Internet financial risk assessment system and method based on multi-dimensional data fusion

Abstract

The invention relates to the field of financial risk assessment, in particular to an internet financial risk assessment system and method based on multi-dimensional data fusion. The method comprises the following steps: setting Internet data points, obtaining user financial data, user information data and a user timestamp, and constructing a user financial tree; monitoring the user financial tree, obtaining a monitoring timestamp and financial monitoring data, and obtaining a user radius, a financial guidance value and a multi-dimensional financial fusion graph according to the monitoring timestamp and the financial monitoring data; analyzing the user financial tree and the multi-dimensional financial fusion graph through the user timestamp to obtain a change time value, and obtaining a risk trend value and a trend time value according to the multi-dimensional financial fusion graph and the change time value; and predicting through the risk trend value and the trend time value to obtain a predicted risk trend value and a predicted trend time value, and evaluating the financial risk of the user. The financial risk coping capability of the user can be improved.

Description

Technical Field

[0001] The present invention relates to the field of financial risk assessment, and particularly to an Internet financial risk assessment system and method based on multi-dimensional data fusion. Background Art

[0002] With the increasing complexity of the global economy and financial markets, the types of risks faced by enterprises are becoming increasingly diverse. In the field of Internet consumer finance, with the rapid growth of users' online lending needs, financial risk management has become particularly important. These risks not only include traditional credit risks, market risks, and liquidity risks, etc., but also involve the influence of external factors such as macroeconomics, industry changes, and policy changes. The data sources are becoming more and more diversified, and the types and forms of data are also very rich.

[0003] A method, system, and device for financial fraud risk assessment with the publication number of CN112419030A disclose a method, system, and device for financial fraud risk assessment. The method includes: obtaining financial statements and extracting financial index information; establishing a training sample set and a prediction sample set; converting financial index information into qualitative indexes and quantitative indexes; predicting the prediction sample set with a random forest model to obtain a first data set of high financial fraud risks; and / or predicting the prediction sample set with an adaptive boosting model to obtain a second data set of high financial fraud risks; and / or predicting the prediction sample set with a bagging model to obtain a third data set of high financial fraud risks; dividing the prediction sample set into sub-prediction sample sets; predicting the sub-prediction sample sets with index data to obtain a fourth data set of high financial fraud risks; extracting data exceeding a preset threshold in the first and / or second and / or third and fourth data sets, constructing a data set of high financial fraud risks, and generating a risk analysis report.

[0004] The transaction speed of Internet consumer finance is fast and the frequency is high. Traditional assessment methods often rely on historical data with a long update cycle and cannot reflect the current financial status and risk changes of users in real time. Most traditional risk assessment models are built based on traditional financial operations and are insufficiently adaptable to the unique scenarios and risk characteristics of Internet consumer finance. The user group of Internet consumer finance is extensive and the business scenarios are complex, and the risk factors vary greatly in different scenarios. The selection of indexes and the determination of weights often rely on the experience and subjective judgment of assessors, resulting in the lack of objectivity and consistency of assessment results and making it difficult to accurately measure the financial risks of Internet consumer finance, which are the problems we need to solve. Summary of the Invention

[0005] The object of the present invention is to propose an Internet financial risk assessment method based on multi-dimensional data fusion for the problems existing in the background art.

[0006] Technical solution of the present invention: An Internet financial risk assessment method based on multi-dimensional data fusion, comprising the following steps:

[0007] S1. Set Internet data points, obtain user financial data, user information data, and user timestamps, and construct a user financial tree through the user financial data and the user timestamps;

[0008] S2. Monitor the user financial tree through the Internet data points, obtain monitoring timestamps and financial monitoring data, analyze the monitoring timestamps and the financial monitoring data, and obtain a user radius, a financial orientation value, and a multi-dimensional financial fusion graph;

[0009] S3. Analyze the user financial tree and the multi-dimensional financial fusion graph through the user timestamps to obtain a change time value, and obtain a risk trend value and a trend time value according to the multi-dimensional financial fusion graph and the change time value;

[0010] S4. Perform prediction through the risk trend value and the trend time value to obtain a predicted risk trend value and a predicted trend time value, and evaluate the financial risk of the user through the predicted risk trend value and the predicted trend time value to obtain an evaluation result.

