Financial risk control method and system

Abstract

The invention discloses a financial risk control method and system, and relates to the technical field of enterprise financial management. The method comprises the following steps: acquiring related data; the related data comprises financial data, market data, operation data and external data; wherein the external data comprises an industry report, competitive product data and policy and regulation change data; constructing a knowledge graph according to the related data; the knowledge graph comprises financial risk types and corresponding risk levels and risk influence factors; based on the knowledge graph, constructing a financial risk prediction model by using a credit risk model and a quantification algorithm; the credit risk model adopts a KMV model; the quantization algorithm comprises a historical simulation method and a Monte Carlo simulation method; and performing real-time monitoring on a target enterprise by using the financial risk prediction model, and performing visual display on a prediction result in a set period. According to the invention, the risk monitoring accuracy and timeliness can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise financial management, and particularly to a financial risk control method and system. Background Art

[0002] In the field of financial risk management, traditional risk assessment methods often rely on single data sources and static analysis, making it difficult to comprehensively capture the dynamic changes of risks. With the increasing complexity of the financial market, enterprises need a method that integrates multiple data sources, dynamically monitors risks, and provides real-time warnings. Summary of the Invention

[0003] The object of the present invention is to provide a financial risk control method and system, which can improve the accuracy and timeliness of risk monitoring.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A financial risk control method includes:

[0006] Obtaining relevant data; the relevant data includes financial data, market data, operation data, and external data; wherein, the external data includes industry reports, competitor data, and policy and regulation change data;

[0007] Constructing a knowledge graph according to the relevant data; the knowledge graph includes financial risk types, corresponding risk levels, and risk influencing factors;

[0008] Based on the knowledge graph, constructing a financial risk prediction model by using a credit risk model and a quantitative algorithm; the credit risk model adopts the KMV model; the quantitative algorithm includes the historical simulation method and the Monte Carlo simulation method;

[0009] Using the financial risk prediction model to conduct real-time monitoring on a target enterprise, and visually displaying the prediction results within a set period.

[0010] Optionally, the financial data includes a balance sheet, an income statement, and a cash flow statement; the market data includes market interest rates, exchange rates, and stock prices; the operation data includes enterprise sales data, cost data, and inventory data.

[0011] Optionally, constructing a knowledge graph according to the relevant data specifically includes:

[0012] Performing data cleaning and data preprocessing on the relevant data to obtain processed data; the data cleaning includes removing incorrect, duplicate, or incomplete data; the data preprocessing includes data normalization and interpolation processing;

[0013] Extract entities and relationships based on the processed data to construct the basic units of the knowledge graph; the entities include financial risk types, risk levels, and risk influencing factors; the relationships include the relationship context between entities.

[0014] Use the Cypher language to create nodes and relationships in the Neo4j graph database to construct the knowledge graph.

[0015] Optionally, the data normalization process includes: converting the data into a consistent format, including but not limited to unifying dates and currency units.

[0016] Optionally, the use of the Cypher language to create nodes and relationships in the Neo4j graph database to construct the knowledge graph specifically includes:

[0017] Use the Cypher language to create nodes and relationships in the Neo4j graph database to construct an initial graph;

[0018] Use incremental learning to update the initial graph in real time to obtain the final knowledge graph.

[0019] Optionally, the financial risk types and corresponding risk influencing factors specifically include:

[0020] Market risk: interest rate risk, exchange rate risk, and stock price fluctuations;

[0021] Credit risk: default and violation events;

[0022] Operating risk: procurement risk, production risk, and inventory liquidation risk;

[0023] Inventory management risk: goods and corresponding inventory red lines.

[0024] Optionally, based on the knowledge graph, use a credit risk model and a quantitative algorithm to construct a financial risk prediction model, specifically including:

[0025] Obtain training data; the training data includes historical data and corresponding training labels;

[0026] Based on the knowledge graph, use a credit risk model and a quantitative algorithm to construct a pre-training network;

[0027] Input the training data into the pre-training network, train with the goal of minimizing the loss between the network output and the training labels, and determine the trained network as the financial risk prediction model.

