CN113159779A - Method and system for monitoring and early warning transaction risk based on business data - Google Patents
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Abstract
The invention discloses a method and a system for monitoring and early warning transaction risk based on business data, and belongs to the technical field of data processing and early warning. The invention comprises the following steps: marking abnormal data and carrying out preliminary early warning; if intersection exists and repeated invoice data exist, marking the enterprise and triggering middle-level early warning; transferring warehousing/ex-warehousing data of a reserve grain depot enterprise, generating warehousing data into a warehousing set, generating ex-warehousing data into an ex-warehousing set, and triggering a middle-level early warning if an intersection exists; if the identified vehicle data of the vehicle entering and exiting the garage is not consistent with the vehicle data recorded by the service, triggering middle-level early warning, and if the vehicle data cannot be identified, triggering middle-level early warning; if all the three middle-level early warnings are triggered, a high-level early warning is triggered. The invention improves the working efficiency of on-site supervision, reduces the cost, is beneficial to a supervision mechanism to put limited resources to other convenience, and improves the overall level of the stored grain management work.
Description
Technical Field
The invention relates to the technical field of data processing and early warning, in particular to a method and a system for monitoring and early warning transaction risk based on business data.
Background
The 'revolving grain' refers to that in the process of executing national policy grain collection and storage and grain storage rotation, enterprises earn subsidy expenses by means of virtual buying and selling, buying and aging, low-income and high-speed rotation, non-rotation and reporting, and the like, and adopting modes of keeping stocks still, revolving accounts or revolving stocks and accounts simultaneously. Therefore, the reserve warehouse and the mutually mature grain trading enterprises are matched for operation, and the profit is obtained in the fictitious logistics and trading links.
At present, the national grain storage supervision has three main bodies, namely a medium grain storage, a national grain administrative management system and a Chinese agricultural development bank, and according to the common supervision responsibility of three parties, the three parties participate in organization and coordination when determining a reserve depot point, so that the links of fixed point, starting, expense allocation, storage, statistics, supervision and the like are ensured to be commonly confirmed and supervised by the three parties, wherein the grain management system bears the administrative supervision responsibility, and the agricultural issue bears the financial loan supervision responsibility.
However, many problems still appear in the actual supervision operation, and firstly, the standards are not uniform, the unit properties of three parties are different, and the execution force is quite insufficient; secondly, the information barrier is serious, and the grain industry belongs to the traditional industry; thirdly, the management cost is too high, the grain storage points need to be distributed in various cities, districts, counties and towns in the district, the geographic positions are generally scattered, and the patrol work aiming at the grain storage points is difficult to implement.
Disclosure of Invention
Aiming at the problems, the invention discloses a method for monitoring and early warning transaction risk based on service data, which comprises the following steps:
calling invoice data of a grain depot, detecting the invoice data, marking the abnormal data and carrying out preliminary early warning when detecting that the abnormal data of an entry ticket source enterprise and an entry ticket selling enterprise are the same enterprise exists in the invoice data;
after the preliminary early warning is received, classifying and sorting the marked abnormal data, generating an entry ticket number array and a sales ticket number array according to the entry ticket amount and the sales ticket amount for the classified abnormal data, generating an entry ticket number array and a sales ticket number array according to the entry ticket amount and the sales ticket amount, generating an entry ticket number array set according to the entry ticket number array, generating a sales ticket number array set according to the sales ticket number array, acquiring an intersection of the entry ticket number array set and the sales ticket number array set, marking the enterprise if the intersection exists and repeated invoice data exists, and triggering the intermediate early warning;
transferring warehousing/ex-warehousing data of a reserve grain depot enterprise, generating warehousing data into a warehousing set, generating ex-warehousing data into an ex-warehousing set, and triggering a middle-level early warning if an intersection exists;
calling monitoring data of a reserved grain depot, identifying vehicle data entering the warehouse, triggering a middle-level early warning if the identified vehicle data entering the warehouse is inconsistent with the vehicle data recorded by the service, and triggering the middle-level early warning if the identified vehicle data is not available;
if all the three middle-level early warnings are triggered, a high-level early warning is triggered.