[0011] Preferably, the process of setting Internet data points, obtaining user financial data, user information data, and user timestamps, and constructing a user financial tree through the user financial data and the user timestamps includes:

[0012] The user financial data includes user behavior data and user credit data; the user behavior data includes consumption amount and consumption category; the user credit data includes credit score, number of loan applications, and overdue duration;

[0013] Set a first-level node, a second-level node, and a third-level node, and obtain a user financial tree according to the respective user information data, corresponding user timestamp data, user financial data, first-level node, second-level node, and third-level node.

[0014] Preferably, the process of monitoring the user financial tree through the Internet data points, obtaining monitoring timestamps and financial monitoring data, and analyzing the monitoring timestamps and the financial monitoring data to obtain a user radius includes:

[0015] Analyze the monitoring timestamps and the financial monitoring data through natural language technology to obtain financial change data, financial non-change data, non-change timestamps, and change timestamps;

[0016] Financial change data includes the amount of consumption change, consumption change category, credit change score, number of loan changes, overdue change duration, and changed income; based on the user's financial data, financial change data, user timestamp, and change timestamp, the change channel is obtained; based on the user timestamp and change channel, the user radius is obtained.

[0017] Preferably, the process of analyzing the monitoring timestamp and financial monitoring data to obtain the financial orientation value is as follows:

[0018] Based on the amount of consumption change and consumption change category, the consumption orientation value D is obtained;

[0019]

[0020] where i is the number of consumption changes, a is the total number of consumption changes, Δx is the difference between the consumption change amount and the consumption amount, g is the income, Δe is the consumption change category, and Δf is the consumption change frequency;

[0021] Based on the credit change score, number of loan changes, and overdue change duration, the credit orientation value X is obtained;

[0022]

[0023] where j is the number of loan changes, b is the total number of loan changes, R is the full score of the credit score, Δr j is the credit change score after the jth loan change, δ j is the weight of the credit change score, and ΔT is the overdue change duration;

[0024]

[0025] where z is the user's expenditure, m is the number of changed income times, c is the total number of changed income times, γ m is the weight coefficient of the mth income change, s m is the changed income corresponding to the mth income change, S is the user's basic income, and cu is the user's savings;

[0026] Based on the consumption orientation value, credit orientation value, and income orientation value, the financial orientation value is obtained.

[0027] Preferably, the process of analyzing the monitoring timestamp and financial monitoring data to obtain the multi-dimensional financial integration map includes:

[0028] Based on the user radius, consumption orientation value, credit orientation value, and income orientation value, the user target circle, consumption circle, credit circle, and income circle are obtained, and based on the user target circle, consumption circle, credit circle, and income circle, the multi-dimensional financial integration map is obtained.

[0029] Preferably, the process of analyzing the user's financial tree and the multi-dimensional financial fusion graph through the user timestamp to obtain the change time value, and obtaining the risk trend value and the trend time value according to the multi-dimensional financial fusion graph and the change time value includes:

[0030] Set the change time threshold and the standard guiding value; obtain the change time value according to the user timestamp linked to the change channel in the user's financial tree and the time corresponding to the change timestamp;

[0031] Analyze the change time value, the change time threshold, the financial guiding value, and the standard guiding value, analyze the financial guiding value greater than or equal to the standard guiding value corresponding to the change time value greater than or equal to the change time threshold to obtain the trend factor, and obtain the risk trend value according to the trend factor and its corresponding financial guiding value, and obtain the trend time value according to the trend factor and the change time value.

[0032] Preferably, the process of predicting through the risk trend value and the trend time value to obtain the predicted risk trend value and the predicted trend time value, and evaluating the user's financial risk through the predicted risk trend value and the predicted trend time value includes:

[0033] Construct a random forest model through a machine learning method, predict the risk trend value and the trend time value through the random forest model, and output the predicted risk trend value and the predicted trend time value; obtain the real-time time, obtain the prediction time according to the real-time time and the trend time value, set the risk trend threshold, when the predicted risk trend value is greater than or equal to the risk trend threshold, the time from the real-time time to the prediction time corresponding to the financial risk is the risk rising period, and send the predicted risk trend value to the Internet side for recording to obtain the evaluation result.

