Automobile financial service data analysis platform based on block chain technology

Abstract

The invention relates to the technical field of automobile financial evaluation, and discloses an automobile financial service data analysis platform based on a block chain technology, which comprises a data acquisition module, a data analysis and evaluation module, a risk management module and a privacy protection module, the data acquisition module comprises a user credit data acquisition unit, a user loan data acquisition unit and a user insurance data acquisition unit, and the data analysis and evaluation module comprises a credit analysis unit, a loan analysis unit and a consumption analysis unit. The platform acquires data from different financial service platforms, lending platforms and insurance service platforms through the user credit data acquisition unit, the user lending data acquisition unit and the user insurance data acquisition unit, so that activity information of users in multiple financial fields can be comprehensively covered, and limitation caused by a single data source is avoided; the financial institution can more comprehensively know the financial behavior and credit status of the user, so that the financial institution can more comprehensively know the financial behavior and credit status of the user.

Description

Technical Field

[0001] The present invention relates to the field of automobile finance evaluation technology, and in particular to an automobile finance service data analysis platform based on blockchain technology. Background Art

[0002] With the development of the global economy and the improvement of consumer purchasing power, the scale of the auto finance market continues to expand. It is expected that the global auto finance market will maintain a growth trend in the next few years, and the market scale will continue to expand. In addition, with the development of technology, the auto finance service industry is undergoing digital transformation. As consumers' demand for auto finance products diversifies, auto finance companies continue to launch new financial products and services, such as financial leasing and auto accessory financing, to meet the needs of the market and consumers. In the existing technology, information asymmetry between consumers and financial service providers may lead to misjudgment in risk assessment, which in turn leads to insufficient basis for personal credit assessment, increasing the risk of loan default to a certain extent. The volatility of the automobile market and economic environment also poses certain challenges to predicting customer repayment ability and asset recovery value. The limitations brought by a single data source will affect the financial service provider's evaluation and credit status of consumer users. Summary of the invention

[0003] In view of the shortcomings of the existing technology, the present invention provides an automobile financial service data analysis platform based on blockchain technology, which has the advantages of analyzing the user's credit status and solves the above-mentioned technical problems.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an automobile financial service data analysis platform based on blockchain technology, including a data collection module, a data analysis and evaluation module, a risk management module and a privacy protection module; The data collection module includes a user credit data collection unit, a user loan data collection unit and a user insurance data collection unit. The user credit data collection unit is used to collect the credit data of the current user on different financial service platforms, the user loan data collection unit is used to collect the loan data of the user on different loan platforms, and the user insurance data collection unit is used to collect the consumption data of the user on different insurance service platforms; The data analysis and evaluation module includes a credit analysis unit, a loan analysis unit and a consumption analysis unit. The credit analysis unit calculates and obtains a first influence coefficient based on the credit data of the current user on different financial service platforms. The loan analysis unit calculates and obtains a second influence coefficient based on the loan data of the user on different loan platforms. The consumption analysis unit calculates and obtains a third influence coefficient based on the consumption data of the user on different insurance service platforms. The risk management module includes a comprehensive analysis unit and a risk assessment unit. The comprehensive analysis unit calculates a comprehensive analysis coefficient based on the first influence coefficient, the second influence coefficient and the third influence coefficient. The risk assessment unit determines whether to issue a risk warning based on the comprehensive analysis coefficient. The privacy protection module includes a data encryption storage unit and a data access control unit. The data encryption storage unit is used to encrypt and classify the user data for storage, and the data access control unit is used to determine the security of data access and call for control.

[0005] As a preferred technical solution of the present invention, the specific expression of the user credit data collection unit for collecting the credit data of the current user on different financial service platforms is as follows: ; in, represents the user credit dataset, Respectively represent the user credit data of the first financial service platform, , No. User credit data of financial service platforms, , No. User credit data of financial service platforms, , the User credit data of financial service platforms The specific expression is as follows: ; in, Indicates Financial services platform days of user credit data, Representing a time dataset The daily user credit data stored in is summed up. Representing a time dataset The number of elements in Indicates User credit data of financial service platforms, .

[0006] As a preferred technical solution of the present invention, the specific expression of the user loan data collection unit for collecting the loan data of users on different loan platforms is as follows: ; in, represents the loan dataset, Respectively represent the user's lending data on the first lending platform, , No. The lending data of lending platforms, , No. The lending data of the lending platform, Lending data of lending platforms The expression is as follows: ; in, Indicates that the user is There are loans on the lending platform. Indicates that the user is There is no borrowing on this lending platform.