[0028] The present invention also provides a financial risk control system, including:

[0029] A data acquisition unit for obtaining relevant data; the relevant data includes financial data, market data, operation data, and external data; wherein, the external data includes industry reports, competitor data, and policy and regulation change data;

[0030] A knowledge graph construction unit for constructing a knowledge graph based on the relevant data; the knowledge graph includes financial risk types and corresponding risk levels and risk influencing factors;

[0031] A model construction unit for constructing a financial risk prediction model based on the knowledge graph, using a credit risk model and a quantitative algorithm; the credit risk model adopts the KMV model; the quantitative algorithms include the historical simulation method and the Monte Carlo simulation method;

[0032] A model prediction unit for using the financial risk prediction model to monitor a target enterprise in real time and visually display the prediction results within a set period.

[0033] According to the specific embodiments provided by the present invention, the following technical effects are disclosed by the present invention:

[0034] The present invention discloses a financial risk control method and system. The method includes obtaining relevant data; the relevant data includes financial data, market data, operation data, and external data; wherein, the external data includes industry reports, competitor data, and policy and regulation change data; constructing a knowledge graph according to the relevant data; the knowledge graph includes financial risk types and corresponding risk levels and risk influencing factors; constructing a financial risk prediction model based on the knowledge graph, using a credit risk model and a quantitative algorithm; the credit risk model adopts the KMV model; the quantitative algorithms include the historical simulation method and the Monte Carlo simulation method; using the financial risk prediction model to monitor a target enterprise in real time and visually display the prediction results within a set period. The present invention can improve the accuracy and timeliness of risk monitoring. Brief Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 It is a flowchart of the financial risk control method of the present invention. Detailed Embodiments

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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.

[0038] The purpose of the present invention is to provide a financial risk control method and system, which can improve the accuracy and timeliness of risk monitoring.

[0039] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] As Figure 1 shown, the present invention provides a financial risk control method, including:

[0041] Step 100: Obtain relevant data; the relevant data includes financial data, market data, operation data, and external data; among them, the external data includes industry reports, competitor data, and policy and regulation change data; the financial data includes balance sheets, income statements, and cash flow statements; the market data includes market interest rates, exchange rates, and stock prices; the operation data includes enterprise sales data, cost data, and inventory data.

[0042] Step 200: Construct a knowledge graph according to the relevant data; the knowledge graph includes financial risk types and corresponding risk levels and risk influencing factors. Among them, the financial risk types and corresponding risk influencing factors specifically include:

[0043] Market risk: interest rate risk, exchange rate risk, and stock price fluctuations;

[0044] Credit risk: default and violation events;

[0045] Operation risk: procurement risk, production risk, and inventory realization risk;

[0046] Inventory management risk: goods and corresponding inventory red lines.

[0047] Step 300: Based on the knowledge graph, construct a financial risk prediction model using a credit risk model and a quantization algorithm; the credit risk model adopts the KMV model; the quantization algorithms include the historical simulation method and the Monte Carlo simulation method.

[0048] Step 400: Use the financial risk prediction model to monitor the target enterprise in real time and visually display the prediction results within a set period.

[0049] As a specific implementation, construct a knowledge graph based on the relevant data, specifically including:

[0050] Perform data cleaning and data preprocessing on the relevant data to obtain processed data; the data cleaning includes removing incorrect, duplicate, or incomplete data; the data preprocessing includes data normalization and interpolation processing; wherein, the process of data normalization includes: converting the data into a consistent format, including but not limited to unifying dates and currency units.

[0051] Extract entities and relationships based on the processed data to construct the basic units of the knowledge graph; the entities include financial risk types, risk levels, and risk influencing factors; the relationships include the relationship context between entities;

[0052] Use the Cypher language to create nodes and relationships in the Neo4j graph database to construct an initial graph, and use incremental learning to update the initial graph in real time to obtain the final knowledge graph.

[0053] As a specific implementation, based on the knowledge graph, construct a financial risk prediction model using a credit risk model and a quantization algorithm, specifically including:

[0054] Obtain training data; the training data includes historical data and corresponding training labels;

[0055] Based on the knowledge graph, construct a pre-training network using a credit risk model and a quantization algorithm;

[0056] Input the training data into the pre-training network, train with the goal of minimizing the loss between the network output and the training labels, and determine the trained network as the financial risk prediction model.

[0057] Based on the above technical solutions, the following embodiments are provided.

[0058] Step 100: Obtain relevant data

[0059] Financial data: Extract the balance sheet, income statement, and cash flow statement from the enterprise's ERP system to ensure the accuracy and timeliness of the data.

