Financial product risk control system and method based on block chain

Abstract

The invention discloses a financial product risk control system and method based on a block chain, and relates to the technical field of financial risk analysis, and the system comprises a block chain unit, a data acquisition unit, a product acquisition unit, a risk control analysis unit, a product monitoring unit, and a risk control execution unit. According to the financial product risk control system and method based on the block chain, the financial product and the user information are monitored in real time, the financial product investment of the user is obtained, the corresponding financial product is searched, and the state of the financial product and the user investment information in the normal state are obtained according to the monitoring node; and carrying out risk trend prediction and user investment level analysis on the financial product, carrying out matching analysis according to a development trend result and the user investment level, and carrying out risk early warning on the financial product of the user through an evaluation result, thereby achieving the purposes of real-time monitoring and reasonable early warning on the financial product.

Description

Technical Field

[0001] The present invention relates to the technical field of financial risk analysis, and in particular to a financial product risk control system and method based on blockchain. Background Art

[0002] Blockchain technology is a distributed ledger technology that ensures data immutability through encryption algorithms and achieves consensus through a consensus mechanism to enable decentralized processing. Its core advantage lies in improving data security and transparency. Currently, financial products on the market mainly include digital currencies, smart contracts, supply chain finance, cross-border payments, supply chain financial products, and insurance products. In the current financial market, there is a wide variety of financial products, especially fund and stock products.

[0003] In the prior art, when choosing funds or stocks, investors often rely on the recommendations of financial institutions and their own limited experience to make purchase decisions. It is difficult to discover the potential product risks in the real-time financial market after purchase. At the same time, ordinary investors lack professional financial knowledge, resulting in a lack of comprehensiveness and accuracy in risk control after purchasing funds or stocks, and it is difficult to comprehensively evaluate the risks of the stocks they purchase for early warning.

[0004] In view of the above technical deficiencies, a solution is proposed. Summary of the Invention

[0005] The purpose of the present invention is to: conduct a trend prediction analysis on existing financial products after a user's purchase, and then combine the user's risk tolerance and investment goals for risk assessment to perform rational monitoring and early warning of financial products.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: a financial product risk control system and method based on blockchain, including a blockchain unit, a data acquisition unit, a product acquisition unit, a risk control analysis unit, a product monitoring unit, and a risk control execution unit;

[0007] The blockchain unit is used to store financial product data and user investment history data on the blockchain through a total control terminal, and divide the nodes of the blockchain into monitoring nodes and storage nodes. The monitoring nodes are used to monitor real-time financial product data, and the storage nodes are used to store historical financial product data and user investment history data, and send the historical financial product data and user investment history data to the data acquisition unit;

[0008] The data acquisition unit is used to obtain historical financial product data, collect the historical information of users, and send it to the risk control analysis unit after preprocessing;

[0009] The product acquisition unit is used to obtain the real-time financial product query requirements of users, query the financial products after purchase according to the financial product selection requirements, and obtain a financial product table that meets the requirements;

[0010] The risk control analysis unit includes a product analysis module and a user analysis module. The product analysis unit is used to obtain historical financial product data, perform product profit analysis based on the historical financial product data, obtain a quarterly profit analysis ranking table corresponding to the historical financial products, predict the quarterly risk trend of the historical financial products based on the quarterly profit analysis ranking table, classify the profit levels of the financial products according to the quarterly risk trend, and send the quarterly risk trend to the product monitoring unit;

[0011] The user analysis module is used to obtain the investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and income situations, establish the investment levels of users who have purchased financial products based on the investment amounts, and generate the analysis results of the investment levels of users who purchase financial products according to the user investment levels;

[0012] The product monitoring unit is used to obtain the analysis results of user investment levels, quarterly risk trends, and profit levels, predict the quarterly risk trends of the financial product table that meets the requirements, generate the financial product development trend results and profit levels, and perform financial product matching analysis in combination with the analysis results of user investment levels, and generate risk warning results according to the financial product matching analysis;

[0013] The risk control execution unit is used to obtain financial products with high matching degrees, generate real-time risk warning instructions through the total control terminal, and transfer the financial product details to the user receiving terminal.

