CA3230500A1 - Method and system for querying abnormal financial data on basis of knowledge map - Google Patents
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Abstract
The present invention makes public a method of and a system for enquiring abnormal financial data based on a knowledge map, whereby abnormal financial data is accurately and quickly identified by means of a knowledge map. The method comprises: designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of intemode relations and nodes; collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map. The system subsumes the method recited in the foregoing technical solution.
Description
METHOD AND SYSTEM FOR QUERYING ABNORMAL FINANCIAL DATA ON
BASIS OF KNOWLEDGE MAP
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of financial anti-fraud technology, and more particularly to a method of and a system for enquiring abnormal financial data based on a knowledge map.
Description of Related Art
BASIS OF KNOWLEDGE MAP
BACKGROUND OF THE INVENTION
Technical Field [0001] The present invention relates to the field of financial anti-fraud technology, and more particularly to a method of and a system for enquiring abnormal financial data based on a knowledge map.
Description of Related Art
[0002] With the development of online finance, loan intermediary has gradually come into being, the middlemen fabricate user materials for populations on the blacklist or the whitelist of credit investigation who would usually find it very hard to pass loan approval, and help them cunningly evade risk control of platforms. Since such customers are mostly not in the capability to normally pay back the loan, loaning to them may engender bad loans for financial platforms and cause capital losses to the financial platforms. Accordingly, to eliminate such fraudulent behaviors, it is of great importance as how to identify the fraudulent behaviors.
[0003] Telephone follow-up or twice confirmation of identification is mainly employed in the state of the art to identify fraudulent behaviors, but it is found in practical application that although the above modes can achieve certain identifying effects with respect to simply answered fraudulent behaviors, it is very difficult for the mode of telephone follow-up or twice confirmation of identification to accurately identify elaborately fabricated fraudulent behaviors, as such behaviors involve a complicated relational network. Therefore, a new challenge has been raised for the identification of fraudulent behaviors.
SUMMARY OF THE INVENTION
SUMMARY OF THE INVENTION
[0004] An objective of the present invention it is to provide a method of and a system for enquiring abnormal financial data based on a knowledge map, whereby abnormal financial data is Date recue/Date received 2024-02-28 accurately and quickly identified by means of a knowledge map.
[0005] In order to achieve the above objective, according to one aspect, the present invention provides a method of enquiring abnormal financial data based on a knowledge map, the method comprises:
[0006] designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
[0007] collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and
[0008] importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map.
[0009] Preferably, the steps of designing structural constitution of a map database according to query requirement of abnormal financial data include:
[0010] the query requirement of abnormal financial data including to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and/or addressee information, and the information includes name data, telephone data, and identification code data; and
[0011] correspondingly setting plural node types based on plural data types, and designing the map database in accordance with a principle of one node corresponding to one piece of data.
[0012] Preferably, the steps of collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database include:
Date recue/Date received 2024-02-28
Date recue/Date received 2024-02-28
[0013] obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data;
[0014] preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data;
[0015] duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and
[0016] subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.
[0017] Optionally, a method of identifying the telephone data and/or the identification code data as invalid is:
[0018] comparing whether the telephone data and/or the identification code data are/is consistent with a standard telephone number and/or a standard identification code in length to judge whether the telephone data and/or the identification code data are/is invalid.
[0019] Preferably, a method of identifying abnormal financial data from the knowledge map includes:
[0020] employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement;
[0021] setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement input according to the query requirement of the abnormal financial data;
Date recue/Date received 2024-02-28
Date recue/Date received 2024-02-28
[0022] scatteredly spreading out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines; and
[0023] screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes.
[0024] Optionally, the step of screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes includes:
[0025] setting an abnormal node identifying threshold, outputting any node consistent in type with the enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold, and obtaining a query result of the illegitimate middleman information.
[0026] Exemplarily, the correlation degree is defined and obtained according to the number of the indicator lines connected with the nodes.
[0027] In comparison with prior-art technology, the method of enquiring abnormal financial data based on a knowledge map provided by the present invention achieves the following advantageous effects.
