Financial credit dynamic evaluation system based on big data

Abstract

The invention discloses a financial credit dynamic evaluation system based on big data, and the system comprises a data collection module, a data preprocessing module, a data storage module, a machine learning module, a dynamic scoring module, and a user interface module. The input end of the data acquisition module is connected to an external data source so as to obtain data related to credit evaluation, the output end of the data acquisition module is electrically connected with the input end of the data preprocessing module, and the output end of the data preprocessing module is electrically connected with the input end of the data storage module; the output end of the data storage module is electrically connected with the input end of a machine learning module and the input end of a dynamic scoring module, and the user interface module is connected with the output end of the dynamic scoring module. According to the financial credit dynamic evaluation system based on the big data provided by the invention, comprehensive and real-time monitoring and evaluation of personal or enterprise credit conditions are realized by integrating a plurality of functional modules such as data acquisition, preprocessing, storage, machine learning and dynamic scoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data evaluation, and in particular to a financial credit dynamic evaluation system based on big data. Background Art

[0002] In the financial industry, credit assessment has always been the basis for important links such as loan approval and interest rate setting. Traditional credit assessment methods mainly rely on historical financial records and personal basic information, and perform static assessments through fixed scoring rules. However, with the development of information technology and the rapid changes in the social and economic environment, this method has shown many shortcomings. First, the traditional assessment model has a long update cycle and it is difficult to reflect the applicant's latest credit status in a timely manner; second, the assessment basis is relatively single, and often ignores the impact of emerging data sources such as social media activities and online behaviors; third, due to the lack of sufficient data processing capabilities, traditional methods cannot effectively process and analyze massive data, resulting in inaccurate assessment results. In addition, the existing credit assessment system also has hidden dangers in data security and privacy protection, and is prone to the risk of data leakage or fraud attacks. These problems limit the risk control ability and market competitiveness of financial institutions. Therefore, there is an urgent need for a new financial credit dynamic assessment system that can be updated in real time, comprehensively considers multiple factors, and has high accuracy and security. Summary of the invention

[0003] 1. Technical Problems Solved

[0004] The technical problems to be solved by the present invention are the various problems mentioned in the above background technology, and a financial credit dynamic evaluation system based on big data is provided.

[0005] 2. Technical Solution

[0006] In order to solve the above technical problems, the technical solution provided by the present invention is: a financial credit dynamic evaluation system based on big data, the system comprising:

[0007] A data collection module for collecting credit-related data from multiple data sources in real time;

[0008] A data preprocessing module, used for cleaning and standardizing the data collected by the data acquisition module;

[0009] A data storage module, used to store the preprocessed data in a distributed database;

[0010] A machine learning module to train models to identify key factors that influence credit scores;

[0011] A dynamic scoring module, used to adjust the credit score in real time according to the analysis results of the machine learning module;

[0012] A user interface module is used to provide users with a credit status inquiry service;

[0013] The input end of the data acquisition module is connected to an external data source to obtain data related to credit assessment, the output end is electrically connected to the input end of the data preprocessing module, the output end of the data preprocessing module is electrically connected to the input end of the data storage module, the output end of the data storage module is electrically connected to the input end of the machine learning module and the input end of the dynamic scoring module, and the user interface module is connected to the output end of the dynamic scoring module.

[0014] As an improvement, the data acquisition module further comprises:

[0015] Internet data collection unit, used to capture information related to user credit from the Internet;

[0016] A social media data collection unit, used to obtain user behavior data from social media platforms;

[0017] The bank transaction data collection unit is used to collect the user's bank transaction records.

[0018] As an improvement, the data preprocessing module further comprises:

[0019] Data cleaning submodule, used to remove invalid or erroneous data entries;

[0020] The data standardization submodule is used to unify the data format for subsequent processing.

[0021] As an improvement, the machine learning module adopts a deep learning algorithm, and the algorithm can automatically identify new credit score influencing factors.

[0022] As an improvement, the dynamic scoring module can automatically update the user's credit score within a preset time interval after receiving new data.

[0023] As an improvement, the user interface module supports access through multiple methods such as mobile applications and web pages, and can provide personalized credit reports.

[0024] As an improvement, a security module is also included to ensure the security of data transmission and the privacy of stored data.

