Financial big data analysis system

Abstract

The invention relates to the technical field of finance, and discloses a financial big data analysis system, which comprises a data acquisition module, a data analysis processing module and an early warning module, and is characterized in that the data acquisition module comprises a market data acquisition unit and an operation data acquisition unit, and the data acquisition unit is used for acquiring historical market data; the operation data acquisition unit is used for acquiring historical internal operation risk data and external event influence risk data and forming a corresponding data set; the data analysis processing module calculates market trend, market fluctuation, an internal operation risk coefficient and an external event influence risk coefficient according to the collected data set, calculates a corresponding risk level, generates a report and an early warning, helps enterprises or individuals to carry out market and operation risk assessment respectively, and improves the risk assessment efficiency. And possible crises caused by markets or operations are avoided or relieved, and default risks are reduced.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a financial big data analysis system. Background Art

[0002] The financial big data analysis system refers to a system specially designed to process and analyze large amounts of data generated in the financial field. It integrates multiple functional modules such as data collection, storage, processing, analysis and visualization. It can extract valuable information from complex data sets and provide in-depth market insights and decision support. The financial big data analysis system is a comprehensive platform that integrates data collection, storage, processing, analysis and visualization. Its core goal is to support financial decision-making, optimize investment strategies, manage risks and improve operational efficiency through in-depth analysis of large amounts of financial data.

[0003] Financial big data analysis systems play an important role in the modern financial industry. They support market forecasting, investment decisions, risk management and customer service by integrating and analyzing massive amounts of data. With the continuous advancement of technology, especially the application of artificial intelligence, real-time data processing and blockchain technology, financial big data analysis systems will become more intelligent, real-time and secure, bringing more opportunities and challenges to the financial industry. In the future, these systems will further enhance data processing capabilities, optimize analysis results, and provide more accurate and efficient decision support for financial institutions and investors.

[0004] Although the existing financial big data analysis system has brought many benefits in practical applications, it also has some defects and problems. It cannot fully identify, evaluate and manage market risks and operational risks, and it is difficult to improve the overall risk management level and business operation efficiency, which leads to the deterioration of the financial conditions of enterprises and individuals, thereby increasing their default risk. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a financial big data analysis system, which has the advantages of calculating market trends, market fluctuations, internal operational risk factors, and external event impact risk factors based on the collected data sets, calculating the corresponding risk levels, and generating reports and warnings, thereby helping enterprises or individuals to conduct market and operational risk assessments respectively, avoid or mitigate possible crises caused by the market or operations, and reduce default risks, etc., solving the problem that market risks and operational risks are difficult to assess well, which leads to increased default risks.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a financial big data analysis system, including a data acquisition module, a data analysis and processing module, and an early warning module:

[0009] The data collection module includes a market data collection unit and an operation data collection unit;

[0010] The market data collection unit is used to collect historical market data SCls, and form a historical market data set with the collected historical market data SCls and send it to the data analysis and processing module; the operation data collection unit is used to collect historical internal operation risk data CZnb and external event impact risk data CZwb, and form a historical internal operation risk data set and an external event impact risk data set and send them to the data analysis and processing module;

[0011] The data analysis and processing module calculates the market trend SCqs and the market volatility SCbd based on the historical market data set, and the data analysis and processing module calculates the internal operation risk coefficient NBxs and the external event impact risk coefficient WBxs based on the historical internal operation risk data set and the external event impact risk data set;

[0012] The data analysis and processing module is internally provided with a market trend threshold QSyz and a market volatility threshold BDyz. The data analysis and processing module compares the market trend SCqs and the market volatility SCbd with the market trend threshold QSyz and the market volatility threshold BDyz respectively, determines the market risk index SCfx and generates a risk report to be sent to the early warning module;

[0013] The data analysis and processing module is internally provided with an internal operation risk threshold NByz and an external event impact risk threshold WByz. The data analysis and processing module compares the internal operation risk coefficient NBxs and the external event impact risk coefficient WBxs with the internal operation risk threshold NByz and the external event impact risk threshold WByz respectively, determines the operation risk index CZfx and generates a risk report to be sent to the early warning module;

[0014] The early warning module provides risk level early warning based on the market risk index SCfx and the operational risk index CZf.

[0015] Preferably, the historical market data set is expressed as: {SCls1, SCls2, SCls3, ..., SCls n}.

[0016] Preferably, the historical internal operational risk data set is expressed as: {CZnb1, CZnb2, CZnb3, ..., CZnb n}, the external event impact risk data set expression is: {CZwb1, CZwb2, CZwb3, ..., CZwb n}.

