Debt risk real-time analysis system and method based on big data and artificial intelligence

Abstract

The invention discloses a debt risk real-time analysis system and method based on big data and artificial intelligence. The system comprises a data acquisition and preprocessing module, a credit risk analysis module, a market risk analysis module, a mobility risk analysis module, a comprehensive risk scoring and early warning module and a risk optimization and decision support module. Each module of the system can perform real-time data analysis and prediction by using machine learning and deep learning algorithms, can generate a credit risk analysis result, a market risk analysis result and a mobility risk analysis result respectively, finally performs weighted summation on each result, and can realize dynamic monitoring and early warning based on the summation result. And enterprises, financial institutions and investors are helped to effectively avoid risks in commercial transactions. Therefore, by adopting the embodiment of the invention, the default risk in the commercial transaction can be obviously reduced, and the decision-making efficiency and the robustness of the financial market can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a real-time debt risk analysis system and method based on big data and artificial intelligence. Background Art

[0002] In commercial transactions, enterprises or financial institutions often need to conduct large-scale debt transactions to meet their capital requirements or achieve their financial goals. These transactions involve huge amounts of funds, and their risks mainly come from the credit status of the debtor, market fluctuations, and the liquidity status of the enterprise. Therefore, accurate assessment of debt risk is crucial for investors, creditors, and financial institutions, which can help them make more informed decisions and reduce potential financial losses.

[0003] In related technologies, traditional debt risk assessment methods mostly rely on static financial data and credit ratings, lacking the ability to respond to real-time market changes. They often only focus on a single risk factor, such as credit risk, while ignoring the combined effects of market risk and liquidity risk. In complex commercial transactions, the assessment of a single risk factor cannot fully reveal the potential risks of debt, which is likely to lead to deviations in risk judgment. Summary of the Invention

[0004] Embodiments of this application provide a real-time debt risk analysis system based on big data and artificial intelligence. To provide a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the subsequent detailed description.

[0005] In a first aspect, embodiments of this application provide a real-time debt risk analysis system based on big data and artificial intelligence. The system includes:

[0006] A data collection and preprocessing module, a credit risk analysis module, a market risk analysis module, a liquidity risk analysis module, a comprehensive risk scoring and early warning module, and a risk optimization and decision support module; wherein,

[0007] The data collection and preprocessing module is used to collect and preprocess various types of data including financial data, market data, debt data, and macroeconomic data in real time from multiple external data sources, and obtain the cleaned data to be analyzed;

[0008] A credit risk analysis module, which is used to select the first data for credit risk analysis from the data to be analyzed, input the first data into a pre-trained credit risk prediction model, and output the credit risk analysis result; the pre-trained credit risk prediction model is trained with the historical enterprise financial data, industry benchmark data, market credit rating data of the object to be analyzed and machine learning algorithms;

[0009] A market risk analysis module, which is used to select the second data for market risk analysis from the data to be analyzed, and perform multi-scenario prediction on the debt risk of the object to be analyzed within a preset future period by using Monte Carlo simulation and generalized autoregressive conditional heteroskedasticity model and the second data to obtain the market risk analysis result;

[0010] A liquidity risk analysis module, which is used to select the third data for liquidity risk analysis from the data to be analyzed, use a time series model and the third data to predict the change of the cash flow of the object to be analyzed within a preset future period, and predict the short-term debt repayment ability of the object to be analyzed according to the preset liquidity index and the third data as the liquidity risk analysis result;

[0011] A comprehensive risk scoring and early warning module, which is used to integrate the credit risk analysis result, market risk analysis result and liquidity risk analysis result through a weighted algorithm to generate a comprehensive risk analysis result, and trigger an early warning mechanism when the comprehensive risk analysis result is greater than a preset threshold to generate a risk notice and feedback it to the early warning client;

[0012] A risk optimization and decision support module, which is used to determine an optimization plan for the debt structure according to the comprehensive risk analysis result, combined with a genetic algorithm or a particle swarm optimization algorithm, and generate strategic suggestions for adjusting the repayment plan or financing structure.

[0013] In a second aspect, a real-time debt risk analysis method based on big data and artificial intelligence, the method includes:

[0014] A data collection and preprocessing module real-time collects and preprocesses various data including financial data, market data, debt data and macroeconomic data from multiple external data sources to obtain the cleaned data to be analyzed;

[0015] A credit risk analysis module selects the first data for credit risk analysis from the data to be analyzed, inputs the first data into a pre-trained credit risk prediction model, and outputs the credit risk analysis result; the pre-trained credit risk prediction model is trained with the historical enterprise financial data, industry benchmark data, market credit rating data of the object to be analyzed and machine learning algorithms;

[0016] The market risk analysis module selects the second data for market risk analysis from the data to be analyzed, and uses Monte Carlo simulation, generalized autoregressive conditional heteroskedasticity model, and the second data to perform multi-scenario predictions on the debt risk of the object to be analyzed within a preset future period, obtaining the market risk analysis result;

