Intelligent enterprise finance and accounting data analysis system and method based on artificial intelligence

Abstract

The invention discloses an intelligent enterprise financial data analysis system and method based on artificial intelligence, and belongs to the technical field of enterprise informatization management and intelligent finance. The objective of the invention is to solve the technical problems of low processing efficiency, insufficient data insight, lagging risk identification, weak intelligent decision-making assistance capability and the like in the existing enterprise financial data analysis process. The system generally integrates a data automatic acquisition and integration module, a financial data deep analysis and modeling engine based on artificial intelligence, a key financial index and operation performance intelligent calculation module, a multi-dimensional financial risk intelligent identification and dynamic early warning module and an interactive visual analysis report and decision support module. The core of the method is that internal and external multi-source heterogeneous financial and operation data of an enterprise are automatically collected and fused, deep processing and intelligent analysis are performed on the data by applying artificial intelligence technologies such as machine learning and natural language processing, and accurate portrait of the financial condition of the enterprise, dynamic evaluation of operation performance and real-time monitoring and prediction of financial risks are realized. And analysis reports and optimization suggestions with insight are generated, so that enterprise managers can make efficient and scientific operation decisions. According to the method, the automation and intelligence level of enterprise financial data analysis can be remarkably improved, the financial information quality and decision support efficiency are improved, the risk prevention capability of enterprises is enhanced, and the enterprises are assisted to realize refined operation and value creation.

Description

Technical Field

[0001] This invention belongs to the field of enterprise information management and intelligent financial technology, specifically involving a system and its implementation method for intelligently collecting, deeply analyzing, monitoring compliance, and supporting decision-making of various financial data and related operational data of enterprises using artificial intelligence technology. Background Technology

[0002] With intensifying market competition and the rapid development of information technology, business operations are becoming increasingly complex, resulting in an explosive growth in the volume of financial data. The types of data are also becoming increasingly diverse, including structured transaction records and financial statements, as well as a large amount of unstructured and semi-structured data, such as contract texts, invoice images, audit reports, and market analyses. Traditional enterprise financial data processing and analysis models often rely on manual operation, spreadsheet tools, or relatively simple traditional financial software. For example, existing expense control systems have the following shortcomings: ① They rely on manual input of invoice information, resulting in insufficient OCR recognition accuracy; ② Compliance monitoring rules are rigid and cannot be dynamically adapted to policy changes through AI models. These limitations are gradually becoming apparent when dealing with the massive, multi-source, high-speed, and ever-changing financial data of modern enterprises.

[0003] On the other hand, the efficiency of data processing and integration between different software systems is low, and the phenomenon of information silos is serious: corporate financial data is often scattered across different business systems (such as ERP, CRM, SCM, procurement systems, expense control systems, etc.), with inconsistent data standards and definitions, resulting in time-consuming, labor-intensive, and error-prone data collection, cleaning, transformation, and integration. Information silos make it difficult for data to be effectively shared and analyzed collaboratively, leaving the finance department overwhelmed with basic data processing and unable to devote more energy to high-value analysis. For example, preparing a comprehensive consolidated financial statement or conducting cross-departmental cost-benefit analysis often requires several days or even weeks of manual data collection and verification.

[0004] Furthermore, the depth of data analysis is insufficient, limiting value extraction. Traditional financial analysis often focuses on ex-post descriptive statistics and simple comparative analysis, making it difficult to quickly and accurately identify deep-seated business models, financial risks, and potential growth opportunities from massive amounts of data. For example, there is often a lack of effective analytical tools and methods for assessing the rationality of expenses, accurately evaluating the return on investment of marketing activities, and identifying supply chain finance risks early on, resulting in insufficient data insights to support corporate decision-making.

[0005] Secondly, the ability to identify and manage financial risks lags behind. In a complex business environment, enterprises face increasingly diverse and concealed financial risks. Traditional risk management methods, which rely on manual audits and internal control processes, often suffer from problems such as untimely response, incomplete coverage, and difficulty in identifying new or complex risk patterns. According to relevant industry reports, losses caused by internal control deficiencies and financial fraud remain high for enterprises.

[0006] Secondly, the lack of intelligent decision support hinders the implementation of strategic goals. Enterprise managers urgently need forward-looking and intelligent financial data support when formulating business strategies, making investment decisions, and optimizing resource allocation. However, existing systems primarily provide historical data reports, lacking data-driven intelligent forecasting, scenario simulation, root cause analysis, and optimization suggestions, making it difficult to meet the needs of enterprises for rapid response and scientific decision-making in a dynamic market environment.

