A fraud risk management method and system based on big data

Abstract

The present invention relates to the technical field of data risk management, and discloses a fraud risk management method and system based on big data. The method comprises: collecting identification strategy content accumulated during the process of identifying counterfeit websites to construct an identification strategy sample set; using a recognition algorithm to discretize the identification strategy content in the identification strategy sample set, and then segmenting each identification strategy in the identification strategy sample set into strategy factors through the discretization process; processing the segmented strategy factors and clustering the processed strategy factors; and achieving risk scoring for websites collected in real time by matching the clustered strategy factors with the website content data collected in real time for semantic relevance. Finally, determining the website fraud risk by setting a website risk score threshold, thereby improving the efficiency and accuracy of website risk identification.

Description

Technical Field

[0001] The present invention relates to the technical field of data risk management, and in particular to a fraud risk management method and system based on big data. Background Art

[0002] Counterfeit websites are characterized by rapid changes and short lifespans. Different identification strategies are required for different types and periods of counterfeit websites. Numerous identification strategies have been accumulated through years of practice. Therefore, the main challenge facing identification algorithms is the ever-increasing library of identification strategies, which leads to a continuous decrease in identification efficiency.

[0003] Currently, the proactive identification algorithm for counterfeit websites uses a single independent strategy. If there are N identification strategies, and each strategy takes an average of M time, the total identification time is T0 = N * M. Since M is a constant, T0 and N have a linear relationship. As N increases, T0 also increases, and identification efficiency decreases.

[0004] The existing technology CN112949954A obtains sample data, cleans the sample data, removes errors, redundancy and noise, and unifies the dimension of the data; at the same time, based on the initially established text framework, it obtains structured data of text information released by sample companies, analyzes the importance and importance ranking of each financial indicator, and formulates model evaluation standards to identify financial fraud based on the standards. However, it does not solve the problem that the recognition efficiency of financial fraud continues to decrease due to the continuous increase in recognition, and has great limitations. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides a fraud risk management method and system based on big data, which has the advantages of real-time and high efficiency, and solves the problem of continuously decreasing efficiency in identifying counterfeit websites.

[0007] (2) Technical solution

[0008] To solve the above-mentioned technical problem of decreasing efficiency in identifying counterfeit websites, the present invention provides the following technical solutions:

[0009] The present invention discloses a fraud risk management method based on big data, which specifically includes the following steps:

[0010] S1. Collect the identification strategy content accumulated in the process of identifying fake websites to build an identification strategy sample set;

[0011] S2. Discretize the recognition strategy content in the recognition strategy sample set using a recognition algorithm, divide each recognition strategy content in the recognition strategy sample set into strategy factors through the discretization process, and save the divided strategy factors to the recognition strategy sample set;

[0012] S3, performing data processing on the strategy factors in the identification strategy sample set to obtain processed strategy factors;

[0013] S4. Clustering the processed strategic factors to obtain clustered strategic factors; the clustering results include: the processed strategic factor content data, the weight of the strategic factor, the number of clusters after clustering, and the number of strategic factors in each cluster;

[0014] S5. Collect website content data in real time, identify the website content data collected in real time based on the clustered strategy factors, and perform risk scoring on the website content data collected in real time;

[0015] The present invention constructs an identification strategy sample set by collecting identification strategy contents accumulated in the process of identifying counterfeit websites, and at the same time, based on the contents of the identification strategies, uses an identification algorithm to discretize the contents of the identification strategies in the identification strategy sample set, and divides each identification strategy content in the identification strategy sample set into strategy factors; and processes the segmented strategy factors and clusters the processed strategy factors; and realizes risk scoring of real-time collected websites by matching the clustered strategy factors with the website content data collected in real time in terms of semantic relevance; finally, judges the website fraud risk by setting a website risk score threshold, thereby improving the efficiency and accuracy of website risk identification.

[0016] Preferably, the discretization processing of the recognition strategy content in the recognition strategy sample set using the recognition algorithm comprises the following steps:

[0017] Extract candidate words from the recognition strategy content and match the extracted candidate words with Wikipedia entries. Calculate the semantic relevance of the candidate words using the document connection information graph and classification structure graph of the Wikipedia knowledge base. Based on the semantic relevance calculation results, construct a candidate word set for the current extraction and recognition strategy content.