[0025] As an improvement, an anomaly detection module is also included to identify and handle possible fraudulent activities.

[0026] 3. Beneficial Effects

[0027] The advantages of the present invention compared with the prior art are:

[0028] The present invention provides a financial credit dynamic assessment system based on big data, which realizes comprehensive and real-time monitoring and assessment of personal or corporate credit status by integrating multiple functional modules such as data collection, preprocessing, storage, machine learning and dynamic scoring. The system can not only obtain credit-related data from multiple data sources such as the Internet, social media and banks in real time, but also ensure the quality of input data through data cleaning and standardization. Using advanced deep learning algorithms, the system can automatically identify key factors affecting credit scores and make dynamic score adjustments accordingly, greatly improving the accuracy and timeliness of credit assessment. Users can access the system through a variety of terminal devices to obtain personalized credit reports, enhancing user experience. In addition, the system also has built-in security modules and anomaly detection modules, which effectively protect the security and privacy of data, and can promptly detect and handle potential fraudulent behaviors, thereby helping financial institutions to better control risks and improve the reliability and practicality of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a structural diagram of a financial credit dynamic evaluation system based on big data in the present invention. DETAILED DESCRIPTION

[0030] The technical solutions 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, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Embodiment 1

[0032] like Figure 1 As shown, a financial credit dynamic evaluation system based on big data, the system includes:

[0033] The data collection module is used to collect credit-related data from multiple data sources in real time. The data collection module further includes: an Internet data collection unit, which is used to capture information related to user credit from the Internet; a social media data collection unit, which is used to obtain user behavior data from social media platforms; and a bank transaction data collection unit, which is used to collect user bank transaction records.

[0034] The data preprocessing module is used to clean and standardize the data collected by the data acquisition module; the data preprocessing module further includes: a data cleaning submodule for removing invalid or erroneous data entries; a data standardization submodule for unifying the data format for subsequent processing.

[0035] A data storage module, used to store the preprocessed data in a distributed database;

[0036] A machine learning module for training a model to identify key factors affecting credit scores, wherein the machine learning module uses a deep learning algorithm that can automatically identify new factors affecting credit scores;

[0037] A dynamic scoring module, used to adjust the credit score in real time according to the analysis results of the machine learning module, and the dynamic scoring module can automatically update the user's credit score within a preset time interval after receiving new data;

[0038] A user interface module is used to provide users with a credit status inquiry service. The user interface module supports multiple access methods such as mobile applications and web pages, and can provide personalized credit reports;

[0039] The input end of the data acquisition module is connected to an external data source to obtain data related to credit assessment, the output end is electrically connected to the input end of the data preprocessing module, the output end of the data preprocessing module is electrically connected to the input end of the data storage module, the output end of the data storage module is electrically connected to the input end of the machine learning module and the input end of the dynamic scoring module, the user interface module is connected to the output end of the dynamic scoring module, and also includes a security module for ensuring the security of data transmission and the privacy of stored data, and also includes an anomaly detection module for identifying and handling possible fraud.

[0040] It should be noted that the data collection module is connected to various data sources (such as social media APIs, bank transaction systems, public record databases, etc.) to obtain data related to credit assessment in real time. This module sends the collected data to the data preprocessing module.

[0041] The data preprocessing module receives data from the data acquisition module, performs preprocessing operations such as cleaning, deduplication, and standardization on the data, and passes the preprocessed clean data to the data storage module.

[0042] The data storage module receives the preprocessed data and stores it in a distributed database, providing a data access interface to the machine learning module and the dynamic scoring module.

[0043] The machine learning module reads data from the data storage module, uses the machine learning algorithm to train the model, identifies key credit scoring indicators, and outputs the trained model to the dynamic scoring module.

[0044] The dynamic scoring module obtains the latest data from the data storage module, analyzes the data using the model provided by the machine learning module, calculates and updates the user's credit score in real time, and stores the results back to the database.

[0045] When actually used, the user interface module will provide an interface for users to query their credit status, request the latest credit score information from the dynamic scoring module, display the user's credit score, and provide corresponding explanations or suggestions.