[0014] Furthermore, the specific process of querying the financial products after purchase according to the financial product selection requirements is as follows:

[0015] S100. After purchasing financial products, establish a personal financial product selection requirement table, and bring the selection requirement table to the requirement query place of the financial product purchase software;

[0016] S101. The requirement query place will identify the content in the selection requirement table, and select the required information according to the content to query the required financial products;

[0017] S102. After the query, initially visually display the financial products that meet the user requirements, and send the financial product table that meets the requirements to the risk control analysis unit.

[0018] Furthermore, the specific process of obtaining the quarterly profit analysis ranking table corresponding to the historical financial products is as follows:

[0019] S200. Obtain the financial products included in the financial product table, obtain the historical profit status of the financial products in real time, and classify the historical profit status by quarter;

[0020] S201. Obtain the historical profit status of the classified financial products, analyze the profit and loss status of each financial product, and obtain a financial product profit analysis table;

[0021] S202. According to the financial product profit analysis table, arrange the financial products according to the size of the profit to obtain a quarterly profit analysis arrangement table.

[0022] Furthermore, the specific process of predicting the quarterly risk trend of the quarterly profit analysis arrangement table is as follows:

[0023] S300. Obtain the quarterly profit analysis arrangement table, analyze all quarterly profits of each financial product, and obtain the difference between each quarterly profit;

[0024] S301. According to the difference c between each quarterly profit, calculate the standard deviation c between quarters. The calculation process is as follows: In the formula, A is the quarterly average amount, g1 is the amount in the first quarter, and g4 is the amount in the fourth quarter;

[0025] S302. Through the quarterly standard deviation, estimate the development trend of the current quarter to be purchased, and calculate the quarterly risk probability L. The calculation process is as follows:

[0026] Furthermore, S400. According to the quarterly risk trend, divide the quarterly development into three categories: safe, low-risk, and high-risk, and divide the risk level of financial products through the development category;

[0027] S401. Preset the financial products into three types: safe profit, low profit, and high profit through the profit analysis table, and distinguish the profits through the financial product profit category;

[0028] S402. Substitute the financial product profit category into the development category to obtain the financial product profit status in each development category;

[0029] Furthermore, the specific process of establishing the user investment level of the financial products purchased based on the investment amount is as follows:

[0030] S500. Obtain the user investment historical data of the users who have purchased financial products through the user control end, including the types of financial products, investment amounts, and income situations;

[0031] S501. Statistically analyze based on the total amount of the user's previous investment. After obtaining the total amount, conduct annual purchase quantity statistics, and calculate the average of the purchase quantity based on the total amount to obtain the average consumption of financial products.

[0032] S502. Obtain the average consumption amount of financial products, and define the consumption amount as primary consumption, secondary consumption, and tertiary consumption, representing one time, two times, and three times the total amount respectively.

[0033] Further, the specific process of generating a risk warning result based on the matching analysis of financial products is as follows:

[0034] S600. Analyze and search for the user's real-time investment amount according to the preset user investment level, and obtain that the preset user investment level is s.

[0035] S601. Obtain the development trend result and profit level of the financial products required by the user, and preset the risk degree as L and the profit level as y.

[0036] S602. Through the profit level y and the risk degree L, combined with the user investment level s, conduct a matching analysis calculation of financial products to obtain the matching degree. The calculation process is as follows:

[0037] The higher the profit level, the lower the risk degree, and the more matching the user investment level is with the profit level, the higher the matching degree.

[0038] S603. Preset three warning levels. When M > h, it indicates that the matching degree is high, representing no risk, in a high-profit state, and the warning is level one.

[0039] When h > M > K, it indicates that the matching degree is in a normal state without risk, in a normal-profit state, and the warning is level two.

[0040] When M < Q, it indicates that the matching degree is low and in a risky state, in a non-profit state, and the warning is level three.