[0018] The semantic relevance calculation result includes the semantic relevance between the extracted candidate words and the Wikipedia entries;

[0019] The semantic relevance calculation is as follows:

[0020]

[0021] in, Indicates that the semantic relevance value of the extracted candidate word and the Wikipedia entry is in the range [-1,1], represents the jth candidate word in the i-th recognition strategy content, Represents the first entry in Wikipedia, Represents the breadth-first traversal path information of the concept node of the j-th candidate word in the i-th recognition strategy content on the classification structure graph, Represents the breadth-first traversal path information of the concept node of the first entry in Wikipedia on the classification structure diagram, Indicates the nearest common root node between the candidate word and the Wikipedia entry in the classification structure diagram. Represents the depth information of the concept node of the jth candidate word in the i-th recognition strategy content on the classification structure diagram, Represents the depth information of the concept node of the first entry in Wikipedia on the classification structure diagram;

[0022] When the semantic relevance value between the extracted candidate word and the Wikipedia entry is in the interval [-1, 0], it means that the extracted candidate word does not match the Wikipedia entry, and the candidate word in the recognition strategy content is re-extracted;

[0023] When the semantic relevance value between the extracted candidate word and the Wikipedia entry is in the interval (0,1], it indicates that the extracted candidate word matches the Wikipedia entry, and the extracted candidate word is added to the candidate word set in the current extraction and recognition strategy content;

[0024] Taking each candidate word in the candidate word set in the current extraction recognition strategy content as a strategy factor of the current recognition strategy;

[0025] The discretization process includes extracting candidate words from the recognition strategy content and calculating semantic relevance;

[0026] Discretization processing is performed on the content of each recognition strategy in the recognition strategy sample set, and the strategy factors of each recognition strategy in the recognition strategy sample set are summarized, and the summarized strategy factors are saved in the recognition strategy sample set.

[0027] The present invention extracts candidate words for each recognition strategy in the recognition strategy sample set, and segments each recognition strategy in the recognition strategy sample set by calculating semantic relevance, and generates strategy factors, thereby reducing the redundancy of fraud risk identification and improving the efficiency of fraud risk identification.

[0028] Preferably, the data processing of the strategy factors in the identification strategy sample set includes the following steps:

[0029] S31. Calculate the weight of each strategy factor;

[0030] S32, performing a deduplication operation on repeated strategy factors in the identification strategy sample set to obtain deduplicated strategy factors;

[0031] Traverse the strategy factors in the strategy sample set. When duplicate strategy factors are detected during the traversal process, retain the first strategy factor and delete the duplicate strategy factors.

[0032] S33, arranging the deduplicated strategy factors according to the calculated weights, and summarizing them to obtain the processed strategy factors;

[0033] Set the one with the largest weight to be ranked first.

[0034] Preferably, the weight calculation formula of the strategy factor is as follows:

[0035]

[0036] in, Indicates the calculated k-th strategy factor weight, represents the frequency of the k-th strategy factor, Indicates the adjustment factor of the set frequency, represents the regional position of the k-th strategy factor, Indicates the adjustment factor of the set regional position, represents the number of identification strategies contained in the k-th strategy factor, Indicates the adjustment factor for the number of identification strategies included in the set strategy factor.

[0037] The present invention pre-processes each strategy factor after segmentation, calculates the weight of each strategy factor, removes duplicate strategy factors, and ensures the efficiency of fraud risk identification.

[0038] Preferably, clustering the processed strategic factors comprises the following steps:

[0039] Set the weight range of the strategy factor and use the bi-kmeans algorithm to cluster the strategy factor within the set weight range:

[0040] Set the number of clusters h in the clustering results and the number of strategy factors r in each cluster;

[0041] S41, considering the strategic factors within the set weight range as a cluster;

[0042] S42, determine whether the number of clusters is less than h, if the number of clusters is less than h, execute S43, if the number of clusters is equal to h, execute S44;

[0043] S43: For each cluster, select a strategic factor in the cluster as the cluster center, calculate the similarity between other strategic factors in the cluster and the cluster center, and divide the cluster based on the calculated similarity value. After the division is completed, execute S42 to determine whether further division is needed;

[0044] S44. Determine whether the number of strategy factors within the cluster is less than r. When the number of strategy factors within the cluster is less than or equal to r, output the clustering result. When the number of strategy factors within the cluster is greater than h, set h=h+1, increase the number of clusters and execute S43 to re-divide the clusters where the number of strategy factors within the cluster is greater than h.

