An integrated anti-fraud platform and method for public security and financial communications

Abstract

The present invention relates to an integrated anti-fraud platform and method for public security finance and communication, wherein the platform comprises: a clue reporting interface, a shared database, an early warning data management unit, a risk query service unit, an anti-fraud measure issuing unit and a coordination unit arranged on an anti-fraud special network; an early warning distribution and disposal unit, a data aggregation unit and a risk identification unit arranged on a public security network; an interface unit arranged on each third-party financial / communication special network; a clue reporting interface receives and uploads anti-fraud clues; a shared database stores event data information uploaded by each unit in a public security system; an early warning data management unit is used to push early warning management data; a risk query service unit provides risk query services; an anti-fraud measure issuing unit is used to issue anti-fraud protection measures; a coordination unit is used to coordinate instruction flow and feedback of disposal data; an early warning distribution and disposal unit is used to divert and dispose of anti-fraud clues; and a risk identification unit adds risk identification to risk targets.

Description

Technical Field

[0001] The present invention relates to a public security financial communication integrated anti-fraud platform and method, belonging to the technical field of anti-fraud. Background Art

[0002] In order to improve the efficiency of anti-fraud work, intelligent anti-fraud platforms have gradually begun to be used in anti-fraud work. Through big data analysis, artificial intelligence algorithms and machine learning technology, intelligent anti-fraud platforms can monitor and identify various fraud behaviors in real time, thereby effectively preventing and combating fraud crimes. Intelligent anti-fraud systems can automatically screen and analyze massive amounts of information, quickly identify potential fraudulent calls, text messages and network information, and promptly remind users to take precautions. This not only improves efficiency, but also greatly reduces labor costs, making anti-fraud work more accurate and efficient.

[0003] However, most of the current anti-fraud platforms are built on anti-fraud dedicated networks, which have no connection with the public security network and third-party dedicated networks (such as bank networks, communication management networks), etc., making data transmission and circulation difficult, reducing the efficiency of anti-fraud work. Summary of the invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes an integrated public security and financial communication anti-fraud platform and method.

[0005] The technical solution of the present invention is as follows:

[0006] On the one hand, the present invention provides a public security financial communication integrated anti-fraud platform, comprising:

[0007] The clue reporting interface, shared database, early warning data management unit, risk query service unit, anti-fraud measures issuing unit and coordination unit set up in the anti-fraud network; the early warning distribution and disposal unit, data aggregation unit and risk identification unit set up in the public security network; the interface unit set up in each third-party financial / communication network;

[0008] The clue reporting interface is used to receive anti-fraud clues reported by various units in the public security system and upload them to the early warning distribution and disposal unit; the shared database is used to store event data information uploaded by various units in the public security system and upload it to the data aggregation unit; the early warning data management unit is used to push early warning management data to the data aggregation unit; the risk query service unit is used to provide query services and interface services for the risk database; the anti-fraud measures issuance unit is used to issue anti-fraud protection measures to the interface units of each third-party financial / communication private network; the collaborative unit is connected to the interface units of each third-party financial / communication private network for collaborative instruction flow and feedback of disposal data; the early warning distribution and disposal unit is connected to the public security network interconnection system for diversion and disposal of anti-fraud clues; the risk identification unit is used to add risk identification to risk targets based on anti-fraud clues and event data information, and upload them to the blockchain-based risk database. The risk targets include risk telephones, risk personnel, risk accounts, risk websites and risk mailboxes.

[0009] As a preferred implementation manner, the anti-fraud clues include call record clues, bank account transaction clues, risky website browsing clues, and email keyword clues.

