CN114862579A - Transaction control method and system for bank intelligent counter - Google Patents

Abstract

The invention provides a transaction control method and system for a bank intelligent counter, which relates to the technical field of financial data processing and includes: determining the environmental risk vector of each ATM; determining the environmental risk vector of the intelligent counter; The environmental risk vector is used to determine the similar ATM corresponding to the smart counter; based on the transaction data of the similar ATM during the night time period, the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter is determined; Transaction data, determine the risk coefficient of the smart counter; for the smart counter, determine the positive risk parameter threshold of the smart counter at night; use the positive risk parameter threshold of the smart counter at night to control customers on the smart counter , when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, stop or reject the current customer's transaction on the smart counter.

Description

Transaction control method and system for bank intelligent counter

technical field

The invention relates to the technical field of financial data processing, in particular to a transaction control method and system for a bank intelligent counter.

Background technique

This section is intended to provide a background or context to the embodiments of the invention recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section.

Currently, bank smart counters typically only serve customers during the day, in part because of the inability to determine the risk of customer transactions during nighttime hours. In actual transactions, the transaction risks in different time periods are different, especially the transaction risks of nighttime transactions are higher than that of daytime transactions, which requires stricter risk control methods, but no relevant technical solutions have been proposed in the prior art.

To sum up, there is an urgent need for a technical solution that can overcome the above-mentioned defects and can effectively control the transaction risk during the night time period.

SUMMARY OF THE INVENTION

In order to solve the problems existing in the prior art, the present invention proposes a transaction control method and system for a bank intelligent counter.

In a first aspect of the embodiments of the present invention, a transaction control method for a bank smart counter is proposed, including:

Obtain the transaction data of multiple transaction locations corresponding to each ATM in the predetermined area during the night time period, and determine the environmental risk vector of each ATM;

Obtain the transaction data of multiple transaction locations corresponding to the smart counter during the night time period, and determine the environmental risk vector of the smart counter;

According to the environmental risk vector of the smart counter and the environmental risk vector of each ATM, determine the similar ATM corresponding to the smart counter;

Based on the transaction data of the similar ATM at night time, determine the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter;

Determine the risk factor of the smart counter according to the transaction data of the smart counter during the day;

For the smart counter, according to the risk coefficient of the smart counter, the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, determine the positive risk parameter threshold of the smart counter at night;

The customer's transactions on the smart counter are controlled by using the positive risk parameter threshold of the smart counter during the night time. When the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, the current customer is stopped or rejected. Transactions on this Smart Counter.

In a second aspect of the embodiments of the present invention, a transaction control system for a bank intelligent counter is proposed, including:

The ATM environmental risk vector determination module is used to obtain transaction data of multiple transaction locations corresponding to each ATM in the predetermined area during the night time period, and to determine the environmental risk vector of each ATM;

The intelligent counter environmental risk vector determination module is used to obtain transaction data of multiple transaction locations corresponding to the intelligent counter in the night time period, and determine the environmental risk vector of the intelligent counter;

A similar ATM determination module is used to determine the similar ATM corresponding to the smart counter according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM;

The corresponding relationship determination module is used to determine the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter based on the transaction data of the similar ATM during the night time;

a risk coefficient determination module, used for determining the risk coefficient of the smart counter according to the transaction data of the smart counter during the daytime;

The positive risk parameter threshold determination module is used for the smart counter to determine the positive risk of the smart counter during night time according to the risk coefficient of the smart counter and the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter parameter threshold;

The transaction control module is used to control the customer's transaction on the smart counter by using the positive risk parameter threshold of the smart counter at night, wherein, when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, Stop or reject the current client's transactions on this smart counter.

In a third aspect of the embodiments of the present invention, a computer device is proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implements a bank when executing the computer program The transaction control method of the smart counter.

In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, a transaction control method for a bank smart counter is implemented.

In a fifth aspect of the embodiments of the present invention, a computer program product is proposed, the computer program product includes a computer program, and when the computer program is executed by a processor, a transaction control method for a bank smart counter is implemented.

The transaction control method and system for a bank intelligent counter provided by the present invention determine the environmental risk vector of each ATM by acquiring transaction data of multiple transaction locations corresponding to each ATM in a predetermined area during the night time period; Determine the environmental risk vector of the smart counter based on the transaction data of each transaction location at night time; determine the similar ATM corresponding to the smart counter according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM; The corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter is determined according to the transaction data of the time period; the risk coefficient of the smart counter is determined according to the transaction data of the smart counter during the daytime period; for the smart counter, according to the smart counter The risk coefficient of the counter, the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, determine the positive risk parameter threshold of the smart counter at night; use the positive risk parameter threshold of the smart counter at night to control customers In the transaction on the smart counter, when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, the current customer's transaction on the smart counter is stopped or rejected. The present invention analyzes the relevant data of the ATM. Set the positive risk parameter threshold of smart counters at night time, so as to control customer transactions at night time, and provide customers with the night service mode of bank smart counters, which greatly facilitates customer business processing, can effectively control risks, and ensure customer transactions. It can improve the customer experience and effectively prevent risks.

Description of drawings

In order to explain the technical solutions of the embodiments of the present application more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. For those of ordinary skill, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a schematic flowchart of a transaction control method for a bank smart counter according to an embodiment of the present invention.