[0034] The present invention also discloses an Internet financial risk assessment system based on multi-dimensional data fusion, including a management center, and the management center is communicatively connected to a data collection module, a data analysis module, a data processing module, and a data evaluation module:

[0035] The data collection module is used to set Internet data points, obtain user financial data, user information data, and user timestamps, and construct a user financial tree through the user financial data and the user timestamps;

[0036] The data analysis module is used to monitor the user's financial tree through the Internet data points, obtain the monitoring timestamp and the financial monitoring data, analyze the monitoring timestamp and the financial monitoring data, and obtain the user radius, the financial guiding value, and the multi-dimensional financial fusion graph;

[0037] The data processing module is used to analyze the user's financial tree and the multi-dimensional financial fusion graph through the user timestamp to obtain the change time value, and obtain the risk trend value and the trend time value according to the multi-dimensional financial fusion graph and the change time value;

[0038] The data evaluation module is used to make predictions through the risk trend value and the trend time value to obtain the predicted risk trend value and the predicted trend time value, and evaluate the financial risk of the user through the predicted risk trend value and the predicted trend time value to obtain the evaluation result.

[0039] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: By constructing the user's financial tree, optimizing the management of the user's financial data, and improving the efficiency of the user's financial data analysis; obtaining the monitoring timestamp and the financial monitoring data, timely obtaining and analyzing the changes in the financial-related data, realizing the dynamic monitoring of the financial trends, improving the timeliness of the data analysis, and adapting to the dynamic environmental changes; through the user radius and the financial orientation value, performing a fusion analysis on the multi-dimensional data, and performing a targeted analysis on the user, improving the accuracy of the financial risk identification, and visually presenting the financial risk status of each user through the multi-dimensional financial fusion graph; by means of the risk trend value and the trend time value, improving the accuracy of the financial risk assessment; the predicted risk trend value and the predicted trend time value improve the accuracy of the risk prediction, help optimize the loan decision-making and the risk control strategy, and improve the efficiency of the decision-making support. Brief Description of the Drawings

[0040] Figure 1 It is a flowchart of an embodiment proposed by the present invention. Detailed Embodiment

[0041] Embodiment 1, as Figure 1 shown, an Internet financial risk assessment method based on multi-dimensional data fusion proposed by the present invention includes the following steps:

[0042] S1. Set Internet data points, obtain the user's financial data, the user's information data, and the user timestamp, and construct the user's financial tree through the user's financial data and the user timestamp;

[0043] S2. Monitor the user's financial tree through the Internet data points, obtain the monitoring timestamp and the financial monitoring data, and analyze the monitoring timestamp and the financial monitoring data to obtain the user radius, the financial orientation value, and the multi-dimensional financial fusion graph;

[0044] S3. Analyze the user's financial tree and the multi-dimensional financial fusion graph through the user timestamp to obtain the change time value, and obtain the risk trend value and the trend time value according to the multi-dimensional financial fusion graph and the change time value;

[0045] S4. Predict through the risk trend value and the trend time value to obtain the predicted risk trend value and the predicted trend time value. Evaluate the financial risk of the user through the predicted risk trend value and the predicted trend time value to obtain the evaluation result.

[0046] It should be further noted that in the specific implementation process, when setting Internet data points and obtaining user financial data, user information data, and user timestamps, the process of constructing a user financial tree through the user financial data and the user timestamp is as follows:

[0047] The user financial data includes user behavior data and user credit data; the user behavior data includes consumption amount and consumption category; the user credit data includes credit score, number of loans, and overdue duration; the user information data includes income and user basic data; the user timestamp refers to the label related to the time sequence information for obtaining each user financial data by the Internet data point;

[0048] It should be further noted that in the specific implementation process, the user basic data includes basic personal information such as user age, occupation, contact information, etc.

[0049] Set a first - layer node, a second - layer node, and a third - layer node, and store each user information data, the corresponding user timestamp data, and the user financial data into the first - layer node, the second - layer node, and the third - layer node respectively, and link the first - layer node, the second - layer node, and the third - layer node related to the same user in sequence to obtain the user financial tree;

[0050] It should be further noted that in the specific implementation process, there is a progressive relationship between the first - layer node, the second - layer node, and the third - layer node, and the link between the first - layer node, the second - layer node, and the third - layer node is the link between each layer.