[0017] The liquidity risk analysis module selects the third data for liquidity risk analysis from the data to be analyzed, uses a time series model and the third data to predict the change in cash flow of the object to be analyzed within a preset future period, and predicts the short-term debt repayment ability of the object to be analyzed based on the preset liquidity indicators and the third data, as the liquidity risk analysis result;

[0018] The comprehensive risk scoring and early warning module integrates the credit risk analysis result, market risk analysis result, and liquidity risk analysis result through a weighted algorithm to generate a comprehensive risk analysis result, and when the comprehensive risk analysis result is greater than the preset threshold, triggers an early warning mechanism to generate a risk notice and feedback it to the early warning client;

[0019] The risk optimization and decision support module determines the optimization plan for the debt structure according to the comprehensive risk analysis result, in combination with a genetic algorithm or a particle swarm optimization algorithm, and generates strategic suggestions for adjusting the repayment plan or financing structure.

[0020] The technical solution provided by the embodiments of the present application may include the following beneficial effects:

[0021] In the embodiments of the present application, the real-time debt risk analysis system based on big data and artificial intelligence can effectively improve the debt risk management ability in commercial transactions. The system can not only dynamically monitor credit, market, and liquidity risks, but also provide real-time early warnings and optimization decision suggestions for enterprises and financial institutions through intelligent algorithms, significantly reducing the default risk in commercial transactions and improving decision-making efficiency and the robustness of the financial market.

[0022] It should be understood that the above general description and subsequent detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application and, together with the specification, are used to explain the principles of the present application.

[0024] Figure 1 is a schematic structural diagram of a real-time debt risk analysis system based on big data and artificial intelligence provided by an embodiment of the present application;

[0025] Figure 2 is a schematic diagram of a code snippet of a credit risk analysis module provided by an embodiment of the present application;

[0026] Figure 3 It is a schematic diagram of a code snippet of a market risk analysis module provided by an embodiment of the present application;

[0027] Figure 4 It is a schematic diagram of a code snippet of a liquidity risk analysis module provided by an embodiment of the present application;

[0028] Figure 5 It is a schematic diagram of a code snippet of a comprehensive risk scoring and early warning module provided by an embodiment of the present application;

[0029] Figure 6 It is a schematic diagram of a code snippet related to a risk optimization and decision support module provided by an embodiment of the present application;

[0030] Figure 7 It is a schematic diagram of the method flow of a method for training a fault early warning analysis model provided by an embodiment of the present application. Detailed implementation manners

[0031] The following description and the drawings fully illustrate the specific implementation manners of the present application so that those skilled in the art can practice them.

[0032] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0033] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are only examples of systems and methods consistent with some aspects of the present application as detailed in the appended claims.

[0034] In the description of the present application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances. In addition, in the description of the present application, unless otherwise stated, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0035] In the embodiments of the present application, a real-time debt risk analysis system based on big data and artificial intelligence can effectively improve the debt risk management ability in commercial transactions. The system can not only dynamically monitor credit, market, and liquidity risks, but also provide real-time early warnings and optimized decision-making suggestions for enterprises and financial institutions through intelligent algorithms, significantly reducing the default risk in commercial transactions, improving decision-making efficiency, and enhancing the robustness of the financial market. The following will be described in detail using exemplary embodiments.

[0036] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the system structure of a real-time debt risk analysis system based on big data and artificial intelligence provided by the embodiments of the present application. The system includes: a data collection and preprocessing module, a credit risk analysis module, a market risk analysis module, a liquidity risk analysis module, a comprehensive risk scoring and early warning module, and a risk optimization and decision support module.

[0037] In some embodiments of the present application, the data collection and preprocessing module is used to collect and preprocess various types of data including financial data, market data, debt data, and macroeconomic data in real time from multiple external data sources to obtain the data to be analyzed after cleaning; the credit risk analysis module is used to select the first data for credit risk analysis from the data to be analyzed, input the first data into a pre-trained credit risk prediction model, and output the credit risk analysis result; the pre-trained credit risk prediction model is trained using the historical enterprise financial data, industry benchmark data, market credit rating data of the object to be analyzed, and machine learning algorithms; the market risk analysis module is used to select the second data for market risk analysis from the data to be analyzed, and use Monte Carlo simulation and generalized autoregressive conditional heteroskedasticity model, and the second data to perform multi-scenario prediction on the debt risk of the object to be analyzed within a preset future period to obtain the market risk analysis result; the liquidity risk analysis module is used to select the third data for liquidity risk analysis from the data to be analyzed, use a time series model and the third data to predict the change in the cash flow of the object to be analyzed within a preset future period, and predict the short-term debt repayment ability of the object to be analyzed based on the preset liquidity index and the third data as the liquidity risk analysis result; the comprehensive risk scoring and early warning module is used to integrate the credit risk analysis result, the market risk analysis result, and the liquidity risk analysis result through a weighted algorithm to generate a comprehensive risk analysis result, and when the comprehensive risk analysis result is greater than a preset threshold, trigger an early warning mechanism to generate a risk notice and feedback it to the early warning client; the risk optimization and decision support module is used to determine an optimized plan for the debt structure according to the comprehensive risk analysis result, combined with a genetic algorithm or a particle swarm optimization algorithm, and generate strategic suggestions for adjusting the repayment plan or financing structure.