FIG. 2 is a schematic diagram of a specific flow of determining the environmental risk vector of each ATM according to a specific embodiment of the present invention.

FIG. 3 is a schematic diagram of a specific flow of determining an environmental risk vector of a smart counter according to a specific embodiment of the present invention.

4 is a schematic diagram of a specific flow of determining a similar ATM corresponding to a smart counter according to a specific embodiment of the present invention.

FIG. 5 is a schematic diagram of a specific flow of determining the corresponding relationship between the forward risk parameter threshold corresponding to the smart counter and the risk coefficient according to a specific embodiment of the present invention.

FIG. 6 is a schematic diagram of a specific flow of determining a risk coefficient of a smart counter according to a specific embodiment of the present invention.

FIG. 7 is a schematic diagram of a specific flow of determining a positive risk parameter threshold of a smart counter at night time according to a specific embodiment of the present invention.

FIG. 8 is a schematic diagram of the structure of a transaction control system of a bank smart counter according to an embodiment of the present invention.

FIG. 9 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

Detailed ways

The principles and spirit of the present invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are only given for those skilled in the art to better understand and implement the present invention, but not to limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

As will be appreciated by those skilled in the art, embodiments of the present invention may be implemented as a system, apparatus, device, method or computer program product. Accordingly, the present disclosure may be embodied in entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

According to the embodiments of the present invention, a transaction control method and system of a bank intelligent counter are proposed, which relate to the technical field of financial data processing.

The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

FIG. 1 is a schematic flowchart of a transaction control method for a bank smart counter according to an embodiment of the present invention. As shown in Figure 1, the method includes:

S1, obtain the transaction data of multiple transaction locations corresponding to each ATM in the predetermined area during the night time period, and determine the environmental risk vector of each ATM;

S2, acquiring transaction data of multiple transaction locations corresponding to the smart counter during the night time period, and determining the environmental risk vector of the smart counter;

S3, according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM, determine the similar ATM corresponding to the smart counter;

S4, based on the transaction data of the similar ATM during the night time period, determine the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter;

S5, determining the risk coefficient of the smart counter according to the transaction data of the smart counter during the daytime;

S6, for the smart counter, according to the risk coefficient of the smart counter, the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, determine the positive risk parameter threshold of the smart counter in the night time period;

S7, using the positive risk parameter threshold of the smart counter at night time to control the customer's transaction on the smart counter, wherein, when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, stop or reject the current The customer's transaction on this smart counter.

By analyzing the relevant data of the ATM, the present invention sets the positive risk parameter threshold of the intelligent counter in the night time period, thereby controlling the customer transaction in the night time period, providing the customer with the night service mode of the bank intelligent counter, which greatly facilitates the customer's business processing, and can Effectively control risks, ensure the security of customer transactions, and realize digital risk control of banks, which can not only improve customer experience, but also effectively prevent risks.

In order to explain more clearly the above-mentioned transaction control method of the bank's smart counter, each step will be described in detail below.

In S1, referring to FIG. 2, the transaction data of multiple transaction locations corresponding to each ATM in the predetermined area during the night time period are obtained, and the specific method for determining the environmental risk vector of each ATM is:

S101, obtain the transaction location of each payment transaction from the payment transaction data of the bank, and form all the payment transaction locations into a transaction location set; wherein, if there is no transaction location in the payment transaction data, the transaction in the payment transaction data is used. The address of the counterparty as the transaction location of the payment transaction data;

S102, for each ATM, use a plurality of transaction locations within the first distance range of the ATM in the transaction location set as a plurality of transaction locations corresponding to the ATM;

S103, determining the risk coefficient of the ATM corresponding to each risk type according to the transaction data of the plurality of transaction locations during the night time period;

S104, taking the risk type with the risk coefficient greater than the first threshold as the nighttime risk type of the ATM, and setting the environmental risk vector of the ATM, wherein each component of the vector corresponds to a risk type, if the risk type corresponding to the component is the The nighttime risk type of the ATM, the value of the component is set to the risk coefficient of the nighttime risk type corresponding to the component corresponding to the ATM, otherwise, the value of the component is set to 0.

In S2, referring to FIG. 3, the transaction data of multiple transaction locations corresponding to the smart counter during the night time period are obtained, and the specific method for determining the environmental risk vector of the smart counter is:

S201, using a plurality of transaction locations within the second distance range of the smart counter in the collection of transaction locations as a plurality of transaction locations corresponding to the smart counter;

S202, determining the risk coefficient of the smart counter corresponding to each risk type according to the transaction data of the multiple transaction locations during the night time period;

S203, taking the risk type with the risk coefficient greater than the second threshold as the nighttime risk type of the smart counter, and setting the environmental risk vector of the smart counter, wherein each component of the vector corresponds to a risk type, if the risk type corresponding to the component is the nighttime risk type of the smart counter, the value of this component is set to the risk coefficient of the nighttime risk type corresponding to the component corresponding to the smart counter, otherwise, the value of this component is set to 0.

