CN114372892A

CN114372892A - Payment data monitoring method, device, equipment and medium

Info

Publication number: CN114372892A
Application number: CN202210073647.1A
Authority: CN
Inventors: 韩晓翠
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2022-01-21
Filing date: 2022-01-21
Publication date: 2022-04-19

Abstract

The invention relates to the field of artificial intelligence and big data, and provides a payment data monitoring method, a device, equipment and a medium, which can automatically identify and predict a risk index based on an index analysis model, reduce manual operation, improve execution efficiency at the same time, execute first risk control on payment data according to the risk index, execute second risk control on the payment data according to a payment threshold and a payment amount corresponding to the payment data after the execution of the first risk control is finished, further realize in-service monitoring on payment in the payment process, execute payment operation based on the payment data after the execution of the second risk control is finished, obtain a payment result, realize in-service early warning and in-service monitoring in the payment process, and further ensure the safety of payment. In addition, the invention also relates to a block chain technology, and the payment result can be stored in the block chain node.

Description

Payment data monitoring method, device, equipment and medium

Technical Field

The invention relates to the technical field of artificial intelligence and big data, in particular to a payment data monitoring method, device, equipment and medium.

Background

In the business process related to insurance, after purchasing insurance, a client has a contractual relationship with an insurance company, and the insurance company needs to provide necessary services for the client in the whole life cycle of the insurance, which is generally divided into security and settlement.

In the existing insurance business system and financial system, the payment task for the client mainly occurs in the client's insurance or claim settlement link, but the risk control for the fund is mainly in the financial system, and at present, only a large amount of risk prompt (for example, when the fund is more than 200 ten thousand, the risk prompt is performed), different risk coping conditions of each organization are not distinguished, and the business characteristics and the corresponding risk characteristics of various professional companies cannot be met.

Moreover, due to the lack of tracking and analysis of the whole payment closed loop of payment data generation, data archive extraction, payment and reimbursement, the lack of analysis and risk prompt of the historical payment data of the customer, and the lack of relevant analysis aiming at specific services, specific customers or specific channels, the fund loss problem is caused by the conditions of staff error, customer fraud, system refund or claim settlement calculation rules and the like.

Disclosure of Invention

In view of the foregoing, there is a need to provide a method, an apparatus, a device and a medium for monitoring payment data, which aim to solve the problem of high risk in the payment process.

A payment data monitoring method, the payment data monitoring method comprising:

when the generation of payment data of a target system is detected, determining a receiver of the payment data, and acquiring historical service data associated with the receiver;

calling a pre-trained index analysis model, and analyzing the historical service data by using the index analysis model to obtain a risk index of the receiver;

performing a first risk control on the payment data according to the risk index;

after the first risk control is executed, acquiring a preset payment threshold value of the receiver;

executing second risk control on the payment data according to the payment threshold and the payment amount corresponding to the payment data;

and after the second risk control is executed, executing payment operation based on the payment data to obtain a payment result.

According to a preferred embodiment of the present invention, before invoking the pre-trained exponential analysis model, the method further comprises:

acquiring a preset logistic regression model;

identifying payment operations generated within a preset time range from the target system;

acquiring payment information, policy information and receiver information of each payment operation as input data, acquiring a risk index of each payment operation mark as output data, and training the logistic regression model;

and when the logistic regression model reaches convergence, stopping training to obtain the index analysis model.

According to a preferred embodiment of the invention, said performing a first risk control on said payment data according to said risk index comprises:

acquiring a pre-configured risk threshold;

when the risk index is larger than or equal to the risk threshold, generating a risk identifier by using the risk threshold, marking the payment data based on the risk identifier, and sending the marked payment data to a specified platform for auditing; or

Generating a payment prompt when the risk index is less than the risk threshold, wherein the payment prompt is used for prompting permission of payment based on the payment data.

According to a preferred embodiment of the present invention, before obtaining the pre-configured payment threshold of the recipient, the method further comprises:

acquiring payment data generated by the receiver in the target system;

acquiring a payment amount corresponding to each piece of payment data from the payment data generated by the receiver in the target system;

determining a median of the payment amount, and determining a quartile of the payment amount;

calculating the product of the quartile of the payment amount and a preset threshold value to obtain a first numerical value;

calculating the sum of the median and the first value as the payout threshold.