[0007] As a preferred technical solution of the present invention, the specific expression of the user insurance data collection unit for collecting the consumption data of users on different insurance service platforms is as follows: ; in, represents the user insurance dataset, Indicates the user's consumption data on the first insurance service platform, , No. Consumption data of insurance service platforms, , No. The consumption data of the insurance service platform Consumption data of insurance service platforms Indicates that the user is The consumption amount of each insurance service platform.

[0008] As a preferred technical solution of the present invention, the specific expression of the first influence coefficient obtained by the credit analysis unit based on the credit data analysis and calculation of the current user on different financial service platforms is as follows: ; in, represents the first influence coefficient, represents the total number of financial service platforms, Express The user credit data of each financial service platform is summed up.

[0009] As a preferred technical solution of the present invention, the specific expression of the second influence coefficient calculated by the lending analysis unit based on the lending data of the user on different lending platforms is as follows: ; in, represents the second influence coefficient, Represents the total number of lending platforms, Indicates total The sum of the lending data of the lending platforms is calculated. Indicates that the base is a natural constant The logarithmic function of .

[0010] As a preferred technical solution of the present invention, the consumption analysis unit calculates the specific expression of the third influence coefficient based on the consumption data of the user on different insurance service platforms as follows: ; in, represents the third influence coefficient, represents a natural constant, represents the total consumption, Expressing The consumption amounts of each insurance service platform are summed up.

[0011] As a preferred technical solution of the present invention, the comprehensive analysis unit calculates the comprehensive analysis coefficient based on the first influence coefficient, the second influence coefficient and the third influence coefficient to obtain the specific expression as follows: ; in, represents the first influence coefficient, represents the second influence coefficient, represents the third influence coefficient, They represent three weight coefficients whose sum is 1, Represents the comprehensive analysis coefficient.

[0012] As a preferred technical solution of the present invention, the specific steps of the risk assessment unit judging whether to issue a risk warning based on the comprehensive analysis coefficient are: Does not exceed the minimum comprehensive risk value When the comprehensive analysis coefficient Exceeds the minimum comprehensive risk value No warning is issued.

[0013] As a preferred technical solution of the present invention, the data access control unit is used to determine the security of data access and call and perform control specifically including using multi-factor authentication technology to verify the identity of the user.

[0014] Compared with the prior art, the present invention provides an automobile financial service data analysis platform based on blockchain technology, which has the following beneficial effects: The present invention collects data from different financial service platforms, loan platforms and insurance service platforms respectively through a user credit data collection unit, a user loan data collection unit and a user insurance data collection unit, which can comprehensively cover the user's activity information in multiple financial fields, avoid the limitations brought by a single data source, and enable financial institutions to have a more comprehensive understanding of the user's financial behavior and credit status. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 , an automotive financial service data analysis platform based on blockchain technology, including data collection module, data analysis and evaluation module, risk management module and privacy protection module; The data collection module includes a user credit data collection unit, a user loan data collection unit and a user insurance data collection unit. The user credit data collection unit is used to collect the credit data of the current user on different financial service platforms, the user loan data collection unit is used to collect the loan data of the user on different loan platforms, and the user insurance data collection unit is used to collect the consumption data of the user on different insurance service platforms; The specific expression of the user credit data collection unit for collecting the credit data of the current user on different financial service platforms is as follows: ; in, represents the user credit dataset, Respectively represent the user credit data of the first financial service platform, , No. User credit data of financial service platforms, , No. User credit data of financial service platforms, , No. User credit data of financial service platforms The specific expression is as follows: ; in, Indicates Financial services platform days of user credit data, Representing a time dataset The daily user credit data stored in is summed up. Representing a time dataset The number of elements in Indicates User credit data of financial service platforms, .

[0018] The specific expression used by the user loan data collection unit to collect the loan data of users on different loan platforms is as follows: ; in, represents the loan dataset, Respectively represent the user's lending data on the first lending platform, , No. The lending data of lending platforms, , No. The lending data of lending platforms, Lending data of lending platforms The expression is as follows: ; in, Indicates that the user is There are loans on the lending platform. Indicates that the user is There is no borrowing on this lending platform.