In S3, referring to FIG. 4, according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM, the specific method for determining the similar ATM corresponding to the smart counter is:

S301, selecting multiple ATMs within a third distance range of the smart counter;

S302, for each ATM selected, determine the difference between the environmental risk vector of the ATM and the environmental risk vector of the smart counter, and determine the difference as the vector difference corresponding to the ATM;

S303, determine the partial order of the ATM according to the corresponding vector difference of the selected multiple ATMs, wherein, the partial order is used to determine whether the first ATM is better than the second ATM in any two ATMs in the selected multiple ATMs;

S304, according to the partial order of the ATM, determine the maximum ATM among the selected multiple ATMs, wherein the maximum ATM is the maximum element of the partial order;

S305, taking the largest ATM among the selected multiple ATMs as the similar ATM of the smart counter.

In one embodiment, (S303) according to the vector difference corresponding to the selected multiple ATMs, a method for determining the partial order of the ATM is:

When determining the partial order of ATMs, for any two ATMs in the selected multiple ATMs, if for each component, the absolute value of the vector difference corresponding to the first ATM of the two ATMs in the value of the component is less than or equal to If the vector difference corresponding to the second ATM of the two ATMs is the absolute value of the value of the component, it is determined that the first ATM is better than the second ATM.

In another embodiment, (S303) according to the vector difference corresponding to the selected multiple ATMs, another method for determining the partial order of the ATM is:

S3031, determine the intersection of the transaction set corresponding to the ATM and the transaction set corresponding to the smart counter;

S3032: Acquire transaction data of the smart counter within a preset time range, and determine a transaction vector of the smart counter, wherein each component of the transaction vector corresponds to the transactions included in the intersection, and the value of each component is equal to The number of transactions corresponding to the component in the transaction data of the smart counter;

S3033, for each selected ATM, obtain transaction data of the ATM within a preset time range, and determine a transaction vector of the ATM, wherein each component of the transaction vector corresponds to the transaction included in the intersection, and each The value of each component is equal to the number of transactions corresponding to the component in the transaction data of the ATM;

S3034, for each selected ATM, determine that the transaction distance corresponding to the ATM is the distance between the transaction vector of the ATM and the transaction vector of the smart counter, and determine that the risk distance corresponding to the ATM is the modulo length of the vector difference corresponding to the ATM ;

The calculation method of the modulo length of the vector is: the square root of the sum of the squares of each component of the vector.

S3035, when determining the partial order of the ATMs, for any two ATMs in the selected multiple ATMs, if the transaction distance corresponding to the first ATM of the two ATMs is less than or equal to the transaction distance corresponding to the second ATM of the two ATMs , and the risk distance corresponding to the first ATM of the two ATMs is less than or equal to the risk distance corresponding to the second ATM of the two ATMs, then it is determined that the first ATM is superior to the second ATM.

It should be noted that the maximal element of the partial order is in the set corresponding to the partial order, and there is no other element superior to the maximal element. The maximal ATM among the selected multiple ATMs is the other ATM that does not exist, so that the other ATM is superior to the maximal ATM.

In one embodiment, (S304), according to the partial order of the ATM, determine the maximum ATM among the selected multiple ATMs, including:

Initialize the partial order maximum value corresponding to each selected ATM to "possible", and initialize the partial order comparison value corresponding to each selected ATM to "yes";

Sort the selected multiple ATMs in ascending order of the modulo lengths of the corresponding vector differences;

Perform the following steps for each selected ATM in sequence until all the selected ATMs have completed the following steps:

For each ATM selected, if the partial order maximum value corresponding to the ATM is not "possible", then the ATM to be compared corresponding to the ATM is set to be empty; otherwise, the ATM to be compared corresponding to the ATM is set to the selected multiple ATMs. Other ATMs in the ATM other than the ATM and the corresponding partial order comparison value is "Yes";

For each ATM to be compared corresponding to the ATM, compare the ATM with the ATM to be compared according to the partial order of the ATM: if the ATM to be compared is better than the ATM, set the maximum value of the partial order corresponding to the ATM to No ; If the ATM to be compared is better than the ATM to be compared, then the partial order maximum value corresponding to the ATM to be compared is set to No, and the ATM to be compared is determined as the secondary ATM of the ATM; if the ATM to be compared is not superior If the ATM is not better than the ATM to be compared, the partial order maxima corresponding to the two ATMs remain unchanged;

If it is determined that all the ATMs to be compared corresponding to the ATM are not better than the ATM, then determine that the ATM is the largest ATM among the selected multiple ATMs, and update the partial order comparison values of all the secondary ATMs of the ATM to No .

In S4, referring to FIG. 5, based on the transaction data of the similar ATM at night time, the specific method for determining the corresponding relationship between the forward risk parameter threshold and the risk coefficient corresponding to the smart counter is:

S401, according to the transaction data of the similar ATM in the night time period, for different positive risk parameter thresholds, screen out the positive risk parameter from the transaction data including the positive risk parameter of the similar ATM in the night time period that the positive risk parameter is smaller than the positive risk The transaction data of the parameter threshold, determine the proportion of risk data in the screened transaction data, use the proportion as the risk coefficient corresponding to the positive risk parameter threshold, and establish the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM ;

S402, the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM is set as the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter.

It should be noted that the positive risk parameter is a transaction parameter. The larger the positive risk parameter, the greater the risk, such as transaction limit, transaction waiting time, etc.