[0051] It should be further noted that in the specific implementation process, when monitoring the user financial tree through the Internet data point, obtaining the monitoring timestamp and the financial monitoring data, and analyzing the monitoring timestamp and the financial monitoring data, the process of obtaining the user radius, the financial orientation value, and the multi - dimensional financial fusion map is as follows:

[0052] The monitoring timestamp refers to the user timestamp corresponding to the financial monitoring data generated by the Internet data point monitoring the user financial tree;

[0053] Analyze the monitoring timestamp and the financial monitoring data through natural language technology to obtain financial change data, financial non - change data, non - change timestamps, and change timestamps;

[0054] The financial change data refers to the data in which user information data and user financial data change during the monitoring process, including the consumption change amount, consumption change category, credit change score, loan change frequency, overdue change duration, and change income;

[0055] Through the monitoring timestamp, update the financial monitoring data into the user's financial tree, correspond the user's financial data with the financial change data, and the user's timestamp with the change timestamp respectively, and construct a link channel, denoted as the change channel; Merge the financial non-change data with the user's financial data;

[0056] Obtain the consumption orientation value D according to the consumption change amount and the consumption change category;

[0057]

[0058] Where i is the consumption change frequency, a is the total consumption change frequency, Δx is the difference between the consumption change amount and the consumption amount, g is the income, Δe is the consumption change category, and Δf is the consumption change frequency;

[0059] Obtain the credit orientation value X according to the credit change score, loan change frequency, and overdue change duration;

[0060]

[0061] Where j is the loan change frequency, b is the total loan change frequency, R is the full score of the credit score, Δr j is the credit change score after the j-th loan change, δ j is the weight of the credit change score, and ΔT is the overdue change duration;

[0062] Obtain the income orientation value S according to the change income and the income;

[0063]

[0064] Where z is the user's expenditure, m is the change income frequency, c is the total change income frequency, γ m is the weight coefficient of the m-th income change, s m is the change income corresponding to the m-th income change, S is the user's basic income, and cu is the user's savings;

[0065] Count the total number of change channels corresponding to the user's timestamp, denoted as the user radius;

[0066] Based on the user radius, consumption orientation value, credit orientation value, and income orientation value, draw a circle with the value of the user radius as the diameter of the circle to obtain the user target circle. Then, add the values of the consumption orientation value, credit orientation value, and income orientation value to the value of the user radius in sequence, and draw circles with the obtained calculation results as the radii and the center of the user target circle as the center of the circle to obtain the consumption circle, credit circle, and income circle in sequence. Coincide the centers of the user target circle, consumption circle, credit circle, and income circle to obtain a multi-dimensional financial integration diagram, and record the consumption orientation value, credit orientation value, and income orientation value as financial orientation values.

[0067] It should be further noted that in the specific implementation process, the process of analyzing the user financial tree and the multi-dimensional financial integration diagram through the user timestamp to obtain the change time value, and obtaining the risk trend value and trend time value based on the multi-dimensional financial integration diagram and the change time value is as follows:

[0068] Set the change time threshold and the standard orientation value; calculate the difference between the user timestamp and the time corresponding to the change timestamp linked to the change channel in the user financial tree to obtain the change time value;

[0069] Analyze the change time value, change time threshold, financial orientation value, and standard orientation value, analyze the financial orientation value greater than or equal to the standard orientation value corresponding to the change time value greater than or equal to the change time threshold to obtain the trend factor, and perform weighted calculation on the trend factor and its corresponding financial orientation value to obtain the risk trend value, and perform multiplication calculation on the trend factor and the change time value to obtain the trend time value;

[0070] It should be further noted that in the specific implementation process, the process of analyzing the financial orientation value greater than or equal to the standard orientation value corresponding to the change time value greater than or equal to the change time threshold is as follows: If the financial orientation value greater than or equal to the standard orientation value corresponding to the change time value greater than or equal to the change time threshold is the consumption orientation value, then respectively obtain the circles adjacent to the consumption circle and larger and smaller than the consumption circle, denoted as the consumption adjacent circle one and the consumption adjacent circle two, calculate the difference in circle area between the consumption adjacent circle one and the consumption adjacent circle two and the consumption circle respectively, and calculate the ratio of the obtained circle area differences to obtain the consumption trend factor. Similarly, if the financial orientation value greater than or equal to the standard orientation value corresponding to the change time value greater than or equal to the change time threshold is the credit orientation value or the income orientation value, then obtain the credit trend factor or the income trend factor. The obtained credit trend factor, income trend factor, or consumption trend factor is collectively referred to as the trend factor.