[0038] In the embodiments of the present application, a real-time debt risk analysis system based on big data and artificial intelligence can effectively improve the debt risk management ability in commercial transactions. The system can not only dynamically monitor credit, market, and liquidity risks, but also provide real-time early warnings and optimized decision-making suggestions for enterprises and financial institutions through intelligent algorithms, significantly reducing the default risk in commercial transactions and enhancing decision-making efficiency and the robustness of the financial market.

[0039] In some embodiments of the present application, the specific process of collecting and preprocessing various types of data including financial data, market data, debt data, and macroeconomic data from multiple external data sources in real time to obtain the data to be analyzed after cleaning includes: collecting various types of data including financial data, market data, debt data, and macroeconomic data from multiple external data sources in real time through Kafka and Flume big data stream processing frameworks; performing distributed preprocessing and real-time analysis on the collected various types of data through the Apache Spark or Flink big data frameworks, and the preprocessing includes data deduplication, outlier processing, and data standardization; storing the preprocessed data through the distributed databases HBase or Cassandra.

[0040] Among them, the data collection and preprocessing module collects data in real time from multiple data sources (including enterprise financial systems, credit rating agencies, market trading platforms, public market data, etc.), and through preprocessing steps such as data cleaning, deduplication, and missing value filling, ensures the quality and consistency of the data.

[0041] For example, collect data from multiple external data sources, including enterprise financial reports, market quotation data (such as interest rates, exchange rates, commodity prices), industry benchmark data, debt historical data, macroeconomic indicators, etc. Realize real-time data collection through big data stream processing frameworks such as Kafka and Flume, and ensure the efficiency and reliability of data transmission. Deduplication processing: Clear duplicate records to ensure data uniqueness. Outlier processing: Use methods such as z-score and IQR to detect and process abnormal data points. Data standardization: Standardize data from different sources so that data in different dimensions can be compared and analyzed under the same standard. Store the cleaned data through a distributed database (such as HBase or Cassandra) for subsequent large-scale parallel computing.

[0042] In the embodiments of the present application, by introducing real-time data collection and processing technologies, the debt risk of enterprises can be dynamically tracked and evaluated.

[0043] Among them, the first data includes enterprise financial indicators, historical default records, and industry benchmark data, and the enterprise financial indicators include asset-liability ratio, current ratio, and accounts receivable turnover ratio.

[0044] In some embodiments of the present application, the specific process of inputting the first data into the pre-trained credit risk prediction model and outputting the credit risk analysis result includes: inputting the enterprise financial indicators, historical default records, and industry benchmark data into the pre-trained credit risk prediction model; outputting the default probability, loss given default, and exposure at default corresponding to the first data; calculating the expected credit loss based on the default probability, loss given default, and exposure at default as the credit risk analysis result; wherein, the pre-trained credit risk prediction model is based on supervised learning, and the model parameters are optimized through the cross-validation method and hyperparameter tuning technology, and the machine learning algorithms are random forest and gradient boosting decision tree algorithms.

[0045] Among them, the credit risk analysis module is used to evaluate the credit risk of the borrower, calculate the probability of default (PD), loss given default (LGD), and exposure at default (EAD). Machine learning algorithms such as random forest or XGBoost are used to train the enterprise's financial data and credit scoring data to establish a credit risk assessment model. The cross-validation (Cross-Validation) and hyperparameter tuning (Hyperparameter Tuning) technologies are adopted to optimize the model parameters to improve the accuracy of the model. The model is updated in real time every time new data is input to ensure that the credit scoring results change with the changes in the market and the enterprise's financial conditions.

[0046] For example, the processing process of the credit risk analysis module includes: obtaining the historical financial data and credit scoring data of the borrower. Using algorithms such as random forest and XGBoost to train the credit risk model. Generating the probability of default based on financial indicators. Estimating the loss given default in combination with the enterprise's asset value. Determining the exposure at default according to the debt scale. Calculating the expected credit loss (ECL) based on PD, LGD, and EAD. The code snippet of the credit risk analysis module is as Figure 2 shown.

[0047] Among them, the second data includes real-time market quotation data.

[0048] In some embodiments of the present application, Monte Carlo simulation, the generalized autoregressive conditional heteroskedasticity (GARCH) model, and secondary data are used to perform multi-scenario prediction on the debt risk of the object to be analyzed within a preset future period. The specific process of obtaining the market risk analysis result includes: using the GARCH model and market quotation data to model market volatility to predict the risk exposure information of the debt under future market conditions; based on the risk exposure information, using Monte Carlo simulation to generate multiple market risk scenarios to determine the risks that occur under different market conditions; based on the risks that occur under different market conditions, predicting the impact of market fluctuations on the debt within a preset future period under a preset confidence interval, calculating the value at risk (VaR), and using the VaR as a key indicator for risk prediction; using the key indicator as the market risk analysis result, and performing a stress test on the potential loss of the debt under simulated preset extreme market conditions to obtain the test result.