According to a preferred embodiment of the invention, the method further comprises:

connecting a hadoop data warehouse tool hive library through python;

after connecting, synchronizing data in the target system to the hive library;

and after the synchronization is finished, training in the hive library to obtain the index analysis model, and configuring the payment threshold of the receiver.

According to a preferred embodiment of the present invention, the performing a second risk control on the payment data according to the payment threshold and the payment amount corresponding to the payment data includes:

when the payment data is larger than or equal to the payment threshold, calling a first approval chain to approve the payment data; or

When the payment data are smaller than the payment threshold value, calling a second approval chain to approve the payment data;

and the number of the nodes of the first approval chain is greater than that of the nodes of the second approval chain.

According to a preferred embodiment of the invention, after obtaining the payment result, the method further comprises:

sending the payment result to the hive library;

within the hive library, optimizing the exponential analysis model and the recipient's payment threshold based on the payment results.

A payment data monitoring apparatus, the payment data monitoring apparatus comprising:

the system comprises a determining unit, a receiving unit and a processing unit, wherein the determining unit is used for determining a receiver of payment data and acquiring historical service data associated with the receiver when the generation of the payment data in a target system is detected;

the analysis unit is used for calling a pre-trained index analysis model and analyzing the historical service data by using the index analysis model to obtain the risk index of the receiver;

an execution unit configured to execute first risk control on the payment data according to the risk index;

the acquiring unit is used for acquiring a preset payment threshold value of the receiver after the first risk control is executed;

the execution unit is further configured to execute second risk control on the payment data according to the payment threshold and the payment amount corresponding to the payment data;

and the execution unit is further used for executing payment operation based on the payment data after the second risk control is executed, so as to obtain a payment result.

A computer device, the computer device comprising:

a memory storing at least one instruction; and

a processor executing instructions stored in the memory to implement the payment data monitoring method.

A computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement the payment data monitoring method.

According to the technical scheme, when the generation of the payment data of the target system is detected, the receiver of the payment data is determined, the historical business data associated with the receiver is obtained, the pre-trained index analysis model is called, the historical business data is analyzed by using the index analysis model to obtain the risk index of the receiver, the risk index is automatically identified and predicted based on the index analysis model, manual operation is reduced, the execution efficiency is improved, the first risk control is executed on the payment data according to the risk index, and different processing is carried out on the payment data with different risk levels: and carrying out risk marking on the high-risk payment data, carrying out secondary audit, and directly allowing payment on the low-risk payment data so as to realize targeted control according to the risk. The safety of payment is further improved by special processing of high-risk payment data, the influence on the payment efficiency due to unnecessary risk control is avoided by direct payment of low-risk payment data, effective advance early warning in the payment process is further realized, a pre-configured payment threshold value of the receiver is obtained after the first risk control is executed, second risk control is executed on the payment data according to the payment threshold value and the payment amount corresponding to the payment data, the approval chain of the payment data exceeding the payment threshold value is complicated, the approval chain of the payment data not exceeding the payment threshold value is simplified, manpower is effectively released, loss caused by human factors can be avoided, the in-process monitoring of payment is further realized in the payment process, and after the second risk control is executed, and executing payment operation based on the payment data to obtain a payment result, realizing early warning and in-service monitoring in the payment process, and further ensuring the payment safety.

Drawings

FIG. 1 is a flow chart of a preferred embodiment of the payment data monitoring method of the present invention.

Fig. 2 is a functional block diagram of a preferred embodiment of the payment data monitoring apparatus of the present invention.

Fig. 3 is a schematic structural diagram of a computer device for implementing a payment data monitoring method according to a preferred embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.

Fig. 1 is a flow chart of a payment data monitoring method according to a preferred embodiment of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.

The payment data monitoring method is applied to one or more computer devices, and the computer devices are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware thereof includes but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive web Television (IPTV), an intelligent wearable device, and the like.

The computer device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.

The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.

Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

The Network in which the computer device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.