[0028] In the method of enquiring abnormal financial data based on a knowledge map provided by the present invention, it is firstly needed to design the structural constitution of the map database according to the user's query requirement of abnormal financial data, when the query requirement of abnormal financial data is to enquire illegitimate middleman information from lenders, considering that the illegitimate middleman information obtainable by a platform not only includes names, but also includes such valid identification information as their telephones and identification codes, etc., so three types of nodes can be adopted in designing the structural constitution of the map database, with one node representing one piece of information data;
relational nodes employ Date recue/Date received 2024-02-28 the mode of association by indicator lines to correspondingly design the structural constitution of the map database, plural pieces of sample source data are thereafter collected from the platform, a CSV file identifiable by the map database is formed after data cleaning, the CSV file is finally imported to the map database to construct a knowledge map of sample data, nodes whose correlation degree is higher than a threshold are screened out of the knowledge map, and corresponding information data in the nodes are extracted and output as abnormal financial data, e.g., such valid identification data as names, telephones or identification codes of illegitimate middlemen.
relational nodes employ Date recue/Date received 2024-02-28 the mode of association by indicator lines to correspondingly design the structural constitution of the map database, plural pieces of sample source data are thereafter collected from the platform, a CSV file identifiable by the map database is formed after data cleaning, the CSV file is finally imported to the map database to construct a knowledge map of sample data, nodes whose correlation degree is higher than a threshold are screened out of the knowledge map, and corresponding information data in the nodes are extracted and output as abnormal financial data, e.g., such valid identification data as names, telephones or identification codes of illegitimate middlemen.
[0029] Seen as such, the present invention employs the mode of inputting great quantities of sample data into the map database to form a knowledge map to identify abnormal financial data, and utilizes the characteristic of the knowledge map that is good at processing complicated network relations to express plural pieces of sample data with a structured network, so as to quickly and accurately identify abnormal financial data therefrom.
[0030] According to the other aspect, the present invention provides a system for enquiring abnormal financial data based on a knowledge map, the system is applied to the method of enquiring abnormal financial data based on a knowledge map as recited in the foregoing technical solutions, and comprises:
[0031] a map designing unit, for designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
[0032] a sample collecting unit, for collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and
[0033] an identifying and outputting unit, for importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the Date recue/Date received 2024-02-28 knowledge map.
[0034] Preferably, the sample collecting unit includes:
[0035] an information collecting module, for obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data;
[0036] a screening module, for preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data;
[0037] a duplicate-checking module, for duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and
[0038] a verifying module, for subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.
[0039] Preferably, the identifying and outputting unit includes:
[0040] a pre-storing module, for employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement;
[0041] a setting module, for setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement input according to the query requirement of the abnormal financial data;
[0042] a processing module, for scatteredly spreading out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines; and Date recue/Date received 2024-02-28
[0043] a query outputting module, for screening out relational nodes from the knowledge map according to an input enquiring statement, then identifying abnormal financial data from the screened relational nodes and outputting the same in the form of a query result.
[0044] In comparison with prior-art technology, the advantageous effects achieved by the system for enquiring abnormal financial data based on a knowledge map provided by the present invention are identical with the advantageous effects achievable by the method of enquiring abnormal financial data based on a knowledge map provided by the foregoing technical solution, so these are not redundantly described in this context.
BRIEF DESCRIPTION OF THE DRAWINGS
BRIEF DESCRIPTION OF THE DRAWINGS
[0045] The drawings described here are meant to provide further understanding of the present invention, and constitute a part of the present invention. The exemplary embodiments of the present invention and the descriptions thereof are meant to explain the present invention, rather than to restrict the present invention. In the drawings:
[0046] Fig. 1 is a flowchart schematically illustrating the method of enquiring abnormal financial data based on a knowledge map in Embodiment 1 of the present invention; and
[0047] Fig. 2 is a block diagram illustrating the structure of the system for enquiring abnormal financial data based on a knowledge map in Embodiment 2 of the present invention.
