Enterprise financial risk intelligent analysis method and system based on big data

Abstract

The invention provides an enterprise financial risk intelligent analysis method and system based on big data, and relates to the technical field of enterprise financial risk analysis. The enterprise financial risk intelligent analysis system based on big data comprises a data collection layer, a data processing layer, a feature engineering layer, an intelligent analysis layer, a knowledge graph layer and an application display layer, the data collection layer is connected with the data processing layer, the data processing layer is connected with the feature engineering layer, the feature engineering layer is connected with the intelligent analysis layer, and the knowledge graph layer is connected with the application display layer. The intelligent analysis layer is connected with a knowledge graph layer, and the knowledge graph layer is connected with an application display layer. According to the technology, the financial risk of the enterprise can be identified and evaluated more accurately, a more intuitive and detailed analysis report can be provided, the enterprise is helped to make a more intelligent decision in a complex and changeable market environment, and the accuracy is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise financial risk analysis, and in particular to an enterprise financial risk intelligent analysis method and system based on big data. Background Art

[0002] Corporate financial risk analysis refers to the comprehensive analysis of the company's financial data, market environment, industry trends and other information to assess the financial risks that the company may face in the course of its operations. These risks include but are not limited to credit risk, liquidity risk, operational risk, market risk and compliance risk. The purpose of corporate financial risk analysis is to help companies better understand their own financial situation, identify potential risk points, and take appropriate measures to manage and control risks, thereby ensuring the healthy and stable development of the company. Financial risk analysis usually involves financial statement analysis, credit assessment, market trend analysis and other aspects, and provides comprehensive risk assessment reports and early warning information through quantitative and qualitative methods.

[0003] Existing corporate financial risk analysis technologies mainly rely on traditional statistical methods and machine learning models. First, data is cleaned and preprocessed by collecting multi-source data such as the company's financial statements, market data, and industry reports. Then, statistical methods (such as regression analysis and factor analysis) and machine learning models (such as decision trees, random forests, and support vector machines) are used to analyze and predict financial indicators. In addition, existing technologies also combine some business rules and expert experience to identify and evaluate financial risks by building a rule engine. In recent years, with the development of big data and artificial intelligence, some companies have also begun to use deep learning models (such as LSTM and GRU) for time series analysis to improve the accuracy of predictions. However, most existing technologies rely on single-modal data and lack the ability to comprehensively analyze multimodal data. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method and system for intelligent analysis of enterprise financial risks based on big data, which solves the problem that most of the prior art technologies rely on single-modal data and lack the ability to comprehensively analyze multi-modal data.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an enterprise financial risk intelligent analysis system based on big data, comprising a data collection layer, a data processing layer, a feature engineering layer, an intelligent analysis layer, a knowledge graph layer and an application display layer, wherein the data collection layer is connected to the data processing layer, the data processing layer is connected to the feature engineering layer, the feature engineering layer is connected to the intelligent analysis layer, the intelligent analysis layer is connected to the knowledge graph layer, and the knowledge graph layer is connected to the application display layer; The data collection layer, data processing layer, feature engineering layer, intelligent analysis layer, knowledge graph layer and application display layer transmit and process information through data flow and function calls.

[0006] Preferably, the data collection layer collects internal enterprise data, public market data, social media data and third-party credit evaluation data from multiple data sources through API interfaces, Web crawlers and data exchange protocols.

[0007] Preferably, the data processing layer uses Pandas, NumPy and Spark tools to clean, preprocess, integrate and standardize the collected data to ensure the quality and consistency of the data.

[0008] Preferably, the feature engineering layer extracts multimodal features from structured and unstructured data through natural language processing, image processing and time series analysis techniques to construct risk feature vectors.

[0009] Preferably, the intelligent analysis layer uses multimodal deep learning models and transfer learning technology to analyze feature data, identify and evaluate financial risks, and continuously optimize early warning strategies through dynamic feature generation and reinforcement learning technology.

[0010] Preferably, the knowledge graph layer constructs a knowledge graph between the enterprise and its related parties through entity recognition, relationship extraction, graph construction and graph reasoning technology, and performs risk propagation path prediction and community detection.