S10, when the target system is detected to have the payment data generated, determining a receiver of the payment data, and acquiring historical service data associated with the receiver.

In at least one embodiment of the present invention, the target system refers to a business system, such as: a security system, a claims settlement system, etc.

In at least one embodiment of the invention, the payment data is an amount of money to be paid to the customer due to the occurrence of the transaction, the payment data being generated by the transaction action.

For example: the payment data may include claims funds, pension, refunds, etc.

In at least one embodiment of the invention, the recipient may include an associated customer, such as a user who purchased insurance at an insurance company.

In at least one embodiment of the present invention, the historical traffic data includes, but is not limited to, one or more of the following combinations of data:

payment data, policy data, user information.

And S11, calling a pre-trained index analysis model, and analyzing the historical service data by using the index analysis model to obtain the risk index of the receiver.

In at least one embodiment of the invention, before invoking the pre-trained exponential analysis model, the method further comprises:

acquiring a preset logistic regression model;

Wherein the payment information may include, but is not limited to, one or a combination of more of the following: the type of service, the payment amount, the payment account, the payment time, etc.

Wherein the policy information may include, but is not limited to, one or more of the following: premium, cumulative premium, etc.

Wherein, the receiving party information may include, but is not limited to, one or more of the following information: city, age, gender, etc.

Wherein the risk index may be marked by the relevant staff, the invention is not limited.

Further, the historical business data is input into the index analysis model for automatic analysis processing, and the output of the index analysis model is obtained to be used as the risk index of the receiver.

Through the implementation mode, the index analysis model can be constructed so as to automatically identify and predict the risk index based on the index analysis model, so that manual operation is reduced, and the execution efficiency is improved.

S12, performing first risk control on the payment data according to the risk index.

In at least one embodiment of the invention, the first risk control occurs prior to payment, belonging to a prior warning of payment.

In at least one embodiment of the invention, said performing a first risk control on said payment data according to said risk index comprises:

acquiring a pre-configured risk threshold;

Wherein, the risk threshold can be configured in a customized way, such as: 80. 85, etc.

It should be noted that when the risk index is greater than or equal to the risk threshold, it indicates that the payment risk of the current payment data is higher, and further auditing needs to be performed.

In this embodiment, the designated platform is a platform capable of performing further auditing on the payment data, and can return an auditing result after auditing is finished.

Specifically, the designated platform may perform automatic review, or perform review in combination with manual assistance, which is not limited in the present invention.

It should be noted that when the risk index is smaller than the risk threshold, it indicates that the payment risk of the current payment data is low, and the payment can be directly made without further auditing.

In the above embodiment, the payment data for different risk levels are processed differently: and carrying out risk marking on the high-risk payment data, carrying out secondary audit, and directly allowing payment on the low-risk payment data so as to realize targeted control according to the risk. Through the special processing to the payment data of high risk, further improved the security of payment, through the direct payment to the payment data of low risk, avoid because unnecessary risk control influences payment efficiency, and then realized effectual early warning in advance among the payment process.

And S13, acquiring a preset payment threshold value of the receiver after the first risk control is executed.

In at least one embodiment of the invention, before obtaining the pre-configured payment threshold for the recipient, the method further comprises:

acquiring payment data generated by the receiver in the target system;

calculating the sum of the median and the first value as the payout threshold.

Wherein the preset threshold may be configured to be 0.5.

Of course, in other embodiments, the payment threshold may be adjusted again in combination with the related business personnel, financial personnel and other personnel to ensure the reasonableness of the payment threshold configuration.

It should be noted that the payment threshold values of all the receivers associated with the target system may be preconfigured, so as to facilitate subsequent direct invocation and improve efficiency.

In the above embodiment, for the receiver with the generally higher historical payment amount, a higher payment threshold (e.g. 200 ten thousand) can be configured, and for the receiver with the generally lower historical payment amount, a lower payment threshold (e.g. 100 ten thousand) can be configured, so as to realize targeted risk control for the receivers with different properties.

Through the implementation mode, the automatic configuration of the payment threshold can be realized, so that risk control is carried out based on the payment threshold, and the payment safety is ensured.