In S5, referring to FIG. 6, according to the transaction data of the smart counter during the daytime, the specific method for determining the risk coefficient of the smart counter is:

S501, dividing the transaction data of the smart counter during the daytime into a plurality of sub-transaction data in chronological order, so that the transaction quantity corresponding to each sub-transaction data is greater than a transaction volume threshold;

S502, for each sub-transaction data, determine the risk coefficient corresponding to the sub-transaction data as the proportion of transaction data involving risk in the sub-transaction data;

S503: Determine the risk coefficient r of the smart counter as the mean value of the risk coefficients corresponding to all the obtained sub-transaction data.

In one embodiment, after the risk coefficient of the smart counter is obtained, the accuracy of the risk coefficient can also be evaluated, and the specific method is as follows:

Based on the obtained risk coefficients corresponding to all sub-transaction data (that is, multiple samples of the same distribution, so that the variance of the distribution can be determined), determine the variance σ of the risk coefficient;

Set the maximum value ε of the acceptable risk coefficient error;

其中，n是所有子交易数据的个数。Then the lower bound of the probability corresponding to the risk coefficient of the smart counter (the lower bound of the probability that the error of the risk coefficient r of the smart counter determined by the mean value in the above embodiment is less than ε) is:

Among them, n is the number of all sub-transaction data.

)，则可以修正该智能柜台的风险系数，具体方法为：In one embodiment, if the lower bound P of the probability that the acceptable risk coefficient error is less than ε is greater than the probability lower bound corresponding to the risk coefficient of the smart counter determined above (that is,

), the risk coefficient of the smart counter can be corrected by:

Based on the lower bound P, the number threshold is determined as

The following steps are performed in a loop until the quantity of all sub-transaction data obtained is greater than the quantity threshold:

The transaction data of a plurality of similar smart counters of the smart counter is divided into a plurality of new sub-transaction data according to the chronological order, so that the number of transactions corresponding to each new sub-transaction data is greater than transaction volume threshold;

For each sub-transaction data, determine the risk coefficient corresponding to the sub-transaction data as the proportion of the transaction data involving risk in the sub-transaction data;

The risk coefficient of the smart counter is determined as the mean value of the risk coefficients corresponding to all the obtained sub-transaction data.

Wherein, the method for determining the similar smart counters of the smart counter may be: determining the distance function corresponding to the smart counter, the distance function can determine the distance between any two smart counters, specifically, the independent variable of the distance function is two smart counters , the corresponding function value is the distance between the environmental risk vectors of the two smart counters; multiple smart counters whose distances from the smart counters are less than the specified threshold are determined as similar smart counters to the smart counters.

In S6, referring to FIG. 7, for the smart counter, according to the risk coefficient of the smart counter, the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter, determine the positive risk parameter threshold of the smart counter during the night time period The specific method is:

S601, from the risk coefficients included in the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, find the maximum risk coefficient that is less than or equal to the risk coefficient of the smart counter, and set the corresponding maximum risk coefficient in the corresponding relationship Positive risk parameter threshold, as the reference positive risk parameter threshold of the smart counter at night;

S602: Adjust the positive risk parameter threshold of the smart counter during the night time to the minimum value between the positive risk parameter threshold of the smart counter during the daytime and the reference positive risk parameter threshold.

In S7, the customer's transaction on the smart counter is controlled by using the positive risk parameter threshold of the smart counter at night time;

Wherein, when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, stop or reject the current customer's transaction on the smart counter.

It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and drawings, this does not require or imply that these operations must be performed in this specific order, or that all shown must be performed. operation to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined to be performed as one step, and/or one step may be decomposed into multiple steps to be performed.

After introducing the method of the exemplary embodiment of the present invention, next, referring to FIG. 8 , the transaction control system of the bank intelligent counter according to the exemplary embodiment of the present invention will be introduced.

For the implementation of the transaction control system of the bank's intelligent counter, reference may be made to the implementation of the above-mentioned method, and the repetition will not be repeated. The term "module" or "unit" used below may be a combination of software and/or hardware that implements predetermined functions. Although the apparatus described in the following embodiments is preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.

Based on the same inventive concept, the present invention also proposes a transaction control system for an intelligent bank counter, as shown in FIG. 8 , the system includes:

The ATM environmental risk vector determination module 810 is used for acquiring transaction data of multiple transaction locations corresponding to each ATM in the predetermined area during the night time period, and determining the environmental risk vector of each ATM;

The smart counter environmental risk vector determination module 820 is used for acquiring transaction data of multiple transaction locations corresponding to the smart counter in the night time period, and determining the environmental risk vector of the smart counter;

The similar ATM determination module 830 is used for determining the similar ATM corresponding to the smart counter according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM;

Correspondence determination module 840, configured to determine the corresponding relationship between the forward risk parameter threshold and the risk coefficient corresponding to the smart counter based on the transaction data of the similar ATM at night time;

a risk coefficient determination module 850, configured to determine the risk coefficient of the smart counter according to the transaction data of the smart counter during the daytime;

The positive risk parameter threshold determination module 860 is used for the smart counter, according to the risk coefficient of the smart counter, the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter, to determine the smart counter in the night time period. Risk parameter thresholds;

The transaction control module 870 is used to control the customer's transaction on the smart counter by using the positive risk parameter threshold of the smart counter at night, wherein, when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold , stop or reject the current customer's transaction on the smart counter.