[0017] Preferably, the market trend SCqs is calculated as follows:

[0018]

[0019] In the above formula: SCls t-k+1 +SCls t-k+2 +…+SCls t-k+3 +SCls t represents the sum of all historical market data sets, k represents the length of the time period for calculating the average, and t represents the time point.

[0020] Preferably, the market volatility SCbd is calculated as follows:

[0021]

[0022] In the above formula, Represents the average value of the historical market data set, SCls i Represents the i-th historical market data, SCls j Represents the i-th specific historical market data.

[0023] Preferably, the internal operation risk coefficient NBxs is calculated as follows:

[0024]

[0025] In the above formula, Represents the sum of all historical internal operational risk data from i=1 to i=n, ​​CZnb i Represents the i-th historical internal operational risk data.

[0026] Preferably, the external event impact risk coefficient WBxs is calculated as follows:

[0027]

[0028] In the above formula, Represents the sum of all external events from i=1 to i=n, ​​CZwb i Represents the risk data affected by the ith external event.

[0029] Preferably, the market risk index SCfx includes market trend SCqs and market volatility SCbd. When SCqs≥QSyz10%, it means that the market trend SCqs is at low risk; when SCqs≥QSyz11%~20%, it means that the market trend SCqs is at medium risk; when SCqs≥QSyz21%, it means that the market trend SCqs is at high risk; when SCbd≤BDyz5%, it means that the market volatility SCbd is at low risk; when SCbd≤BDyz6%~10%, it means that the market volatility SCbd is at medium risk; when SCbd>BDyz10%, it means that the market volatility SCbd is at high risk.

[0030] Preferably, the operational risk index CZfx includes an internal operational risk coefficient NBxs and an external event impact risk coefficient WBxs. When NBxs≥NByz10%, it means that the internal operational risk coefficient NBxs is at low risk; when NBxs≥NByz11%~20%, it means that the internal operational risk coefficient NBxs is at medium risk; when NBxs≥NByz21%, it means that the internal operational risk coefficient NBxs is at high risk; when WBxs≥WByz10%, it means that the external event impact risk coefficient NBxs is at low risk; when WBxs≥WByz11%~20%, it means that the external event impact risk coefficient NBxs is at medium risk; when WBxs≥WByz21%, it means that the external event impact risk coefficient NBxs is at high risk.

[0031] Preferably, the early warning and alarm module generates corresponding risk level reports according to different risk levels, and simultaneously generates reminder signals of different levels and sends them to customers.

[0032] Compared with the prior art, the present invention provides a financial big data analysis system with the following beneficial effects:

[0033] 1. The present invention can help enterprises and individuals understand market dynamics in a timely manner by comparing market trends and market fluctuations, and then sending risk index reports to enterprises and individuals in a timely manner according to low risk, medium risk and high risk. It is helpful to optimize investment strategies, improve risk management capabilities, and promote the healthy development of the market and financial innovation. It can identify low-risk states and encourage investors to adopt stable investment strategies, thereby achieving gradual growth while maintaining asset security, providing timely warnings for the market, prompting all parties to pay attention to potential dangers in the market and take necessary preventive measures, thereby avoiding or mitigating possible market crises.

[0034] 2. The present invention first makes the reference data more comprehensive and thorough by comparing the historical internal operational risk data set with the external event impact risk data set, and then conducts historical operational risk assessment to demonstrate the company's good risk management capabilities to the market, enhance the confidence of investors and partners, and improve the company's market reputation and brand image. It can also timely discover and correct potential risk factors to prevent these factors from evolving into bigger problems, thereby improving the institution's risk management level and market competitiveness, while enabling investors to have a more comprehensive understanding and facilitating enterprises and individuals to make corresponding financial policy adjustments based on risk levels. The risk level report facilitates enterprises or individuals to query and understand historical information, facilitates later reference and review, and is sent to customers at the same time to avoid customers missing out, thereby improving the overall risk management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the system flow of the present invention; DETAILED DESCRIPTION

[0036] 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.

[0037] See also Figure 1 , a financial big data analysis system, including a data acquisition module, a data analysis and processing module and an early warning module:

[0038] The data collection module includes a market data collection unit and an operation data collection unit;

[0039] The market data collection unit is used to collect historical market data SCls, and form a historical market data set {SCls1, SCls2, SCls3, ..., SCls n The operation data collection unit is used to collect historical internal operation risk data CZnb and external event impact risk data CZwb, and form a historical internal operation risk data set {CZnb1, CZnb2, CZnb3, ..., CZnb n} and the external event impact risk dataset {CZwb1, CZwb2, CZwb3, …, CZwb n}Send to the data analysis and processing module;

[0040] The data analysis and processing module calculates the market trend SCqs and market volatility SCbd based on the historical market data set;