Optionally, the marked abnormal data is sorted, specifically: and sorting the marked abnormal data by taking the goods name and the specification as standards.
Optionally, the warehouse-in/warehouse-out data includes vehicle license number data and weighing data.
Optionally, the vehicle data includes license plate number data and vehicle type data.
The invention also provides a system for monitoring and early warning transaction risk based on the service data, which comprises the following steps:
the monitoring module is used for calling invoice data of a reserved grain depot, detecting the invoice data, marking the abnormal data and carrying out preliminary early warning when detecting that the abnormal data of an entry ticket source enterprise and an entry ticket selling enterprise are the same enterprise exists in the invoice data;
the invoice data detection module is used for classifying and sorting the marked abnormal data after receiving the preliminary early warning, generating an entry ticket number array and a sales ticket number array according to the amount of the entry tickets and the amount of the sales tickets, generating an entry ticket number array set from the entry ticket number array, generating a sales ticket number array set from the sales ticket number array, acquiring an intersection of the entry ticket number array set and the sales ticket number array set, marking enterprises if the intersection exists and repeated invoice data exists, and triggering the intermediate early warning;
the business data detection module is used for calling the warehousing/ex-warehousing data of the grain depot enterprise, generating warehousing data into a warehousing set, generating ex-warehousing data into an ex-warehousing set, and triggering middle-level early warning if intersection exists;
the monitoring data detection module is used for calling monitoring data of the grain storage, identifying vehicle data entering and leaving the warehouse, triggering medium-level early warning if the identified vehicle data entering and leaving the warehouse is inconsistent with the vehicle data recorded by the business, and triggering the medium-level early warning if the vehicle data cannot be identified;
and the early warning module is used for triggering the high-grade early warning if all the three middle-grade early warnings are detected to be triggered.
Optionally, the marked abnormal data is sorted, specifically: and sorting the marked abnormal data by taking the goods name and the specification as standards.
Optionally, the warehouse-in/warehouse-out data includes vehicle license number data and weighing data.
Optionally, the vehicle data includes license plate number data and vehicle type data.
The invention does not need to rely on the manual experience, and can effectively investigate the potential risk transaction behavior by only using the data precipitated by the service system and adding a special algorithm, thereby providing a reference basis for the development of actual supervision work;
the early warning effect provided by the invention enables a supervisor to purposefully carry out field supervision work and field inspection work, improves the efficiency and reduces the cost.
The invention improves the working efficiency of on-site supervision, reduces the cost, is beneficial to a supervision organization to put limited resources to other places for convenience, and improves the overall level of the stored grain management work
Drawings
FIG. 1 is a flow chart of a method for monitoring and early warning transaction risk based on business data in accordance with the present invention;
fig. 2 is a structural diagram of a system for monitoring and warning transaction risk based on business data according to the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
The invention discloses a method for monitoring and early warning transaction risk based on business data, which comprises the following steps of:
calling invoice data of a grain depot, detecting the invoice data, marking the abnormal data and carrying out preliminary early warning when detecting that the abnormal data of an entry ticket source enterprise and an entry ticket selling enterprise are the same enterprise exists in the invoice data;
after the preliminary early warning is received, classifying and sorting the marked abnormal data, generating an entry ticket number array and a sales ticket number array according to the entry ticket amount and the sales ticket amount for the classified abnormal data, generating an entry ticket number array and a sales ticket number array according to the entry ticket amount and the sales ticket amount, generating an entry ticket number array set according to the entry ticket number array, generating a sales ticket number array set according to the sales ticket number array, acquiring an intersection of the entry ticket number array set and the sales ticket number array set, marking the enterprise if the intersection exists and repeated invoice data exists, and triggering the intermediate early warning;
transferring warehousing/ex-warehousing data of a reserve grain depot enterprise, generating warehousing data into a warehousing set, generating ex-warehousing data into an ex-warehousing set, and triggering a middle-level early warning if an intersection exists;
calling monitoring data of a reserved grain depot, identifying vehicle data entering the warehouse, triggering a middle-level early warning if the identified vehicle data entering the warehouse is inconsistent with the vehicle data recorded by the service, and triggering the middle-level early warning if the identified vehicle data is not available;
if all the three middle-level early warnings are triggered, a high-level early warning is triggered.