In one embodiment, the ATM environmental risk vector determination module is specifically used for:

From the payment transaction data of the bank, the transaction location of each payment transaction is obtained, and all the payment transaction locations are formed into a transaction location set; wherein, if there is no transaction location in the payment transaction data, the counterparty in the payment transaction data is used. address as the transaction location of the payment transaction data;

For each ATM, use a plurality of transaction locations within the first distance range of the ATM in the transaction location set as the plurality of transaction locations corresponding to the ATM;

Determine the risk coefficient of the ATM corresponding to each risk type according to the transaction data of the multiple transaction locations during the night time;

Taking the risk type with the risk coefficient greater than the first threshold as the nighttime risk type of the ATM, and setting the environmental risk vector of the ATM, where each component of the vector corresponds to a risk type, if the risk type corresponding to the component is the risk type of the ATM nighttime risk type, the value of this component is set to the risk coefficient of the nighttime risk type corresponding to this component corresponding to the ATM, otherwise, the value of this component is set to 0.

In one embodiment, the intelligent counter environment risk vector determination module is specifically used for:

Using the plurality of transaction locations within the second distance range of the smart counter in the transaction location set as the plurality of transaction locations corresponding to the smart counter;

Determine the risk coefficient of the smart counter corresponding to each risk type according to the transaction data of the multiple transaction locations during the night time;

The risk type with the risk coefficient greater than the second threshold is used as the nighttime risk type of the smart counter, and the environmental risk vector of the smart counter is set, wherein each component of the vector corresponds to a risk type, if the risk type corresponding to the component is this The nighttime risk type of the smart counter, the value of this component is set to the risk coefficient of the nighttime risk type corresponding to the component corresponding to the smart counter, otherwise, the value of this component is set to 0.

In one embodiment, the similar ATM determination module is specifically used for:

select multiple ATMs within the third distance range of the smart counter;

For each ATM selected, determine the difference between the environmental risk vector of the ATM and the environmental risk vector of the smart counter, and determine the difference as the vector difference corresponding to the ATM;

Determine the partial order of the ATM according to the corresponding vector differences of the selected multiple ATMs, wherein the partial order is used to determine whether the first ATM is better than the second ATM in any two ATMs in the selected multiple ATMs;

According to the partial order of the ATM, determine the maximum ATM among the selected multiple ATMs, wherein the maximum ATM is the maximum element of the partial order;

The largest ATM among the selected multiple ATMs is used as the similar ATM of the intelligent counter.

In one embodiment, the similar ATM determination module is specifically used for:

When determining the partial order of ATMs, for any two ATMs in the selected multiple ATMs, if for each component, the absolute value of the vector difference corresponding to the first ATM of the two ATMs in the value of the component is less than or equal to If the vector difference corresponding to the second ATM of the two ATMs is the absolute value of the value of the component, it is determined that the first ATM is better than the second ATM.

In one embodiment, the similar ATM determination module is specifically used for:

Determine the intersection of the transaction set corresponding to the ATM and the transaction set corresponding to the smart counter;

Obtain the transaction data of the smart counter within a preset time range, and determine the transaction vector of the smart counter, wherein each component of the transaction vector corresponds to the transactions included in the intersection, and the value of each component is equal to the smart counter The number of transactions corresponding to this component in the transaction data of the counter;

For each selected ATM, acquire transaction data of the ATM within a preset time range, and determine a transaction vector of the ATM, wherein each component of the transaction vector corresponds to the transactions included in the intersection, and each component The value of is equal to the number of transactions corresponding to this component in the transaction data of the ATM;

For each ATM selected, determine that the transaction distance corresponding to the ATM is the distance between the transaction vector of the ATM and the transaction vector of the smart counter, and determine that the risk distance corresponding to the ATM is the modulo length of the vector difference corresponding to the ATM;

When determining the partial order of ATMs, for any two ATMs in the selected multiple ATMs, if the transaction distance corresponding to the first ATM of the two ATMs is less than or equal to the transaction distance corresponding to the second ATM of the two ATMs, and If the risk distance corresponding to the first ATM of the two ATMs is less than or equal to the risk distance corresponding to the second ATM of the two ATMs, it is determined that the first ATM is better than the second ATM.

In one embodiment, the corresponding relationship determination module is specifically used for:

According to the transaction data of the similar ATM during the night time, for different positive risk parameter thresholds, the positive risk parameter is selected from the transaction data containing the positive risk parameter of the similar ATM during the night time, and the positive risk parameter is smaller than the positive risk parameter threshold. determine the proportion of risk data in the screened transaction data, use the proportion as the risk coefficient corresponding to the positive risk parameter threshold, and establish the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM;

The corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM is set as the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter.

In one embodiment, the positive risk parameter threshold determination module is specifically used for:

From the risk coefficients included in the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, find the maximum risk coefficient that is less than or equal to the risk coefficient of the smart counter, and assign the positive value corresponding to the maximum risk coefficient in the corresponding relationship. The risk parameter threshold, as the reference positive risk parameter threshold of the smart counter at night;

The positive risk parameter threshold of the smart counter in the night time period is adjusted to the minimum value between the positive risk parameter threshold of the smart counter in the daytime period and the reference positive risk parameter threshold.

It should be noted that although several modules of the transaction control system of the bank smart counter are mentioned in the above detailed description, this division is merely exemplary and not mandatory. Indeed, in accordance with embodiments of the present invention, the features and functions of two or more modules described above may be embodied in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules to be embodied.