[0049] Among them, the market risk analysis module evaluates the potential impact of market fluctuations (such as interest rate, exchange rate, and commodity price fluctuations) on the debt. The market risk analysis module collects real-time market quotation data, such as interest rate, exchange rate, commodity price, etc., and evaluates its potential impact on the debt through time series analysis and volatility modeling. The GARCH model is used to model market volatility to predict the risk exposure of the debt under future market conditions. Monte Carlo simulation is used to generate multiple market risk scenarios to evaluate the worst-case losses that may occur under different market conditions. By simulating the impact of future market fluctuations on the debt, the value at risk (VaR) is calculated and used as a key indicator for risk prediction. A stress test is performed on the potential loss of the debt under extreme market conditions (such as financial crisis or economic recession), and the result is fed back to the comprehensive risk scoring and early warning module.

[0050] For example, the market risk analysis module includes data input: collecting real-time market data (interest rate, exchange rate, commodity price, etc.). GARCH model: predicting market volatility. Monte Carlo simulation: simulating risks under different market scenarios. VaR calculation: evaluating the maximum possible loss under a specific confidence interval. Code snippets of the market risk analysis module are as follows Figure 3 shown.

[0051] Among them, the third data includes the cash flow data and financial statements of the enterprise; the time series model is a long short-term memory network.

[0052] In some embodiments of the present application, a time series model and third data are used to predict the change in the cash flow of the object to be analyzed within a preset future period, and the short-term debt repayment ability of the object to be analyzed is predicted based on a preset liquidity index and the third data. The specific process of the liquidity risk analysis result includes: based on the enterprise's cash flow data and financial statements, using a long short-term memory network to predict the cash flow within a preset future period to obtain a cash flow prediction result; based on the enterprise's cash flow data and financial statements, calculating the enterprise's liquidity ratio index; according to the enterprise's liquidity ratio index and in combination with the cash flow prediction result, calculating the short-term debt repayment ability of the object to be analyzed as the liquidity risk analysis result.

[0053] Further, when it is detected that the financial data of the object to be analyzed is updated, the process of predicting the short-term debt repayment ability of the object to be analyzed is re-executed to re-evaluate the liquidity risk; when the evaluation result indicates that the liquidity risk value of the object to be analyzed is greater than a preset threshold, a risk notice is generated and fed back to the warning client.

[0054] Among them, the liquidity risk analysis module refers to whether an enterprise can liquidate assets or obtain financial support at a reasonable price when needed to fulfill its debt obligations. The liquidity risk analysis module is based on the enterprise's historical cash flow data and uses an LSTM (long short-term memory network) model to predict the future cash flow and evaluate its future short-term cash flow situation. Using liquidity indicators such as the quick ratio and current ratio of the enterprise and combining with the cash flow prediction result, the short-term debt repayment ability of the enterprise is calculated. The system re-evaluates the liquidity risk every time the financial data is updated. If it detects an increase in the liquidity risk, it issues a warning in advance.

[0055] For example, the liquidity risk analysis module includes data input: obtaining the enterprise's cash flow data and financial statements. Cash flow prediction: using the LSTM model to predict the future cash flow. Liquidity index calculation: calculating indicators such as the quick ratio and current ratio to evaluate the short-term debt repayment ability. The code snippet of the liquidity risk analysis module is as follows Figure 4 shown.

[0056] In some embodiments of the present application, the specific process of integrating the credit risk analysis result, market risk analysis result, and liquidity risk analysis result through a weighted algorithm to generate a comprehensive risk analysis result includes: using the analytic hierarchy process algorithm to calculate the weights of the credit risk analysis result, market risk analysis result, and liquidity risk analysis result; integrating the credit risk analysis result, market risk analysis result, and liquidity risk analysis result and their weights through a fuzzy comprehensive evaluation model to obtain a comprehensive risk score as the comprehensive risk analysis result.

[0057] Among them, the comprehensive risk scoring and early warning module is used to integrate the analysis results of credit risk, market risk, and liquidity risk, generate a comprehensive risk score, and issue an early warning signal when necessary. The comprehensive risk scoring and early warning module integrates credit risk, market risk, and liquidity risk through the fuzzy comprehensive evaluation method. The AHP (Analytic Hierarchy Process) is used to calculate the weights of various risks to generate a comprehensive risk score. A risk score threshold is set, and when the comprehensive risk score exceeds the preset threshold, the system automatically triggers an early warning. The early warning notice can be sent to the debtor, investor, or risk manager through various channels (such as text messages, emails, or app push notifications). The system provides a historical trend analysis of the risk score, and visualizes the time series of risk changes to help managers identify potential risks in advance.

[0058] For example, the comprehensive risk scoring and early warning module includes: integrating risk assessment results: comprehensively calculating credit risk, market risk, and liquidity risk. Comprehensive scoring: integrating multiple risks using the fuzzy comprehensive evaluation method or the AHP method. Early warning trigger: when the risk score exceeds the threshold, send an early warning notice. Code snippets of the comprehensive risk scoring and early warning module are as follows Figure 5 shown.