[0019] The specific expression used by the user insurance data collection unit to collect the consumption data of users on different insurance service platforms is as follows: ; in, represents the user insurance dataset, Indicates the user's consumption data on the first insurance service platform, , No. Consumption data of insurance service platforms, , No. Consumption data of insurance service platforms, Consumption data of insurance service platforms Indicates that the user is The amount of consumption on each insurance service platform; The user credit data collection unit, user loan data collection unit and user insurance data collection unit collect data from different financial service platforms, loan platforms and insurance service platforms respectively, which can comprehensively cover the user's activity information in multiple financial fields. This provides a rich data basis for subsequent analysis and evaluation, avoids the limitations brought by a single data source, and enables financial institutions to have a more comprehensive understanding of the user's financial behavior and credit status; The data analysis and evaluation module includes a credit analysis unit, a loan analysis unit and a consumption analysis unit. The credit analysis unit calculates and obtains a first impact coefficient based on the credit data of the current user on different financial service platforms. The loan analysis unit calculates and obtains a second impact coefficient based on the loan data of the user on different loan platforms. The consumption analysis unit calculates and obtains a third impact coefficient based on the consumption data of the user on different insurance service platforms. The credit analysis unit, the loan analysis unit and the consumption analysis unit analyze the user data from different angles and calculate the first impact coefficient, the second impact coefficient and the third impact coefficient. These coefficients comprehensively reflect the performance of the user in terms of credit, loan and consumption, and provide a more comprehensive and accurate basis for risk assessment for financial institutions. The specific expression of the first influence coefficient obtained by the credit analysis unit based on the credit data analysis of the current user on different financial service platforms is as follows: ; in, represents the first influence coefficient, represents the total number of financial service platforms, Express The specific expression of the second influence coefficient calculated by the lending analysis unit based on the user's lending data on different lending platforms is as follows: ; in, represents the second influence coefficient, Represents the total number of lending platforms, Indicates total The sum of the lending data of the lending platforms is calculated. Indicates that the base is a natural constant The consumption analysis unit calculates the third impact coefficient based on the consumption data of users on different insurance service platforms as follows: ; in, represents the third influence coefficient, represents a natural constant, Indicates the total amount of consumption. The total amount of consumption here is provided by the user. If the user does not authorize, it is set , Expressing The consumption amount of each insurance service platform is summed up; The risk management module includes a comprehensive analysis unit and a risk assessment unit. The comprehensive analysis unit calculates the comprehensive analysis coefficient based on the first influence coefficient, the second influence coefficient and the third influence coefficient. The risk assessment unit determines whether to issue a risk warning based on the comprehensive analysis coefficient. After the platform is promoted and applied in the entire automotive finance industry, it can improve the risk management level of the entire industry. Through the analysis and risk assessment of a large amount of user data, financial institutions can better grasp market trends and risk conditions, formulate response strategies in advance, avoid the occurrence of systemic financial risks, and maintain the stability of the financial market. The specific expression of the comprehensive analysis coefficient obtained by the comprehensive analysis unit based on the comprehensive calculation of the first influence coefficient, the second influence coefficient and the third influence coefficient is as follows: ; in, represents the first influence coefficient, represents the second influence coefficient, represents the third influence coefficient, They represent three weight coefficients whose sum is 1, Represents the comprehensive analysis coefficient, when When Divide equally The specific steps for the risk assessment unit to determine whether to issue a risk warning based on the comprehensive analysis coefficient are as follows: Does not exceed the minimum comprehensive risk value When the comprehensive analysis coefficient Exceeds the minimum comprehensive risk value No warning is issued; The privacy protection module includes a data encryption storage unit and a data access control unit. The data encryption storage unit is used to encrypt and classify the user data, store the user's credit data, loan data and insurance data separately, and set different encryption levels and access rights according to the sensitivity and importance of the data. The data access control unit is used to determine the security of data access and call and control it.

[0020] The data access control unit is used to determine the security of data access and call control, including the use of multi-factor authentication technology to verify the user's identity. When a user attempts to access data, the platform will require the user to provide multiple authentication factors, for example, first enter the user name and password, and then the platform will send a one-time verification code to the user's bound mobile phone. The user needs to enter the correct verification code to continue access. For data access with a higher security level, biometric identification may also be required. Through this multi-factor authentication technology, illegal users can be effectively prevented from impersonating legitimate users to obtain data, greatly improving the security of data access. In addition, the data access control unit will also record and audit the call of data in detail so that it can be traced and investigated when a data security incident occurs. Multi-factor authentication technology verifies the user's identity, including knowledge and information known by the user (such as passwords, answers to security questions), physical devices owned by the user (such as mobile phones, smart cards), and the user's own biometrics (such as fingerprints, facial recognition, iris scanning) and other methods for identity authentication; The present invention only provides a feasible solution and does not represent the optimal solution. The setting of the threshold value is for the convenience of comparison. The size of the threshold value depends on the amount of sample data and the cardinality set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value, the size of the weight can be determined by technical personnel in this field based on each sample data and multiple rounds of experiments.