[0041] The present invention also provides a financial product risk control method based on blockchain, including the following steps:

[0042] Step 1: Store financial product data and user investment history data on the blockchain through the total control terminal, and divide the nodes of the blockchain into monitoring nodes and storage nodes. The monitoring nodes are used to monitor real-time financial product data and store historical financial product data and user investment history data in the blockchain storage nodes.

[0043] Step 2: Obtain the real-time user financial product query requirement, and query the purchased financial products according to the financial product selection requirement.

[0044] Step 3: Obtain historical financial product data, conduct product profitability analysis based on the historical financial product data to obtain a monthly profitability analysis ranking list of historical financial products, predict the quarterly risk trend of historical financial products based on the quarterly profitability analysis ranking, and classify the profitability levels of financial products according to the quarterly risk trend;

[0045] Step 4: Obtain the investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and income situations. Establish the investment levels of users who have purchased financial products based on the investment amounts, and generate the analysis results of the investment levels of users who purchase financial products according to the user investment levels;

[0046] Step 5: According to the results of the financial product development trend, conduct financial product matching analysis through the analysis results of user investment levels, generate risk warning results according to the financial product matching analysis, generate real-time risk warning instructions through the total control terminal, and transmit the details of financial products to the user receiving terminal.

[0047] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0048] The financial product risk control system and method based on blockchain monitor the financial products and user information in real time, find the corresponding financial products after the users purchase the financial products, obtain the status of financial products and user investment information under normal conditions according to the monitoring nodes, and conduct risk trend prediction of financial products and analysis of user investment levels. Conduct matching analysis according to the development trend results and user investment levels, comprehensively evaluate the risks of users' financial products through the matching analysis, and issue risk warnings for users' financial products based on the evaluation results, so as to achieve the purpose of real-time monitoring and reasonable warning of financial products. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Shows the schematic diagram of the system flow structure of the present invention;

[0050] Figure 2 Shows the schematic diagram of the method step structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1:

[0053] AsFigure 1 As shown, it includes a blockchain unit, a data acquisition unit, a product acquisition unit, a risk control analysis unit, a product monitoring unit, and a risk control execution unit;

[0054] The blockchain unit is used to store financial product data and user investment history data on the blockchain through the total control terminal, and divide the nodes of the blockchain into monitoring nodes and storage nodes. The monitoring nodes are used to monitor real-time financial product data, and the storage nodes are used to store historical financial product data and user investment history data, and send the historical financial product data and user investment history data to the data acquisition unit;

[0055] The data acquisition unit is used to obtain historical financial product data and collect the historical information of users, and after preprocessing, send it to the risk control analysis unit;

[0056] The product acquisition unit is used to obtain the real-time user financial product query requirements, query the purchased financial products according to the financial product selection requirements, and obtain a financial product table that meets the requirements;

[0057] The risk control analysis unit includes a product analysis module and a user analysis module. The product analysis unit is used to obtain historical financial product data, and conduct product profit analysis based on the historical financial product data to obtain a quarterly profit analysis ranking table corresponding to the historical financial products. Based on the quarterly profit analysis ranking table, predict the quarterly risk trend of the historical financial products, classify the profit levels of the financial products according to the quarterly risk trend, and send the quarterly risk trend to the product monitoring unit;

[0058] The user analysis module is used to obtain the user investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and income situations. Based on the investment amounts, establish the user investment levels of users who have purchased financial products, and generate user investment level analysis results for purchasing financial products;

[0059] The product monitoring unit is used to obtain user investment level analysis results, quarterly risk trends, and profit levels, predict the quarterly risk trends of the financial product table that meets the requirements, generate financial product development trend results and profit levels, and conduct financial product matching analysis in combination with the user investment level analysis results, and generate risk warning results according to the financial product matching analysis;

[0060] The risk control execution unit is used to obtain financial products with high matching degrees, generate real-time risk warning instructions through the total control terminal, and transfer the financial product details to the user receiving end.

[0061] The specific process of querying the purchased financial products according to the financial product selection requirements is as follows:

[0062] S100. After purchasing a financial product, create a personal financial product selection requirements form and bring the form to the requirements query section of the financial product purchase software.