[0059] In some embodiments of the present application, according to the comprehensive risk analysis results, combining the genetic algorithm or the particle swarm optimization algorithm to determine the optimization plan of the debt structure, and the specific process of generating the strategy suggestions for adjusting the repayment plan or the financing structure includes: obtaining the due date, repayment method, and interest rate of the debt of the object to be analyzed; according to the comprehensive risk analysis results, due date, repayment method, and interest rate, and combining the genetic algorithm or the particle swarm optimization algorithm to optimize the debt structure of the enterprise, and output the optimal debt repayment and financing structure plan as the optimization plan of the debt structure; according to the comprehensive risk analysis results, generate multiple financing plans, and evaluate the risk-return ratio of each financing plan to obtain the risk optimization result; according to the risk optimization result, generate decision suggestions for adjusting the debt maturity, changing the financing method, or prepaying high-risk debts to reduce the comprehensive risk exposure as the strategy suggestions for adjusting the repayment plan or the financing structure.

[0060] Among them, the risk optimization and decision support module can provide optimized debt management solutions and strategic suggestions based on the comprehensive risk assessment results, including adjusting the repayment plan, changing the financing structure, etc. The risk optimization and decision support module optimizes the enterprise's debt structure through the genetic algorithm (GA) or the particle swarm optimization algorithm (PSO). The inputs include key factors such as the maturity time, repayment method, and interest rate of the debt, and the output is the optimal debt repayment and financing structure plan. Considering the future cash flow prediction results of the enterprise and the market risk assessment, multiple financing plans are generated, and the risk-return ratio of each plan is evaluated. The system puts forward specific decision suggestions according to the risk optimization results, such as adjusting the debt term, changing the financing method, or prepaying high-risk debts in advance, so as to reduce the comprehensive risk exposure. The proposed plan is backtested through historical market and financial data to ensure that the proposed debt optimization plan is feasible and robust in different market environments.

[0061] For example, the risk optimization and decision support module uses the genetic algorithm or the particle swarm optimization algorithm to optimize the debt management plan. According to the optimization results, suggestions for adjusting the debt structure are provided. Code snippets involved in the risk optimization and decision support module are for example Figure 6 as shown.

[0062] In some embodiments of the present application, the credit risk analysis module is further used to analyze the time variation trend of the enterprise financial data of the object to be analyzed based on the time series model of the LSTM neural network, and predict the financial status and potential default risk of the enterprise within a preset period in the future; the liquidity risk analysis module simulates the cash flow performance of the object to be analyzed under different market conditions and financial status by introducing scenario analysis and stress testing, and predicts the liquidity risk faced by the enterprise under extreme market fluctuations; the risk notification notifies the debtor, investor or risk manager by means of email, text message or real-time notification.

[0063] In the embodiments of the present application, the system can immediately update the risk assessment results when the market environment, the enterprise financial status or the industry benchmark changes, ensuring that the analysis and prediction have high timeliness.

[0064] In the embodiments of the present application, when the risk index exceeds the preset threshold, the system will automatically trigger an early warning to prompt the relevant parties (such as creditors, risk managers) to take measures in time to reduce the debt default risk. This automated function greatly reduces the latency problem of traditional manual monitoring.

[0065] In the embodiments of the present application, through the comprehensive integration of credit risk, market risk and liquidity risk, and using technologies such as fuzzy comprehensive evaluation and AHP (Analytic Hierarchy Process), a more comprehensive risk score can be provided. Compared with traditional single risk assessment methods, the present application can accurately analyze the mutual relationship between various risks and provide more in-depth and extensive risk analysis results.

[0066] In the embodiments of the present application, by processing tens of thousands of real-time data streams per minute and through an efficient stream processing framework (such as Kafka, Spark Streaming), the efficiency and accuracy of large-scale data processing are ensured, enabling the risk assessment results to have the ability to be updated at high frequencies.

[0067] In the embodiments of the present application, by introducing deep learning algorithms (such as LSTM) and machine learning models (such as random forest, XGBoost), the present application exhibits extremely high prediction accuracy in aspects such as credit scoring, market volatility prediction, and cash flow prediction. Especially when dealing with complex non-linear market environments, the model can better capture the potential change trends of debt risks.

[0068] In the embodiments of the present application, through genetic algorithms and particle swarm optimization algorithms, it is possible to provide optimized debt structure adjustment plans for different market conditions and corporate financial structures, help enterprises reduce the overall risk exposure, and propose optimal financing and debt repayment strategies to enhance the risk resistance ability of enterprises.

[0069] In the embodiments of the present application, by performing deep learning modeling on the historical cash flow data of enterprises and combining the industry characteristics and debt structures of enterprises, a dedicated liquidity risk assessment report can be generated for each enterprise. This report not only includes the prediction of future cash flows but also provides detailed liquidity optimization suggestions.