[0045] Preferably, the calculating the similarity between other strategy factors in the cluster and the cluster center, and dividing the cluster based on the calculated similarity value comprises the following steps:

[0046] The similarity calculation is as follows:

[0047]

[0048] in, Represents the similarity value between other strategic factors in the cluster and the cluster center. The calculated similarity value range is [-1,1]. represents a strategic factor within the cluster, Indicates the currently selected cluster center B;

[0049] Since the calculated similarity value range is in the interval [-1,1];

[0050] When the similarity value between other strategic factors in the cluster and the cluster center is in the interval [-1,0], it is determined that other strategic factors in the cluster are not similar to the cluster center;

[0051] When the similarity value between other strategy factors in the cluster and the cluster center is in the interval (0,1], it is determined that other strategy factors in the cluster are similar to the cluster center;

[0052] Based on the similar results of other strategic factors within the cluster and the cluster center, the cluster is divided into two.

[0053] The present invention achieves clustering of strategic factors by using a clustering algorithm, thereby further improving the efficiency of fraud risk identification.

[0054] Preferably, the step of identifying the website content data collected in real time based on the clustered strategy factors and performing risk scoring on the website content data collected in real time comprises the following steps:

[0055] S51, collecting website content data in real time, and extracting keywords from the website content data collected in real time;

[0056] S52, matching the keywords in the website content data collected in real time with the strategic factors in the clustering results one by one based on a semantic relevance calculation method;

[0057] Set the system to accumulate the weights of the strategy factors for each successful match, and use the accumulated weights as the risk score for the website content data collected in real time.

[0058] S53. Setting a risk score threshold for website content data collected in real time;

[0059] When the risk score of the accumulated real-time collected website content data is greater than or equal to the set threshold, the current website is determined to be a fake website;

[0060] When the accumulated risk score of the real-time collected website content data is less than the set threshold, the current website is determined to be a normal website.

[0061] The present invention compares and matches the clustered strategy factors with the website content data collected in real time to achieve the identification of the websites collected in real time. At the same time, the fraud risk level of the websites is judged by performing risk scoring on the websites collected in real time and setting a scoring threshold, thereby ensuring the accuracy of fraud risk identification.

[0062] The present invention also discloses a fraud risk management system based on big data, comprising: a database, a semantic recognition module, a data collection module, a data processing module, a clustering module and a risk scoring module;

[0063] The database is used to store various accumulated recognition strategies;

[0064] The semantic recognition module is used to extract various recognition strategies from the database, segment the content of various recognition strategies, and generate strategy factors;

[0065] The data processing module is used to process the strategy factors generated by segmentation and transmit the processed strategy factors to the clustering module;

[0066] The clustering module is used to cluster the received strategic factors and output the clustering results to be stored in the database;

[0067] The data collection module is used to collect website content data in real time;

[0068] The risk scoring module is used to score and display the websites collected in real time according to the clustered strategic factors.

[0069] (3) Beneficial effects

[0070] Compared with the existing technology, the present invention provides a fraud risk management method and system based on big data, which has the following beneficial effects:

[0071] 1. The invention constructs an identification strategy sample set by collecting identification strategy contents accumulated in the process of identifying counterfeit websites, and at the same time, based on the contents of the identification strategies, uses an identification algorithm to discretize the contents of the identification strategies in the identification strategy sample set, and divides each identification strategy content in the identification strategy sample set into strategy factors; and processes the divided strategy factors and clusters the processed strategy factors; and realizes risk scoring of real-time collected websites by matching the clustered strategy factors with the real-time collected website content data through semantic relevance matching. Finally, the website fraud risk is judged by setting a website risk score threshold, thereby improving the efficiency and accuracy of website risk identification.

