Data sharing analysis system and methods

Abstract

A data sharing analysis system includes a processing unit and a storage unit. The processing unit is configured to: receive communication data to be verified; determine whether the communication data to be verified matches one of multiple reference customer communication data; when a match is determined, obtain related person characteristic data and shared characteristic data based on the target customer's basic information and the basic information of a reference customer corresponding to the matching reference customer communication data; generate a data sharing assessment result using a smart decision-making model based on the related person characteristic data, the shared characteristic data, and the reference customer risk data corresponding to the basic information of the reference customer corresponding to the matching reference customer communication data; and transmit a message content corresponding to the data sharing assessment result to the client electronic device.

Claims

1. A data sharing and analysis system, suitable for communicating with a client electronic device operated by a target customer, and comprising: a processing unit, electrically connected to the client electronic device; a storage unit, electrically connected to the processing unit, storing a target customer basic information corresponding to the target customer, multiple reference customer basic information corresponding to multiple reference customers, multiple reference customer risk information corresponding to the reference customers, and a smart decision-making model implemented using a machine learning algorithm, wherein each reference customer basic information includes reference customer communication information; the processing unit receives a data change request from the client electronic device, the data change request including a pair of communication information to be verified corresponding to the target customer; the processing unit determines, based on the data change request, whether the communication information to be verified matches one of the reference customer communication information; When the processing unit determines that the communication data to be verified matches one of the reference customer communication data, it obtains a related party characteristic data and a shared characteristic data based on the target customer's basic information and the basic information of the reference customer corresponding to the matching reference customer communication data. The related party characteristic data indicates the association information between the target customer and the corresponding reference customer, and the shared characteristic data indicates the characteristic information that the communication data to be verified is shared. The processing unit uses the intelligent decision-making model to generate a data sharing assessment result based on the related party characteristic data, the shared characteristic data, and the reference customer risk data corresponding to the basic information of the reference customer corresponding to the matching reference customer communication data. The processing unit transmits a message content corresponding to the data sharing assessment result to the client electronic device, and the message content indicates whether the data change request is allowed.

2. The data sharing and analysis system as described in claim 1, wherein, This intelligent decision-making model contains multiple decision trees, each of which outputs a trust score; For each trust score, the processing unit generates a decision result corresponding to the trust score based on the trust score and a first preset rule; the processing unit selects a target decision result from the decision results as the data sharing evaluation result based on the decision results and a second preset rule.

3. The data sharing and analysis system as described in claim 1, wherein, Each reference client risk profile includes a high-risk characteristic value, a historical risk characteristic value, and a time factor characteristic value.

4. The data sharing and analysis system as described in claim 1, wherein, The related person information includes a table showing the relationship between a person and a legal entity, a table showing the relationship between a parent and child account, a table showing the relationship between a legal representative and a trusted contact.

5. The data sharing and analysis system as described in claim 1, wherein, The shared feature data includes a blacklist shared feature value, a relative shared feature value, and a device shared feature value.

6. A data sharing analysis method, implemented by a data sharing analysis system, the data sharing analysis system being adapted to communicate with a client electronic device operated by a target customer, and comprising a processing unit and a storage unit, the storage unit storing target customer basic information corresponding to the target customer, multiple reference customer basic information corresponding to multiple reference customers, multiple reference customer risk information corresponding to the reference customers, and a smart decision-making model implemented using a machine learning algorithm, each reference customer basic information containing reference customer communication data; the data sharing analysis method comprising: (A) the processing unit receiving a data change request from the client electronic device, the data change request containing a pair of communication data to be verified corresponding to the target customer; (B) the processing unit determining, based on the data change request, whether the communication data to be verified matches one of the reference customer communication data; (C) When the processing unit determines that the communication data to be verified matches one of the reference customer communication data, it obtains a related person characteristic data and a shared characteristic data based on the target customer's basic information and the reference customer's basic information corresponding to the matching reference customer communication data. The related person characteristic data indicates the association information between the target customer and the corresponding reference customer, and the shared characteristic data indicates the characteristic information that the communication data to be verified is shared; (D) The processing unit uses the intelligent decision model to generate a data sharing assessment result based on the related person characteristic data, the shared characteristic data, and the reference customer risk data corresponding to the reference customer's basic information corresponding to the matching reference customer communication data; and (E) The processing unit transmits a message content corresponding to the data sharing assessment result to the client electronic device, and the message content indicates whether the data change request is allowed.

7. The data sharing analysis method as described in claim 6, wherein, This intelligent decision-making model is implemented using machine learning algorithms and contains multiple decision trees, each of which outputs a confidence score. For each trust score, the processing unit generates a decision result corresponding to the trust score based on the trust score and a first preset rule; the processing unit selects a target decision result from the decision results as the data sharing evaluation result based on the decision results and a second preset rule.

8. The data sharing analysis method as described in claim 6, wherein, Each reference client risk profile includes a high-risk characteristic value, a historical risk characteristic value, and a time factor characteristic value.

9. The data sharing analysis method as described in claim 6, wherein, The related person information includes a table showing the relationship between a person and a legal entity, a table showing the relationship between a parent and child account, a table showing the relationship between a legal representative and a trusted contact.

10. The data sharing analysis method as described in claim 6, wherein, The shared feature data includes a blacklist shared feature value, a relative shared feature value, and a device shared feature value.

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