[0011] Preferably, the application presentation layer presents the analysis results to the user using natural language generation and visualization technology, and provides corresponding suggestions and solutions.

[0012] A method for intelligent analysis of enterprise financial risks based on big data, specifically comprising the following steps: S1. Data collection and preprocessing Collect data from multiple data sources through API interfaces, web crawlers, and data exchange protocols, and use Pandas, NumPy, and Spark tools to clean, preprocess, integrate, and standardize the collected data to ensure high quality and consistency; S2. Multimodal feature extraction and risk assessment Use natural language processing, image processing and time series analysis techniques to extract multimodal features from structured and unstructured data, construct risk feature vectors, and analyze feature data through multimodal deep learning models and transfer learning techniques to identify and assess financial risks; S3. Dynamic optimization and result display The early warning strategy is continuously optimized through dynamic feature generation and reinforcement learning technology, and the analysis results are presented to users using natural language generation and visualization technology, and corresponding suggestions and solutions are provided. At the same time, user feedback is collected to further optimize system performance.

[0013] The present invention provides a method and system for intelligent analysis of enterprise financial risks based on big data. It has the following beneficial effects: The present invention provides a method and system for intelligent analysis of enterprise financial risks based on big data. The present invention can provide a more comprehensive and accurate risk assessment by combining structured data and unstructured data, such as text, images, and sounds. The present invention adopts dynamic feature generation and reinforcement learning technology, and can dynamically adjust risk scores and early warning strategies according to real-time data streams and changes in market environment. The present invention uses an explainable machine learning model decision tree, rule learning, and natural language generation technology to make the decision-making process of the model transparent, so that users can better understand and trust the risk assessment results provided by the system. DETAILED DESCRIPTION

[0014] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Example

[0015] The embodiment of the present invention provides an enterprise financial risk intelligent analysis system based on big data, including a data collection layer, a data processing layer, a feature engineering layer, an intelligent analysis layer, a knowledge graph layer and an application display layer, wherein the data collection layer is connected to the data processing layer, the data processing layer is connected to the feature engineering layer, the feature engineering layer is connected to the intelligent analysis layer, the intelligent analysis layer is connected to the knowledge graph layer, and the knowledge graph layer is connected to the application display layer; Information is transmitted and processed between the data collection layer, data processing layer, feature engineering layer, intelligent analysis layer, knowledge graph layer and application display layer through data flow and function calls.

[0016] The data collection layer collects internal enterprise data, public market data, social media data, and third-party credit rating data from multiple data sources through API interfaces, web crawlers, and data exchange protocols. The data processing layer uses Pandas, NumPy, and Spark tools to clean, preprocess, integrate, and standardize the collected data to ensure data quality and consistency. The feature engineering layer extracts multimodal features from structured and unstructured data through natural language processing, image processing, and time series analysis techniques to construct risk feature vectors. The intelligent analysis layer uses multimodal deep learning models and transfer learning techniques to analyze feature data, identify and evaluate financial risks, and continuously optimizes early warning strategies through dynamic feature generation and reinforcement learning techniques. The knowledge graph layer constructs a knowledge graph between the enterprise and its related parties through entity recognition, relationship extraction, graph construction, and graph reasoning techniques to predict risk propagation paths and detect communities. The application presentation layer presents the analysis results to users using natural language generation and visualization techniques, and provides corresponding suggestions and solutions.

[0017] In the data collection layer, we collect the company's internal financial statements, audit reports, project management data, and public market data, industry reports, news reports, social media data, and third-party credit rating data. In this process, we use Python's Requests library to call API interfaces to obtain structured data from the company's internal systems and third-party service platforms. We use Scrapy or BeautifulSoup libraries to obtain text, image, and video unstructured data from public websites. We can also obtain data from partners through FTP and SFTP protocols.

[0018] Then use the drop_duplicates method of Pandas to remove duplicate records in the data. Use the fillna method of Pandas to fill missing values, which can be done by interpolation, mean, and median methods. Use the Isolation Forest and BoxPlot methods to detect and handle outliers.