Based on the foregoing inventive concept, as shown in FIG. 9 , the present invention further provides a computer device 900, including a memory 910, a processor 920, and a computer program 930 stored in the memory 910 and running on the processor 920, the When the processor 920 executes the computer program 930, the aforementioned transaction control method of the bank smart counter is implemented.

Based on the foregoing inventive concept, the present invention proposes a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, implements the aforementioned transaction control method for a bank smart counter.

Based on the foregoing inventive concept, the present invention proposes a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, implements a transaction control method for a bank smart counter.

The transaction control method and system for a bank intelligent counter provided by the present invention determine the environmental risk vector of each ATM by acquiring transaction data of multiple transaction locations corresponding to each ATM in a predetermined area during the night time period; Determine the environmental risk vector of the smart counter based on the transaction data of each transaction location at night time; determine the similar ATM corresponding to the smart counter according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM; The corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter is determined based on the transaction data of the time period; the risk coefficient of the smart counter is determined according to the transaction data of the smart counter during the daytime period; for the smart counter, according to the smart counter The risk coefficient of the counter, the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, determine the positive risk parameter threshold of the smart counter at night time; use the positive risk parameter threshold of the smart counter at night time to control customers In the transaction on the smart counter, when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, the current customer's transaction on the smart counter is stopped or rejected. The present invention analyzes the relevant data of the ATM. Set the positive risk parameter threshold of smart counters at night time, so as to control customer transactions at night time, and provide customers with the night service mode of bank smart counters, which greatly facilitates customer business processing, can effectively control risks, and ensure customer transactions. It can improve the customer experience and effectively prevent risks.

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, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods and computer program products according to embodiments of the invention. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Finally, it should be noted that the above-mentioned embodiments are only specific implementations of the present invention, and are used to illustrate the technical solutions of the present invention, but not to limit them. The protection scope of the present invention is not limited thereto, although referring to the foregoing The embodiment has been described in detail the present invention, those of ordinary skill in the art should understand: any person skilled in the art who is familiar with the technical field within the technical scope disclosed by the present invention can still modify the technical solutions described in the foregoing embodiments. Or can easily think of changes, or equivalently replace some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be covered in the present invention. within the scope of protection. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (19)