[0048] Reference numerals:
[0049] 1 ¨ map designing unit 2 ¨ sample collecting unit
[0050] 3 ¨ identifying and outputting unit 21 ¨ information collecting module
[0051] 22 ¨ screening module 23 ¨ duplicate-checking module
[0052] 24¨ verifying module 31 ¨ pre-storing module
[0053] 32 ¨ setting module 33 ¨ processing module Date recue/Date received 2024-02-28
[0054] 34¨ query outputting module DETAILED DESCRIPTION OF THE INVENTION
[0055] To make the objectives, features and advantages of the present invention more lucid and clear, the technical solutions in the embodiments of the present invention are clearly and comprehensively described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the embodiments as described are merely partial, rather than the entire, embodiments of the present invention. All other embodiments obtainable by persons ordinarily skilled in the art on the basis of the embodiments in the present invention without spending creative effort shall all fall within the protection scope of the present invention.
[0056] Embodiment 1
[0057] Fig. 1 is a flowchart schematically illustrating the method of enquiring abnormal financial data based on a knowledge map in Embodiment 1 of the present invention. Please refer to Fig. 1, this embodiment provides a method of enquiring abnormal financial data based on a knowledge map, the method comprises:
[0058] designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes; collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map.
[0059] In the method of enquiring abnormal financial data based on a knowledge map provided by this embodiment, it is firstly needed to design the structural constitution of the map database according to the user's query requirement of abnormal financial data, when the query requirement of abnormal financial data is to enquire illegitimate middleman information from lenders, Date recue/Date received 2024-02-28 considering that the illegitimate middleman information obtainable by a platform not only includes names, but also includes such valid identification information as their telephones and identification codes, etc., so three types of nodes can be adopted in designing the structural constitution of the map database, with one node representing one piece of information data;
relational nodes employ the mode of association by indicator lines to correspondingly design the structural constitution of the map database, plural pieces of sample source data are thereafter collected from the platform, a CSV file identifiable by the map database is formed after data cleaning, the CSV file is finally imported to the map database to construct a knowledge map of sample data, nodes whose correlation degree is higher than a threshold are screened out of the knowledge map, and corresponding information data in the nodes are extracted and output as abnormal financial data, e.g., such valid identification data as names, telephones or identification codes of illegitimate middlemen.
relational nodes employ the mode of association by indicator lines to correspondingly design the structural constitution of the map database, plural pieces of sample source data are thereafter collected from the platform, a CSV file identifiable by the map database is formed after data cleaning, the CSV file is finally imported to the map database to construct a knowledge map of sample data, nodes whose correlation degree is higher than a threshold are screened out of the knowledge map, and corresponding information data in the nodes are extracted and output as abnormal financial data, e.g., such valid identification data as names, telephones or identification codes of illegitimate middlemen.
[0060] Seen as such, this embodiment employs the mode of inputting great quantities of sample data in the map database to form a knowledge map to identify abnormal financial data, and utilizes the characteristic of the knowledge map that is good at processing complicated network relations to express plural pieces of sample data with a structured network, so as to quickly and accurately identify abnormal financial data therefrom.
[0061] Specifically, the method of designing structural constitution of a map database according to query requirement of abnormal financial data in the foregoing embodiment includes:
[0062] the query requirement of abnormal financial data including to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and/or addressee information, wherein the information includes name data, telephone data, and identification code data; and correspondingly setting plural node types based on plural data types, and designing the map database in accordance with a principle of one node corresponding to one piece of data.
Date recue/Date received 2024-02-28
Date recue/Date received 2024-02-28
[0063] During specific implementation, in order to facilitate comprehension, installment loan shopping is now taken for example for explanation, in the process of searching for any illegitimate middleman of an installment loan shopping activity, the lenders, the addressees of purchased commodities, and the relevant transferors must be taken as ingress to sort out suspectable clues and to find out illegitimate middlemen therefrom; since the information of the relevant personnel obtainable by the platform includes name data, telephone data, and identification code data, so when the structural constitution of the map database is designed, three types of nodes can be correspondingly set in the map database to correspond to the above three types of data, and nodes with higher correlation degree are screened out therefrom to find out suspectable illegitimate middlemen after having subjected the plural pieces of installment loan shopping data to knowledge map analysis.
[0064] Specifically, the method of collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database in the foregoing embodiment includes:
[0065] obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data;
preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data; duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.
preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data; duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.
