Credit data mining processing system and method based on block chain fusion

Abstract

The invention discloses a credit data mining processing system and method based on block chain fusion, relates to the technical field of credit data mining processing, and solves the technical problem that in the prior art, multi-stage data acquisition and mining cannot be performed for an acquisition stage, a development stage and a retention stage of a user. Specifically, an acquisition stage analysis unit performs credit data mining analysis on a user in an acquisition stage, acquires acquisition stage selection data and acquisition stage development data, and deduces whether the current user is further expanded or not through data analysis; after it is determined that an authorization request is initiated to the development user after expansion, and after authorization is completed, the current development user is set as a financial auxiliary user; the development stage analysis unit performs risk assessment on the development users in the development stage; and the retention stage analysis unit performs user credit analysis on the retention stage.

Description

Technical Field

[0001] The present invention relates to the technical field of credit data mining and processing, and specifically to a credit data mining and processing system and method based on blockchain fusion. Background Art

[0002] Credit data mining refers to the process of extracting valuable information, patterns and knowledge from a large amount of credit-related data; these credit data can include personal credit records, corporate financial statements, transaction data, credit rating information and many other aspects; through data mining technology, it can help financial institutions, credit rating companies, etc. better assess credit risks, detect fraud, optimize credit decisions and many other important matters.

[0003] However, in the existing technology, it is impossible to conduct data collection and mining at multiple stages for the user's acquisition stage, development stage, and retention stage, so that it is impossible to conduct user financial data mining accurately in real time, which increases the risk of deviation in user credit data mining processing.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose a credit data mining processing system and method based on blockchain fusion.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A credit data mining and processing system based on blockchain integration includes a data processing platform, the data processing platform is connected to the block in communication, and the block and the data processing platform are connected to an acquisition phase analysis unit, a development phase analysis unit, and a retention phase analysis unit;

[0008] The acquisition phase analysis unit conducts credit data mining and analysis on users in the acquisition phase, collects acquisition phase selection data and acquisition phase development data, and infers whether the current users will be further expanded through data analysis;

[0009] After the extension is confirmed, an authorization request is initiated to the development user and after the authorization is completed, the current development user is set as a financial auxiliary user; the development stage analysis unit conducts risk assessment on the development user during the development stage, collects demand growth information and auxiliary risk information, and infers whether the development user at the current development stage has credit risk based on information processing;

[0010] The overdue periods of credit risk users and the overdue periods of credit safety users that exceed the number of overdue times are cumulatively marked as the retention stage. The retention stage analysis unit conducts user credit analysis on the retention stage, collects retention risk frequency data and retention risk duration data, and regulates the retention stage of development users based on data analysis.

[0011] As a preferred implementation mode of the present invention, the acquisition stage selection data and the acquisition stage development data are respectively the highest proportion of the number of times that users in the acquisition stage use advance consumption payment methods in the historical consumption process to the total number of times that consumption payment methods are selected, and the corresponding numerical ratio of the increase span in the number of payment terminals for advance consumption payment methods used by users in the acquisition stage in the historical consumption process to the proportion of the remaining balances of existing payment terminals.

[0012] As a preferred implementation of the present invention, if the data selected in the acquisition stage exceeds the highest percentage threshold of the number of times, or the data carried out in the acquisition stage exceeds the span-amount value ratio threshold, the corresponding user will be marked as a development user; if the data selected in the acquisition stage does not exceed the highest percentage threshold of the number of times, and the data carried out in the acquisition stage does not exceed the span-amount value ratio threshold, the corresponding user will be marked as a non-development user.

[0013] As a preferred embodiment of the present invention, the demand growth information and the auxiliary risk information are respectively the sum of the maximum growth span of the shortening speed of the financial assistance usage cycle corresponding to the development users in the development stage and the corresponding span value ratio of the increase span of the usage frequency within the real-time financial assistance usage cycle, and the corresponding span value ratio of the increase span of the consumption limit filling amount proportion at the end of the financial assistance usage cycle corresponding to the development users in the development stage and the increase span of the consumption limit to be filled during the decrease of the proportion.

[0014] As a preferred implementation of the present invention, if the demand growth information exceeds the span value and the threshold, or the auxiliary risk information does not exceed the span value ratio threshold, the corresponding development user will be marked as a credit risk user; if the demand growth information does not exceed the span value and the threshold, and the auxiliary risk information exceeds the span value ratio threshold, the corresponding development user will be marked as a credit safety user.

