CN111177250A - Abnormal transaction monitoring method, system and storage medium - Google Patents
Abnormal transaction monitoring method, system and storage medium Download PDFInfo
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- CN111177250A CN111177250A CN201911412059.0A CN201911412059A CN111177250A CN 111177250 A CN111177250 A CN 111177250A CN 201911412059 A CN201911412059 A CN 201911412059A CN 111177250 A CN111177250 A CN 111177250A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
- G06F16/287—Visualization; Browsing
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Abstract
The invention discloses an abnormal transaction monitoring system, which comprises: the data acquisition module is used for acquiring conventional data from a database through a data source interface and acquiring real-time data from a message queue system; the intelligent judgment module is used for judging abnormal transactions according to the acquired conventional data and the acquired real-time data; compared with the prior art, the invention greatly reduces the time delay of data in a synchronous mode from a relational database, can more quickly discover a transaction action occurring and give an alarm in time, adopts a distributed storage and calculation framework to further improve the operation efficiency, and improves the judgment accuracy of the abnormal transaction monitoring model through the retest of the intelligent judgment module and the intelligent analysis module.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an abnormal transaction monitoring system.
Background
Since the financial crisis of 2008, the regulatory risk management of global financial enterprises has been a major challenge for company operations. According to the statistics of Reuters, from 2008 to 2015, 20% of the major banks or dealer in the world will pay a cumulative penalty of more than 2350 billion dollars (about 1517 billion pounds) due to careless mistakes in financial regulation, which number roughly corresponds to the GNP in Ireland. The trend of strengthening regulation has been increasing in china, particularly in the securities industry, over 2015. According to incomplete statistics, in 2017, the number of important supervision documents which are issued by the financial supervision department in China is more than 20, the administrative penalty is more than 2700, and the penalty amount is more than 80 hundred million yuan. With more than 20% of the regulatory penalties being directly or indirectly due to anomalous transactions. Thus, financial institutions, for their own development, must also serve their customers with innovative solutions to address the ever-increasing wind control and compliance challenges, particularly with respect to monitoring of anomalous transactions.
Most of the existing security company abnormal transaction monitoring systems are based on a rule matching technology and a relational database, calculation and storage are all carried out on the same server, namely, data storage, calculation and analysis are all completed through the database, and the database is installed on one server. Firstly, in the aspect of data synchronization, because a reading and writing bottleneck exists in a relational database and real-time data synchronization is not advantageous, delay in data timeliness is large, and data delay of ten minutes level may occur under the condition of large data backlog, so that the instantaneity of abnormal transaction monitoring is greatly reduced by a security company, abnormal transactions cannot be found in time, and corresponding reminding service or help is provided for customers. In addition, the storage and the calculation of the existing system are all completed through a relational database and are deployed on the same server, so that the mutual influence and the restraint of the real-time calculation and the storage are caused, the performance of a machine cannot be fully exerted, and some complex calculations cannot be implemented, thereby influencing the accuracy of monitoring abnormal transactions.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an abnormal transaction monitoring system which can reduce data delay in the abnormal transaction monitoring system and improve the accuracy of abnormal transaction monitoring.
The invention is realized by the following technical scheme:
in a first aspect, an abnormal transaction monitoring method is provided, including:
acquiring data, namely acquiring conventional data from a database through a data source interface and acquiring real-time data from a message queue system;
intelligent judgment, namely judging abnormal transactions according to the acquired conventional data and the acquired real-time data;
and storing and displaying, namely storing the related data of the transaction monitoring system and displaying the abnormal transaction monitoring result.
With reference to the first aspect, further, the regular data includes: basic information data of clients, stocks, funds and futures, and the real-time data comprises client transaction data and market quotation data.
With reference to the first aspect, further, the intelligent determination includes establishing an abnormal transaction monitoring model, where the abnormal transaction monitoring model includes abnormal transaction description information, abnormal transaction classification information, abnormal transaction feature information, and abnormal transaction threshold information.
With reference to the first aspect, further, the intelligent determination further includes mail analysis, and the intelligent analysis is performed on the supervision mail sent by the transaction to generate a new abnormal transaction monitoring model.