[0072] 2. This invention extracts candidate words for each recognition strategy in the recognition strategy sample set, and segments each recognition strategy in the recognition strategy sample set by calculating semantic relevance, and generates strategy factors, thereby reducing the redundancy of fraud risk identification and improving the efficiency of fraud risk identification.

[0073] 3. This invention calculates the weight of each strategy factor by pre-processing each strategy factor after segmentation, removes duplicate strategy factors, and ensures the efficiency of fraud risk identification.

[0074] 4. This invention achieves clustering of strategic factors by using clustering algorithms, thereby further improving the efficiency of fraud risk identification.

[0075] 5. This invention achieves the identification of websites collected in real time by comparing and matching the clustered strategy factors with the website content data collected in real time. At the same time, it determines the fraud risk level of the website by scoring the risk of the websites collected in real time and setting a scoring threshold, thereby ensuring the accuracy of fraud risk identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a schematic diagram of the process structure of the fraud risk management method for big data of the present invention. DETAILED DESCRIPTION

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0078] The present invention discloses a fraud risk management method based on big data, which specifically includes the following steps:

[0079] S1. Collect the identification strategy content accumulated in the process of identifying fake websites to build an identification strategy sample set;

[0080] Set the constructed recognition strategy sample set to X={ };

[0081] in, represents the first recognition strategy in the recognition strategy sample set, Represents the nth recognition strategy in the recognition strategy sample set;

[0082] S2. Discretize the recognition strategy content in the recognition strategy sample set using a recognition algorithm, divide each recognition strategy content in the recognition strategy sample set into strategy factors through the discretization process, and save the divided strategy factors to the recognition strategy sample set;

[0083] Discretizing the content of the recognition strategies in the recognition strategy sample set using the recognition algorithm includes the following steps:

[0084] Extract candidate words from the recognition strategy content and match the extracted candidate words with Wikipedia entries. Calculate the semantic relevance of the candidate words using the document connection information graph and classification structure graph of the Wikipedia knowledge base. Based on the semantic relevance calculation results, construct a candidate word set for the current extraction and recognition strategy content.

[0085] The semantic relevance calculation result includes the semantic relevance between the extracted candidate words and the Wikipedia entries;

[0086] The semantic relevance calculation is as follows:

[0087]

[0088] in, Indicates that the semantic relevance value of the extracted candidate word and the Wikipedia entry is in the range [-1,1], represents the jth candidate word in the i-th recognition strategy content, Represents the first entry in Wikipedia, Represents the breadth-first traversal path information of the concept node of the j-th candidate word in the i-th recognition strategy content on the classification structure graph, Represents the breadth-first traversal path information of the concept node of the first entry in Wikipedia on the classification structure diagram, Indicates the nearest common root node between the candidate word and the Wikipedia entry in the classification structure diagram. Represents the depth information of the concept node of the jth candidate word in the i-th recognition strategy content on the classification structure diagram, Represents the depth information of the concept node of the first entry in Wikipedia on the classification structure diagram;

[0089] When the semantic relevance value between the extracted candidate word and the Wikipedia entry is in the interval [-1, 0], it means that the extracted candidate word does not match the Wikipedia entry, and the candidate word in the recognition strategy content is re-extracted;

[0090] When the semantic relevance value between the extracted candidate word and the Wikipedia entry is in the interval (0,1], it indicates that the extracted candidate word matches the Wikipedia entry, and the extracted candidate word is added to the candidate word set in the current extraction and recognition strategy content;

[0091] Further, each candidate word in the candidate word set in the current extraction recognition strategy content is used as a strategy factor of the current recognition strategy;

[0092] Furthermore, the discretization process includes extracting candidate words from the recognition strategy content and calculating semantic relevance;

[0093] Furthermore, the content of each recognition strategy in the recognition strategy sample set is discretized, and the strategy factors of each recognition strategy in the recognition strategy sample set are summarized, and the summarized strategy factors are saved in the recognition strategy sample set;

[0094] S3, performing data processing on the strategy factors in the identification strategy sample set to obtain processed strategy factors;

[0095] Data processing of the strategy factors in the identification strategy sample set includes the following steps:

[0096] S31. Calculate the weight of each strategy factor;

[0097] The weight calculation formula of the strategy factor is as follows:

[0098]

[0099] in, Indicates the calculated k-th strategy factor weight, represents the frequency of the k-th strategy factor, Indicates the adjustment factor of the set frequency, represents the regional position of the k-th strategy factor, Indicates the adjustment factor of the set regional position, represents the number of identification strategies contained in the k-th strategy factor, Indicates the adjustment factor for the number of identification strategies included in the set strategy factor;

[0100] S32, performing a deduplication operation on repeated strategy factors in the identification strategy sample set to obtain deduplicated strategy factors;

[0101] Traverse the strategy factors in the strategy sample set. When duplicate strategy factors are detected during the traversal process, retain the first strategy factor and delete the duplicate strategy factors.

[0102] S33, arranging the deduplicated strategy factors according to the calculated weights, and summarizing them to obtain the processed strategy factors;

[0103] Set the one with the largest weight to be ranked first;

[0104] S4. Clustering the processed strategic factors to obtain clustered strategic factors; the clustering results include: the processed strategic factor content data, the weight of the strategic factor, the number of clusters after clustering, and the number of strategic factors in each cluster;

[0105] Clustering the processed strategic factors includes the following steps:

[0106] Set the weight range of the strategy factor and use the bi-kmeans algorithm to cluster the strategy factor within the set weight range:

[0107] Set the number of clusters h in the clustering results and the number of strategy factors r in each cluster;

[0108] S41, considering the strategic factors within the set weight range as a cluster;

[0109] S42, determine whether the number of clusters is less than h, if the number of clusters is less than h, execute S43, if the number of clusters is equal to h, execute S44;

[0110] S43. For each cluster, select a strategic factor in the cluster as the cluster center, calculate the similarity between other strategic factors in the cluster and the cluster center, and divide the cluster based on the calculated similarity value;

[0111] The similarity calculation is as follows:

[0112]

[0113] in, Represents the similarity value between other strategic factors in the cluster and the cluster center. The calculated similarity value range is [-1,1]. represents a strategic factor within the cluster, Indicates the currently selected cluster center B;

[0114] Furthermore, since the calculated similarity value range is in the interval [-1,1];

[0115] When the similarity value between other strategic factors in the cluster and the cluster center is in the interval [-1,0], it is determined that other strategic factors in the cluster are not similar to the cluster center;

[0116] When the similarity value between other strategy factors in the cluster and the cluster center is in the interval (0,1], it is determined that other strategy factors in the cluster are similar to the cluster center;

[0117] Based on the similar results of other strategic factors within the cluster and the cluster center, the cluster is divided into two;

[0118] Further, after the division is completed, S42 is executed to determine whether further division is required;

[0119] S44: Determine whether the number of strategy factors within the cluster is less than r. If the number of strategy factors within the cluster is less than or equal to r, output the clustering result. If the number of strategy factors within the cluster is greater than h, set h=h+1, increase the number of clusters, and execute S43 to re-divide the clusters where the number of strategy factors within the cluster is greater than h.

[0120] S5. Collect website content data in real time, identify the website content data collected in real time based on the clustered strategy factors, and perform risk scoring on the website content data collected in real time;

[0121] Identifying the website content data collected in real time based on the clustered strategic factors and scoring the website content data collected in real time includes the following steps:

[0122] S51, collecting website content data in real time, and extracting keywords from the website content data collected in real time;

[0123] S52, matching the keywords in the website content data collected in real time with the strategic factors in the clustering results one by one based on a semantic relevance calculation method;

[0124] Set the system to accumulate the weights of the strategy factors for each successful match, and use the accumulated weights as the risk score for the website content data collected in real time.