[0015] As a preferred implementation of the present invention, the retention risk frequency data and the retention risk duration data are respectively the corresponding numerical ratio of the frequency of financial assistance usage cycle adjustment for users developed during the retention stage before and after the overdue deadline, and the shortest duration of the use of the increased credit limit of the financial assistance terminal during the stage when the overdue frequency of users developed during the retention stage continues to increase.

[0016] As a preferred embodiment of the present invention, if the retention risk frequency data does not exceed the frequency value ratio threshold, or the retention risk duration data does not exceed the minimum duration threshold, a retention phase shortening signal is generated and sent to the data processing platform; if the retention risk frequency data exceeds the frequency value ratio threshold, and the retention risk duration data exceeds the minimum duration threshold, a retention phase extension signal is generated and sent to the data processing platform.

[0017] The credit data mining and processing method based on blockchain integration is as follows:

[0018] Acquisition phase analysis: credit data mining and analysis of users in the acquisition phase, collection of acquisition phase selection data and acquisition phase development data, and inference through data analysis whether the current users should be further expanded;

[0019] After confirming the extension, an authorization request is initiated to the development user and after the authorization is completed, the current development user is set as a financial auxiliary user;

[0020] Development stage analysis, risk assessment of development users during the development stage, collection of demand growth information and auxiliary risk information, inference based on information processing whether the development users at the current development stage have credit risks; cumulatively mark the overdue periods of credit risk users and overdue periods of credit safety users that exceed the number of overdue times as the retention stage;

[0021] Retention stage analysis, conduct user credit analysis on the retention stage, collect retention risk frequency data and retention risk duration data, and regulate the retention stage of development users based on data analysis.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] In the present invention, credit data mining and analysis is performed on users in the acquisition stage, wherein the acquisition stage is represented by the terminal currently executing the credit data mining system performing credit data analysis on the customers to be expanded, so as to ensure whether the current customers to be expanded can continue to be expanded, thereby improving the accuracy of user credit identification; development stage analysis is performed on the development users, and risk assessment is performed on the development users during the development stage to infer whether there is risk in the use of financial assistance by the development users during the development stage, thereby avoiding the reduction of the security performance of the execution of financial assistance and causing operational risks of the terminal, wherein the development stage is represented by the stage in which the development users authorize to receive financial assistance, and the financial assistance stage is represented by setting the advance consumption limit;

[0024] Conduct user credit analysis during the retention phase to infer whether the credit analysis results of different users during the retention phase are abnormal, so as to carry out targeted control according to different users. At the same time, conduct user financial assistance setting planning based on the credit data mined during the retention phase, so as to minimize the impact of terminal operational risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0026] Figure 1 It is a system principle block diagram of the present invention;

[0027] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] See also Figure 1 As shown, the credit data mining and processing system based on blockchain fusion includes a data processing platform, which is connected to several blocks in communication, and the blocks and the data processing platform are connected with an acquisition phase analysis unit, a development phase analysis unit and a retention phase analysis unit, wherein the blocks serve as data collection terminals, cooperate with the acquisition phase analysis unit, the development phase analysis unit and the retention phase analysis unit to collect data, and upload and aggregate the blockchain data to the data processing platform, and the data processing platform makes user execution decisions based on the credit data;

[0031] The data processing platform performs credit data mining and analysis on users, where the users can be individuals or enterprises, and generates data collection instructions to each block at the same time. The block performs data collection, generates an acquisition phase analysis signal and sends the acquisition phase analysis signal to the acquisition phase analysis unit. After receiving the acquisition phase analysis signal, the acquisition phase analysis unit performs credit data mining and analysis on users in the acquisition phase. The acquisition phase is represented by the terminal currently executing the credit data mining system performing credit data analysis on the customers to be expanded to ensure whether the current customers to be expanded can continue to be expanded, thereby improving the accuracy of user credit identification. The terminal is a financial institution or platform, etc.