In at least one embodiment of the invention, the method further comprises:

connecting a hadoop data warehouse tool hive library through python;

after connecting, synchronizing data in the target system to the hive library;

It can be understood that, because the payment data volume in the system may reach tens of millions, and the traditional relational database cannot support the calculation of a large data volume, the embodiment synchronizes the data to the hadoop platform, and trains and calculates the model on the hadoop platform, thereby effectively improving the calculation efficiency.

And S14, executing second risk control on the payment data according to the payment threshold and the payment amount corresponding to the payment data.

In at least one embodiment of the invention, the second risk control occurs during the payment process, belonging to the in-flight monitoring of the payment.

In at least one embodiment of the present invention, the performing of the second risk control on the payment data according to the payment threshold and the payment amount corresponding to the payment data includes:

For example: when the receiving party A organization has more than 200 ten thousand payment data and more than 100 ten thousand payment threshold values of the A organization, the first approval chain carries out approval: the first approval chain is approved by a business manager, a business department manager, a financial manager and a financial manager in sequence; when the first institution has no more than 10 ten thousand payment data and the payment threshold of the first institution is not exceeded by 100 ten thousand, the approval is carried out by a second approval chain: only need to be examined and approved by a financial operator.

In this embodiment, the first approval chain and the second approval chain may also be adjusted in due time according to actual manpower, which is not limited in the present invention.

Through the implementation mode, the approval chain of the payment data exceeding the payment threshold is complicated, the approval chain of the payment data not exceeding the payment threshold is simplified, manpower is effectively released, loss caused by human factors can be avoided, and in-process monitoring of payment is achieved in the payment process.

And S15, after the second risk control is executed, executing payment operation based on the payment data to obtain a payment result.

Through the implementation mode, in the payment process, the early warning and the in-service monitoring are realized, and the payment safety is further ensured.

Specifically, after obtaining the payment result, the method further comprises:

sending the payment result to the hive library;

Through the implementation mode, the exponential analysis model and the payment threshold value of the receiver can be continuously optimized and iterated based on a big data technology, so that the data payment process can be effectively controlled every time, and the post analysis of the payment process is further realized.

It should be noted that, in order to further improve the security of the data and avoid malicious tampering of the data, the payment result may be stored in the blockchain node.

Fig. 2 is a functional block diagram of a payment data monitoring apparatus according to a preferred embodiment of the present invention. The payment data monitoring apparatus 11 includes a determining unit 110, an analyzing unit 111, an executing unit 112, and an obtaining unit 113. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.

When it is detected that the target system has payment data generated, the determining unit 110 determines a receiver of the payment data and acquires historical service data associated with the receiver.

For example: the payment data may include claims funds, pension, refunds, etc.

payment data, policy data, user information.

The analysis unit 111 calls a pre-trained index analysis model, and analyzes the historical service data by using the index analysis model to obtain the risk index of the receiver.

In at least one embodiment of the invention, a pre-set logistic regression model is obtained before a pre-trained exponential analysis model is called;

The execution unit 112 performs a first risk control on the payment data according to the risk index.

In at least one embodiment of the present invention, the performing unit 112 performs a first risk control on the payment data according to the risk index includes:

acquiring a pre-configured risk threshold;

The obtaining unit 113 obtains a pre-configured payment threshold of the receiving party after the first risk control is executed.

In at least one embodiment of the present invention, before obtaining a pre-configured payment threshold of the recipient, obtaining payment data generated by the recipient within the target system;

calculating the sum of the median and the first value as the payout threshold.

Wherein the preset threshold may be configured to be 0.5.

In at least one embodiment of the invention, the hadoop data warehouse tool hive library is connected by python;

after connecting, synchronizing data in the target system to the hive library;

The execution unit 112 executes a second risk control on the payment data according to the payment threshold and the payment amount corresponding to the payment data.

In at least one embodiment of the present invention, the executing unit 112, according to the payment threshold and the payment amount corresponding to the payment data, executing second risk control on the payment data includes:

After the execution of the second risk control is completed, the execution unit 112 executes a payment operation based on the payment data, and obtains a payment result.