The marked abnormal data is classified and sorted, and the method specifically comprises the following steps: and sorting the marked abnormal data by taking the goods name and the specification as standards.
The warehouse-in/warehouse-out data comprises vehicle license number data and weighing data.
The vehicle data comprises license plate number data and vehicle type data.
The invention is based on the invoice data of the grain depot, the business data of grain warehouse entry and exit and the video monitoring data of the grain depot, and carries out cross validation under the condition that each item of data is self-consistent and complete, thereby checking the authenticity of the grain depot business and checking the transaction risk, namely the risk of 'turning grains'.
Checking invoice data;
the invoice data can reflect the operation condition of a real enterprise from one angle, serve the requirement of supervision and exhibition, obtain the entry invoice data and the sale invoice data of a reserved grain and grain depot through a tax disk, arrange and compare the entry invoice data and the sale invoice data, and can discover some risk signals by adopting multiple rule verification, wherein the period can be 1 year, also can be a reserved period of 4 years, or a rotation and overhead period of 6 months.
Firstly, in the actual business operation of the grain storage company, the purchasing and selling actions of the same company are avoided to the utmost extent, and special instructions are also needed if the actions occur and are not normal business actions generally. Secondly, when the reserved grain company and the trade enterprise intend to perform the operation of "revolving grain", the period is usually prolonged, and the contract quantity is broken up to perform the operation, that is, 5000 tons of old grain is sold to the company in 6 months, and then 2000 tons, 2000 tons and 1000 tons of old grain are purchased back after 9 months and 10 months, so as to confuse the supervision.
Therefore, the invoice checking is divided into the following three steps:
in the invoice data of a reserved grain depot, if an entry ticket source enterprise and an entry ticket selling client enterprise are the same company, the business operation of the grain depot is proved to have illegal and abnormal operations, important attention is needed, and primary early warning is triggered. The invoice entering and selling the company also belongs to abnormal invoice data and is to be verified in the next step.
The abnormal invoice data of the same company are sorted, and the invoices with the same goods name and specification are classified together to form
The amount of the entry ticket forms a series { entry 1, entry 2, entry 3 …, entry n },
the amount of the sales ticket forms the sequence of numbers { sales item 1, sales item 2, sales item 3 … sales item n }.
Then the number series of the amount of the entering item and the amount of the canceling item are respectively arranged and combined to form a plurality of combined number series, namely, the amount of the entering item forms
…
Up to
Array of numbers
Forming the amount of the sales ticket
…
Up to
Array of numbers
Will be assembled
And collections
And (6) solving intersection.
If there is an intersection between the two sets, i.e., there are duplicate numbers, then the representative may be the possibility of splitting the contract and repurchasing.
If the invoice data contained in the repeated digital invoice appears, the invoice data is marked as high-risk business data, which indicates that the reserve grain depot is likely to have a 'grain rotation' transaction with the trading company indeed, and a middle-level early warning is triggered.
Checking service data;
the data detection is only carried out through the invoice data, the possibility of 'turning grains' operation cannot be completely explained, the business data of grain warehouse entry and exit needs to be used, and the weighing data of grain warehouse entry and exit weighing data of the grain is detected in the wagon balance for verification. 4G or Nb-iot Internet of things gateway equipment is additionally arranged on the wagon balance of the grain depot, weighing data are directly collected, and manual modification of data in later period can be avoided.
In the checking of the in-out data, the number plate numbers in the warehouse and the number plate numbers out of the warehouse are respectively arranged into two sets, the intersection of the two sets is solved, namely, whether the same number plate number has both the condition of the in-warehouse record and the condition of the out-warehouse record is checked, and if the condition exists, the business record belongs to abnormal business data.