[0032] Obtain the highest proportion of the number of advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the total number of consumption payment methods selected. At the same time, obtain the corresponding numerical ratio of the increase span of the number of payment terminals for advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the remaining balance ratio of existing payment terminals. The numerical ratio only collects the ratio of the numerical value of the data, does not consider the problem of inconsistent units, and only analyzes the floating impact of the numerical value;

[0033] The highest proportion of the number of advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the total number of consumption payment method selections, and the corresponding numerical ratio of the increase span of the number of payment terminals for advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the remaining balance of existing payment terminals are marked as acquisition stage selection data and acquisition stage development data, and are compared with the highest proportion threshold of the number of times and the span amount numerical ratio threshold respectively:

[0034] If the highest proportion of the number of advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the total number of consumption payment methods selected exceeds the highest proportion threshold, or the corresponding numerical ratio of the increase span of the number of payment terminals of advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the remaining balance of existing payment terminals exceeds the span balance ratio threshold, it is inferred that the users currently in the acquisition stage can be further expanded, and the corresponding users are marked as development users;

[0035] If the highest proportion of the number of advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the total number of consumption payment methods selected does not exceed the highest proportion threshold, and the corresponding numerical ratio of the increase span of the number of payment terminals for advance consumption payment methods used by users in the acquisition stage during the historical consumption process to the remaining balance of existing payment terminals does not exceed the span balance ratio threshold, it is inferred that the users currently in the acquisition stage cannot be further expanded, and the corresponding users are marked as non-development users;

[0036] An authorization request is initiated to the development user and after the authorization is completed, the current development user is set as a financial assistance user; at the same time, a development stage analysis signal is generated and sent to the development stage analysis unit. After receiving the development stage analysis signal, the development stage analysis unit performs a development stage analysis on the development user, performs a risk assessment on the development user during the development stage, and infers whether there is a risk in the development user's use of financial assistance during the development stage, so as to avoid a decrease in the security performance of the financial assistance execution and cause operational risks of the terminal, wherein the development stage is represented by the stage in which the development user authorizes to receive financial assistance, and the financial assistance stage is represented by setting an advance consumption limit;

[0037] The sum of the maximum growth span of the shortening speed of the financial assistance use cycle corresponding to the development users in the development stage and the corresponding span value of the increase span of the use frequency within the real-time financial assistance use cycle is obtained, where the value and the value of the progress span are added, and the problem of inconsistent units is not considered. Only the impact analysis of the data is performed, and the sum of the maximum growth span of the shortening speed of the financial assistance use cycle corresponding to the development users in the development stage and the corresponding span value of the increase span of the use frequency within the real-time financial assistance use cycle is marked as demand growth information;

[0038] Obtain the corresponding span value ratio of the rising span of the consumption limit filling amount ratio at the end of the financial assistance use period corresponding to the development users in the development stage and the rising span of the consumption limit to be filled during the process of decreasing the proportion, and mark the corresponding span value ratio of the rising span of the consumption limit filling amount ratio at the end of the financial assistance use period corresponding to the development users in the development stage and the rising span of the consumption limit to be filled during the process of decreasing the proportion as auxiliary risk information;

[0039] And the sum of the maximum growth span of the shortening speed of the financial assistance use cycle corresponding to the development users in the development stage and the span corresponding to the increase span of the use frequency in the real-time financial assistance use cycle, and the span value ratio of the rising span of the consumption amount to be filled in the process of the consumption amount to be filled in the process of the decline of the consumption amount corresponding to the span value ratio at the end of the financial assistance use cycle of the development users in the development stage are compared with the span value and threshold and the span value ratio threshold respectively:

[0040] If the sum of the maximum growth span of the shortening speed of the corresponding financial assistance use cycle of the development user in the development stage and the corresponding span value of the increase span of the use frequency in the real-time financial assistance use cycle exceeds the span value and the threshold, or the corresponding span value ratio of the rising span of the consumption limit filling amount proportion at the end of the corresponding financial assistance use cycle of the development user in the development stage and the rising span of the consumption limit to be filled during the decline of the proportion does not exceed the span value ratio threshold, it is inferred that the development user in the current development stage has credit risk, and the corresponding development user is marked as a credit risk user;

[0041] If the sum of the maximum growth span of the shortening speed of the corresponding financial assistance use cycle of the development user in the development stage and the corresponding span value of the increase span of the use frequency in the real-time financial assistance use cycle does not exceed the span value and the threshold, and the corresponding span value ratio of the rising span of the consumption limit filling amount proportion at the end of the corresponding financial assistance use cycle of the development user in the development stage and the rising span of the consumption limit to be filled during the decrease of the proportion exceeds the span value ratio threshold, it is inferred that the development user in the current development stage does not have a credit risk, and the corresponding development user is marked as a credit-safe user;