With reference to the first aspect, further, the intelligent judgment module further includes a retest and a test calculation, and the retest is used for retesting historical transaction data; trial calculation is used for modifying the monitoring logic and the monitoring threshold value of the abnormal transaction monitoring model, and trial calculation is carried out on the modified model through historical transaction data.
With reference to the first aspect, further, the mail analysis includes:
NLP processing, namely performing natural language processing analysis on the supervision function, and extracting key entities in the supervision function to generate structured data;
intelligent analysis: performing semantic analysis, rule feature extraction and threshold extraction on the generated structured data; combining the obtained semantic analysis result, the characteristic data and the threshold value with the historical transaction data
And (4) performing machine learning to form a new abnormal transaction monitoring model.
In a second aspect, there is provided an anomalous transaction monitoring system comprising:
the data acquisition module is used for acquiring conventional data from a database through a data source interface and acquiring real-time data from a message queue system;
the intelligent judgment module is used for judging abnormal transactions according to the acquired conventional data and the acquired real-time data;
and the storage and display module is used for storing the related data of the transaction monitoring system and displaying the abnormal transaction monitoring result.
With reference to the second aspect, further, the monitoring system is deployed in a distributed manner on a plurality of servers, and each server includes a data acquisition module, an intelligent determination module, and a storage and display module.
In a third aspect, there is provided an abnormal transaction monitoring system, including: a memory and a processor;
the memory is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of any of the first aspects.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method of any of the first aspects.
The invention has the following beneficial effects:
1. the invention receives market quotation data and customer transaction data in real time in a real-time stream form from the message queue system, greatly reduces the time delay of the data compared with the prior art in a synchronous mode from a relational database, can more quickly discover a transaction action occurring in the prior art and give an alarm in time.
2. The invention further improves the operation efficiency by adopting the framework of distributed storage and distributed computation.
3. The accuracy of judgment of the abnormal transaction monitoring model is improved through the retesting of the intelligent judgment module and the intelligent analysis module.
Drawings
FIG. 1 is a schematic block diagram of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a schematic diagram of an intelligent judgment module according to the present invention.
Detailed Description
The invention provides a novel abnormal transaction real-time monitoring system for security companies by using a new method and a new framework for the limitation of the prior art, improves timeliness, realizes the real-time monitoring of abnormal transactions, and simultaneously improves the monitoring accuracy to meet the new requirements of current-stage supervision, thereby better serving clients.
The invention is further described with reference to the accompanying drawings.
As shown in fig. 1 to 3, the present invention provides an abnormal transaction monitoring method, which mainly includes the following steps:
and data acquisition, namely, data of different sources, different types and different purposes can be acquired by accessing different data sources. Various data source interfaces such as relational databases, key value databases, fast cache system databases, distributed databases, streaming data, etc. The judgment results of abnormal transactions in the invention are obtained by data analysis, and the data for subsequent judgment and analysis mainly comprises two parts: regular data and real-time data. The conventional data (such as basic information data of clients, stocks, funds and futures) has low read-write requirements, and can be stored by a conventional database such as a relational database, and the data is read out from the database to a cache (breaking through a read bottleneck from the database in each use) during initialization, so that the subsequent processing speed can be increased; however, for data with high real-time requirements (such as customer transaction data and market quotation data), a message queue system (for example, Kafka, ActiveMQ and the like) is adopted by adopting a traditional database, the data is stored in the message queue system in a data stream form, and the data is directly read from the message queue through a data source interface when needed, so that the problem of data delay in the abnormal transaction monitoring system in the prior art can be solved (the data transmission and calculation delay are reduced to millisecond level).