[0070] In the embodiments of the present application, through market extreme scenario simulation and stress testing, it helps enterprises identify potential liquidity crises in advance and provides corresponding risk response strategies to enhance the response ability of enterprises in extreme market environments.

[0071] In the embodiments of the present application, this system is not only applicable to enterprise debt risk assessment but can also be extended to other fields, such as government debt management, loan risk control of financial institutions, etc. The system has a high degree of adaptability and can be quickly deployed and adjusted for different industries and application scenarios.

[0072] In the embodiments of the present application, through a distributed architecture design, when the system processes large-scale real-time data, it has good horizontal scalability and can adapt to the processing requirements of enterprises and data sets of different scales, ensuring the stability and efficiency of the system.

[0073] In the embodiments of the present application, with the increasing uncertainty of the global financial market, the debt risks faced by enterprises are becoming increasingly complex. Through intelligent and automated debt risk assessment and management means, the present invention can help enterprises effectively reduce default risks and enhance the credit level and market competitiveness of enterprises.

[0074] In the embodiments of the present application, the system can help financial institutions such as banks and investment institutions conduct more accurate risk assessment and management of loans and investment projects, reduce the occurrence probability of systemic financial risks, and enhance the stability of the financial system.

[0075] Please refer to Figure 7 , which is a schematic flowchart of a real-time debt risk analysis method based on big data and artificial intelligence provided by the embodiments of the present application. As Figure 7 shown, the detection method of the embodiments of the present application may include the following steps:

[0076] S101, the data collection and preprocessing module collects and preprocesses various types of data including financial data, market data, debt data, and macroeconomic data in real time from multiple external data sources to obtain the cleaned data to be analyzed;

[0077] S102, the credit risk analysis module selects the first data for credit risk analysis from the data to be analyzed, inputs the first data into a pre-trained credit risk prediction model, and outputs the credit risk analysis result; the pre-trained credit risk prediction model is trained using the historical enterprise financial data, industry benchmark data, market credit rating data of the object to be analyzed, and machine learning algorithms;

[0078] S103, the market risk analysis module selects the second data for market risk analysis from the data to be analyzed, and uses Monte Carlo simulation and generalized autoregressive conditional heteroskedasticity model, and the second data to perform multi-scenario prediction on the debt risk of the object to be analyzed within a future preset period to obtain the market risk analysis result;

[0079] S104, the liquidity risk analysis module selects the third data for liquidity risk analysis from the data to be analyzed, uses a time series model and the third data to predict the change of the cash flow of the object to be analyzed within a future preset period, and predicts the short-term debt repayment ability of the object to be analyzed according to the preset liquidity index and the third data, as the liquidity risk analysis result;

[0080] S105, the comprehensive risk scoring and early warning module integrates the credit risk analysis result, the market risk analysis result, and the liquidity risk analysis result through a weighted algorithm to generate a comprehensive risk analysis result, and when the comprehensive risk analysis result is greater than a preset threshold, triggers an early warning mechanism to generate a risk notice and feedback it to the early warning client;

[0081] S106, the risk optimization and decision support module determines an optimization plan for the debt structure according to the comprehensive risk analysis result, in combination with a genetic algorithm or a particle swarm optimization algorithm, and generates strategic suggestions for adjusting the repayment plan or financing structure.

[0082] For example, large enterprises usually have complex debt structures and multiple financing channels, including bank loans, bond issuances, commercial papers, etc. With the fluctuations in the market environment, changes in interest rates, and adjustments to the internal financial conditions of enterprises, the debt risks faced by enterprises are constantly changing. Traditional debt management methods often rely on historical financial statements and annual audits and are difficult to cope with the real-time changing market environment. Enterprises can use this method to conduct real-time tracking and dynamic risk assessment of their debt situations, and at any time understand their credit risks, market risks, and liquidity risks. It can automatically generate future debt risk predictions and repayment optimization plans based on current market data and enterprise financial indicators. When the market fluctuates or the liquidity situation of the enterprise deteriorates, the system will issue early warnings to help enterprise management make timely risk response decisions, such as adjusting the financing structure, deferring payments, or repaying debts in advance. A large manufacturing enterprise uses this method to conduct real-time monitoring of its multiple financing channels. Through the multi-scenario stress testing function of the method, it successfully identified the potential liquidity crisis that might occur during an economic recession and took preventive measures in advance, reducing the debt default risk.

[0083] For example, financial institutions can use this method to collect, analyze, and evaluate the financial and market data of all their enterprise customers in real time, and provide automated credit scoring, probability of default, and loan risk rating assessments. According to the historical credit records of customers, industry benchmarks, and market risk situations, dynamically adjust loan terms or interest rates to ensure that financial institutions can respond more flexibly to risk changes. When the liquidity risk or market risk of an enterprise rises to a certain level, the system will automatically issue a risk warning to the financial institution and recommend risk control measures, such as increasing collateral or adjusting the loan limit. A large bank uses this method to conduct dynamic risk monitoring of its loan portfolio and successfully avoided several potential high-risk loan default events, reducing the default rate of the overall loan portfolio.