[0071] It should be further noted that in the specific implementation process, prediction is carried out through the risk trend value and the trend time value to obtain the predicted risk trend value and the predicted trend time value. The process of evaluating the financial risk of the user through the predicted risk trend value and the predicted trend time value to obtain the evaluation result is as follows:

[0072] A random forest model is constructed through a machine learning method. The risk trend value and the trend time value are predicted through the random forest model, and the predicted risk trend value and the predicted trend time value are output; the real-time time is obtained, and the real-time time and the trend time value are added and calculated to obtain the predicted time. A risk trend threshold is set. When the predicted risk trend value is greater than or equal to the risk trend threshold, the time from the real-time time to the predicted time corresponding to the financial risk is the risk rising period, and the predicted risk trend value is sent to the Internet side for recording to obtain the evaluation result; when the predicted risk trend value is less than the risk trend threshold, the user's financial data is continuously monitored.

[0073] Embodiment 2. An Internet financial risk assessment system based on multi-dimensional data fusion proposed in the present invention is applied to an Internet financial risk assessment method based on multi-dimensional data fusion described in Embodiment 1, and specifically includes a management center. The management center is communicatively connected to a data collection module, a data analysis module, a data processing module, and a data evaluation module:

[0074] The data collection module is used to set Internet data points, obtain user financial data, user information data, and user timestamps, and construct a user financial tree through the user financial data and the user timestamps;

[0075] The data analysis module is used to monitor the user financial tree through the Internet data points, obtain the monitoring timestamp and the financial monitoring data, and analyze the monitoring timestamp and the financial monitoring data to obtain the user radius, the financial orientation value, and the multi-dimensional financial fusion map;

[0076] The data processing module is used to analyze the user financial tree and the multi-dimensional financial fusion map through the user timestamp to obtain the change time value, and obtain the risk trend value and the trend time value according to the multi-dimensional financial fusion map and the change time value;

[0077] The data evaluation module is used to perform prediction through the risk trend value and the trend time value to obtain the predicted risk trend value and the predicted trend time value, and evaluate the financial risk of the user through the predicted risk trend value and the predicted trend time value to obtain the evaluation result.

[0078] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made without departing from the spirit of the present invention within the knowledge scope of those skilled in the art to which the present invention pertains.

Claims

1. An Internet financial risk assessment method based on multi-dimensional data fusion, characterized in that It includes the following steps: S1. Set up Internet data points, obtain user financial data, user information data, and user timestamps, and construct a user financial tree through the user financial data and user timestamps; S2. Monitor the user financial tree through the Internet data points, obtain monitoring timestamps and financial monitoring data, analyze the monitoring timestamps and financial monitoring data, and obtain the user radius, financial orientation value, and multi-dimensional financial fusion map; S3. Analyze the user financial tree and the multi-dimensional financial fusion map through the user timestamps to obtain the change time value, and obtain the risk trend value and trend time value based on the multi-dimensional financial fusion map and the change time value; S4. Make a prediction through the risk trend value and trend time value to obtain the predicted risk trend value and predicted trend time value, and evaluate the financial risk of the user through the predicted risk trend value and predicted trend time value to obtain the evaluation result.

2. The internet financial risk assessment method based on multi-dimensional data fusion according to claim 1, characterized in that, The process of setting up Internet data points, obtaining user financial data, user information data, and user timestamps, and constructing a user financial tree includes: The user financial data includes user behavior data and user credit data; the user behavior data includes consumption amount and consumption category; the user credit data includes credit score, number of loans, and overdue duration; Set up a first-level node, a second-level node, and a third-level node, and obtain the user financial tree based on each user information data, the corresponding user timestamp data, user financial data, the first-level node, the second-level node, and the third-level node.

3. The internet financial risk assessment method based on multi-dimensional data fusion according to claim 2, wherein The process of monitoring the user financial tree through the Internet data points, obtaining monitoring timestamps and financial monitoring data, and analyzing the monitoring timestamps and financial monitoring data to obtain the user radius includes: Analyze the monitoring timestamps and financial monitoring data through natural language technology to obtain financial change data, financial non-change data, non-change timestamps, and change timestamps; The financial change data includes consumption change amount, consumption change category, credit change score, loan change number, overdue change duration, and change income; obtain the change channel based on the user financial data, financial change data, user timestamps, and change timestamps; obtain the user radius based on the user timestamps and the change channel.