[0010] As a preferred implementation, the steps of adding risk identification to risk targets according to anti-fraud clues and event data information and uploading them to the blockchain-based risk database are as follows:

[0011] Input the anti-fraud clues and event data containing the same target information into the pre-trained neural network-based risk identification model to identify whether the corresponding target is a risk target, and add a risk tag to the identified risk target;

[0012] Classify different targets and obtain several subsets consisting of different targets;

[0013] For any subset of risk targets, risk propagation processing is performed within the subset based on event data, and the risk infection degree of all targets in the associated set is calculated;

[0014] Based on the basic attributes of risk targets and targets with a risk infection degree greater than 0, a hash calculation is performed, and the calculated hash value is used as the block address to chain the risk targets and targets with a risk infection degree greater than 0 to the blockchain-based risk database.

[0015] As a preferred implementation, the method of performing risk propagation processing in a subset based on event data and calculating the risk infection degree of all targets in the associated set is specifically:

[0016] Traverse the event data and obtain all targets in the sub-collection that have events associated with the risk target;

[0017] The risk infection degree of any target with which a risk target has an associated event is calculated according to the following formula:

[0018] ;

[0019] in, The risk of infection is is the number of associated events actively initiated by the corresponding target, is the number of associated events actively initiated by the corresponding risk target, For the The event feature value of the associated event actively initiated by the corresponding target, For the The event characteristic value of the associated event actively initiated by the corresponding risk target, and are the preset active weight coefficient and passive weight coefficient respectively, and + =1; is the number of risk events corresponding to the risk target, For the The event characteristic value of the secondary risk event.

[0020] As a preferred implementation, the anti-fraud network is further provided with a risk classification unit;

[0021] The risk classification unit is used to set risk thresholds of different sizes, and obtain the risk infection degree of all targets in the risk database through the risk query service unit;

[0022] Different targets are divided into different risk levels according to the risk threshold and the risk infection degree of the corresponding target.

[0023] As a preferred implementation, upon receiving the instruction to issue anti-fraud protection measures, the anti-fraud measure issuing unit formulates and issues different anti-fraud protection measures according to the risk level of the corresponding target.

[0024] As a preferred implementation, the risk identification unit is also used to dynamically adjust the risk infection level of each target, and the specific method is:

[0025] For each target uploaded to the risk database, the risk infection level is dynamically adjusted using the following formula:

[0026] ;

[0027] in, Indicates the risk infection degree after dynamic adjustment, Indicates the risk infection level before dynamic adjustment; Indicates the time interval from the last association event between the corresponding target and the risk target to the present; , , are different time length thresholds, and > > .

[0028] On the other hand, the present invention also provides a public security financial communication integrated anti-fraud method, comprising the following steps:

[0029] Set up clue reporting interface, shared database, early warning data management unit, risk query service unit, anti-fraud measures issuing unit and coordination unit in the anti-fraud network; set up early warning distribution and disposal unit, data aggregation unit and risk identification unit in the public security network; set up interface units in each third-party financial / communication network;

[0030] Receive anti-fraud clues reported by various units in the public security system through the clue reporting interface and upload them to the early warning distribution and disposal unit; receive event data information uploaded by various units in the public security system through the shared database and upload them to the data aggregation unit; push early warning management data to the data aggregation unit through the early warning data management unit; provide query services and interface services for the risk database through the risk query service unit; issue anti-fraud protection measures to the interface units of various third-party financial / communication private networks through the anti-fraud measures issuance unit; connect with the interface units of various third-party financial / communication private networks through the collaborative unit to conduct collaborative instruction flow and feedback on disposal data; connect to the public security network interconnection system through the early warning distribution and disposal unit to divert and dispose of anti-fraud clues; add risk identification to risk targets according to anti-fraud clues and event data information through the risk identification unit, and upload them to the risk database based on blockchain. The risk targets include risk telephones, risk personnel, risk accounts, risk websites and risk mailboxes;

[0031] Among them, an RPA robot module is set in the anti-fraud measures issuance unit, and a variety of anti-fraud protection measures issuance rules are pre-customized in the RPA robot module. When any anti-fraud protection measures issuance rule is triggered, the RPA robot module automatically generates the corresponding anti-fraud protection measures issuance instruction.

[0032] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the integrated anti-fraud method for public security and financial communications as described in any embodiment of the present invention is implemented.