[0084] In the embodiment of this application, the real-time debt risk analysis system based on big data and artificial intelligence can effectively improve the debt risk management ability in commercial transactions. The system can not only dynamically monitor credit, market, and liquidity risks, but also provide real-time warnings and optimization decision-making suggestions for enterprises and financial institutions through intelligent algorithms, significantly reducing the default risk in commercial transactions and improving decision-making efficiency and the robustness of the financial market.

[0085] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program for real-time debt risk analysis based on big data and artificial intelligence can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above various methods. Among them, the storage medium of the program for real-time debt risk analysis based on big data and artificial intelligence can be a magnetic disk, an optical disc, a read-only memory, or a random access memory, etc.

[0086] The above-disclosed is only the preferred embodiment of the present application. Of course, the scope of rights of the present application cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A real-time debt risk analysis system based on big data and artificial intelligence, characterized in that: The system comprises: Data collection and preprocessing module, credit risk analysis module, market risk analysis module, liquidity risk analysis module, comprehensive risk scoring and early warning module, risk optimization and decision support module; among them, The data collection and preprocessing module is used to collect and preprocess various types of data including financial data, market data, debt data and macroeconomic data from multiple external data sources in real time to obtain cleaned data to be analyzed; The credit risk analysis module is used to select first data for credit risk analysis from the data to be analyzed, input the first data into a pre-trained credit risk prediction model, and output a credit risk analysis result; the pre-trained credit risk prediction model is obtained by training with historical corporate financial data, industry benchmark data, market credit rating data and a machine learning algorithm of the object to be analyzed; The market risk analysis module is used to select second data for market risk analysis from the data to be analyzed, and use Monte Carlo simulation and generalized autoregressive conditional heteroskedasticity model and the second data to perform multi-scenario prediction on the debt risk of the object to be analyzed within a preset period in the future to obtain a market risk analysis result; The liquidity risk analysis module is used to select third data for liquidity risk analysis from the data to be analyzed, use the time series model and the third data to predict the change of cash flow of the object to be analyzed within a preset period in the future, and predict the short-term debt repayment ability of the object to be analyzed based on the preset liquidity index and the third data as the liquidity risk analysis result; The comprehensive risk scoring and early warning module is used to integrate the credit risk analysis result, the market risk analysis result and the liquidity risk analysis result through a weighted algorithm to generate a comprehensive risk analysis result, and trigger an early warning mechanism when the comprehensive risk analysis result is greater than a preset threshold, so as to generate a risk notification feedback to the early warning client; The risk optimization and decision support module is used to determine the optimization plan of the debt structure based on the comprehensive risk analysis results in combination with a genetic algorithm or a particle swarm optimization algorithm, and to generate strategic recommendations for adjusting the repayment plan or financing structure.

2. The method according to claim 1, characterized in that The real-time collection and preprocessing of various data including financial data, market data, debt data and macroeconomic data from multiple external data sources to obtain cleaned data to be analyzed includes: Collect various data including financial data, market data, debt data and macroeconomic data from multiple external data sources in real time through Kafka and Flume big data stream processing framework; Use Apache Spark or Flink big data framework to perform distributed preprocessing and real-time analysis on various types of collected data. Preprocessing includes data deduplication, outlier processing, and data standardization. The preprocessed data is stored in a distributed database such as HBase or Cassandra.

3. The method according to claim 1, characterized in that The first data includes enterprise financial indicators, historical default records, and industry benchmark data, and the enterprise financial indicators include asset-liability ratio, current ratio, and accounts receivable turnover rate; The step of inputting the first data into a pre-trained credit risk prediction model and outputting a credit risk analysis result includes: Inputting the enterprise's financial indicators, historical default records, and industry benchmark data into a pre-trained credit risk prediction model; Outputting the default probability, default loss rate and risk exposure corresponding to the first data; Based on the default probability, default loss rate and risk exposure, the expected credit loss is calculated as the credit risk analysis result; The pre-trained credit risk prediction model is based on supervised learning and is obtained by optimizing model parameters through a cross-validation method and hyperparameter tuning technology. The machine learning algorithms are random forest and gradient boosting decision tree algorithms.

4. The method according to claim 1, characterized in that The second data includes real-time market data; The Monte Carlo simulation and the generalized autoregressive conditional heteroskedasticity model and the second data are used to perform multi-scenario forecasts on the debt risk of the object to be analyzed within a preset future period to obtain market risk analysis results, including: The generalized autoregressive conditional heteroskedasticity model and market data are used to model market volatility in order to predict the risk exposure information of debt under future market conditions; Generate multiple market risk scenarios using Monte Carlo simulations based on the risk exposure information to identify risks arising under different market conditions; Based on the risks arising under different market conditions, the impact of market fluctuations on debt within a preset period in the future is predicted within a preset confidence interval, and the risk value is calculated, and the risk value is used as a key indicator for risk prediction; The key indicators are used as market risk analysis results, and stress tests are performed on the potential losses of debts under simulated preset extreme market conditions to obtain test results.