[0125] S53. Setting a risk score threshold for website content data collected in real time;

[0126] When the risk score of the accumulated real-time collected website content data is greater than or equal to the set threshold, the current website is determined to be a fake website;

[0127] When the accumulated risk score of the real-time collected website content data is less than the set threshold, the current website is determined to be a normal website;

[0128] The present invention also discloses a fraud risk management system based on big data, comprising: a database, a semantic recognition module, a data collection module, a data processing module, a clustering module and a risk scoring module;

[0129] The database is used to store various accumulated recognition strategies;

[0130] The semantic recognition module is used to extract various recognition strategies from the database, segment the content of various recognition strategies, and generate strategy factors;

[0131] The data processing module is used to process the strategy factors generated by segmentation and transmit the processed strategy factors to the clustering module;

[0132] The clustering module is used to cluster the received strategic factors and output the clustering results to be stored in the database;

[0133] The data collection module is used to collect website content data in real time;

[0134] The risk scoring module is used to score and display the websites collected in real time according to the clustered strategic factors.

[0135] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A fraud risk management method based on big data, characterized in that: The following steps are involved: S1. Collect the identification strategy content accumulated in the process of identifying fake websites to build an identification strategy sample set; S2. Discretize the recognition strategy content in the recognition strategy sample set using a recognition algorithm, divide each recognition strategy content in the recognition strategy sample set into strategy factors through the discretization process, and save the divided strategy factors to the recognition strategy sample set; S3, performing data processing on the strategy factors in the identification strategy sample set to obtain processed strategy factors; S4. Clustering the processed strategic factors to obtain clustered strategic factors; The clustering results include: processed strategic factor content data, strategic factor weights, the number of clusters after clustering, and the number of strategic factors in each cluster; S5. Collect website content data in real time, identify the website content data collected in real time based on the clustered strategy factors, and perform risk scoring on the website content data collected in real time; S2 specifically includes: extracting candidate words from the recognition strategy content, and matching the extracted candidate words in Wikipedia entries, calculating semantic relevance of the candidate words by using the document connection information diagram and classification structure diagram of the Wikipedia knowledge base, and constructing a set of candidate words in the current extracted recognition strategy content based on the semantic relevance calculation results; using each candidate word in the set of candidate words in the current extracted recognition strategy content as a strategy factor of the current recognition strategy; the discretization processing includes extracting candidate words from the recognition strategy content and the semantic relevance calculation process; discretizing each recognition strategy content in the recognition strategy sample set, and summarizing the strategy factors of each recognition strategy in the recognition strategy sample set, and saving the summarized strategy factors to the recognition strategy sample set.

2. The fraud risk management method based on big data according to claim 1, characterized in that: The semantic relevance calculation result includes the semantic relevance between the extracted candidate words and the Wikipedia entries; The semantic relevance calculation is as follows: in, Indicates that the semantic relevance value of the extracted candidate word and the Wikipedia entry is in the range [-1,1], represents the jth candidate word in the i-th recognition strategy content, Represents the first entry in Wikipedia, Represents the breadth-first traversal path information of the concept node of the j-th candidate word in the i-th recognition strategy content on the classification structure graph, Represents the breadth-first traversal path information of the concept node of the first entry in Wikipedia on the classification structure diagram, Indicates the nearest common root node between the candidate word and the Wikipedia entry in the classification structure diagram. Represents the depth information of the concept node of the jth candidate word in the i-th recognition strategy content on the classification structure diagram, Represents the depth information of the concept node of the first entry in Wikipedia on the classification structure diagram; The method of constructing a candidate word set in the current extraction and recognition strategy content based on the semantic relevance calculation result specifically includes: When the semantic relevance value between the extracted candidate word and the Wikipedia entry is in the interval [-1,0], it means that the extracted candidate word does not match the Wikipedia entry, and the candidate word in the recognition strategy content is re-extracted; when the semantic relevance value between the extracted candidate word and the Wikipedia entry is in the interval (0,1], it means that the extracted candidate word matches the Wikipedia entry, and the extracted candidate word is added to the candidate word set in the current extraction and recognition strategy content.

3. The fraud risk management method based on big data according to claim 2, characterized in that: The pair Identifying the strategic factors in the strategic sample set for data processing includes the following steps: S31. Calculate the weight of each strategy factor; S32, performing a deduplication operation on repeated strategy factors in the identification strategy sample set to obtain deduplicated strategy factors; Traverse the strategy factors in the strategy sample set. When duplicate strategy factors are detected during the traversal process, retain the first strategy factor and delete the duplicate strategy factors. S33, arranging the deduplicated strategy factors according to the calculated weights, and summarizing them to obtain the processed strategy factors; Set the one with the largest weight to be ranked first.