[0019] Convert data from different sources to a consistent format, such as unifying all timestamps into ISO 8601 format. Use NumPy or Scikit-learn's StandardScaler to standardize numerical data to ensure that data with different features are at the same level. Use Pandas' merge method to merge data from different data sources. Ensure that the merged data is aligned on the key fields of time and company ID.

[0020] Use NLTK or spaCy for sentiment analysis to extract sentiment in text. Use spaCy for named entity recognition to extract key entities in text. Use the pre-trained BERT model for event extraction to obtain major events in financial reports and news reports. Use OpenCV for image preprocessing, such as scaling, cropping, and grayscale. Use convolutional neural networks (CNNs) to extract image features, such as using ResNet or Inception models. Use Librosa for audio preprocessing, such as sampling rate conversion and noise removal. Use MFCC (Mel-frequency cepstral coefficients) to extract audio features. Use Apache Kafka and Apache Flink to process real-time data streams and generate dynamic features, such as real-time stock price fluctuations and public opinion trends. Use ARIMA and LSTM time series analysis methods to generate trend features of historical data, such as revenue growth rate and market share changes.

[0021] Use spaCy or BERT for entity recognition to identify companies, competitors, and partners from the text. Extract the relationships between entities from the text, such as cooperative relationships, competitive relationships, and financial transactions, using SpaCy's dependency parsing or rule matching methods. Store the identified entities and relationships in a graph database (such as Neo4j) to build a dynamic knowledge graph. Use Neo4j's graph algorithms (such as PageRank and Degree Centrality) to calculate the centrality of a company in its ecological network. Use the Louvain algorithm for community detection to extract the attribute characteristics of the community, such as stability and diversity.

[0022] Through the multimodal fusion network, you can use Transformer as the basic model, combined with CNN and LSTM to process image and time series data. Introduce the attention mechanism in the fusion network, and use Self-Attention or Cross-Attention to improve the overall performance of the model.

[0023] Use the pre-trained BERT and ViT models to adapt to the specific task of corporate financial risk analysis through fine-tuning. According to the specific situation of the enterprise and the market environment, improve the flexibility and accuracy of the model by adaptively adjusting the model parameters. Define the financial situation of the enterprise and the market environment as the environment for reinforcement learning, and use OpenAI Gym or other frameworks to define the environment. Design a reasonable reward mechanism, such as using the accuracy of the risk score as part of the reward function. Through interaction with the environment, use DQN (Deep Q-Network) and PPO (Proximal Policy Optimization) algorithms to continuously optimize the early warning strategy and improve the performance of the system.

[0024] Import data into the graph database using Neo4j's Cypher language. Update the graph regularly to ensure the timeliness and accuracy of the data. Updates can be made using batch or stream processing. Use GNN (such as GraphSAGE, GCN) for path prediction to identify potential risk propagation paths. Assess the potential impact of risks through graph algorithms (such as propagation models). Use the Louvain algorithm or the Infomap algorithm for community detection. Extract attribute features from the detected communities, such as community stability and diversity. Example

[0025] The embodiment of the present invention provides a method for intelligent analysis of enterprise financial risks based on big data, which specifically includes the following steps: S1. Data collection and preprocessing Collect data from multiple data sources (including internal corporate data, public market data, social media data, and third-party credit rating data) through API interfaces, web crawlers, and data exchange protocols, and use Pandas, NumPy, and Spark tools to clean, preprocess, integrate, and standardize the collected data to ensure high quality and consistency; S2. Multimodal feature extraction and risk assessment Use natural language processing (NLP), image processing and time series analysis techniques to extract multimodal features from structured and unstructured data, construct risk feature vectors, and analyze feature data through multimodal deep learning models and transfer learning techniques to identify and assess financial risks; S3. Dynamic optimization and result display The early warning strategy is continuously optimized through dynamic feature generation and reinforcement learning technology, and the analysis results are presented to users using natural language generation and visualization technology, and corresponding suggestions and solutions are provided. At the same time, user feedback is collected to further optimize system performance.