1. a transaction control method of a bank intelligent counter, is characterized in that, comprises: Obtain the transaction data of multiple transaction locations corresponding to each ATM in the predetermined area during the night time period, and determine the environmental risk vector of each ATM; Obtain the transaction data of multiple transaction locations corresponding to the smart counter during the night time period, and determine the environmental risk vector of the smart counter; According to the environmental risk vector of the smart counter and the environmental risk vector of each ATM, determine the similar ATM corresponding to the smart counter; Based on the transaction data of the similar ATM at night time, determine the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter; Determine the risk factor of the smart counter according to the transaction data of the smart counter during the day; For the smart counter, according to the risk coefficient of the smart counter, the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, determine the positive risk parameter threshold of the smart counter at night; The customer's transactions on the smart counter are controlled by using the positive risk parameter threshold of the smart counter during the night time. When the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, the current customer is stopped or rejected. Transactions on this Smart Counter. 2. The method according to claim 1, characterized in that, acquiring transaction data of multiple transaction locations corresponding to each ATM in the predetermined area at night time, and determining the environmental risk vector of each ATM, comprising: From the payment transaction data of the bank, the transaction location of each payment transaction is obtained, and all the payment transaction locations are formed into a transaction location set; wherein, if there is no transaction location in the payment transaction data, the counterparty in the payment transaction data is used. address as the transaction location of the payment transaction data; For each ATM, use a plurality of transaction locations within the first distance range of the ATM in the transaction location set as the plurality of transaction locations corresponding to the ATM; Determine the risk coefficient of the ATM corresponding to each risk type according to the transaction data of the multiple transaction locations at night; The risk type with the risk coefficient greater than the first threshold is used as the nighttime risk type of the ATM, and the environmental risk vector of the ATM is set, wherein each component of the vector corresponds to a risk type, if the risk type corresponding to the component is the risk type of the ATM. nighttime risk type, the value of this component is set as the risk coefficient of the ATM corresponding to the nighttime risk type corresponding to this component, otherwise, the value of this component is set to 0. 3. The method according to claim 1, characterized in that, acquiring transaction data of multiple transaction locations corresponding to the smart counter at night time, and determining the environmental risk vector of the smart counter, comprising: Using the plurality of transaction locations within the second distance range of the smart counter in the transaction location set as the plurality of transaction locations corresponding to the smart counter; Determine the risk coefficient of the smart counter corresponding to each risk type according to the transaction data of the multiple transaction locations during the night time; The risk type with the risk coefficient greater than the second threshold is used as the nighttime risk type of the smart counter, and the environmental risk vector of the smart counter is set, wherein each component of the vector corresponds to a risk type, if the risk type corresponding to the component is this The nighttime risk type of the smart counter, the value of this component is set to the risk coefficient of the nighttime risk type corresponding to the component corresponding to the smart counter, otherwise, the value of this component is set to 0. 4. The method according to claim 1, wherein, according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM, determining the similar ATM corresponding to the smart counter, comprising: select multiple ATMs within the third distance range of the smart counter; For each ATM selected, determine the difference between the environmental risk vector of the ATM and the environmental risk vector of the smart counter, and determine the difference as the vector difference corresponding to the ATM; Determine the partial order of the ATM according to the corresponding vector differences of the selected multiple ATMs, wherein the partial order is used to determine whether the first ATM is better than the second ATM in any two ATMs in the selected multiple ATMs; According to the partial order of the ATM, determine the maximum ATM among the selected multiple ATMs, wherein the maximum ATM is the maximum element of the partial order; The largest ATM among the selected multiple ATMs is used as the similar ATM of the intelligent counter. 5. method according to claim 4 is characterized in that, according to the vector difference corresponding to a plurality of ATMs selected, determine the partial order of ATM, comprising: When determining the partial order of ATMs, for any two ATMs in the selected multiple ATMs, if for each component, the absolute value of the vector difference corresponding to the first ATM of the two ATMs in the value of the component is less than or equal to If the vector difference corresponding to the second ATM of the two ATMs is the absolute value of the value of the component, it is determined that the first ATM is better than the second ATM. 6. method according to claim 4 is characterized in that, according to the corresponding vector difference of a plurality of ATMs selected, determine the partial order of ATM, comprising: Determine the intersection of the transaction set corresponding to the ATM and the transaction set corresponding to the smart counter; Obtain the transaction data of the smart counter within a preset time range, and determine the transaction vector of the smart counter, wherein each component of the transaction vector corresponds to the transactions included in the intersection, and the value of each component is equal to the smart counter The number of transactions corresponding to this component in the transaction data of the counter; For each selected ATM, acquire transaction data of the ATM within a preset time range, and determine a transaction vector of the ATM, wherein each component of the transaction vector corresponds to the transactions included in the intersection, and each component The value of is equal to the number of transactions corresponding to this component in the transaction data of the ATM; For each ATM selected, determine that the transaction distance corresponding to the ATM is the distance between the transaction vector of the ATM and the transaction vector of the smart counter, and determine that the risk distance corresponding to the ATM is the modulo length of the vector difference corresponding to the ATM; When determining the partial order of ATMs, for any two ATMs in the selected multiple ATMs, if the transaction distance corresponding to the first ATM of the two ATMs is less than or equal to the transaction distance corresponding to the second ATM of the two ATMs, and If the risk distance corresponding to the first ATM of the two ATMs is less than or equal to the risk distance corresponding to the second ATM of the two ATMs, it is determined that the first ATM is better than the second ATM. 7 . The method according to claim 1 , wherein, based on the transaction data of the similar ATM during night time, determining the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter, comprising: 8 . According to the transaction data of the similar ATM during the night time, for different positive risk parameter thresholds, the positive risk parameter is selected from the transaction data containing the positive risk parameter of the similar ATM during the night time, and the positive risk parameter is smaller than the positive risk parameter threshold. determine the proportion of risk data in the screened transaction data, use the proportion as the risk coefficient corresponding to the positive risk parameter threshold, and establish the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM; The corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM is set as the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter. 8 . The method according to claim 1 , wherein, for the smart counter, according to the risk coefficient of the smart counter, the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter, it is determined that the smart counter is at night. 9 . Positive risk parameter thresholds for the time period, including: From the risk coefficients included in the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, find the maximum risk coefficient that is less than or equal to the risk coefficient of the smart counter, and assign the positive value corresponding to the maximum risk coefficient in the corresponding relationship. The risk parameter threshold, as the reference positive risk parameter threshold of the smart counter at night; The positive risk parameter threshold of the smart counter in the night time period is adjusted to the minimum value between the positive risk parameter threshold of the smart counter in the daytime period and the reference positive risk parameter threshold. 