[0021] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The automotive financial service data analysis platform based on blockchain technology is characterized by: It includes data collection module, data analysis and evaluation module, risk management module and privacy protection module; The data collection module includes a user credit data collection unit, a user loan data collection unit and a user insurance data collection unit. The user credit data collection unit is used to collect the credit data of the current user on different financial service platforms, the user loan data collection unit is used to collect the loan data of the user on different loan platforms, and the user insurance data collection unit is used to collect the consumption data of the user on different insurance service platforms; The data analysis and evaluation module includes a credit analysis unit, a loan analysis unit and a consumption analysis unit. The credit analysis unit calculates and obtains a first influence coefficient based on the credit data of the current user on different financial service platforms. The loan analysis unit calculates and obtains a second influence coefficient based on the loan data of the user on different loan platforms. The consumption analysis unit calculates and obtains a third influence coefficient based on the consumption data of the user on different insurance service platforms. The risk management module includes a comprehensive analysis unit and a risk assessment unit. The comprehensive analysis unit calculates a comprehensive analysis coefficient based on the first influence coefficient, the second influence coefficient and the third influence coefficient. The risk assessment unit determines whether to issue a risk warning based on the comprehensive analysis coefficient. The privacy protection module includes a data encryption storage unit and a data access control unit. The data encryption storage unit is used to encrypt and classify the user data for storage, and the data access control unit is used to determine the security of data access and call and control it.

2. The blockchain-based automobile financial service data analysis platform according to claim 1 is characterized by: The specific expression of the user credit data collection unit for collecting the credit data of the current user on different financial service platforms is as follows: ； in, represents the user credit dataset, Respectively represent the user credit data of the first financial service platform, , No. User credit data of financial service platforms, , No. User credit data of financial service platforms, , the User credit data of financial service platforms The specific expression is as follows: ； in, Indicates Financial services platform days of user credit data, Representing a time dataset The daily user credit data stored in is summed up. Representing a time dataset The number of elements in Indicates User credit data of financial service platforms, .

3. The blockchain-based automobile financial service data analysis platform according to claim 2 is characterized by: The specific expression for the user loan data collection unit to collect the loan data of users on different loan platforms is as follows: ； in, represents the loan dataset, Respectively represent the user's lending data on the first lending platform, , No. The lending data of lending platforms, , No. The lending data of the lending platform, Lending data of lending platforms The expression is as follows: ； in, Indicates that the user There are loans on the lending platform. Indicates that the user is There is no borrowing on this lending platform.

4. The blockchain-based automobile financial service data analysis platform according to claim 3 is characterized by: The specific expression for the user insurance data collection unit to collect the consumption data of users on different insurance service platforms is as follows: ； in, represents the user insurance dataset, Indicates the user's consumption data on the first insurance service platform, , No. Consumption data of insurance service platforms, , No. The consumption data of the insurance service platform Consumption data of insurance service platforms Indicates that the user is The consumption amount of each insurance service platform.

5. The blockchain-based automobile financial service data analysis platform according to claim 4 is characterized by: The specific expression of the first influence coefficient obtained by the credit analysis unit based on the credit data of the current user on different financial service platforms is as follows: ； in, represents the first influence coefficient, represents the total number of financial service platforms, Express The user credit data of each financial service platform is summed up.

6. The blockchain-based automobile financial service data analysis platform according to claim 5 is characterized by: The specific expression of the second influence coefficient calculated by the lending analysis unit based on the lending data of the user on different lending platforms is as follows: ； in, represents the second influence coefficient, Represents the total number of lending platforms, Indicates total The sum of the lending data of the lending platforms is calculated. Indicates that the base is a natural constant The logarithmic function of .

7. The blockchain-based automobile financial service data analysis platform according to claim 6 is characterized by: The consumption analysis unit calculates the third influence coefficient based on the consumption data of the user on different insurance service platforms. The specific expression is as follows: ； in, represents the third influence coefficient, represents a natural constant, represents the total consumption, Expressing The consumption amounts of each insurance service platform are summed up.

8. The blockchain-based automobile financial service data analysis platform according to claim 7 is characterized by: The specific expression of the comprehensive analysis coefficient obtained by the comprehensive analysis unit based on the comprehensive calculation of the first influence coefficient, the second influence coefficient and the third influence coefficient is as follows: ； in, represents the first influence coefficient, represents the second influence coefficient, represents the third influence coefficient, They represent three weight coefficients whose sum is 1, Represents the comprehensive analysis coefficient.

9. The blockchain-based automobile financial service data analysis platform according to claim 8 is characterized by: The specific steps of the risk assessment unit judging whether to issue a risk warning based on the comprehensive analysis coefficient are: Does not exceed the minimum comprehensive risk value When the comprehensive analysis coefficient Exceeds the minimum comprehensive risk value No warning is issued.

10. The blockchain-based automobile financial service data analysis platform according to claim 9, characterized in that: The data access control unit is used to determine the security of data access and call and perform control, specifically including using multi-factor authentication technology to verify user identity.

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