[0033] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the integrated public security financial communication anti-fraud method as described in any embodiment of the present invention.

[0034] The present invention has the following beneficial effects:

[0035] 1. The present invention proposes an integrated anti-fraud platform for public security finance and communication, which sets corresponding functional units in the anti-fraud network, public security network and third-party financial / communication network, and realizes data aggregation, measure issuance, early warning management and risk identification of anti-fraud information through the cooperation of various functional units, thereby effectively improving the efficiency of anti-fraud work.

[0036] 2. The present invention proposes an integrated anti-fraud platform for public security finance and communications, which stores risk targets and targets at risk of being infected by risk transmission through blockchain, thereby ensuring the reliability of targets at risk of anti-fraud incidents.

[0037] 3. The present invention proposes an integrated anti-fraud platform for public security, finance and communications, which quantifies the risk infection degree of different targets and improves the effectiveness of anti-fraud investigation.

[0038] 4. The present invention proposes an integrated anti-fraud platform for public security finance and communications, which classifies risk levels based on the degree of risk infection, and then formulates different anti-fraud protection measures according to the risk levels to improve the efficiency of anti-fraud work. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a schematic diagram of a platform framework according to Embodiment 1 of the present invention;

[0040] Figure 2 This is a schematic diagram of the method flow of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0043] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0044] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0045] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0046] Embodiment 1:

[0047] See also Figure 1 This embodiment provides a public security financial communication integrated anti-fraud platform, including:

[0048] The clue reporting interface, shared database, early warning data management unit, risk query service unit, anti-fraud measures issuing unit and coordination unit set up in the anti-fraud network; the early warning distribution and disposal unit, data aggregation unit and risk identification unit set up in the public security network; the interface unit set up in each third-party financial / communication network;

[0049] The clue reporting interface is used to receive anti-fraud clues reported by various units in the public security system and upload them to the early warning distribution and disposal unit to achieve rapid transmission and efficient processing of clues.

[0050] The shared database is used to store event data information uploaded by various units in the public security system and uploaded to the data aggregation unit, as well as financial transaction data and communication data received from the third-party financial / communication private network interface unit. After preprocessing and standardization, these data are formed into a unified data format to facilitate subsequent risk analysis and early warning management.

[0051] The early warning data management unit is used to push the early warning management data to the data aggregation unit, and generate risk early warning information according to the processing results of the data aggregation unit for reference and decision-making by various units in the public security system.

[0052] The risk query service unit is used to provide query services and interface services for the risk database, so that the public security system and other relevant units can query risk information in real time and improve anti-fraud efficiency. The risk database contains data such as various fraud cases, fraud methods, and information on fraud-related personnel, providing strong data support for anti-fraud work. At the same time, the risk query service unit also provides a wealth of interface services to facilitate integration and calling of other systems or applications, and realize information sharing and collaborative work.

[0053] The anti-fraud measures issuing unit is used to issue anti-fraud protection measures to the interface units of each third-party financial / communication private network, and formulate corresponding anti-fraud strategies according to the risk level and actual situation, so as to effectively prevent the occurrence of financial fraud and communication fraud.

[0054] The coordination unit is connected to the interface units of each third-party financial / communication private network to coordinate the flow of instructions and the feedback of processing data, thereby ensuring the efficient implementation of anti-fraud measures and real-time synchronization of information.

[0055] The early warning distribution and disposal unit is connected to the public security network interconnection system to divert and dispose of anti-fraud clues; the unit can distribute the clues to the corresponding public security system units or departments for further investigation and processing based on the urgency, type, and geographical location of the anti-fraud clues. At the same time, the early warning distribution and disposal unit also has the function of real-time tracking and monitoring the status of clue processing to ensure that each clue can be processed in a timely and effective manner. In addition, the unit can also feedback the processing results to the relevant reporting units or departments to form a closed-loop management and improve the efficiency and accuracy of anti-fraud work.