[0060] Market data: Obtain key market indicators such as market interest rates, exchange rates, and stock prices through financial data providers to evaluate market risks.

[0061] Operational data: Extract sales data, cost data, and inventory data from the enterprise's sales, procurement, and inventory management systems to analyze the enterprise's operating efficiency and cost control capabilities.

[0062] External data: Obtain industry reports, competitor data, and policy and regulatory change data through industry research institutions, competitor analysis platforms, and government regulatory agencies to understand the impact of the external environment on the enterprise's financial risks.

[0063] Step 200: Construct a knowledge graph

[0064] Data cleaning and preprocessing:

[0065] Remove incorrect, duplicate, or incomplete data to ensure data quality.

[0066] Normalize the data, including unifying date formats, currency units, and measurement standards.

[0067] Interpolate missing data to improve data integrity and usability.

[0068] Entity and relationship extraction:

[0069] Identify and extract entities related to financial risks, such as types of financial risks (market risk, credit risk, operational risk, inventory management risk, etc.), risk levels, and risk influencing factors (such as interest rate changes, exchange rate fluctuations, default events, rising procurement costs, etc.).

[0070] Construct the relationship context between entities, such as the association between market risk and interest rate risk, the association between credit risk and default events, etc.

[0071] Knowledge graph construction:

[0072] Use the Cypher language to create nodes and relationships in the Neo4j graph database to construct an initial knowledge graph.

[0073] Real-time update the initial graph through an incremental learning algorithm to ensure the accuracy and timeliness of the graph. The incremental learning algorithm can automatically detect entities and relationships in new data and integrate them into the existing graph.

[0074] Step 300: Construct a financial risk prediction model

[0075] Obtain training data:

[0076] Screen out the dataset containing financial risk events from historical data and assign corresponding training labels (such as high risk, medium risk, low risk) to it.

[0077] Ensure the diversity and representativeness of the training data to improve the generalization ability of the model.

[0078] Construct a pre-trained network:

[0079] Based on the knowledge graph, a pre-trained network is constructed by combining the KMV model (used to evaluate credit risk) and the historical simulation method and Monte Carlo simulation method (used to quantify market risk and operating risk).

[0080] The pre-trained network is trained using deep learning algorithms (such as convolutional neural networks, recurrent neural networks, or graph neural networks) to improve its ability to process complex data.

[0081] Model training and optimization:

[0082] The training data is input into the pre-trained network, and the network parameters are adjusted through optimization algorithms (such as gradient descent method, Adam optimizer, etc.) to minimize the loss between the network output and the training labels.

[0083] Strategies such as cross-validation and early stopping are adopted to prevent model overfitting and improve the generalization performance of the model.

[0084] When the model performance reaches the preset standard, stop training and determine the trained network as the financial risk prediction model.

[0085] Step 400: Real-time monitoring and visualization display

[0086] Deploy the financial risk prediction model to the enterprise's risk management system to achieve real-time monitoring of the target enterprise.

[0087] Visualize the prediction results within a set period (such as daily, weekly, or monthly), including risk types, risk levels, risk influencing factors, and their change trends, etc.

[0088] The visualization display can be in the form of charts, dashboards, etc., so that the enterprise leadership and relevant departments can intuitively understand the financial risk situation and take corresponding measures in a timely manner.

[0089] III. Implementation effects and evaluation

[0090] By implementing this method, the enterprise has successfully constructed a financial risk prediction model based on the knowledge graph and quantitative algorithms, achieving real-time monitoring and early warning of financial risks. This model can accurately identify and warn of potential financial risks, providing effective decision-making support for the enterprise. At the same time, through visualization display, the enterprise leadership can intuitively understand the financial risk situation, formulate and adjust risk management strategies in a timely manner, and effectively reduce the enterprise's financial risk level.

[0091] During the implementation process, the enterprise also regularly evaluates and updates the model performance to ensure its adaptation to the changing external environment and internal enterprise situations. Through continuous optimization and improvement, this method provides long-term and stable financial risk control support for the enterprise.

[0092] Therefore, the present invention proposes a financial risk control method, aiming to comprehensively identify risk types and their influencing factors by integrating financial data, market data, operation data and external data, constructing a knowledge graph, and using the KMV model and quantitative algorithms to construct a financial risk prediction model to achieve real-time monitoring and risk warning of the target enterprise. This method can not only provide more accurate risk assessment, but also help decision-makers quickly respond to risk changes through visual display.