[0007] In recent years, the rapid development of artificial intelligence (AI) technologies, particularly machine learning, deep learning, natural language processing (NLP), and knowledge graphs, has provided new technological pathways for solving the aforementioned problems. Some companies have already begun exploring the application of AI technology in scenarios such as financial robots (RPA), intelligent expense reimbursement, and compliance monitoring, achieving some success. For example, OCR technology is used to identify invoice information, and rule engines are used for preliminary judgments on expense compliance. However, the market currently lacks a systematic end-to-end intelligent solution that deeply integrates artificial intelligence into the entire process of enterprise financial data analysis, achieving everything from intelligent integration of multi-source heterogeneous data, in-depth insights into complex financial problems, intelligent early warning of multi-dimensional risks, to interactive decision support. Summary of the Invention

[0008] To address the aforementioned technical issues, this invention provides an intelligent analysis system and method for enterprise financial data based on artificial intelligence. The system aims to significantly improve the automation and intelligence of enterprise financial data analysis, enhance the quality of financial information and the efficiency of decision support, strengthen enterprise risk prevention capabilities, and help enterprises achieve refined operations and value creation.

[0009] The objective of this invention is achieved through the following technical solution: an intelligent analysis system for enterprise financial data based on artificial intelligence, characterized in that it comprises:

[0010] The data acquisition and integration module is configured to automatically collect and integrate multi-source heterogeneous financial data and operational data from one or more internal business systems and designated external data sources, and to preprocess the collected data to form a standardized dataset to be analyzed.

[0011] An artificial intelligence analysis engine, connected to the data acquisition and integration module, is configured to load and execute at least one preset artificial intelligence analysis model to perform in-depth analysis on the standardized dataset to be analyzed.

[0012] The financial indicators and performance calculation module is connected to the artificial intelligence analysis engine and is configured to calculate key financial indicators and operating performance indicators of the enterprise in real time or periodically based on the results of the deep analysis.

[0013] The risk intelligent early warning and compliance monitoring module is connected to the artificial intelligence analysis engine and the financial indicator and performance calculation module. It is configured to automatically identify potential financial risks and compliance issues based on the risk characteristics or abnormal fluctuations of the key financial indicators identified by the deep analysis, combined with the preset corporate financial system rule library and industry risk model, and generate early warning information.

[0014] The interactive analysis and decision support module is connected to the artificial intelligence analysis engine, the financial indicator and performance calculation module, and the risk intelligent early warning and compliance monitoring module. It is configured to present analysis results, financial indicators, performance data, and early warning information in a visual manner, and provide interactive data extraction, intelligent query, and decision support suggestions.

[0015] Preferably, the data acquisition and integration module is further configured to connect with the enterprise resource planning system, customer relationship management system, supply chain management system, e-invoice platform, bank interface and other third-party data services to achieve comprehensive coverage and automated aggregation of financial data.

[0016] Preferably, the artificial intelligence analysis engine employs an artificial intelligence analysis model including any one or a combination of machine learning models, deep learning models, natural language processing models, and knowledge graph models, used to extract patterns, correlations, trends, or anomalies from the dataset to be analyzed.

[0017] Preferably, the in-depth analysis includes at least one or more of the following: intelligent assessment of financial condition, dynamic analysis of operating performance, intelligent identification of financial risks, or prediction of future financial trends.

[0018] Preferably, the risk intelligent early warning and compliance monitoring module is further configured to classify and process the early warning information according to the type, impact and probability of occurrence of risk events, and provide risk tracing analysis function to assist users in tracing the causes and transmission paths of risks. It also uses natural language processing technology to parse financial and tax policy documents in real time, extract key compliance clauses and update them to the enterprise's financial and accounting system rule base, so as to realize the dynamic adaptation of compliance rules.

[0019] Preferably, the interactive analysis and decision support module is further configured to receive natural language queries from users, convert the natural language queries into structured queries on the dataset to be analyzed through the artificial intelligence analysis engine, and generate corresponding analysis results.

[0020] Furthermore, using the aforementioned system, the intelligent analysis method for enterprise financial data includes the following steps:

[0021] (1) Automatically collect and integrate multi-source heterogeneous financial data and operational data from one or more business systems within the enterprise and designated external data sources, and preprocess the collected data to form a standardized dataset to be analyzed.

[0022] (2) Using an artificial intelligence analysis engine to load and execute at least one preset artificial intelligence analysis model to perform in-depth analysis on the standardized dataset to be analyzed. The in-depth analysis includes at least one or more of the following: intelligent assessment of financial status, dynamic analysis of operating performance, intelligent identification of financial risks, or prediction of future financial trends.

[0023] (3) Based on the results of the in-depth analysis, calculate the enterprise’s key financial indicators and operating performance indicators in real time or periodically.

[0024] (4) Based on the risk characteristics identified by the in-depth analysis or the abnormal fluctuations of the key financial indicators, combined with the preset corporate financial system rule library and industry risk model, potential financial risks and compliance issues are automatically identified and early warning information is generated.

