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Method and system for intelligently adding and clearing bank notes for bank ATMs based on machine learning

An ATM machine and machine learning technology, applied in the field of big data analysis in the financial industry, can solve the problems of waste of funds, increase in the cost of adding banknotes, and more non-interest-bearing assets, so as to reduce waste and reduce bank operating costs.

Active Publication Date: 2019-04-12
山东省城市商业银行合作联盟有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] The amount and frequency of refilling and clearing of ATMs by existing banks are completely based on manual experience judgments, and in order to avoid cash shortages in self-service equipment, frequent refills are required, usually each time the refill will be much higher Due to the actual withdrawal demand, the amount of added banknotes is more than the actual withdrawal amount, which will lead to excessive non-interest-earning assets and waste of funds
Moreover, the increase in the number of banknotes will also lead to an increase in costs

Method used

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  • Method and system for intelligently adding and clearing bank notes for bank ATMs based on machine learning
  • Method and system for intelligently adding and clearing bank notes for bank ATMs based on machine learning
  • Method and system for intelligently adding and clearing bank notes for bank ATMs based on machine learning

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Embodiment 1

[0049] Such as figure 1 As shown, the embodiment of the present invention provides a method for intelligently adding banknotes to bank ATM machines based on machine learning, and the method includes the following steps:

[0050] S1: Wide table processing and sample splitting of bank ATM machine historical transaction data and holiday data. The realization principle and process of the whole steps are as follows:

[0051] 1) Screening, calculating, and summarizing the transaction date, card withdrawal amount of the bank, and card withdrawal amount of other banks in the original transaction data.

[0052] 2) Summarize the data of the bank's card withdrawal amount and other banks' withdrawal amount data to obtain the total daily withdrawal amount; process the daily withdrawal amount of each ATM machine and the value of the date-related dependent variable according to the transaction date and the total daily withdrawal amount.

[0053] 3) According to the annual holiday calendar,...

Embodiment 2

[0092] On the basis of the method in embodiment 1, embodiment 2 provides a method based on machine learning to intelligently add banknotes to bank ATM machines, and the method is as follows Figure 4 As shown, on the basis of the steps in Embodiment 1, the subsequent step S4 is added.

[0093] The implementation process of step S4 is: use the verification sample to generate the clearing and adding banknote strategy. The amount and date of adding and clearing banknotes need to be determined based on three factors: the total daily withdrawal amount, the cost of adding banknotes, and the coverage rate.

[0094] The principle of step S4 is as follows:

[0095] 1. ARIMA can predict the total amount of daily withdrawals in the next 7 days (that is, one week). Considering the actual situation of the business, different recharge times are preset, for example, recharge every Monday and Thursday, recharge every Tuesday and Friday, and recharge every Monday, Wednesday, and Five plus ban...

Embodiment 3

[0101] On the basis of the method in embodiment 2, embodiment 3 provides a kind of method based on machine learning to bank ATM machine intelligently clearing money, described method is as follows Figure 7 As shown, on the basis of the steps in Embodiment 2, the subsequent step S5 is added.

[0102] The process realized in step S5 is: run the model prediction every day to obtain the predicted withdrawal amount in the next 7 days, and obtain the predicted withdrawal amount based on the frequency of adding money, the number of days between intervals, and the rising ratio of the added money amount compared to the predicted withdrawal amount in the money-adding and clearing strategy. The date of the next refill and the amount of refill.

[0103] The realization of step S5 is based on the realization of step S4. Using the verification sample, a strategy of adding and clearing money is selected, and the daily withdrawal total confidence interval rises, and the weekly adding money d...

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Abstract

The invention provides a method and system for intelligently adding and clearing bank notes for bank ATMs based on machine learning. The method comprises the steps: S1, historical transaction data andholiday data of the bank ATMs are subjected to wide table processing and sample splitting; S2, modeling is conducted based on an ARIMA model of the time sequence, and model training is conducted through a training sample; and S3, after model training is completed, a predicting sample is input, and the model can output the predicting result, namely the daily withdrawal amount of the next seven days. The system comprises a wide table processing module, a sample splitting module, a model building module, a sample training module and a predicting module. On the basis of the ARIMA model of the time sequence, the future withdrawal amount is predicted through historical withdrawal data of the self-service equipment, and by combining with the cost and the coverage rate, the optimal bank note adding amount and the optimal bank note adding frequency of all the self-service equipment within the next seven days are given.

Description

technical field [0001] The present application relates to the field of big data analysis in the financial industry, and in particular to a method and system for bank ATM machine tools plus banknote clearing strategy based on machine learning big data analysis technology. Background technique [0002] The amount and frequency of refilling and clearing of ATMs by existing banks are completely based on manual experience judgments, and in order to avoid cash shortages in self-service equipment, frequent refills are required, usually each time the refill will be much higher Due to the actual withdrawal demand, the amount of added banknotes is more than the actual withdrawal amount, which will lead to excessive non-interest-earning assets and waste of funds. Moreover, the increase in the number of banknotes will also lead to an increase in cost. Contents of the invention [0003] In view of the above shortcomings, the present invention proposes a method and system for intellige...

Claims

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Application Information

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IPC IPC(8): G07D11/13
Inventor 周静方明永寇少敏王晓李霞蒋明润张利朋刘殿生
Owner 山东省城市商业银行合作联盟有限公司