And comparing the gross weighing of the same vehicle, if the gross weighing of the vehicle in the warehouse and the vehicle in the warehouse are found to be completely consistent, representing that the vehicle possibly has the condition that the original vehicle is warehoused again after grain is delivered out of the warehouse, and the service record belongs to high-risk service data. A medium warning should be triggered.
Checking video data;
video monitoring is standard distribution of the current digital grain depot system, and the digital grain depot system and a provincial grain supervision platform can store video clips or picture records of grain depot input depot services. And respectively using the license plate recognition algorithm and the vehicle recognition algorithm to detect the monitoring video and recognize risks.
And detecting the video monitoring of grain warehousing and warehousing by using a license plate recognition algorithm, recognizing the actual license plate of each warehousing and warehousing business under the condition that the license plate recognition accuracy is more than 95%, and if the monitoring result of the license plate recognition algorithm is inconsistent with the license plate recorded by the business, triggering middle-level early warning, wherein the business record belongs to high-risk business data.
And detecting the grain warehouse-in and warehouse-out video monitoring by using a vehicle identification algorithm, identifying vehicles of warehouse-in and warehouse-out services every time under the condition that the vehicle identification accuracy is more than 95%, if the vehicles cannot be detected by the vehicle identification algorithm, identifying that the warehouse-in and warehouse-out operation is possibly counterfeit service data, and triggering intermediate-level early warning if the service record belongs to high-risk service data.
Comprehensively judging;
the cross validation of three types of data including invoice, business record and video record is comprehensive and multidimensional for checking the business behavior of the grain depot.
If invoices, business records, video records all produce high risk business data, advanced early warning should be triggered.
The present invention further provides a system 200 for monitoring and warning transaction risk based on service data, as shown in fig. 2, including:
the monitoring module 201 is used for calling invoice data of a reserved grain depot, detecting the invoice data, marking the abnormal data and carrying out preliminary early warning when detecting that the invoice data contains abnormal data of an entry ticket source enterprise and an entry ticket selling enterprise which are the same enterprise;
the invoice data detection module 202 is used for classifying and sorting the marked abnormal data after receiving the preliminary early warning, generating an entry ticket number list and a sales ticket number list according to the amount of the entry tickets and the amount of the sales tickets, generating an entry ticket number list set from the entry ticket number list, generating a sales ticket number list set from the sales ticket number list, acquiring an intersection of the entry ticket number list set and the sales ticket number list set, marking enterprises if the intersection exists and repeated invoice data exists, and triggering the intermediate early warning;
the business data detection module 203 is used for calling the warehousing/ex-warehousing data of the grain depot enterprise, generating warehousing data into a warehousing set, generating ex-warehousing data into an ex-warehousing set, and triggering middle-level early warning if intersection exists;
the monitoring data detection module 204 is used for calling monitoring data of a reserved grain depot, identifying vehicle data entering the warehouse or leaving the warehouse, triggering medium-level early warning if the identified vehicle data entering the warehouse is inconsistent with the vehicle data recorded by the service, and triggering the medium-level early warning if the identified vehicle data is not available;
and the early warning module 205 triggers the high-level early warning if all the three medium-level early warnings are detected to be triggered.
The marked abnormal data is classified and sorted, and the method specifically comprises the following steps: and sorting the marked abnormal data by taking the goods name and the specification as standards.
The warehouse-in/warehouse-out data comprises vehicle license number data and weighing data.
The vehicle data comprises license plate number data and vehicle type data.
The invention does not need to rely on the manual experience, and can effectively investigate the potential risk transaction behavior by only using the data precipitated by the service system and adding a special algorithm, thereby providing a reference basis for the development of actual supervision work;
the early warning effect provided by the invention enables a supervisor to purposefully carry out field supervision work and field inspection work, improves the efficiency and reduces the cost.