[0066] During specific implementation, after the plural pieces of sample source data have been obtained, any sample source data that does not conform to the structural constitution of the map database is eliminated; if the same and single lender has plural loan records, the platform records plural pieces of lender registration information of the same and single lender, and there might be Date recue/Date received 2024-02-28 repetitive lender registration information, accordingly, the sample source data will be duplicate-removed when the sample source data is obtained, and the duplicate-checked sample source data is thereafter subjected to legitimacy verification to remove any sample source data whose telephone data and/or identification code data are/is invalid, and to finally retain valid sample data.
The method of identifying the telephone data and/or the identification code data as invalid is:
comparing whether the telephone data and/or the identification code data are/is consistent with a standard telephone number and/or a standard identification code in length to judge whether the telephone data and/or the identification code data are/is invalid, for instance, any telephone number not being of 11 bits and any identification code not being of 18 bits in the sample source data are judged as invalid.
The method of identifying the telephone data and/or the identification code data as invalid is:
comparing whether the telephone data and/or the identification code data are/is consistent with a standard telephone number and/or a standard identification code in length to judge whether the telephone data and/or the identification code data are/is invalid, for instance, any telephone number not being of 11 bits and any identification code not being of 18 bits in the sample source data are judged as invalid.
[0067] Preferably, the method of identifying abnormal financial data from the knowledge map in the foregoing embodiment includes:
[0068] employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement; setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement input according to the query requirement of the abnormal financial data; scatteredly spreading out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines;
and screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes.
and screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes.
[0069] During specific implementation, when the map database is used for enquiry and retrieval, it is required in each enquiry to employ a Cypher statement to compile an enquiring command identifiable by the map database, only then can the map database correspondingly output a query Date recue/Date received 2024-02-28 result. This is obviously not easily manipulable to business personnel without the computer professional background, so there are lot of inconveniences in use. In order to solve such problem, this embodiment employs an enquiring module with preset Cypher statements well compiled on the enquiring interface of the platform, such as an illegitimate middleman name enquiring module or an illegitimate middleman telephone enquiring module, so that, while enquiring the name of an illegitimate middleman, the business personnel can directly drag the illegitimate middleman name enquiring module to the enquiring box of the platform for searching, the program screens out relational nodes from the knowledge map after having received an enquiring instruction, the relational nodes here include name data, telephone data, and identification code data of illegitimate middlemen, and name data of the illegitimate middleman is finally searched out from the relational nodes and a result is output.
[0070] Understandably, the method of scatteredly spreading out the plural pieces of sample source data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines is as follows:
[0071] Since each piece of sample data includes three types of data, namely names data, telephones data, and identification code data, three nodes can be correspondingly constructed with reference to each piece of sample data during the process of constructing the knowledge map, so that each node represents one piece of data, at the same time, the three nodes in the same piece of sample data are associatively expressed in the mode of indicator lines; after the nodes to which the plural pieces of sample data correspond have been constructed to completion, nodes of the same data are duplicate-removed, the indicator lines originally connected with the removed nodes are thereafter re-connected to the nodes that remain after the duplicate-removal, and the knowledge map is finally formed.
[0072] As can be known from the above implementation process, this embodiment achieves the following advantages.
[0073] 1. Enquiring complexity of the map database can be simplified¨languages and grammars Date recue/Date received 2024-02-28 dedicated to the map database formerly were mastered only by professional data analyzers and engineers, but now they can also be utilized by business personnel without knowledge of computer programming languages to perform enquiring operations.
[0074] 2. The cost of communication between the business personnel and the developing personnel can be reduced ¨ it was formerly required for the business personnel to undergo a cooperative procedure through multiple depaiiments such as writing requirement description ¨
scheduling dates by R&D department ¨ realizing the requirement by R&D
depaiiment, but it is now only required for the R&D depaitment to import data in the map database, while subsequent use can be carried out by the business personnel on their own initiative.
scheduling dates by R&D department ¨ realizing the requirement by R&D
depaiiment, but it is now only required for the R&D depaitment to import data in the map database, while subsequent use can be carried out by the business personnel on their own initiative.
[0075] 3. Enquiring efficiency is enhanced ¨ formerly, the analysis result derived by the data analyzing personnel could only be interacted by means of Cypher statements on the map database, and the map data could be used by the business department only after it was restored to a datasheet structure, but now the map database is set up on the platform, whereby the business personnel can directly obtain the query result, and the whole process is convenient and speedy.