4. The internet financial risk assessment method based on multi-dimensional data fusion according to claim 3, wherein, The process of analyzing the monitoring timestamps and financial monitoring data to obtain the financial orientation value is: Obtain the consumption orientation value D based on the consumption change amount and consumption change category; where i is the number of consumption changes, a is the total number of consumption changes, Δx is the difference between the consumption change amount and the consumption amount, g is the income, Δe is the consumption change category, and Δf is the consumption change frequency; Obtain the credit orientation value X based on the credit change score, loan change number, and overdue change duration; where j is the number of loan changes, b is the total number of loan changes, R is the full score of credit score, and Δr j is the credit change score after the j-th loan change, and δ j is the weight of the credit change score, and ΔT is the overdue change duration; where z is the user's expenditure, m is the number of variable income times, c is the total number of variable income times, γ m is the weight coefficient of the m-th income change, s m is the variable income corresponding to the m-th income change, S is the user's basic income, and cu is the user's savings; Obtain the financial orientation value based on the consumption orientation value, credit orientation value, and income orientation value.

5. The method for evaluating Internet financial risks based on multi-dimensional data fusion according to claim 4, characterized in that The process of analyzing the monitoring timestamps and financial monitoring data to obtain the multi-dimensional financial fusion map includes: According to the user radius, consumption orientation value, credit orientation value, and income orientation value, obtain the user target circle, consumption circle, credit circle, and income circle. According to the user target circle, consumption circle, credit circle, and income circle, obtain the multi-dimensional financial integration diagram.

6. A method for evaluating Internet financial risks based on multi-dimensional data fusion according to claim 1 or 5, characterized in that The process of analyzing the user's financial tree and the multi-dimensional financial integration diagram through the user timestamp to obtain the change time value, and obtaining the risk trend value and trend time value according to the multi-dimensional financial integration diagram and the change time value includes: Set the change time threshold and the standard orientation value; according to the user timestamp linked to the change channel in the user financial tree and the time corresponding to the change timestamp, obtain the change time value; Analyze the change time value, change time threshold, financial orientation value, and standard orientation value. Analyze the financial orientation value greater than or equal to the standard orientation value corresponding to the change time value greater than or equal to the change time threshold to obtain the trend factor. According to the trend factor and its corresponding financial orientation value, obtain the risk trend value. According to the trend factor and the change time value, obtain the trend time value.

7. A method for evaluating Internet financial risks based on multi-dimensional data fusion according to claim 6, characterized in that, The process of predicting through the risk trend value and the trend time value to obtain the predicted risk trend value and the predicted trend time value, and evaluating the user's financial risk through the predicted risk trend value and the predicted trend time value includes: Construct a random forest model through machine learning methods. Use the random forest model to predict the risk trend value and the trend time value, and output the predicted risk trend value and the predicted trend time value; obtain the real-time time. According to the real-time time and the trend time value, obtain the prediction time. Set the risk trend threshold. When the predicted risk trend value is greater than or equal to the risk trend threshold, the time from the real-time time to the prediction time corresponding to the financial risk is the risk rising period. Send the predicted risk trend value to the Internet side for recording to obtain the evaluation result.

8. An Internet financial risk assessment system based on multi-dimensional data fusion, specifically applied to an Internet financial risk assessment method based on multi-dimensional data fusion according to any one of claims 1 to 7, including a management center, characterized in that, The management center is communicatively connected to a data collection module, a data analysis module, a data processing module, and a data evaluation module: The data collection module is used to set Internet data points, obtain user financial data, user information data, and user timestamps, and construct a user financial tree through the user financial data and the user timestamps; The data analysis module is used to monitor the user financial tree through the Internet data points, obtain the monitoring timestamp and the financial monitoring data, and analyze the monitoring timestamp and the financial monitoring data to obtain the user radius, the financial orientation value, and the multi-dimensional financial integration diagram; The data processing module is used to analyze the user financial tree and the multi-dimensional financial integration diagram through the user timestamp to obtain the change time value, and obtain the risk trend value and the trend time value according to the multi-dimensional financial integration diagram and the change time value; The data evaluation module is used to predict through the risk trend value and the trend time value to obtain the predicted risk trend value and the predicted trend time value, and evaluate the user's financial risk through the predicted risk trend value and the predicted trend time value to obtain the evaluation result.

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