[0063] S101. The requirements query section will identify the content in the selection requirements form and select the required information according to the content to query the required financial products.

[0064] S102. After the query, initially visually display the financial products that meet the user's requirements and send the financial product list that meets the requirements to the risk control analysis unit.

[0065] The specific process of obtaining the quarterly profit analysis ranking table corresponding to historical financial products is as follows:

[0066] S200. Obtain the financial products included in the financial product list, obtain the historical profit status of the financial products in real time, and classify the historical profit status by quarter.

[0067] S201. Obtain the historical profit status of the classified financial products, analyze the profit and loss status of each financial product, and obtain the financial product profit analysis table.

[0068] S202. According to the financial product profit analysis table, rank the financial products according to the size of the profit to obtain the quarterly profit analysis ranking table.

[0069] The specific process of the quarterly profit analysis ranking table predicting the quarterly risk trend of financial products is as follows:

[0070] S300. Obtain the quarterly profit analysis ranking table, analyze all quarterly profits of each financial product, and obtain the difference between each quarterly profit.

[0071] S301. According to the difference c between each quarterly profit, calculate the standard deviation c between quarters. The calculation process is as follows: In the formula, A is the quarterly average amount, g1 is the amount in the first quarter, and g4 is the amount in the fourth quarter.

[0072] S302. Through the quarterly standard deviation, estimate the development trend of the current quarter to be purchased, and calculate the quarterly risk probability L. The calculation process is as follows:

[0073] The specific process of classifying the profit level of financial products according to the quarterly risk trend is as follows:

[0074] S400. According to the quarterly risk trend, divide the quarterly development into three categories: safe, low-risk, and high-risk, and classify the risk level of financial products through the development category.

[0075] S401. Preset financial products into three types: safe profit, low profit, and high profit through a profit analysis table, and distinguish profits by the profit category of financial products;

[0076] S402. Substitute the profit category of financial products into the development category to obtain the profit status of financial products in each development category;

[0077] The specific process of establishing the investment level of users who have purchased financial products based on the investment amount is as follows:

[0078] S500. Obtain the investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and income situations;

[0079] S501. Conduct statistics based on the total amount of users' previous investment amounts. After obtaining the total amount, conduct annual purchase quantity statistics, and perform an averaging calculation on the purchase quantity based on the total amount to obtain the average consumption of financial products;

[0080] S502. Obtain the average consumption amount of financial products, and divide the consumption amount into first-level consumption, second-level consumption, and third-level consumption, representing one time, two times, and three times the total amount.

[0081] The specific process of generating a risk warning result based on the matching analysis of financial products is as follows:

[0082] S600. Analyze and search for the user's real-time investment amount according to the preset user investment level, and obtain that the preset user investment level is s;

[0083] S601. Obtain the development trend result and profit level of the financial products required by the user, and preset the risk degree as L and the profit level as y;

[0084] S602. Through the profit level y and the risk degree L, combined with the user investment level s, perform a matching analysis calculation of financial products to obtain the matching degree. The calculation process is as follows:

[0085] The higher the profit level, the lower the risk degree, and the more matching the user investment level and the profit level, the higher the matching degree;

[0086] S603. Preset three warning levels. When M > h, it indicates that the matching degree is high, representing no risk, in a high-profit state, and the warning is level one;

[0087] When h > M > K, it indicates that the matching degree is in a normal state without risk, in a normal-profit state, and the warning is level two;

[0088] When M < Q, it indicates that the matching degree is low and in a risky state, in a non-profit state, and the warning is level three.