[0076] Moreover, the method of screening out relational nodes from the knowledge map according to an input enquiring statement, and then searching out illegitimate middleman information from the screened relational nodes includes:
[0077] setting an abnormal node identifying threshold, outputting any node consistent in type with the enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold, and obtaining a query result of the illegitimate middleman information. The correlation degree is defined and obtained according to the number of the indicator lines connected with the nodes.
[0078] Embodiment 2
[0079] Please refer to Fig. 1 and Fig. 2, this embodiment provides a system for enquiring abnormal financial data based on a knowledge map, the system comprises:
Date recue/Date received 2024-02-28
Date recue/Date received 2024-02-28
[0080] a map designing unit 1, for designing structural constitution of a map database according to query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
[0081] a sample collecting unit 2, for collecting plural pieces of sample source data, and subjecting the same to data cleaning to obtain plural pieces of sample data that conform to the structural constitution of the map database; and
[0082] an identifying and outputting unit 3, for importing the sample data to the map database to output a knowledge map, and thereafter searching out the abnormal financial data from the knowledge map.
[0083] Preferably, the sample collecting unit 2 includes:
[0084] an information collecting module 21, for obtaining plural pieces of lender registration information from a database, and extracting lender information, contact information, transferor information, and/or addressee information from each piece of lender registration information to serve as sample source data;
[0085] a screening module 22, for preliminarily screening the sample source data, and eliminating any sample source data that contains no name data, telephone data, or identification code data;
[0086] a duplicate-checking module 23, for duplicate-checking the remaining sample source data, and deleting repetitive sample source data; and
[0087] a verifying module 24, for subjecting the duplicate-checked sample source data to legitimacy verification, removing any sample source data whose telephone data and/or identification code data are/is invalid, and finally retaining valid sample data.
[0088] Preferably, the identifying and outputting unit 3 includes:
[0089] a pre-storing module 31, for employing a Cypher language to preset plural abnormal Date recue/Date received 2024-02-28 financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, or an abnormal identification code enquiring statement;
[0090] a setting module 32, for setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, or the abnormal identification code enquiring statement on an enquiring interface in a modular form, so that a user correspondingly selects enquiring statement input according to the query requirement of the abnormal financial data;
[0091] a processing module 33, for scatteredly spreading out the plural pieces of sample data in the form of nodes, and associatively forming relational nodes into a knowledge map through indicator lines; and
[0092] a query outputting module 34, for screening out relational nodes from the knowledge map according to an input enquiring statement, then identifying abnormal financial data from the screened relational nodes and outputting the same in the form of a query result.
[0093] In comparison with prior-art technology, the advantageous effects achieved by the system for enquiring abnormal financial data based on a knowledge map provided by this embodiment of the present invention are identical with the advantageous effects achievable by the method of enquiring abnormal financial data based on a knowledge map provided by the foregoing Embodiment 1, so these are not redundantly described in this context.
[0094] As understandable to persons ordinarily skilled in the art, the entire or partial steps realizing the method of the present invention can be completed via a program to instruct relevant hardware. The program can be stored in a computer-readable storage medium, and various steps of the method in the foregoing embodiment are involved when it is executed, and the storage medium can be an ROM/RAM, a magnetic disk, an optical disk, or a memory card, etc.
[0095] What the above describes is merely directed to specific modes of execution of the present invention, but the protection scope of the present invention is not restricted thereby. Any change Date recue/Date received 2024-02-28 or replacement easily conceivable to persons skilled in the art within the technical range disclosed by the present invention shall be covered by the protection scope of the present invention.
Accordingly, the protection scope of the present invention shall be based on the protection scope as claimed in the Claims.
Date recue/Date received 2024-02-28
Accordingly, the protection scope of the present invention shall be based on the protection scope as claimed in the Claims.
Date recue/Date received 2024-02-28
Claims (20)
1. A device comprising:
a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
an identifying and outputting unit, configured to:
import clean sample data to the map database to output a knowledge map;
search out the abnomial financial data from the knowledge map;
a sample collecting unit, configured to:
collect plural pieces of sample source data; and subject plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.
a map designing unit, configured to design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
an identifying and outputting unit, configured to:
import clean sample data to the map database to output a knowledge map;
search out the abnomial financial data from the knowledge map;
a sample collecting unit, configured to:
collect plural pieces of sample source data; and subject plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.