[0056] The risk identification unit is used to add risk identification to risk targets based on anti-fraud clues and event data information, and upload them to a blockchain-based risk database.

[0057] The risk identification unit can accurately identify potential risk targets through an algorithm model and automatically add corresponding risk identification according to their risk levels. These risk identifications not only help various units within the public security system to quickly identify and respond to risks, but also provide strong support for subsequent risk management and decision-making. At the same time, uploading risk data to a blockchain-based risk database can ensure the immutability and security of the data, providing more reliable data protection for anti-fraud work.

[0058] Among them, risk targets include risky telephones, risky personnel, risky accounts, risky websites and risky mailboxes. After these risky targets are identified by the risk identification unit, they can be shared and managed more effectively within the public security system. Risky telephones and risky mailboxes can be monitored and tracked to prevent the spread of fraudulent information; the information of risky personnel can be integrated and analyzed to assist the police in the investigation and tracking of suspects; risky accounts can be frozen or restricted to prevent the flow of fraudulent funds; risky websites can be blocked or warned to protect the public from phishing websites and malicious software. In this way, the public security financial communication integrated anti-fraud platform can achieve a full-scale and multi-level crackdown on fraudulent behavior.

[0059] As a preferred implementation manner of this embodiment, the anti-fraud clues include call record clues, bank account transaction clues, risky website browsing clues, and email keyword clues.

[0060] These clue types cover multiple aspects such as communications, financial transactions, and online behaviors, providing a comprehensive and detailed source of information for anti-fraud work. Call record clues can reveal the suspect's social network and communication patterns, helping analysts track and identify potential fraud. Bank account transaction clues can show the flow of funds, helping to discover abnormal transaction patterns and money laundering. Risky website browsing clues can warn users to avoid visiting malicious websites and reduce the risk of being defrauded. Email keyword clues can detect and prevent the spread of fraudulent emails in a timely manner by monitoring sensitive words in the email content. The integration and analysis of these clues can greatly improve the efficiency and accuracy of anti-fraud work.

[0061] As a preferred implementation of this embodiment, the steps of adding risk identification to risk targets according to anti-fraud clues and event data information and uploading them to the blockchain-based risk database are as follows:

[0062] The anti-fraud clues and related event data containing the same target information are input into a pre-trained neural network-based risk identification model to identify whether these targets are potential risk targets. Once a target is identified as a risk target, the system will automatically add a risk tag to the target for subsequent tracking and processing.

[0063] In order to effectively manage and process various different targets, a classification processing method is adopted to subdivide these targets into several subsets. Each subset consists of different targets with similar characteristics or attributes, so that a more accurate and efficient processing strategy can be formulated for each subset.

[0064] When processing a subset containing at least one risk target, it is first necessary to process the risk propagation within the subset based on the event data. This process involves a detailed analysis of all targets in the subset to determine the possible risk associations between them. Through such analysis, the risk infection level of each target in the associated set can be calculated, thereby confirming the risk level of the entire subset.

[0065] Based on the basic attributes of risk targets and targets with a risk infection degree greater than 0, a hash calculation is performed, and the calculated hash value is used as the block address to chain the risk targets and targets with a risk infection degree greater than 0 to the blockchain-based risk database.

[0066] This step ensures that all targets identified as risks, as well as those that are not directly identified as risks but have a risk infection level greater than 0, can be recorded in a secure and tamper-proof manner on the blockchain. Using the distributed nature of the blockchain, these data can be shared among multiple institutions or departments, thereby improving the transparency and traceability of information. At the same time, the use of hash calculations also ensures the integrity and uniqueness of the data, so that each target can be accurately identified and located on the blockchain. The establishment of this blockchain-based risk database will not only help improve the efficiency and accuracy of anti-fraud work, but also provide strong support for subsequent case detection and data analysis.