Intelligent judgment, namely judging abnormal transactions according to the acquired conventional data and real-time data; the method mainly comprises the steps of establishing an abnormal transaction monitoring model, wherein the abnormal transaction monitoring model comprises abnormal transaction description information, abnormal transaction classification information, abnormal transaction characteristic information and abnormal transaction threshold value information. The algorithm of the abnormal transaction monitoring model can be provided by a market supervision department, the intelligent judgment further comprises letter analysis, automatic intelligent analysis can be carried out on supervision letters sent by a transaction, and the intelligent analysis mainly comprises letter NLP processing and letter intelligent analysis. The function of processing the mail NLP is mainly to process and analyze the mail in natural language, extract the key entity in the mail to generate the structured data and store the structured data in the database. The intelligent analysis function of the mail mainly performs semantic analysis, rule feature extraction, threshold extraction and the like on a result processed by a Natural Language Processing (NLP), and relearns is performed by combining similar abnormal transaction historical data by using machine learning algorithms such as a Global Boot Regression (GBRT) Tree and a deep neural network, so as to form a new abnormal transaction monitoring model and update the model to a database. The intelligent judgment also comprises retesting and trial calculation, the retesting can be carried out according to the currently set abnormal transaction detection algorithm aiming at the historical transaction data in a certain time period, and the retesting result is stored in the storage display module. The trial calculation can modify the logic conditions and detection threshold values of different abnormal transaction type detection methods, the trial calculation is carried out on historical transaction data within a certain time period, the trial calculation result is stored in the storage and display module, a data closed loop can be formed through the back test and the trial calculation, and the abnormal transaction monitoring model is optimized, so that the abnormal transaction is judged more accurately.
And storing and displaying, namely storing the related data of the transaction monitoring system and displaying the abnormal transaction monitoring result. The data storage submodule is mainly used for persistence of abnormal transaction early warning information and persistence of abnormal transaction detail information, and a conventional single machine or a distributed relational database can be selected. The data display sub-module is mainly used for displaying abnormal transaction rules, abnormal transaction early warning information, abnormal transaction detail information and other related statistical lists, charts and other information.
The abnormal transaction monitoring system provided by the embodiment of the invention can be used for loading and executing the abnormal transaction monitoring method, and comprises the following steps:
the data acquisition module is used for acquiring conventional data from a database through a data source interface and acquiring real-time data from a message queue system;
the intelligent judgment module (intelligent algorithm platform) is used for judging abnormal transactions according to the acquired conventional data and the acquired real-time data;
and the storage and display module is used for storing the related data of the transaction monitoring system and displaying the abnormal transaction monitoring result.
The intelligent judgment module of the system adopts a three-layer design and mainly comprises a logic layer submodule, a realization layer submodule and an execution layer submodule.
The logic layer sub-module mainly comprises information such as an abnormal transaction detection algorithm model, a preprocessing algorithm and a public calculation module. The abnormal transaction algorithm model comprises information such as abnormal transaction description, abnormal transaction classification, abnormal transaction characteristic conditions and abnormal transaction threshold values. The algorithm model can be provided by a market supervision department, can also be managed and stored after external language information is converted into a logic description language by analyzing and extracting supervision functions sent by the supervision department, and can also be manually set and maintained by a user. The implementation layer submodule has the main function of instantiating an abnormal transaction detection algorithm model in the logic layer submodule, and implementing various abnormal transaction algorithms through JAVA, Python, R and other programming languages to form an executable program or script. In addition, the implementation layer sub-module also has the functions of example management and scheduling, and schedules executable program parameters and the like to the next layer of execution layer. The execution layer sub-module receives an executable program transmitted by the implementation layer scheduling and corresponding real-time data in a certain time period, calculates a specific index model by combining related conventional data obtained during initialization, and finally persists a calculation result to the storage module. The execution layer sub-module may be a distributed real-time computing engine such as Storm, a distributed big data computing engine such as Spark, or other stand-alone or distributed computing tools.
The monitoring system can be distributed on a plurality of servers, each server comprises a data acquisition module, an intelligent judgment module and a storage and display module, and the intelligent judgment module can perform distributed calculation on an engine or a stand-alone calculation when performing abnormal transaction monitoring calculation. By the method, storage and calculation are divided into a plurality of servers, the storage and operation pressure of each server is reduced, and the bottleneck of data reading, writing and processing is further reduced.
The abnormal transaction monitoring system provided by the invention can also comprise a memory and a processor; the memory is to store instructions;
the processor is used for operating according to the instruction to execute the steps of the abnormal transaction monitoring method.