[0089] Embodiment 2:

[0090] As Figure 2 shown, the present invention also provides a risk control method for financial products based on blockchain, including the following steps:

[0091] Step 1: Store financial product data and user investment history data on the blockchain through the total control terminal, and divide the nodes of the blockchain into monitoring nodes and storage nodes. The monitoring nodes are used to monitor real-time financial product data, and store historical financial product data and user investment history data in the blockchain storage nodes;

[0092] Step 2: Obtain the real-time user financial product query requirement, and query the purchased financial products according to the financial product selection requirement;

[0093] Step 3: Obtain historical financial product data, conduct product profit analysis based on the historical financial product data to obtain a monthly profit analysis ranking list of historical financial products, predict the quarterly risk trend of historical financial products based on the quarterly profit analysis ranking, and classify the profit levels of financial products according to the quarterly risk trend;

[0094] Step 4: Obtain the user investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and income situations. Establish the user investment levels of users who have purchased financial products based on the investment amounts, and generate an analysis result of the user investment levels for purchasing financial products according to the user investment levels;

[0095] Step 5: According to the financial product development trend result, conduct financial product matching analysis through the user investment level analysis result, generate a risk warning result according to the financial product matching analysis, generate a real-time risk warning instruction through the total control terminal, and transmit the financial product details to the user receiving end.

[0096] The setting of the size of the interval and threshold is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the number of base numbers set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0097] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation;

[0098] In the two embodiments provided by the present application, it should be understood that the disclosed systems and methods can be implemented in other ways; for example, the system and method embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices or modules, and can be in electrical, mechanical or other forms;

[0099] As mentioned above, the above is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A financial product risk control system based on blockchain, characterized in that: It includes blockchain unit, data acquisition unit, product acquisition unit, risk control analysis unit, product monitoring unit and risk control execution unit; The blockchain unit is used to store the financial product data and the user's investment history data on the blockchain through the general control terminal, and divide the nodes of the blockchain into monitoring nodes and storage nodes, wherein the monitoring nodes are used to monitor the real-time financial product data, and the storage nodes are used to store the historical financial product data and the user's investment history data, and send the historical financial product data and the user's investment history data to the data acquisition unit; The data acquisition unit is used to acquire historical financial product data and collect historical information of users, and send it to the risk control analysis unit after preprocessing; The product acquisition unit is used to acquire real-time user financial product query requirements, query purchased financial products according to the financial product selection requirements, and obtain a list of financial products that meet the requirements; The risk control analysis unit includes a product analysis module and a user analysis module. The product analysis unit is used to obtain historical financial product data, and perform product profit analysis based on the historical financial product data to obtain a quarterly profit analysis ranking table corresponding to the historical financial products, predict the quarterly risk trend of the historical financial products based on the quarterly profit analysis ranking table, classify the financial products into profit levels according to the quarterly risk trend, and send the quarterly risk trend to the product monitoring unit; The user analysis module is used to obtain the investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and returns, establish the investment level of users who have purchased financial products based on the investment amount, and generate investment level analysis results of users who purchased financial products based on the user investment level; The product monitoring unit is used to obtain the user's investment level analysis results, quarterly risk trends and profit levels, generate financial product development trend results and profit levels, and conduct financial product matching analysis in combination with the user's investment level analysis results, and generate risk warning results based on the financial product matching analysis; The risk control execution unit is used to obtain risk warning results, generate real-time warning instructions through the main control terminal, and transmit the real-time warning instructions to the user receiving terminal.

2. The blockchain-based financial product risk control system according to claim 1 is characterized in that: The specific process of querying purchased financial products according to financial product selection requirements is as follows: S100. After purchasing a financial product, a personal financial product selection requirement table is created, and the selection requirement table is brought into the requirement query section of the financial product purchase software; S101, the demand query section will identify and select the content in the demand table, and select the required information according to the content to query the required financial products; S102: After the query is made, the financial products that meet the user's requirements are preliminarily visualized and a table of financial products that meet the requirements is sent to the risk control analysis unit.

3. The blockchain-based financial product risk control system according to claim 2 is characterized in that: The specific process of obtaining the quarterly profit analysis table corresponding to the historical financial products is as follows: S200, obtaining the financial products contained in the financial product table, obtaining the historical profitability of the financial products in real time, and classifying the historical profitability by quarter; S201. Obtain the historical profit status of the classified financial products, analyze the profit and loss status of each financial product, and obtain a financial product profit analysis table; S202. According to the financial product profit analysis table, the financial products are arranged according to the size of the profit to obtain a quarterly profit analysis arrangement table.