5. The method according to claim 1, characterized in that: The third data includes the cash flow data and financial statements of the enterprise; the time series model is a long short-term memory network; The time series model and the third data are used to predict the change of the cash flow of the object to be analyzed within a preset period in the future, and the short-term debt repayment ability of the object to be analyzed is predicted according to the preset liquidity index and the third data as the liquidity risk analysis result, including: Based on the cash flow data and financial statements of the enterprise, a long short-term memory network is used to predict the cash flow within a future preset period to obtain a cash flow prediction result; Calculate the liquidity ratio indicators of the enterprise based on the cash flow data and financial statements of the enterprise; According to the liquidity ratio index of the enterprise and in combination with the cash flow forecast result, the short-term debt repayment ability of the object to be analyzed is calculated as the liquidity risk analysis result.

6. The method according to claim 5, characterized in that The method further comprises: When it is monitored that the financial data of the object to be analyzed is updated, the process of predicting the short-term debt repayment ability of the object to be analyzed is re-executed to re-evaluate the liquidity risk; When the evaluation result indicates that the liquidity risk value of the object to be analyzed is greater than a preset threshold, a risk notification is generated and fed back to the early warning client.

7. The method according to claim 1, characterized in that The credit risk analysis result, the market risk analysis result and the liquidity risk analysis result are integrated through a weighted algorithm to generate a comprehensive risk analysis result, including: Calculate the weights of the credit risk analysis result, the market risk analysis result and the liquidity risk analysis result using a hierarchical analysis algorithm; The credit risk analysis result, the market risk analysis result, the liquidity risk analysis result and their weights are integrated through a fuzzy comprehensive evaluation model to obtain a comprehensive risk score as the comprehensive risk analysis result.

8. The method according to claim 1, characterized in that: Determining an optimization plan for debt structure based on the comprehensive risk analysis results in combination with a genetic algorithm or a particle swarm optimization algorithm, and generating strategic recommendations for adjusting a repayment plan or a financing structure, include: Obtaining the due date, repayment method, and interest rate of the debt of the object to be analyzed; Optimizing the debt structure of the enterprise based on the comprehensive risk analysis results, the maturity date, repayment method, interest rate, and in combination with a genetic algorithm or a particle swarm optimization algorithm, and outputting an optimal debt repayment and financing structure plan as a debt structure optimization plan; Generate multiple financing plans based on the comprehensive risk analysis results, and evaluate the risk-return ratio of each financing plan to obtain a risk optimization result; Based on the risk optimization results, decision recommendations are generated to adjust debt terms, change financing methods, or repay high-risk debts in advance to reduce overall risk exposure, which can serve as strategic recommendations for adjusting repayment plans or financing structures.

9. The method according to claim 1, characterized in that: The credit risk analysis module is also used to analyze the time variation trend of the enterprise financial data of the object to be analyzed based on the time series model of the LSTM neural network, and predict the financial status and potential default risk of the enterprise within a preset period in the future; The liquidity risk analysis module simulates the cash flow performance of the object to be analyzed under different market conditions and financial conditions by introducing scenario analysis and stress testing, and predicts the liquidity risk faced by the enterprise under extreme market fluctuations; The risk notification is notified to the debtor, investor or risk manager via email, text message or real-time notification.

10. A real-time debt risk analysis method based on big data and artificial intelligence implemented by the system according to any one of claims 1 to 9, characterized in that: The method comprises: The data collection and preprocessing module collects and preprocesses various data including financial data, market data, debt data and macroeconomic data from multiple external data sources in real time to obtain cleaned data to be analyzed; The credit risk analysis module selects first data for credit risk analysis from the data to be analyzed, inputs the first data into a pre-trained credit risk prediction model, and outputs a credit risk analysis result; the pre-trained credit risk prediction model is trained using historical corporate financial data, industry benchmark data, market credit rating data, and a machine learning algorithm of the object to be analyzed; The market risk analysis module selects second data for market risk analysis from the data to be analyzed, uses Monte Carlo simulation and generalized autoregressive conditional heteroskedasticity model and the second data to perform multi-scenario prediction on the debt risk of the object to be analyzed within a preset period in the future, and obtains a market risk analysis result; The liquidity risk analysis module selects third data for liquidity risk analysis from the data to be analyzed, uses a time series model and the third data to predict changes in cash flow of the object to be analyzed within a preset period in the future, and predicts the short-term debt repayment ability of the object to be analyzed based on preset liquidity indicators and the third data as a liquidity risk analysis result; The comprehensive risk scoring and early warning module integrates the credit risk analysis result, the market risk analysis result and the liquidity risk analysis result through a weighted algorithm to generate a comprehensive risk analysis result, and triggers an early warning mechanism when the comprehensive risk analysis result is greater than a preset threshold, so as to generate a risk notification feedback to the early warning client; The risk optimization and decision support module determines the optimization plan of the debt structure based on the comprehensive risk analysis results and combines the genetic algorithm or the particle swarm optimization algorithm, and generates strategic suggestions for adjusting the repayment plan or financing structure.

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