[0042] The overdue periods of credit risk users and overdue periods of credit safety users exceeding the number of overdue times are cumulatively marked as the retention stage, and a retention stage analysis signal is generated and sent to the retention stage analysis unit. After receiving the retention stage analysis signal, the retention stage analysis unit performs user credit analysis on the retention stage, and infers whether the credit analysis results of different users in the retention stage are abnormal, so as to carry out targeted control according to different users, and at the same time, user financial auxiliary setting planning is carried out according to the credit data mined in the retention stage, so as to minimize the impact of terminal operation risks;

[0043] Obtain the numerical ratio of the frequency of financial assistance use cycle adjustment performed by the users developed in the retention stage before and after the overdue deadline, and mark the numerical ratio of the frequency of financial assistance use cycle adjustment performed by the users developed in the retention stage before and after the overdue deadline as the retention risk frequency data;

[0044] Obtain the shortest value of the usage maintenance duration of the financial auxiliary terminal growth credit limit during the period when the overdue frequency of developed users continues to increase during the retention period, and mark the shortest value of the usage maintenance duration of the financial auxiliary terminal growth credit limit during the period when the overdue frequency of developed users continues to increase during the retention period as the retention risk duration data;

[0045] And the corresponding numerical ratio of the frequency of financial assistance use cycle adjustment of users developed in the retention stage before and after the overdue deadline, and the shortest value of the use maintenance time of the financial assistance terminal growth quota in the stage where the overdue frequency of users developed in the retention stage continues to increase are compared with the frequency numerical ratio threshold and the shortest time threshold respectively:

[0046] If the corresponding numerical ratio of the frequency of financial assistance use cycle adjustment performed by the development user in the retention stage before and after the overdue deadline does not exceed the frequency numerical ratio threshold, or the shortest value of the use maintenance time of the financial assistance terminal growth quota in the stage of continuous increase in the overdue frequency of the development user in the retention stage does not exceed the shortest time threshold, it is inferred that the retention risk of the development user in the current retention stage is high, and a retention stage shortening signal is generated and sent to the data processing platform;

[0047] If the corresponding numerical ratio of the frequency of financial assistance use cycle adjustment performed by the development users in the retention stage before and after the overdue deadline exceeds the frequency numerical ratio threshold, and the shortest value of the use maintenance time of the financial assistance terminal growth quota in the stage of continuous increase in the overdue frequency of the development users in the retention stage exceeds the shortest time threshold, it is inferred that the retention risk of the development users in the current retention stage is low, and a retention stage extension signal is generated and sent to the data processing platform;

[0048] After receiving the retention phase shortening signal, the data processing platform shortens the retention phase period of the development user and controls the financial assistance quota, and reduces the number and frequency of adjustments in the financial assistance usage period. After receiving the retention phase extension signal, the retention phase period of the development user is extended; the block stores and records the data collected during the user acquisition phase, development phase, and retention phase;

[0049] See also Figure 2 As shown in the figure, the credit data mining and processing method based on blockchain integration is as follows:

[0050] Acquisition phase analysis: credit data mining and analysis of users in the acquisition phase, collection of acquisition phase selection data and acquisition phase development data, and inference through data analysis whether the current users should be further expanded;

[0051] After confirming the extension, an authorization request is initiated to the development user and after the authorization is completed, the current development user is set as a financial auxiliary user;

[0052] Development stage analysis, risk assessment of development users during the development stage, collection of demand growth information and auxiliary risk information, inference based on information processing whether the development users at the current development stage have credit risks; cumulatively mark the overdue periods of credit risk users and overdue periods of credit safety users that exceed the number of overdue times as the retention stage;

[0053] Retention stage analysis, conduct user credit analysis on the retention stage, collect retention risk frequency data and retention risk duration data, and regulate the retention stage of development users based on data analysis.