[0067] As a preferred implementation of this embodiment, the method of performing risk propagation processing in a subset based on event data and calculating the risk infection degree of all targets in the associated set is specifically:

[0068] Traverse the event data and obtain all targets in the sub-collection that have events associated with the risk target;

[0069] The risk infection degree of any target with which a risk target has an associated event is calculated according to the following formula:

[0070] ;

[0071] in, The risk of infection is is the number of associated events actively initiated by the corresponding target, is the number of associated events actively initiated by the corresponding risk target, For the The event feature value of the associated event actively initiated by the corresponding target, For the The event characteristic value of the associated event actively initiated by the corresponding risk target, and are the preset active weight coefficient and passive weight coefficient respectively, and + =1; is the number of risk events corresponding to the risk target, For the The event characteristic value of the secondary risk event.

[0072] As a preferred implementation of this embodiment, a risk classification unit is also provided in the anti-fraud network;

[0073] The risk classification unit is used to set risk thresholds of different sizes, and obtain the risk infection degree of all targets in the risk database through the risk query service unit;

[0074] Different targets are divided into different risk levels according to the risk threshold and the risk infection degree of the corresponding target.

[0075] Specifically, the risk classification unit first presets multiple risk thresholds, which represent different levels of risk. After the risk query service unit obtains the risk infection level of each target from the risk database, the risk classification unit compares these risk infection levels with the preset risk thresholds. Based on the comparison results, the risk classification unit can classify the target into different risk levels such as low risk, medium risk and high risk. Such risk classification helps the anti-fraud platform take different treatment measures for targets of different risk levels, thereby improving anti-fraud efficiency and accuracy.

[0076] As a preferred implementation of this embodiment, when the anti-fraud measures issuing unit receives the instruction to issue anti-fraud protection measures, it formulates and issues different anti-fraud protection measures according to the risk level of the corresponding target. Specifically, the anti-fraud measures issuing unit will formulate targeted anti-fraud protection measures for targets of different risk levels according to the risk level information provided by the risk classification unit. For example, for targets at low risk levels, the anti-fraud measures issuing unit may take simple reminders or warning measures; for targets at medium risk levels, more stringent monitoring and restriction measures may be taken; and for targets at high risk levels, emergency protection measures such as freezing accounts and intercepting transactions may be taken immediately. These anti-fraud protection measures are intended to block fraudulent behavior in a timely and effective manner and protect the user's financial security. At the same time, the anti-fraud measures issuing unit will also issue the formulated anti-fraud protection measures information to the relevant executive agencies or systems to ensure that the measures are implemented in a timely and effective manner. Through such an anti-fraud measures issuing mechanism, the integrated anti-fraud platform for public security finance and communications can achieve precise strikes against targets at different risk levels and improve the efficiency and accuracy of anti-fraud work.

[0077] As a preferred implementation, the risk identification unit is also used to dynamically adjust the risk infection level of each target, and the specific method is:

[0078] For each target uploaded to the risk database, the risk infection level is dynamically adjusted using the following formula:

[0079] ;

[0080] in, Indicates the risk infection degree after dynamic adjustment, Indicates the risk infection level before dynamic adjustment; Indicates the time interval from the last association event between the corresponding target and the risk target to the present; , , are different time length thresholds, and > > .

[0081] When the time interval Less than the time length threshold When , it means that the event of association between the target and the risk target is relatively recent, so the risk infection degree after dynamic adjustment will be relatively high; when the time interval is Greater than the time length threshold And less than the time length threshold When , it means that the associated events between the target and the risk target are gradually moving away from the current time, so the risk infection degree after dynamic adjustment will be relatively reduced; when the time interval Greater than the time length threshold And less than the time length threshold When , it means that the associated event between the target and the risk target is far away from the current time, so the risk infection degree after dynamic adjustment will be further reduced. Greater than the time length threshold , it means that the association event between the target and the risk target has far exceeded the current time, so the risk infection degree after dynamic adjustment is 0. Through such a dynamic adjustment method, the degree of association between each target and the risk target can be more accurately reflected, providing a more accurate decision-making basis for subsequent risk control and anti-fraud measures.

