination apparatus according to an embodiment of the present invention, as shown in fig. 3, the apparatus including:
the data acquisition module 301 is used for acquiring personnel data and financial data of a plurality of ATMs;
a characteristic data obtaining module 302, configured to longitudinally aggregate, for each ATM, personal data and financial data of the ATM to obtain characteristic data of the ATM;
a tag data determination module 303 for determining, for each ATM, tag data for the ATM based on financial data for the ATM;
a training data obtaining module 304, configured to transversely aggregate feature data of all ATMs to obtain training data;
a training label obtaining module 305, configured to transversely aggregate label data of all ATMs to obtain training labels;
the training module 306 is used for training the ATM revocation model based on the training data and the training labels to obtain a trained ATM revocation model;
and a banning analysis module 307, configured to input the obtained personal data and financial data of the new ATM into the trained ATM banning model to obtain an ATM banning analysis result.
In one embodiment, the people data of the ATM comprises one or any combination of the number of people entering the shooting range of the ATM camera, the number of people approaching the ATM and the number of people operating the ATM in a preset period.
In one embodiment, the financial data of the ATM includes one or any combination of a procedure fee earned daily, a daily average turnover of a teller machine at a network site, a daily transaction amount of a cash deposit at the ATM, a daily transaction amount of a withdrawal, a transfer transaction amount, a peak deposit value, a peak withdrawal value, a time length for replenishing money, a loss fee, and a maintenance and operation number.
Fig. 4 is a schematic diagram of a tag data determining module in an embodiment of the present invention, as shown in fig. 4, in an embodiment, the tag data determining module includes:
a first calculation module 401 for obtaining the bank income and cost expenditure for the transaction through the ATM based on the financial data of the ATM;
a second calculation module 402, configured to determine that the tag data of the ATM is not banned when the bank income is greater than the cost expenditure; otherwise, determining the label data of the ATM as banned data.
In summary, in the apparatus provided in the embodiment of the present invention, the personal data and the financial data of a plurality of ATMs are collected; for each ATM, longitudinally aggregating personnel data and financial data of the ATM to obtain characteristic data of the ATM; for each ATM, determining tag data for the ATM based on financial data for the ATM; transversely aggregating the characteristic data of all ATMs to obtain training data; transversely aggregating the label data of all ATMs to obtain training labels; training an ATM (automatic teller machine) rejection model based on the training data and the training labels to obtain a trained ATM rejection model; and inputting the obtained personnel data and financial data of the new ATM into the trained ATM rejection model to obtain an ATM rejection analysis result. In the above embodiment, the accuracy of the obtained training data and training labels is higher through longitudinal aggregation and transverse aggregation, so that the accuracy of the trained ATM banning module obtained through final training is high, and whether the ATM of a certain network point should be banned can be accurately judged by using the model.
An embodiment of the present application further provides a computer device, and fig. 5 is a schematic diagram of a computer device in an embodiment of the present invention, where the computer device is capable of implementing all steps in the ATM revocation determination method in the foregoing embodiment, and the computer device specifically includes the following contents:
a processor (processor)501, a memory (memory)502, a communication Interface (Communications Interface)503, and a communication bus 504;
the processor 501, the memory 502 and the communication interface 503 complete mutual communication through the communication bus 504; the communication interface 503 is used for implementing information transmission between related devices such as server-side devices, detection devices, and user-side devices;
the processor 501 is configured to call a computer program in the memory 502, and when the processor executes the computer program, the processor implements all the steps of the ATM banning determination method in the above embodiments.
An embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps in the ATM revocation determination method in the above-described embodiment, the computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements all the steps of the ATM revocation determination method in the above-described embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An ATM ban determination method, comprising:
collecting personnel data and financial data of a plurality of ATMs;
for each ATM, longitudinally aggregating personnel data and financial data of the ATM to obtain characteristic data of the ATM;
for each ATM, determining tag data for the ATM based on financial data for the ATM;
transversely aggregating the characteristic data of all ATMs to obtain training data;
transversely aggregating the label data of all ATMs to obtain training labels;
training an ATM (automatic teller machine) rejection model based on the training data and the training labels to obtain a trained ATM rejection model;
and inputting the obtained personnel data and financial data of the new ATM into the trained ATM rejection model to obtain an ATM rejection analysis result.
2. The ATM banning determination method according to claim 1, wherein the person data of the ATM includes one or any combination of a number of persons who enter a photographing range of the ATM camera for a preset period, a number of persons who approach the ATM, and a number of persons who operate the ATM.
3. The ATM banning determination method of claim 1, wherein the financial data of the ATM includes one or any combination of a procedure fee earned per day, a daily average turnover number of a teller machine at a network site, a daily transaction amount of an ATM cash deposit, a daily transaction amount of a withdrawal, a transfer transaction amount, a deposit peak value, a withdrawal peak value, a money replenishing duration, a loss fee, and a maintenance and transportation number.
4. The ATM banquet determination method as recited in claim 1 wherein for each ATM, determining label data for the ATM based on financial data for the ATM comprises:
obtaining bank income and cost expenditure for conducting transactions through the ATM according to the financial data of the ATM;
determining that the tag data of the ATM is not banned when the bank income is greater than the cost expenditure; otherwise, determining the label data of the ATM as banned data.
5. An ATM banning determination device, comprising:
the data acquisition module is used for acquiring personnel data and financial data of a plurality of ATMs;
the characteristic data acquisition module is used for longitudinally aggregating personnel data and financial data of each ATM to acquire the characteristic data of the ATM;
a tag data determination module for determining, for each ATM, tag data for the ATM based on financial data for the ATM;
the training data acquisition module is used for transversely aggregating the characteristic data of all ATMs to acquire training data;
the training label obtaining module is used for transversely aggregating the label data of all ATMs to obtain training labels;
the training module is used for training the ATM revocation model based on the training data and the training labels to obtain a trained ATM revocation model;
and the rejection analysis module is used for inputting the personnel data and the financial data of the new ATM into the trained ATM rejection model after obtaining the personnel data and the financial data of the new ATM, and obtaining an ATM rejection analysis result.
6. The ATM banning determination device according to claim 5, wherein the ATM personal data includes one or any combination of a number of persons who enter a range of the ATM camera for a preset period, a number of persons who approach the ATM, and a number of persons who operate the ATM.
7. An ATM banning determination device as claimed in claim 5 wherein the financial data of the ATM includes one or any combination of a procedure fee earned daily, a daily average turnover of a teller machine at a network site, a daily transaction amount for an ATM cash deposit, a daily transaction amount for a withdrawal day, a transaction amount for a transfer, a peak deposit value, a peak withdrawal value, a length of time for replenishing the cash, a loss fee, and a number of maintenance operations.
8. An ATM banning determination device as recited in claim 5, wherein the tag data determination module is specifically configured to:
obtaining bank income and cost expenditure for conducting transactions through the ATM according to the financial data of the ATM;
determining that the tag data of the ATM is not banned when the bank income is greater than the cost expenditure; otherwise, determining the label data of the ATM as banned data.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 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.
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| CN115331360A (en) * | 2022-08-09 | 2022-11-11 | 中国银行股份有限公司 | ATM emergency protection method and device |
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