4. The blockchain-based financial product risk control system according to claim 3 is characterized in that: The specific process of using the quarterly profit analysis table to predict the quarterly risk trend of financial products is as follows: S300, obtaining a quarterly profit analysis ranking table, analyzing all quarterly profits of each financial product, and obtaining the difference between each quarterly profit; S301. Calculate the standard deviation c between quarters based on the difference c between each quarter's profits. The calculation process is as follows: Where A is the quarterly average amount, g1 is the amount of the first quarter, and g4 is the amount of the fourth quarter; S302. The development trend of the current quarter to be purchased is estimated through the quarterly standard deviation, and the calculation process of the quarterly risk probability L is as follows:

5. The blockchain-based financial product risk control system according to claim 4 is characterized in that: The specific process of classifying the profitability of financial products according to quarterly risk trends is as follows: S400, according to the quarterly risk trend, the quarterly development is divided into three categories: safe, low risk and high risk, and the risk level of financial products is divided by development category; S401. Preset the financial products into three categories: safe profit, low profit and high profit through the profit analysis table, and differentiate the profits by the profit categories of the financial products; S402. The financial product profit category is brought into the development category to obtain the financial product profit status in each development category.

6. The blockchain-based financial product risk control system according to claim 5 is characterized in that: The specific process of establishing the investment level of users who have purchased financial products based on the investment amount is as follows: S500, obtaining the investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and income; S501. Count the total amount of previous investments made by the user, and then count the annual purchase quantity after obtaining the total amount. Calculate the average of the purchase quantity using the total amount to obtain the average consumption of the financial product. S502. Obtain the average consumption amount of financial products, and define the consumption amount as primary consumption, secondary consumption, and tertiary consumption, representing one, two, and three times the total amount.

7. The blockchain-based financial product risk control system and method according to claim 6 is characterized in that: The specific process of generating risk warning results based on financial product matching analysis is as follows: S600, analyzing and searching the user's real-time investment amount according to the preset user investment level, and obtaining the user investment level preset as s; S601. Obtain the development trend results and profit level of the financial products required by the user, with the risk level preset as L and the profit level as y; S602, by using the profit level y and the risk level L, combined with the user's investment level s, a financial product matching analysis is performed to calculate the matching degree. The calculation process is as follows: The higher the profit level, the lower the risk level, the more the user's investment level matches the profit level, and the higher the matching degree, the lower the risk warning result will be; S603. Three warning levels are preset. When M>h, it means that a high matching degree means no risk, which is a high profit state, and the warning is level one; When h>M>K, it means that the matching degree is in a normal state without risk, it is in a normal profit state, and the warning is level 2; When M<Q, it means that the matching degree is low and is in a risky state, which is non-profitable and the warning is level three.

8. The blockchain-based financial product risk control method according to claim 1, characterized in that: The following steps are involved: Step 1: The financial product data and the user's investment history data are stored on the blockchain through the general control terminal, and the nodes of the blockchain are divided into monitoring nodes and storage nodes. The monitoring nodes are used to monitor the real-time financial product data, and the historical financial product data and the user's investment history data are stored in the blockchain storage nodes; Step 2: Obtain real-time user financial product query needs, and query purchased financial products based on financial product selection needs; Step 3: Obtain historical financial product data, perform product profitability analysis based on the historical financial product data, obtain a monthly profitability analysis ranking table for historical financial products, predict the quarterly risk trend of historical financial products based on the quarterly profitability analysis ranking, and classify financial products into profitability levels based on the quarterly risk trend; Step 4: Obtain investment history data of users who have purchased financial products through the user control terminal, including the types of financial products, investment amounts, and returns; establish investment levels of users who have purchased financial products based on the investment amounts; and generate investment level analysis results of users who have purchased financial products based on the user investment levels; Step 5: Based on the results of the financial product development trend, financial product matching analysis is performed through the user investment level analysis results, risk warning results are generated based on the financial product matching analysis, real-time risk warning instructions are generated through the general control terminal, and the financial product details are transmitted to the user receiving end.

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