[0082] Embodiment 2:

[0083] See also Figure 2 This embodiment provides a public security financial communication integrated anti-fraud method, including the following steps:

[0084] S100, set up functional units in the anti-fraud network, the public security network and the third-party financial / communication network; specifically, the clue reporting interface, shared database, early warning data management unit, risk query service unit, anti-fraud measures issuing unit and coordination unit set up in the anti-fraud network; the early warning distribution and disposal unit, data aggregation unit and risk identification unit set up in the public security network; and the interface units set up in each third-party financial / communication network;

[0085] S200, processing anti-fraud information and issuing anti-fraud protection measures through various functional units, specifically including: receiving anti-fraud clues reported by various units of the public security system through the clue reporting interface and uploading them to the early warning distribution and disposal unit; receiving event data information uploaded by various units in the public security system through the shared database and uploading them to the data aggregation unit; pushing early warning management data to the data aggregation unit through the early warning data management unit; providing query services and interface services for the risk database through the risk query service unit; issuing anti-fraud protection measures to the interface units of various third-party financial / communication private networks through the anti-fraud measures issuing unit; connecting with the interface units of various third-party financial / communication private networks through the collaborative unit to conduct collaborative instruction flow and feedback of disposal data; connecting to the public security network interconnection system through the early warning distribution and disposal unit to divert and dispose of anti-fraud clues; adding risk identification to risk targets according to anti-fraud clues and event data information through the risk identification unit, and uploading them to the risk database based on blockchain, wherein the risk targets include risk telephones, risk personnel, risk accounts, risk websites and risk mailboxes;

[0086] Among them, an RPA robot module is set in the anti-fraud measures issuance unit, and a variety of anti-fraud protection measures issuance rules are pre-customized in the RPA robot module. When any anti-fraud protection measures issuance rule is triggered, the RPA robot module automatically generates the corresponding anti-fraud protection measures issuance instruction.

[0087] As a preferred implementation manner of this embodiment, the anti-fraud clues include call record clues, bank account transaction clues, risky website browsing clues, and email keyword clues.

[0088] As a preferred implementation of this embodiment, the steps of adding risk identification to risk targets according to anti-fraud clues and event data information and uploading them to the blockchain-based risk database are as follows:

[0089] Input the anti-fraud clues and event data containing the same target information into the pre-trained neural network-based risk identification model to identify whether the corresponding target is a risk target, and add a risk tag to the identified risk target;

[0090] Classify different targets and obtain several subsets consisting of different targets;

[0091] For any subset of risk targets, risk propagation processing is performed within the subset based on event data, and the risk infection degree of all targets in the associated set is calculated;

[0092] Based on the basic attributes of risk targets and targets with a risk infection degree greater than 0, a hash calculation is performed, and the calculated hash value is used as the block address to chain the risk targets and targets with a risk infection degree greater than 0 to the blockchain-based risk database.

[0093] As a preferred implementation of this embodiment, the method of performing risk propagation processing in a subset based on event data and calculating the risk infection degree of all targets in the associated set is specifically:

[0094] Traverse the event data and obtain all targets in the sub-collection that have events associated with the risk target;

[0095] The risk infection degree of any target with which a risk target has an associated event is calculated according to the following formula:

[0096] ;

[0097] in, The risk of infection is is the number of associated events actively initiated by the corresponding target, is the number of associated events actively initiated by the corresponding risk target, For the The event feature value of the associated event actively initiated by the corresponding target, For the The event characteristic value of the associated event actively initiated by the corresponding risk target, and are the preset active weight coefficient and passive weight coefficient respectively, and + =1; is the number of risk events corresponding to the risk target, For the The event characteristic value of the secondary risk event.

[0098] As a preferred implementation of this embodiment, a risk classification unit is also provided in the anti-fraud network;

[0099] The risk classification unit is used to set risk thresholds of different sizes, and obtain the risk infection degree of all targets in the risk database through the risk query service unit;

[0100] Different targets are divided into different risk levels according to the risk threshold and the risk infection degree of the corresponding target.