[0054] When the present invention is in use, the acquisition stage analysis unit performs credit data mining and analysis on users in the acquisition stage, collects acquisition stage selection data and acquisition stage development data, and infers whether the current user will be further expanded through data analysis; after determining the expansion, an authorization request is initiated to the development user and after completing the authorization, the current development user is set as a financial assistance user; the development stage analysis unit performs risk assessment on the development user during the development stage; the overdue periods of credit risk users and the overdue periods of credit safety users that exceed the number of overdue times are cumulatively marked as the retention stage, and the retention stage analysis unit performs user credit analysis on the retention stage.

[0055] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The credit data mining and processing system based on blockchain integration is characterized by: The data processing platform includes a data processing platform, the data processing platform is communicatively connected with a block, and an acquisition phase analysis unit, a development phase analysis unit, and a retention phase analysis unit are connected between the block and the data processing platform; The acquisition phase analysis unit conducts credit data mining and analysis on users in the acquisition phase, collects acquisition phase selection data and acquisition phase development data, and infers whether the current users will be further expanded through data analysis; After the extension is confirmed, an authorization request is initiated to the development user and after the authorization is completed, the current development user is set as a financial auxiliary user; the development stage analysis unit conducts risk assessment on the development user during the development stage, collects demand growth information and auxiliary risk information, and infers whether the development user at the current development stage has credit risk based on information processing; The overdue periods of credit risk users and the overdue periods of credit safety users that exceed the number of overdue times are cumulatively marked as the retention stage. The retention stage analysis unit conducts user credit analysis on the retention stage, collects retention risk frequency data and retention risk duration data, and regulates the retention stage of development users based on data analysis.

2. The credit data mining and processing system based on blockchain fusion according to claim 1 is characterized in that: The acquisition stage selection data and the acquisition stage development data are respectively the highest proportion of the number of times users in the acquisition stage use advance consumption payment methods in the historical consumption process to the total number of times consumption payment methods are selected, and the corresponding numerical ratio of the increase span in the number of payment terminals for advance consumption payment methods used by users in the acquisition stage in the historical consumption process to the proportion of the remaining balance of existing payment terminals.

3. The credit data mining and processing system based on blockchain fusion according to claim 2 is characterized in that: If the selected data in the acquisition stage exceeds the highest percentage threshold of the number of times, or the development data in the acquisition stage exceeds the span-amount value ratio threshold, the corresponding user will be marked as a development user; if the selected data in the acquisition stage does not exceed the highest percentage threshold of the number of times, and the development data in the acquisition stage does not exceed the span-amount value ratio threshold, the corresponding user will be marked as a non-development user.

4. The credit data mining and processing system based on blockchain fusion according to claim 1 is characterized in that: The demand growth information and auxiliary risk information are respectively the sum of the maximum growth span of the shortening speed of the financial assistance usage cycle of the development users in the development stage and the corresponding span value ratio of the increase span of the usage frequency within the real-time financial assistance usage cycle, and the corresponding span value ratio of the increase span of the consumption quota filling ratio at the end of the financial assistance usage cycle of the development users in the development stage and the increase span of the consumption quota to be filled during the decrease of the proportion.

5. The credit data mining and processing system based on blockchain fusion according to claim 4 is characterized in that: If the demand growth information exceeds the span value and the threshold, or the auxiliary risk information does not exceed the span value than the threshold, the corresponding development user will be marked as a credit risk user; If the demand growth information does not exceed the span value and threshold, and the auxiliary risk information exceeds the span value ratio threshold, the corresponding development user will be marked as a credit-safe user.

6. The credit data mining and processing system based on blockchain fusion according to claim 1 is characterized in that: The retention risk frequency data and the retention risk duration data are respectively the corresponding numerical ratio of the frequency of financial assistance usage cycle adjustment for users developed during the retention stage before and after the overdue deadline, and the shortest duration of use of the growth quota of the financial assistance terminal during the stage when the overdue frequency of users developed during the retention stage continues to increase.

7. The credit data mining and processing system based on blockchain fusion according to claim 6 is characterized in that: If the retention risk frequency data does not exceed the frequency value ratio threshold, or the retention risk duration data does not exceed the minimum duration threshold, a retention phase shortening signal is generated and sent to the data processing platform; If the retention risk frequency data exceeds the frequency value ratio threshold, and the retention risk duration data exceeds the minimum duration threshold, a retention phase extension signal is generated and sent to the data processing platform.

8. A credit data mining and processing method based on blockchain integration, characterized in that: A credit data mining and processing system based on blockchain fusion as described in any one of claims 1 to 7 above is applied.

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