[0041] The calculation method of market trend SCqs is as follows:

[0042]

[0043] In the above formula: SCls t-k+1 +SCls t-k+2 +…+SCls t-k+3 +SCls t represents the sum of all historical market data sets, k represents the length of the time period for calculating the average, and t represents the time point;

[0044] The calculation method of market volatility SCbd is as follows:

[0045]

[0046] In the above formula, Represents the average value of the historical market data set, SCls i Represents the i-th historical market data, SCls j Represents the i-th specific historical market data;

[0047] The data analysis and processing module calculates the internal operation risk coefficient NBxs and the external event impact risk coefficient WBxs based on the historical internal operation risk data set and the external event impact risk data set;

[0048] The internal operational risk factor NBxs is calculated as follows:

[0049]

[0050] In the above formula, Represents the sum of all historical internal operational risk data from i=1 to i=n, ​​CZnb i represents the i-th historical internal operational risk data;

[0051] The calculation method of the risk factor WBxs of external events is as follows:

[0052]

[0053] In the above formula, Represents the sum of all external events from i=1 to i=n, ​​CZwb i Represents the risk data of the impact of the ith external event;

[0054] The data analysis and processing module is internally provided with a market trend threshold QSyz and a market volatility threshold BDyz. The data analysis and processing module compares the market trend SCqs and the market volatility SCbd with the market trend threshold QSyz and the market volatility threshold BDyz respectively, determines the market risk index SCfx and generates a risk report to be sent to the early warning module;

[0055] The market risk index SCfx includes the market trend SCqs and the market volatility SCbd. When SCqs≥QSyz10%, it means that the market trend SCqs is at low risk; when SCqs≥QSyz11%~20%, it means that the market trend SCqs is at medium risk; when SCqs≥QSyz21%, it means that the market trend SCqs is at high risk; when SCbd≤BDyz5%, it means that the market volatility SCbd is at low risk; when SCbd≤BDyz6%~10%, it means that the market volatility SCbd is at medium risk; when SCbd>BDyz10%, it means that the market volatility SCbd is at high risk;

[0056] By comparing market trends and market fluctuations, and then sending risk index reports to companies and individuals in a timely manner based on low risk, medium risk, and high risk, we can help companies and individuals understand market dynamics in a timely manner, help optimize investment strategies, improve risk management capabilities, and promote the healthy development of the market and financial innovation. Identifying low-risk conditions encourages investors to adopt sound investment strategies, thereby achieving gradual growth while maintaining asset security, providing timely warnings to the market, and prompting all parties to pay attention to potential dangers in the market and take necessary precautions, thereby avoiding or mitigating possible market crises.

[0057] The data analysis and processing module is internally set with an internal operational risk threshold NByz and an external event impact risk threshold WByz. The data analysis and processing module compares the internal operational risk coefficient NBxs and the external event impact risk coefficient WBxs with the internal operational risk threshold NByz and the external event impact risk threshold WByz respectively, determines the operational risk index CZfx, and generates a risk report to be sent to the early warning module;

[0058] The operational risk index CZfx includes the internal operational risk coefficient NBxs and the external event impact risk coefficient WBxs. When NBxs ≥ NByz 10%, it means that the internal operational risk coefficient NBxs is at low risk; when NBxs ≥ NByz 11% to 20%, it means that the internal operational risk coefficient NBxs is at medium risk; when NBxs ≥ NByz 21%, it means that the internal operational risk coefficient NBxs is at high risk; when WBxs ≥ WByz 10%, it means that the external event impact risk coefficient NBxs is at low risk; when WBxs ≥ WByz 11% to 20%, it means that the external event impact risk coefficient NBxs is at medium risk; when WBxs ≥ WByz 21%, it means that the external event impact risk coefficient NBxs is at high risk;

[0059] First, the historical internal operational risk data set and the external event impact risk data set make the reference data more comprehensive and thorough. The historical operational risk assessment then demonstrates the company's good risk management capabilities to the market, enhances the confidence of investors and partners, improves the company's market reputation and brand image, and can promptly discover and correct potential risk factors to prevent these factors from evolving into bigger problems, thereby improving the institution's risk management level and market competitiveness. At the same time, it enables investors to have a more comprehensive understanding and facilitates corporate individuals to make corresponding financial policy adjustments based on risk levels.

[0060] The early warning module issues risk level early warnings based on the market risk index SCfx and the operational risk index CZf;

[0061] The early warning and alert module generates corresponding risk level reports according to different risk levels, and also generates reminder signals of different levels and sends them to customers.

[0062] The risk level report makes it easy for enterprises or individuals to query and understand historical information, and facilitates later reference. At the same time, it can be sent to customers to avoid customers missing information, thereby improving the overall risk management level.