The invention improves the working efficiency of on-site supervision, reduces the cost, is beneficial to a supervision organization to put limited resources to other places for convenience, and improves the overall level of the stored grain management work
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 scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
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.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (8)
1. A method of monitoring and forewarning transaction risk based on business data, the method comprising:
calling invoice data of a grain depot, detecting the invoice data, marking the abnormal data and carrying out preliminary early warning when detecting that the abnormal data of an entry ticket source enterprise and an entry ticket selling enterprise are the same enterprise exists in the invoice data;
after the preliminary early warning is received, classifying and sorting the marked abnormal data, generating an entry ticket number array and a sales ticket number array according to the entry ticket amount and the sales ticket amount for the classified abnormal data, generating an entry ticket number array and a sales ticket number array according to the entry ticket amount and the sales ticket amount, generating an entry ticket number array set according to the entry ticket number array, generating a sales ticket number array set according to the sales ticket number array, acquiring an intersection of the entry ticket number array set and the sales ticket number array set, marking the enterprise if the intersection exists and repeated invoice data exists, and triggering the intermediate early warning;
transferring warehousing/ex-warehousing data of a reserve grain depot enterprise, generating warehousing data into a warehousing set, generating ex-warehousing data into an ex-warehousing set, and triggering a middle-level early warning if an intersection exists;
calling monitoring data of a reserved grain depot, identifying vehicle data entering the warehouse, triggering a middle-level early warning if the identified vehicle data entering the warehouse is inconsistent with the vehicle data recorded by the service, and triggering the middle-level early warning if the identified vehicle data is not available;
if all the three middle-level early warnings are triggered, a high-level early warning is triggered.
2. The method according to claim 1, wherein the sorting of the marked abnormal data includes: and sorting the marked abnormal data by taking the goods name and the specification as standards.
3. The method of claim 1, wherein the warehouse entry/exit data comprises warehouse entry vehicle license number data and weighing data.
4. The method of claim 1, the vehicle data comprising license plate number data and vehicle model data.
5. A system for monitoring and pre-warning of transaction risk based on business data, the system comprising:
the monitoring module is used for calling invoice data of a reserved grain depot, detecting the invoice data, marking the abnormal data and carrying out preliminary early warning when detecting that the abnormal data of an entry ticket source enterprise and an entry ticket selling enterprise are the same enterprise exists in the invoice data;
the invoice data detection module is used for classifying and sorting the marked abnormal data after receiving the preliminary early warning, generating an entry ticket number array and a sales ticket number array according to the amount of the entry tickets and the amount of the sales tickets, generating an entry ticket number array set from the entry ticket number array, generating a sales ticket number array set from the sales ticket number array, acquiring an intersection of the entry ticket number array set and the sales ticket number array set, marking enterprises if the intersection exists and repeated invoice data exists, and triggering the intermediate early warning;
the business data detection module is used for calling the warehousing/ex-warehousing data of the grain depot enterprise, generating warehousing data into a warehousing set, generating ex-warehousing data into an ex-warehousing set, and triggering middle-level early warning if intersection exists;
the monitoring data detection module is used for calling monitoring data of the grain storage, identifying vehicle data entering and leaving the warehouse, triggering medium-level early warning if the identified vehicle data entering and leaving the warehouse is inconsistent with the vehicle data recorded by the business, and triggering the medium-level early warning if the vehicle data cannot be identified;
and the early warning module is used for triggering the high-grade early warning if all the three middle-grade early warnings are detected to be triggered.
6. The system according to claim 5, wherein the marked abnormal data is sorted by: and sorting the marked abnormal data by taking the goods name and the specification as standards.
7. The system of claim 5, the warehouse entry/exit data comprising warehouse entry vehicle license number data and weighing data.
8. The system of claim 5, the vehicle data comprising license plate number data and vehicle model data.
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| CN116976655A (en) * | 2023-06-15 | 2023-10-31 | 四川仕虹腾飞信息技术有限公司 | Distributed banking website transaction flow supervision system and method thereof |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116976655A (en) * | 2023-06-15 | 2023-10-31 | 四川仕虹腾飞信息技术有限公司 | Distributed banking website transaction flow supervision system and method thereof |
| CN116976655B (en) * | 2023-06-15 | 2024-05-24 | 四川仕虹腾飞信息技术有限公司 | Distributed banking website transaction flow supervision system and method thereof |
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