[0101] As a preferred implementation mode of this embodiment, upon receiving the instruction to issue anti-fraud protection measures, the anti-fraud measure issuing unit formulates and issues different anti-fraud protection measures according to the risk level of the corresponding target.

[0102] As a preferred implementation, the risk identification unit is also used to dynamically adjust the risk infection level of each target, and the specific method is:

[0103] For each target uploaded to the risk database, the risk infection level is dynamically adjusted using the following formula:

[0104] ;

[0105] in, Indicates the risk infection degree after dynamic adjustment, Indicates the risk infection level before dynamic adjustment; Indicates the time interval from the last association event between the corresponding target and the risk target to the present; , , are different time length thresholds, and > > .

[0106] Embodiment three:

[0107] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the integrated public security financial communication anti-fraud method as described in any embodiment of the present invention is implemented.

[0108] Embodiment 4:

[0109] This embodiment proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the integrated public security and financial communication anti-fraud method as described in any embodiment of the present invention is implemented.

[0110] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0111] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0112] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0114] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A public security financial communication integrated anti-fraud platform, characterized in that: include: The clue reporting interface, shared database, early warning data management unit, risk query service unit, anti-fraud measures distribution unit and coordination unit set up on the anti-fraud network; The early warning distribution and disposal unit, data aggregation unit and risk identification unit set up in the public security network; the interface unit set up in each third-party financial / communication network; The clue reporting interface is used to receive anti-fraud clues reported by various units in the public security system and upload them to the early warning distribution and disposal unit; the shared database is used to store event data information uploaded by various units in the public security system and upload them to the data aggregation unit; the early warning data management unit is used to push early warning management data to the data aggregation unit; the risk query service unit is used to provide query services and interface services for the risk database; the anti-fraud measures issuing unit is used to issue anti-fraud protection measures to the interface units of various third-party financial / communication private networks; The coordination unit is connected to the interface unit of each third-party financial / communication network to coordinate the flow of instructions and the feedback of processing data; the early warning distribution and processing unit is connected to the public security network interconnection system to divert and process anti-fraud clues; the risk identification unit is used to add risk identification to risk targets based on anti-fraud clues and event data information, and upload them to the risk database based on blockchain, specifically to classify different targets and obtain a number of subsets consisting of different targets; For any subset of risk targets, risk propagation processing is performed within the subset based on event data, and the risk infection degree of all targets in the associated set is calculated, specifically: Traverse the event data and obtain all targets in the sub-collection that have events associated with the risk target; The risk infection degree of any target with which a risk target has an associated event is calculated according to the following formula: ； in, The risk of infection is is the number of associated events actively initiated by the corresponding target, is the number of associated events actively initiated by the corresponding risk target, For the The event feature value of the associated event actively initiated by the corresponding target, For the The event characteristic value of the associated event actively initiated by the corresponding risk target, and are the preset active weight coefficient and passive weight coefficient respectively, and + =1; is the number of risk events corresponding to the risk target, For the Event characteristic value of the secondary risk event; Based on the basic attributes of risk targets and targets with a risk infection degree greater than 0, hash calculation is performed, and the calculated hash value is used as the block address to chain the risk targets and targets with a risk infection degree greater than 0 to the blockchain-based risk database; The risk targets include risk telephones, risk personnel, risk accounts, risk websites and risk mailboxes.

2. According to claim 1, the integrated anti-fraud platform for public security finance and communication is characterized by: The anti-fraud clues include call record clues, bank account transaction clues, risky website browsing clues, and email keyword clues.

3. The integrated anti-fraud platform for public security finance and communication according to claim 1 is characterized in that: The steps of adding risk identification to risk targets according to anti-fraud clues and event data information by the risk identification unit are specifically as follows: The anti-fraud clues and event data containing the same target information are input into the pre-trained neural network-based risk identification model to identify whether the corresponding target is a risk target and add a risk label to the identified risk target.