[0046] When the present invention is actually used, the communication mechanism between modules is as follows:

[0047] Data acquisition module → data preprocessing module: transmit raw data through message queue or directly call API;

[0048] Data preprocessing module → data storage module: save the processed data to persistent storage through database insertion operation;

[0049] Data storage module → machine learning module: exchange data through database query / write interface;

[0050] Machine Learning Module → Dynamic Scoring Module: passing model parameters via file transfer or API call;

[0051] Data storage module → dynamic scoring module: obtain the latest data through database query;

[0052] Dynamic scoring module → data storage module: update scoring results by writing into the database;

[0053] User interface module → dynamic scoring module: request scoring information through API calls;

[0054] User Interface Module → User: Displays credit score results through the web or mobile application interface.

[0055] The working principle of the present invention: the system mainly includes a data acquisition module, a data preprocessing module, a data storage module, a machine learning module, a dynamic scoring module and a user interface module. The data acquisition module is responsible for collecting credit-related data from multiple data sources in real time, including but not limited to public information on the Internet, user behavior data on social media, and bank transaction records; the data preprocessing module cleans and standardizes the received data, removes invalid or erroneous data entries, and unifies the data format for subsequent processing; the preprocessed data is stored in a distributed database and managed by the data storage module; the machine learning module uses a deep learning algorithm to train the model, which can automatically identify new factors that affect credit scores; the dynamic scoring module adjusts the credit score in real time according to the analysis results of the machine learning module, and automatically updates the user's credit score within a preset time interval after receiving new data; the user interface module supports access to the system through multiple methods such as mobile applications and web pages, and provides personalized credit reports for users to query. In addition, the system also includes a security module to ensure the security of data transmission and the privacy of stored data; the anomaly detection module is used to identify and handle possible fraudulent behaviors, thereby ensuring the reliability and accuracy of the system. Through such a design, the system can efficiently integrate data from multiple sources, provide real-time updated credit assessment results, and enhance the risk control capabilities of financial institutions.

[0056] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0057] 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.

[0058] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A financial credit dynamic assessment system based on big data, characterized in that: The system comprises: A data collection module for collecting credit-related data from multiple data sources in real time; A data preprocessing module, used for cleaning and standardizing the data collected by the data acquisition module; A data storage module, used to store the preprocessed data in a distributed database; A machine learning module to train models to identify key factors that influence credit scores; A dynamic scoring module, used to adjust the credit score in real time according to the analysis results of the machine learning module; A user interface module is used to provide users with a credit status inquiry service; The input end of the data acquisition module is connected to an external data source to obtain data related to credit assessment, the output end is electrically connected to the input end of the data preprocessing module, the output end of the data preprocessing module is electrically connected to the input end of the data storage module, the output end of the data storage module is electrically connected to the input end of the machine learning module and the input end of the dynamic scoring module, and the user interface module is connected to the output end of the dynamic scoring module.

2. According to the big data-based financial credit dynamic evaluation system of claim 1, it is characterized in that: The data acquisition module further comprises: Internet data collection unit, used to capture information related to user credit from the Internet; A social media data collection unit, used to obtain user behavior data from social media platforms; The bank transaction data collection unit is used to collect the user's bank transaction records.

3. According to the big data-based financial credit dynamic evaluation system of claim 1, it is characterized in that: The data preprocessing module further comprises: Data cleaning submodule, used to remove invalid or erroneous data entries; The data standardization submodule is used to unify the data format for subsequent processing.

4. According to the big data-based financial credit dynamic evaluation system of claim 1, it is characterized in that: The machine learning module adopts a deep learning algorithm, and the algorithm can automatically identify new credit score influencing factors.

5. According to the big data-based financial credit dynamic assessment system of claim 1, it is characterized in that: The dynamic scoring module can automatically update the user's credit score within a preset time interval after receiving new data.

6. A financial credit dynamic evaluation system based on big data according to claim 1, characterized in that: The user interface module supports access through multiple methods such as mobile applications and web pages, and can provide personalized credit reports.

7. A financial credit dynamic evaluation system based on big data according to claim 1, characterized in that: It also includes a security module to ensure the security of data transmission and the privacy of stored data.

8. The financial credit dynamic evaluation system based on big data according to claim 1 is characterized in that: It also includes an anomaly detection module to identify and address possible fraudulent behavior.

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