2. The device of claim 1, wherein the sample collecting unit further comprises:
an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender infomiation, contact infomiation, transferor information, and addressee infommtion from each piece of the lender registration infomiation to serve as the sample source data;
a screening module, configured to:
Date recue/Date received 2024-02-28 preliminarily screen the sample source data;
eliminate any sample source data containing no name data, no telephone data, or no identification code data;
a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.
an information collecting module, configured to:
obtain plural pieces of lender registration information from a database;
extract lender infomiation, contact infomiation, transferor information, and addressee infommtion from each piece of the lender registration infomiation to serve as the sample source data;
a screening module, configured to:
Date recue/Date received 2024-02-28 preliminarily screen the sample source data;
eliminate any sample source data containing no name data, no telephone data, or no identification code data;
a duplicate-checking module, configured to:
duplicate-check remaining sample source data;
delete repetitive sample source data;
a verifying module, configured to:
subject duplicate-checked sample source data to legitimacy verification;
remove any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.
3. The device of claim 2, wherein the identifying and outputting unit further comprises:
a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnomial identification code enquiring statement;
a setting module, configured to set up the abnomial name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular fonn, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
Date recue/Date received 2024-02-28 randomly spread out plural pieces of sample data in form of nodes;
associatively fonn relational nodes into the knowledge map through indicator lines;
a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnormal financial data from screened relational nodes; and output the abnormal financial data as a query result.
a pre-storing module, configured to employ a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone enquiring statement, and an abnomial identification code enquiring statement;
a setting module, configured to set up the abnomial name enquiring statement, the abnormal telephone enquiring statement, and the abnormal identification code enquiring statement on an enquiring interface in a modular fonn, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
a processing module, configured to:
Date recue/Date received 2024-02-28 randomly spread out plural pieces of sample data in form of nodes;
associatively fonn relational nodes into the knowledge map through indicator lines;
a query outputting module, configured to:
screen out relational nodes from the knowledge map of an input enquiring statement;
identify the abnormal financial data from screened relational nodes; and output the abnormal financial data as a query result.
4. The device of claim 3, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and identifying the abnormal financial data from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the abnormal financial data.
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the abnormal financial data.
5. The device of claim 4, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes; and wherein plural pieces of the sample source data are collected from a platform.
6. The device of claim 5, wherein personnel directly drags illegitimate middleman name enquiring module to enquiring box of the platform for searching; and wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one Date recue/Date received 2024-02-28 piece of data.
7. A method comprising:
designing structural constitution of a map database of query requirement of abnomial financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
importing clean sample data to the map database to output a knowledge map;
searching out the abnormal financial data from the knowledge map.
collecting plural pieces of sample source data; and subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.
designing structural constitution of a map database of query requirement of abnomial financial data, wherein the structural constitution includes expressions of intemode relations and nodes;
importing clean sample data to the map database to output a knowledge map;
searching out the abnormal financial data from the knowledge map.
collecting plural pieces of sample source data; and subjecting plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.
8. The method of claim 7, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
the query requirement of the abnormal financi al data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.
the query requirement of the abnormal financi al data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.
9. The method of claim 8, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces Date recue/Date received 2024-02-28 of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data;
subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data;
subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are invalid; and retaining valid sample data.
10. The method of claim 9, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid;
wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone Date recue/Date received 2024-02-28 enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnomial name enquiring statement, the abnomial telephone enquiring statement, and the abnomial identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnomial financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid;
wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnormal name enquiring statement, an abnormal telephone Date recue/Date received 2024-02-28 enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnomial name enquiring statement, the abnomial telephone enquiring statement, and the abnomial identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnomial financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement; and searching out the illegitimate middleman information from screened relational nodes.
11. The method of claim 10, wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.
setting an abnormal node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman information.
12. The method of claim 11, wherein the correlation degree is defined and obtained of number of the indicator lines connected with the nodes; and wherein plural pieces of the sample source data are collected from a platform.