Specifically, after a payment result is obtained, the payment result is sent to the hive library;

Fig. 3 is a schematic structural diagram of a computer device for implementing the payment data monitoring method according to a preferred embodiment of the present invention.

The computer device 1 may comprise a memory 12, a processor 13 and a bus, and may further comprise a computer program, such as a payment data monitoring program, stored in the memory 12 and executable on the processor 13.

It will be understood by those skilled in the art that the schematic diagram is merely an example of the computer device 1, and does not constitute a limitation to the computer device 1, the computer device 1 may have a bus-type structure or a star-shaped structure, the computer device 1 may further include more or less other hardware or software than those shown, or different component arrangements, for example, the computer device 1 may further include an input and output device, a network access device, etc.

It should be noted that the computer device 1 is only an example, and other electronic products that are currently available or may come into existence in the future, such as electronic products that can be adapted to the present invention, should also be included in the scope of the present invention, and are included herein by reference.

The memory 12 includes at least one type of readable storage medium, which includes flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the computer device 1, for example a removable hard disk of the computer device 1. The memory 12 may also be an external storage device of the computer device 1 in other embodiments, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device 1. Further, the memory 12 may also include both an internal storage unit and an external storage device of the computer device 1. The memory 12 may be used not only to store application software installed in the computer device 1 and various types of data, such as codes of a payment data monitoring program, etc., but also to temporarily store data that has been output or is to be output.

The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the computer device 1, connects various components of the whole computer device 1 by using various interfaces and lines, and executes various functions and processes data of the computer device 1 by running or executing programs or modules (for example, executing a payment data monitoring program and the like) stored in the memory 12 and calling data stored in the memory 12.

The processor 13 executes the operating system of the computer device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in the various payment data monitoring method embodiments described above, such as the steps shown in fig. 1.

Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the computer device 1. For example, the computer program may be divided into a determination unit 110, an analysis unit 111, an execution unit 112, an acquisition unit 113.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute parts of the payment data monitoring method according to the embodiments of the present invention.

The integrated modules/units of the computer device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.

Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), random-access Memory, or the like.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one line is shown in FIG. 3, but this does not mean only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.

Although not shown, the computer device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 13 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The computer device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

Further, the computer device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the computer device 1 and other computer devices.

Optionally, the computer device 1 may further comprise a user interface, which may be a Display (Display), an input unit, such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the computer device 1 and for displaying a visualized user interface.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

Fig. 3 shows only the computer device 1 with the components 12-13, and it will be understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the computer device 1 and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.

With reference to fig. 1, the memory 12 of the computer device 1 stores a plurality of instructions to implement a payment data monitoring method, and the processor 13 can execute the plurality of instructions to implement:

Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.

It should be noted that all the data involved in the present application are legally acquired.

In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A payment data monitoring method, the payment data monitoring method comprising:

2. The payment data monitoring method of claim 1, wherein prior to invoking the pre-trained exponential analysis model, the method further comprises:

acquiring a preset logistic regression model;

3. The payment data monitoring method of claim 1, wherein the performing a first risk control on the payment data according to the risk index comprises:

acquiring a pre-configured risk threshold;

4. The payment data monitoring method of claim 1, wherein prior to obtaining a pre-configured payment threshold for the recipient, the method further comprises:

acquiring payment data generated by the receiver in the target system;

calculating the sum of the median and the first value as the payout threshold.

5. The payment data monitoring method of claim 1, wherein the method further comprises:

connecting a hadoop data warehouse tool hive library through python;

after connecting, synchronizing data in the target system to the hive library;

6. The payment data monitoring method of claim 1, wherein the performing a second risk control on the payment data based on the payment threshold and the payment amount corresponding to the payment data comprises:

7. The payment data monitoring method of claim 5, wherein upon obtaining the payment result, the method further comprises:

sending the payment result to the hive library;

8. A payment data monitoring apparatus, comprising:

9. A computer device, characterized in that the computer device comprises:

a memory storing at least one instruction; and

a processor executing instructions stored in the memory to implement a payment data monitoring method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium characterized by: the computer-readable storage medium having stored therein at least one instruction for execution by a processor in a computer device to implement a payment data monitoring method as claimed in any one of claims 1 to 7.