[0026] The present invention can provide a more comprehensive and accurate risk assessment by combining structured data and unstructured data, such as text, images, and sounds. For example, using BERT for text analysis can extract information from financial reports and news reports, using CNN to extract image features of financial charts, and capturing visual information of data. By extracting emotional information from meeting records through audio processing technology, the attitude and decision of corporate management can be better understood. The present invention uses dynamic feature generation and reinforcement learning technology, and can dynamically adjust risk scores and early warning strategies according to real-time data streams and market environment changes. For example, dynamic features are generated by real-time data stream processing to ensure the timeliness and accuracy of early warnings; and early warning strategies are continuously optimized through reinforcement learning to improve the flexibility and adaptability of the system. The present invention uses interpretable machine learning model decision trees, rule learning, and natural language generation technology to make the decision process of the model transparent, so that users can better understand and trust the risk assessment results provided by the system. For example, by generating easy-to-understand decision rules, users can know which factors affect the risk score; through natural language generation technology, the system can automatically generate detailed reports to explain the prediction process and results of the model.

[0027] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise financial risk intelligent analysis system based on big data, including a data collection layer, a data processing layer, a feature engineering layer, an intelligent analysis layer, a knowledge graph layer and an application display layer, characterized in that: The data collection layer is connected to the data processing layer, the data processing layer is connected to the feature engineering layer, the feature engineering layer is connected to the intelligent analysis layer, the intelligent analysis layer is connected to the knowledge graph layer, and the knowledge graph layer is connected to the application display layer; The data collection layer, data processing layer, feature engineering layer, intelligent analysis layer, knowledge graph layer and application display layer transmit and process information through data flow and function calls.

2. According to claim 1, the enterprise financial risk intelligent analysis system based on big data is characterized by: The data collection layer collects internal enterprise data, public market data, social media data and third-party credit evaluation data from multiple data sources through API interfaces, Web crawlers and data exchange protocols.

3. The enterprise financial risk intelligent analysis system based on big data according to claim 1 is characterized by: The data processing layer uses Pandas, NumPy and Spark tools to clean, preprocess, integrate and standardize the collected data to ensure the quality and consistency of the data.

4. The enterprise financial risk intelligent analysis system based on big data according to claim 1 is characterized by: The feature engineering layer extracts multimodal features from structured and unstructured data through natural language processing, image processing and time series analysis techniques to construct risk feature vectors.

5. The enterprise financial risk intelligent analysis system based on big data according to claim 1 is characterized by: The intelligent analysis layer uses multimodal deep learning models and transfer learning technology to analyze feature data, identify and evaluate financial risks, and continuously optimizes early warning strategies through dynamic feature generation and reinforcement learning technology.

6. The enterprise financial risk intelligent analysis system based on big data according to claim 1 is characterized by: The knowledge graph layer constructs a knowledge graph between the enterprise and its related parties through entity recognition, relationship extraction, graph construction and graph reasoning technology, and performs risk propagation path prediction and community detection.

7. The enterprise financial risk intelligent analysis system based on big data according to claim 1 is characterized by: The application presentation layer presents the analysis results to users using natural language generation and visualization technology, and provides corresponding suggestions and solutions.

8. The method for intelligent analysis of enterprise financial risks based on big data according to any one of claims 1 to 7, characterized in that: The specific steps include: S1. Data collection and preprocessing Collect data from multiple data sources through API interfaces, web crawlers, and data exchange protocols, and use Pandas, NumPy, and Spark tools to clean, preprocess, integrate, and standardize the collected data to ensure high quality and consistency; S2. Multimodal feature extraction and risk assessment Use natural language processing, image processing and time series analysis techniques to extract multimodal features from structured and unstructured data, construct risk feature vectors, and analyze feature data through multimodal deep learning models and transfer learning techniques to identify and assess financial risks; S3. Dynamic optimization and result display The early warning strategy is continuously optimized through dynamic feature generation and reinforcement learning technology, and the analysis results are presented to users using natural language generation and visualization technology, and corresponding suggestions and solutions are provided. At the same time, user feedback is collected to further optimize system performance.