4. The integrated anti-fraud platform for public security finance and communication according to claim 3 is characterized in that: The anti-fraud network is also provided with a risk classification unit; The risk classification unit is used to set risk thresholds of different sizes, and obtain the risk infection degree of all targets in the risk database through the risk query service unit; Different targets are divided into different risk levels according to the risk threshold and the risk infection degree of the corresponding target.

5. The integrated anti-fraud platform for public security finance and communication according to claim 4 is characterized by: When receiving the instruction to issue anti-fraud protection measures, the anti-fraud measure issuing unit formulates and issues different anti-fraud protection measures according to the risk level of the corresponding target.

6. The integrated anti-fraud platform for public security finance and communication according to claim 1 is characterized in that: The risk identification unit is also used to dynamically adjust the risk infection level of each target, and the specific method is as follows: For each target uploaded to the risk database, the risk infection level is dynamically adjusted using the following formula: ； in, Indicates the risk infection degree after dynamic adjustment, Indicates the risk infection level before dynamic adjustment; Indicates the time interval from the last association event between the corresponding target and the risk target to the present; , , are different time length thresholds, and > > .

7. A public security financial communication integrated anti-fraud method, characterized in that: The following steps are involved: Set up clue reporting interface, shared database, early warning data management unit, risk query service unit, anti-fraud measures issuing unit and coordination unit in the anti-fraud network; set up early warning distribution and disposal unit, data aggregation unit and risk identification unit in the public security network; set up interface units in each third-party financial / communication network; Receive anti-fraud clues reported by various units in the public security system through the clue reporting interface and upload them to the early warning distribution and disposal unit; receive event data information uploaded by various units in the public security system through the shared database and upload them to the data aggregation unit; push early warning management data to the data aggregation unit through the early warning data management unit; provide query services and interface services for the risk database through the risk query service unit; and issue anti-fraud protection measures to the interface units of various third-party financial / communication private networks through the anti-fraud measures issuance unit; Through the collaborative unit, the interface units of the third-party financial / communication networks are connected to coordinate the flow of instructions and feedback of processing data; through the early warning distribution and processing unit, the public security network interconnection system is connected to divert and process anti-fraud clues; The risk identification unit adds risk identification to risk targets based on anti-fraud clues and event data information, and uploads it to the blockchain-based risk database. Specifically, different targets are classified and processed to obtain several subsets consisting of different targets; For any subset of risk targets, risk propagation processing is performed within the subset based on event data, and the risk infection degree of all targets in the associated set is calculated, specifically: Traverse the event data and obtain all targets in the sub-collection that have events associated with the risk target; The risk infection degree of any target with which a risk target has an associated event is calculated according to the following formula: ； in, The risk of infection is is the number of associated events actively initiated by the corresponding target, is the number of associated events actively initiated by the corresponding risk target, For the The event feature value of the associated event actively initiated by the corresponding target, For the The event characteristic value of the associated event actively initiated by the corresponding risk target, and are the preset active weight coefficient and passive weight coefficient respectively, and + =1; is the number of risk events corresponding to the risk target, For the Event characteristic value of the secondary risk event; Based on the basic attributes of risk targets and targets with a risk infection degree greater than 0, hash calculation is performed, and the calculated hash value is used as the block address to chain the risk targets and targets with a risk infection degree greater than 0 to the blockchain-based risk database; The risk targets include risk telephones, risk personnel, risk accounts, risk websites and risk mailboxes; Among them, an RPA robot module is set in the anti-fraud measures issuance unit, and a variety of anti-fraud protection measures issuance rules are pre-customized in the RPA robot module. When any anti-fraud protection measures issuance rule is triggered, the RPA robot module automatically generates the corresponding anti-fraud protection measures issuance instruction.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the integrated public security financial communication anti-fraud method as described in claim 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the integrated public security financial communication anti-fraud method as described in claim 7 is implemented.

Images