[0093] In addition, the present invention also provides a financial risk control system, including:

[0094] A data acquisition unit for acquiring relevant data; the relevant data includes financial data, market data, operation data and external data; wherein, the external data includes industry reports, competitor data and policy and regulation change data;

[0095] A graph construction unit for constructing a knowledge graph according to the relevant data; the knowledge graph includes financial risk types and corresponding risk levels and risk influencing factors;

[0096] A model construction unit for constructing a financial risk prediction model based on the knowledge graph by using a credit risk model and quantitative algorithms; the credit risk model adopts the KMV model; the quantitative algorithms include the historical simulation method and the Monte Carlo simulation method;

[0097] A model prediction unit for using the financial risk prediction model to conduct real-time monitoring of the target enterprise and visually display the prediction results within a set period.

[0098] Each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0099] Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the core idea of the present invention; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A financial risk control method, characterized in that: include: Obtain relevant data; the relevant data includes financial data, market data, operational data and external data; wherein the external data includes industry reports, competitive product data and policy and regulatory change data; Constructing a knowledge graph based on the relevant data; the knowledge graph includes financial risk types and corresponding risk levels and risk influencing factors; Based on the knowledge graph, a financial risk prediction model is constructed using a credit risk model and a quantitative algorithm; the credit risk model adopts a KMV model; the quantitative algorithm includes a historical simulation method and a Monte Carlo simulation method; The financial risk prediction model is used to monitor the target enterprise in real time and to visualize the prediction results within a set period.

2. The financial risk control method according to claim 1, characterized in that: The financial data includes balance sheet, income statement and cash flow statement; the market data includes market interest rate, exchange rate and stock price; the operating data includes enterprise sales data, cost data and inventory data.

3. The financial risk control method according to claim 1, characterized in that: Constructing a knowledge graph based on the relevant data specifically includes: Performing data cleaning and data preprocessing on the relevant data to obtain processed data; the data cleaning includes removing erroneous, duplicated or incomplete data; the data preprocessing includes data normalization and interpolation processing; Entities and relationships are extracted based on the processed data to construct a basic unit of the knowledge graph; the entities include financial risk types, risk levels and risk influencing factors; the relationships include the relationship context between entities; Use the Cypher language to create nodes and relationships in the Neo4j graph database and build a knowledge graph.

4. The financial risk control method according to claim 3, characterized in that: The data normalization process includes: converting the data into a consistent format, including but not limited to unifying dates and currency units.

5. The financial risk control method according to claim 3, characterized in that: The use of Cypher language to create nodes and relationships in the Neo4j graph database and construct a knowledge graph specifically includes: Use Cypher language to create nodes and relationships in Neo4j graph database and build the initial graph; The initial graph is updated in real time using incremental learning to obtain a final knowledge graph.

6. The financial risk control method according to claim 1, characterized in that: The types of financial risks and corresponding risk influencing factors specifically include: Market risk: interest rate risk, exchange rate risk and stock price fluctuations; Credit risk: events of default and non-compliance; Operational risks: procurement risk, production risk and inventory liquidation risk; Inventory management risks: goods and corresponding inventory red lines.

7. The financial risk control method according to claim 1, characterized in that: Based on the knowledge graph, a financial risk prediction model is constructed using a credit risk model and a quantitative algorithm, specifically including: Acquire training data; the training data includes historical data and corresponding training labels; Based on the knowledge graph, a pre-trained network is constructed using a credit risk model and a quantitative algorithm; The training data is input into the pre-trained network, training is performed with the goal of minimizing the loss between the network output and the training label, and the trained network is determined as a financial risk prediction model.

8. A financial risk control system, characterized in that: include: A data collection unit, used to obtain relevant data; the relevant data includes financial data, market data, operational data and external data; wherein the external data includes industry reports, competitive product data and policy and regulatory change data; A graph construction unit, used to construct a knowledge graph based on the relevant data; the knowledge graph includes financial risk types and corresponding risk levels and risk influencing factors; A model building unit, used to build a financial risk prediction model based on the knowledge graph using a credit risk model and a quantitative algorithm; the credit risk model adopts a KMV model; the quantitative algorithm includes a historical simulation method and a Monte Carlo simulation method; The model prediction unit is used to use the financial risk prediction model to monitor the target enterprise in real time and to visualize the prediction results within a set period.

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