4. The fraud risk management method based on big data according to claim 3, characterized in that: The weight calculation formula of the strategy factor is as follows: in, Indicates the calculated k-th strategy factor weight, represents the frequency of the k-th strategy factor, Indicates the adjustment factor of the set frequency, represents the regional position of the k-th strategy factor, Indicates the adjustment factor of the set regional position, represents the number of identification strategies contained in the k-th strategy factor, Indicates the adjustment factor for the number of identification strategies included in the set strategy factor.

5. The fraud risk management method based on big data according to claim 4, characterized in that: The pair Clustering the processed strategic factors includes the following steps: Set the weight range of the strategy factor and use the bi-kmeans algorithm to cluster the strategy factor within the set weight range: Set the number of clusters h in the clustering results and the number of strategy factors r in each cluster; S41, considering the strategic factors within the set weight range as a cluster; S42, determine whether the number of clusters is less than h, if the number of clusters is less than h, execute S43, if the number of clusters is equal to h, execute S44; S43: For each cluster, select a strategic factor in the cluster as the cluster center, calculate the similarity between other strategic factors in the cluster and the cluster center, and divide the cluster based on the calculated similarity value. After the division is completed, execute S42 to determine whether further division is needed; S44. Determine whether the number of strategy factors within the cluster is less than r. When the number of strategy factors within the cluster is less than or equal to r, output the clustering result. When the number of strategy factors within the cluster is greater than h, set h=h+1, increase the number of clusters and execute S43 to re-divide the clusters where the number of strategy factors within the cluster is greater than h.

6. The fraud risk management method based on big data according to claim 5, characterized in that: The meter Calculate the similarity between other strategic factors in the cluster and the cluster center, and divide the cluster based on the calculated similarity value, including the following steps: The similarity calculation is as follows: in, Represents the similarity value between other strategic factors in the cluster and the cluster center. The calculated similarity value range is [-1,1]. represents a strategic factor within the cluster, Indicates the currently selected cluster center B; Since the calculated similarity value range is in the interval [-1,1]; When the similarity value between other strategic factors in the cluster and the cluster center is in the interval [-1,0], it is determined that other strategic factors in the cluster are not similar to the cluster center; When the similarity value between other strategy factors in the cluster and the cluster center is in the interval (0,1], it is determined that other strategy factors in the cluster are similar to the cluster center; Based on the similar results of other strategic factors within the cluster and the cluster center, the cluster is divided into two.

7. The fraud risk management method based on big data according to claim 1, characterized in that: The base Identifying the real-time collected website content data based on the clustered strategic factors and performing risk scoring on the real-time collected website content data includes the following steps: S51, collecting website content data in real time, and extracting keywords from the website content data collected in real time; S52, matching the keywords in the website content data collected in real time with the strategic factors in the clustering results one by one based on a semantic relevance calculation method; Set the system to accumulate the weights of the strategy factors for each successful match, and use the accumulated weights as the risk score for the website content data collected in real time. S53. Setting a risk score threshold for website content data collected in real time; When the risk score of the accumulated real-time collected website content data is greater than or equal to the set threshold, the current website is determined to be a fake website; When the accumulated risk score of the real-time collected website content data is less than the set threshold, the current website is determined to be a normal website.

8. A method for implementing the fraud risk management method based on big data according to any one of claims 1 to 7 The fraud risk management system is characterized in that Including database, semantic recognition module, data collection module, data processing module, and clustering module; The database is used to store various accumulated recognition strategies; The semantic recognition module is used to extract various recognition strategies from the database, segment the content of various recognition strategies, and generate strategy factors; The data processing module is used to process the strategy factors generated by segmentation and transmit the processed strategy factors to the clustering module; The clustering module is used to cluster the received strategic factors and output the clustering results to be stored in the database; The data collection module is used to collect website content data in real time.

9. The fraud risk management system based on big data according to claim 8, characterized in that: Also includes The risk scoring module is used to score and display the websites collected in real time according to the clustered strategic factors.

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