[0063] 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. A financial big data analysis system, characterized by: Including data acquisition module, data analysis and processing module and early warning module: The data collection module includes a market data collection unit and an operation data collection unit; The market data collection unit is used to collect historical market data SCls, and form a historical market data set with the collected historical market data SCls and send it to the data analysis and processing module; the operation data collection unit is used to collect historical internal operation risk data CZnb and external event impact risk data CZwb, and form a historical internal operation risk data set and an external event impact risk data set and send them to the data analysis and processing module; The data analysis and processing module calculates the market trend SCqs and the market volatility SCbd based on the historical market data set, and the data analysis and processing module calculates the internal operation risk coefficient NBxs and the external event impact risk coefficient WBxs based on the historical internal operation risk data set and the external event impact risk data set; The data analysis and processing module is internally provided with a market trend threshold QSyz and a market volatility threshold BDyz. The data analysis and processing module compares the market trend SCqs and the market volatility SCbd with the market trend threshold QSyz and the market volatility threshold BDyz respectively, determines the market risk index SCfx and generates a risk report to be sent to the early warning module; The data analysis and processing module is internally provided with an internal operation risk threshold NByz and an external event impact risk threshold WByz. The data analysis and processing module compares the internal operation risk coefficient NBxs and the external event impact risk coefficient WBxs with the internal operation risk threshold NByz and the external event impact risk threshold WByz respectively, determines the operation risk index CZfx and generates a risk report to be sent to the early warning module; The early warning module provides risk level early warning based on the market risk index SCfx and the operational risk index CZf.

2. A financial big data analysis system according to claim 1, characterized in that: The historical market data set expression is: {SCls1, SCls2, SCls3, ···, SCls n }.

3. A financial big data analysis system according to claim 2, characterized in that: The historical internal operation risk data set expression is: {CZnb1, CZnb2, CZnb3, . . . , CZnb n }, the external event impact risk data set expression is: {CZwb1, CZwb2, CZwb3, . . . , CZwb n }.

4. A financial big data analysis system according to claim 3, characterized in that: The market trend SCqs is calculated as follows: In the above formula: SCls t-k+1 +SCls t-k+2 +···+SCls t-k+3 +SCls t represents the sum of all historical market data sets, k represents the length of the time period for calculating the average, and t represents the time point.

5. A financial big data analysis system according to claim 4, characterized in that: The market volatility SCbd is calculated as follows: In the above formula, Represents the average value of the historical market data set, SCls i Represents the i-th historical market data, SCls j Represents the i-th specific historical market data.

6. A financial big data analysis system according to claim 5, characterized in that: The internal operation risk coefficient NBxs is calculated as follows: In the above formula, Represents the sum of all historical internal operational risk data from i=1 to i=n, ​​CZnb i Represents the i-th historical internal operational risk data.

7. A financial big data analysis system according to claim 6, characterized in that: The calculation method of the risk factor WBxs of the external event impact is as follows: In the above formula, Represents the sum of all external events from i=1 to i=n, ​​CZwb i Represents the risk data affected by the ith external event.

8. A financial big data analysis system according to claim 7, characterized in that: The market risk index SCfx includes market trend SCqs and market volatility SCbd. When SCqs≥QSyz10%, it means that the market trend SCqs is at low risk; when SCqs≥QSyz11%~20%, it means that the market trend SCqs is at medium risk; when SCqs≥QSyz21%, it means that the market trend SCqs is at high risk; when SCbd≤BDyz5%, it means that the market volatility SCbd is at low risk; when SCbd≤BDyz6%~10%, it means that the market volatility SCbd is at medium risk; when SCbd>BDyz10%, it means that the market volatility SCbd is at high risk.

9. A financial big data analysis system according to claim 8, characterized in that: The operational risk index CZfs includes the internal operational risk coefficient NBxs and the external event impact risk coefficient WBxs. When NBxs≥NByz10%, it means that the internal operational risk coefficient NBxs is at low risk; when NBxs≥NByz11%~20%, it means that the internal operational risk coefficient NBxs is at medium risk; when NBxs≥NByz21%, it means that the internal operational risk coefficient NBxs is at high risk; when WBxs≥WByz10%, it means that the external event impact risk coefficient NBxs is at low risk; when WBxs≥WByz11%~20%, it means that the external event impact risk coefficient NBxs is at medium risk; when WBxs≥WByz21%, it means that the external event impact risk coefficient NBxs is at high risk.

10. A financial big data analysis system according to claim 9, characterized in that: The early warning module generates corresponding risk level reports according to different risk levels, and also generates reminder signals of different levels and sends them to customers.

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