13. The method of claim 12, wherein any sample source data which does not conform to the structural constitution of the map database is eliminated; and wherein personnel directly Date recue/Date received 2024-02-28 drags illegitimate middleman name enquiring module to enquiring box of the platform for searching.
14. The method of claim 13, wherein each piece of the sample data includes three types of data, including the names data, the telephones data, and the identification code data, three nodes are constructed with reference to each piece of the sample data during constructing the knowledge map, wherein each node represents one piece of data.
15. The method of claim 14, wherein the three nodes in same piece of the sample data are associatively expressed in mode of the indicator lines; and wherein nodes of same data are duplicate, are removed.
16. The method of claim 15, wherein the indicator lines originally connected with removed nodes are re-connected to the nodes which remain after the duplicate are removed, and the knowledge map is fomied.
17. A computer readable physical memory having stored thereon a computer program executed by a computer configured to:
design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
import clean sample data to the map database to output a knowledge map;
search out the abnomial financial data from the knowledge map.
collect plural pieces of sample source data; and subject plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.
design structural constitution of a map database of query requirement of abnormal financial data, wherein the structural constitution includes expressions of internode relations and nodes;
import clean sample data to the map database to output a knowledge map;
search out the abnomial financial data from the knowledge map.
collect plural pieces of sample source data; and subject plural pieces of the sample source data to data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database.
18. The memory of claim 17, wherein designing the structural constitution of the map database of the query requirement of the abnormal financial data comprises:
Date recue/Date received 2024-02-28 the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.
Date recue/Date received 2024-02-28 the query requirement of the abnormal financial data includes to search out illegitimate middleman information from registration information of plural lenders, wherein the registration information of the lenders includes lender information, contact information, transferor information and addressee information, wherein the registration information includes name data, telephone data, and identification code data;
setting plural node types based on plural data types; and designing the map database in accordance with a principle of one node corresponding to one piece of data.
19. The memory of claim 18, wherein collecting plural pieces of the sample source data, and subjecting plural pieces of the sample source data to the data cleaning to obtain plural pieces of the clean sample data which conforms to the structural constitution of the map database comprises:
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data;
subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are Date recue/Date received 2024-02-28 invalid; and retaining valid sample data.
obtaining plural pieces of lender registration information from a database;
extracting the lender information, the contact information, the transferor information, and the addressee information from each piece of the lender registration information to serve as the sample source data;
preliminarily screening the sample source data;
eliminating any sample source data containing no name data, no telephone data, or no identification code data;
duplicate-checking remaining sample source data;
deleting repetitive sample source data;
subjecting duplicate-checked sample source data to legitimacy verification;
removing any sample source data whose telephone data and identification code data are Date recue/Date received 2024-02-28 invalid; and retaining valid sample data.
20. The memory of claim 19, wherein identifying the telephone data and the identification code data as invalid comprises:
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid;
wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnomial name enquiring statement, an abnomial telephone enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnomial identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement;
searching out the illegitimate middleman infomiation from screened relational nodes;
Date recue/Date received 2024-02-28 wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnomial node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman infomiation.
Date recue/Date received 2024-02-28
comparing the telephone data and the identification code data are consistent with a standard telephone number and a standard identification code in length to judge the telephone data and the identification code data are invalid;
wherein identifying the abnormal financial data from the knowledge map comprises:
employing a Cypher language to preset plural abnormal financial data enquiring statements, including an abnomial name enquiring statement, an abnomial telephone enquiring statement, and an abnormal identification code enquiring statement;
setting up the abnormal name enquiring statement, the abnormal telephone enquiring statement, and the abnomial identification code enquiring statement on an enquiring interface in a modular form, wherein a user correspondingly selects enquiring statement input of the query requirement of the abnormal financial data;
randomly spreading out plural pieces of sample data in form of nodes;
associatively forming relational nodes into the knowledge map through indicator lines;
screening out the relational nodes from the knowledge map of an input enquiring statement;
searching out the illegitimate middleman infomiation from screened relational nodes;
Date recue/Date received 2024-02-28 wherein screening out the relational nodes from the knowledge map of the input enquiring statement, and searching out the illegitimate middleman information from the screened relational nodes comprises:
setting an abnomial node identifying threshold;
outputting any node consistent in type with the input enquiring statement in the relational nodes, when a correlation degree of the relational nodes is greater than the threshold; and obtaining a query result of the illegitimate middleman infomiation.