9. A transaction control system for a bank intelligent counter, characterized in that, comprising: The ATM environmental risk vector determination module is used to obtain transaction data of multiple transaction locations corresponding to each ATM in the predetermined area during the night time period, and to determine the environmental risk vector of each ATM; The intelligent counter environmental risk vector determination module is used to obtain transaction data of multiple transaction locations corresponding to the intelligent counter in the night time period, and determine the environmental risk vector of the intelligent counter; A similar ATM determination module is used to determine the similar ATM corresponding to the smart counter according to the environmental risk vector of the smart counter and the environmental risk vector of each ATM; The corresponding relationship determination module is used to determine the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter based on the transaction data of the similar ATM during the night time; a risk coefficient determination module, used for determining the risk coefficient of the smart counter according to the transaction data of the smart counter during the daytime; The positive risk parameter threshold determination module is used for the smart counter to determine the positive risk of the smart counter during night time according to the risk coefficient of the smart counter and the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter parameter threshold; The transaction control module is used to control the customer's transaction on the smart counter by using the positive risk parameter threshold of the smart counter at night, wherein, when the value corresponding to the transaction parameter in the transaction is greater than the positive risk parameter threshold, Stop or reject the current client's transactions on this smart counter. 10. The system according to claim 9, wherein the ATM environment risk vector determination module is specifically used for: From the payment transaction data of the bank, the transaction location of each payment transaction is obtained, and all the payment transaction locations are formed into a transaction location set; wherein, if there is no transaction location in the payment transaction data, the counterparty in the payment transaction data is used. address as the transaction location of the payment transaction data; For each ATM, use a plurality of transaction locations within the first distance range of the ATM in the transaction location set as the plurality of transaction locations corresponding to the ATM; Determine the risk coefficient of the ATM corresponding to each risk type according to the transaction data of the multiple transaction locations at night; The risk type with the risk coefficient greater than the first threshold is used as the nighttime risk type of the ATM, and the environmental risk vector of the ATM is set, wherein each component of the vector corresponds to a risk type, if the risk type corresponding to the component is the risk type of the ATM. nighttime risk type, the value of this component is set as the risk coefficient of the ATM corresponding to the nighttime risk type corresponding to this component, otherwise, the value of this component is set to 0. 11. The system according to claim 9, wherein the intelligent counter environment risk vector determination module is specifically used for: Using the plurality of transaction locations within the second distance range of the smart counter in the transaction location set as the plurality of transaction locations corresponding to the smart counter; Determine the risk coefficient of the smart counter corresponding to each risk type according to the transaction data of the multiple transaction locations during the night time; The risk type with the risk coefficient greater than the second threshold is used as the nighttime risk type of the smart counter, and the environmental risk vector of the smart counter is set, wherein each component of the vector corresponds to a risk type, if the risk type corresponding to the component is this The nighttime risk type of the smart counter, the value of this component is set to the risk coefficient of the nighttime risk type corresponding to the component corresponding to the smart counter, otherwise, the value of this component is set to 0. 12. system according to claim 9, is characterized in that, similar ATM determines that module is specially used for: select multiple ATMs within the third distance range of the smart counter; For each ATM selected, determine the difference between the environmental risk vector of the ATM and the environmental risk vector of the smart counter, and determine the difference as the vector difference corresponding to the ATM; Determine the partial order of the ATM according to the corresponding vector differences of the selected multiple ATMs, wherein the partial order is used to determine whether the first ATM is better than the second ATM in any two ATMs in the selected multiple ATMs; According to the partial order of the ATM, determine the maximum ATM among the selected multiple ATMs, wherein the maximum ATM is the maximum element of the partial order; The largest ATM among the selected multiple ATMs is used as the similar ATM of the intelligent counter. 13. The system according to claim 12, wherein the similar ATM determination module is specifically used for: When determining the partial order of ATMs, for any two ATMs in the selected multiple ATMs, if for each component, the absolute value of the vector difference corresponding to the first ATM of the two ATMs in the value of the component is less than or equal to If the vector difference corresponding to the second ATM of the two ATMs is the absolute value of the value of the component, it is determined that the first ATM is better than the second ATM. 14. The system according to claim 12, wherein the similar ATM determination module is specifically used for: Determine the intersection of the transaction set corresponding to the ATM and the transaction set corresponding to the smart counter; Obtain the transaction data of the smart counter within a preset time range, and determine the transaction vector of the smart counter, wherein each component of the transaction vector corresponds to the transactions included in the intersection, and the value of each component is equal to the smart counter The number of transactions corresponding to this component in the transaction data of the counter; For each selected ATM, acquire transaction data of the ATM within a preset time range, and determine a transaction vector of the ATM, wherein each component of the transaction vector corresponds to the transactions included in the intersection, and each component The value of is equal to the number of transactions corresponding to this component in the transaction data of the ATM; For each ATM selected, determine that the transaction distance corresponding to the ATM is the distance between the transaction vector of the ATM and the transaction vector of the smart counter, and determine that the risk distance corresponding to the ATM is the modulo length of the vector difference corresponding to the ATM; When determining the partial order of ATMs, for any two ATMs in the selected multiple ATMs, if the transaction distance corresponding to the first ATM of the two ATMs is less than or equal to the transaction distance corresponding to the second ATM of the two ATMs, and If the risk distance corresponding to the first ATM of the two ATMs is less than or equal to the risk distance corresponding to the second ATM of the two ATMs, it is determined that the first ATM is better than the second ATM. 15. The system according to claim 9, wherein the corresponding relationship determination module is specifically used for: According to the transaction data of the similar ATM during the night time, for different positive risk parameter thresholds, the positive risk parameter is selected from the transaction data containing the positive risk parameter of the similar ATM during the night time, and the positive risk parameter is smaller than the positive risk parameter threshold. determine the proportion of risk data in the screened transaction data, use the proportion as the risk coefficient corresponding to the positive risk parameter threshold, and establish the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM; The corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the similar ATM is set as the corresponding relationship between the positive risk parameter threshold and the risk coefficient corresponding to the smart counter. 16. The system according to claim 9, wherein the positive risk parameter threshold determination module is specifically used for: From the risk coefficients included in the corresponding relationship between the positive risk parameter threshold corresponding to the smart counter and the risk coefficient, find the maximum risk coefficient that is less than or equal to the risk coefficient of the smart counter, and assign the positive value corresponding to the maximum risk coefficient in the corresponding relationship. The risk parameter threshold, as the reference positive risk parameter threshold of the smart counter at night; The positive risk parameter threshold of the smart counter in the night time period is adjusted to the minimum value between the positive risk parameter threshold of the smart counter in the daytime period and the reference positive risk parameter threshold. 17. A computer device comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements any of claims 1 to 8 when the processor executes the computer program the method. 18 . A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method of any one of claims 1 to 8 is implemented. 19. A computer program product, characterized in that the computer program product comprises a computer program, and when the computer program is executed by a processor, implements the method of any one of claims 1 to 8.

Images