The present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the aforementioned abnormal transaction monitoring method.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. An anomalous transaction monitoring method, comprising:
acquiring data, namely acquiring conventional data from a database through a data source interface and acquiring real-time data from a message queue system;
intelligent judgment, namely judging abnormal transactions according to the acquired conventional data and the acquired real-time data;
and storing and displaying, namely storing the related data of the transaction monitoring system and displaying the abnormal transaction monitoring result.
2. The anomalous transaction monitoring method of claim 1 wherein said regular data includes: basic information data of clients, stocks, funds and futures, and the real-time data comprises client transaction data and market quotation data.
3. The abnormal transaction monitoring system method according to claim 1, wherein the intelligent judgment comprises establishment of an abnormal transaction monitoring model, and the abnormal transaction monitoring model comprises abnormal transaction description information, abnormal transaction classification information, abnormal transaction feature information and abnormal transaction threshold information.
4. The abnormal transaction monitoring method of claim 3, wherein the intelligent judgment further comprises a mail analysis, and the intelligent analysis is performed on the monitoring mail sent by the transaction to generate a new abnormal transaction monitoring model.
5. The abnormal transaction monitoring method according to claim 3, wherein the intelligent judgment module further comprises a retest and a test calculation, wherein the retest is used for retest of historical transaction data; trial calculation is used for modifying the monitoring logic and the monitoring threshold value of the abnormal transaction monitoring model, and trial calculation is carried out on the modified model through historical transaction data.
6. The anomalous transaction monitoring method of claim 4 wherein said mail analysis includes:
NLP processing, namely performing natural language processing analysis on the supervision function, and extracting key entities in the supervision function to generate structured data;
intelligent analysis: performing semantic analysis, rule feature extraction and threshold extraction on the generated structured data; and performing machine learning on the obtained semantic analysis result, the characteristic data and the threshold value in combination with historical transaction data to form a new abnormal transaction monitoring model.
7. An anomalous transaction monitoring system, comprising:
the data acquisition module is used for acquiring conventional data from a database through a data source interface and acquiring real-time data from a message queue system;
the intelligent judgment module is used for judging abnormal transactions according to the acquired conventional data and the acquired real-time data;
and the storage and display module is used for storing the related data of the transaction monitoring system and displaying the abnormal transaction monitoring result.
8. The abnormal transaction monitoring system of claim 7, wherein the monitoring system is distributed on a plurality of servers, and each server comprises a data acquisition module, an intelligent judgment module and a storage and display module.
9. An anomalous transaction monitoring system, comprising: a memory and a processor;
the memory is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method of any one of claims 1 to 6.
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| CN111737080A (en) * | 2020-06-11 | 2020-10-02 | 北京向上一心科技有限公司 | Abnormal transaction suspicion monitoring method and device, computer equipment and storage medium |
| CN111817914A (en) * | 2020-06-30 | 2020-10-23 | 晨边高地(无锡)科技有限公司 | Medium-high frequency algorithm transaction interface intelligent system |
| CN112001730A (en) * | 2020-08-25 | 2020-11-27 | 徐鹏飞 | Data security detection method based on block chain and digital currency and cloud computing center |
| CN112667678A (en) * | 2020-12-16 | 2021-04-16 | 珠海格力电器股份有限公司 | Energy data abnormity determining method and device and intelligent energy management system |
| CN112991056A (en) * | 2021-02-05 | 2021-06-18 | 深圳华锐金融技术股份有限公司 | Wind control platform parameter optimization method and device, computer equipment and storage medium |
| CN113011877A (en) * | 2021-02-23 | 2021-06-22 | 国网山东省电力公司 | Capital payment risk monitoring and early warning system and method |
| CN113592508A (en) * | 2021-09-29 | 2021-11-02 | 深圳市新鹏城网络科技有限公司 | Mobile phone payment safety protection system |
| CN117368670A (en) * | 2023-11-07 | 2024-01-09 | 东莞市一丁精密模具组件有限公司 | Method and system for flexibly detecting discharge characteristic of mold |
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| CN117368670A (en) * | 2023-11-07 | 2024-01-09 | 东莞市一丁精密模具组件有限公司 | Method and system for flexibly detecting discharge characteristic of mold |
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