Date recue/Date received 2024-02-28
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811588282.6 | 2018-12-25 | ||
| CN201811588282.6A CN109491995A (en) | 2018-12-25 | 2018-12-25 | Knowledge based map inquires the method and system of financial abnormal data |
| CA3179620A CA3179620A1 (en) | 2018-12-25 | 2019-09-18 | Method and system for querying abnormal financial data on basis of knowledge map |
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| Application Number | Title | Priority Date | Filing Date |
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| CA3179620A Division CA3179620A1 (en) | 2018-12-25 | 2019-09-18 | Method and system for querying abnormal financial data on basis of knowledge map |
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| CA3230500A Pending CA3230500A1 (en) | 2018-12-25 | 2019-09-18 | Method and system for querying abnormal financial data on basis of knowledge map |
| CA3179620A Pending CA3179620A1 (en) | 2018-12-25 | 2019-09-18 | Method and system for querying abnormal financial data on basis of knowledge map |
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| CN109491995A (en) * | 2018-12-25 | 2019-03-19 | 苏宁易购集团股份有限公司 | Knowledge based map inquires the method and system of financial abnormal data |
| CN110321438A (en) * | 2019-06-14 | 2019-10-11 | 北京奇艺世纪科技有限公司 | Real-time fraud detection method, device and electronic equipment based on complex network |
| CN110609905A (en) * | 2019-09-12 | 2019-12-24 | 深圳众赢维融科技有限公司 | Method and device for recognizing type of over-point and processing graph data |
| CN110837538A (en) * | 2019-10-24 | 2020-02-25 | 北京中科捷信信息技术有限公司 | Financial knowledge map visual query and multidimensional analysis system |
| TWI736233B (en) * | 2020-04-23 | 2021-08-11 | 兆豐國際商業銀行股份有限公司 | Pre-loan investigation system and pre-loan investigation method |
| CN112632132B (en) * | 2020-12-31 | 2024-04-12 | 中国农业银行股份有限公司 | Processing method, device and equipment for abnormal imported data |
| CN113486090A (en) * | 2021-01-20 | 2021-10-08 | 何青波 | Financial service inquiry request processing method based on big data |
| CN113469697B (en) * | 2021-06-30 | 2022-12-06 | 重庆富民银行股份有限公司 | Unsupervised anomaly detection method and unsupervised anomaly detection device based on knowledge graph |
| CN115269879B (en) * | 2022-09-05 | 2023-05-05 | 北京百度网讯科技有限公司 | Knowledge structure data generation method, data search method and risk warning method |
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| CN108038700A (en) * | 2017-12-22 | 2018-05-15 | 上海前隆信息科技有限公司 | A kind of anti-fraud data analysing method and system |
| CN108280760A (en) * | 2018-01-25 | 2018-07-13 | 树根互联技术有限公司 | A kind of financial risks on-line monitoring method and apparatus |
| CN108492173A (en) * | 2018-03-23 | 2018-09-04 | 上海氪信信息技术有限公司 | A kind of anti-Fraud Prediction method of credit card based on dual-mode network figure mining algorithm |
| CN109033135A (en) * | 2018-06-06 | 2018-12-18 | 北京大学 | A kind of natural language querying method and system of software-oriented project knowledge map |
| CN109002470A (en) * | 2018-06-12 | 2018-12-14 | 东方银谷（北京）投资管理有限公司 | Knowledge mapping construction method and device, client |
| CN109064313A (en) * | 2018-07-20 | 2018-12-21 | 重庆富民银行股份有限公司 | Warning monitoring system after the loan of knowledge based graphical spectrum technology |
| CN109064318A (en) * | 2018-08-24 | 2018-12-21 | 苏宁消费金融有限公司 | A kind of internet financial risks monitoring system of knowledge based map |
| CN109491995A (en) * | 2018-12-25 | 2019-03-19 | 苏宁易购集团股份有限公司 | Knowledge based map inquires the method and system of financial abnormal data |
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