[0025] (5) Present analysis results, financial indicators, performance data and early warning information in a visual manner, and provide interactive data extraction, intelligent query and decision support suggestions.

[0026] Furthermore, step (2) specifically includes the following steps:

[0027] (2.1) Apply machine learning algorithms, such as classification, regression or clustering algorithms, to identify specific patterns in financial data and predict the probability or value of future financial events.

[0028] (2.2) Apply natural language processing technology to automatically extract and analyze key financial information and risk points in unstructured data such as financial reports, contract texts, and audit records. Compare the parsing results of unstructured financial statements with the structured general ledger data in the enterprise resource planning system. If the difference exceeds 1%, trigger the manual review process.

[0029] Furthermore, the in-depth analysis includes: (1) intelligently evaluating the rationality and effectiveness of various expenses of the enterprise, and identifying potential cost waste or optimization space;

[0030] (2) Dynamically monitor and intelligently predict corporate cash flow. The cash flow prediction module models and analyzes the cash inflows / outflows of operating activities, investing activities and financing activities according to the classification standards of cash flow in the Accounting Standards for Business Enterprises; analyzes the efficiency of capital turnover and provides early warning of potential liquidity risks.

[0031] Furthermore, step (4) further includes: intelligently comparing real-time financial behavior data with enterprise internal control processes, tax regulations and industry compliance requirements to identify non-compliant operations; and using a risk evolution model based on historical data to predict the development trend and potential impact of specific risk events.

[0032] The beneficial effects of this invention are as follows: Through the above technical solution, this invention can realize the intelligent and automated analysis of enterprise financial data, significantly improve data processing efficiency and analysis depth, enhance the enterprise's ability to identify, warn and control financial risks, provide timely, accurate and comprehensive decision support information for enterprise management, thereby optimizing resource allocation, improving operating efficiency and helping enterprises maintain their advantages in fierce market competition. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the overall functional modules of an intelligent analysis system for enterprise financial data based on artificial intelligence, provided in an embodiment of the present invention.

[0034] Figure 2 This is a flowchart illustrating the intelligent analysis method for enterprise financial data based on artificial intelligence, as described in this embodiment of the invention.

[0035] Figure 3 This is a schematic diagram of the internal components of the artificial intelligence analysis engine in an embodiment of the present invention.

[0036] Figure 4 This is a schematic diagram illustrating the working logic of the risk intelligent early warning and compliance monitoring module in this embodiment of the invention.

[0037] Figure 5 This is a schematic diagram of the user interface of the interactive analysis and decision support module in an embodiment of the present invention. Detailed Implementation

[0038] Example 1: System Architecture and Core Module Function Introduction

[0039] Reference Figure 1 This embodiment discloses an intelligent enterprise financial data analysis system 10 based on artificial intelligence. System 10 is designed to help enterprises efficiently process and deeply analyze their financial and related operational data to improve management efficiency and decision-making quality. System 10 mainly consists of the following modules:

[0040] 1. Data Acquisition and Integration Module 101

[0041] This module 101 is the system's data entry point, responsible for automatically collecting financial data and auxiliary data related to business operations from various sources both inside and outside the enterprise.

[0042] (1) Internal Data Source Integration: Through API interfaces, direct database connections, ETL tools, or RPA robots, integrate with the enterprise's existing ERP systems (such as SAP, Oracle EBS, Yonyou, Kingdee, etc.), financial accounting software, CRM systems, SCM systems, MES systems, HRM systems, electronic invoice management platforms, expense control and reimbursement systems, etc., to obtain transaction records, general ledger details, account balances, customer orders, supplier information, inventory data, production costs, employee salaries, invoice images, and structured data. For example, newly added accounting vouchers and business documents in the ERP system can be automatically synchronized hourly or daily.

[0043] (2) External data source access: Configure the interface to obtain information from specified external data sources, such as bank account statements (through direct bank-enterprise connection or authorized query), tax system declaration data, capital market data (such as stock price, industry price-earnings ratio), macroeconomic indicators (such as GDP growth rate, CPI, PPI), industry average financial ratio, public credit information of suppliers and customers (such as business registration information, judicial litigation records), updates of laws, regulations and tax policies, etc.

[0044] (3) Data fusion and preprocessing: The collected multi-source heterogeneous data (including structured data, database tables, Excel files, CSV files, semi-structured data such as JSON and XML, and unstructured data such as PDF contracts, Word audit reports, scanned invoice images, etc.) first enter the preprocessing pipeline.

[0045] (4) Data cleaning: Automatically identify and process missing values ​​(e.g., for key numerical data, if the missing rate is less than 5%, the mean / median can be used to fill the missing values; if the missing rate is high, it will be marked and manual intervention will be requested), outliers (e.g., use box plots or the 3-sigma principle to identify and correct extreme outliers in transaction amounts; for transactions with a single amount exceeding 3 times the standard deviation of the historical mean and without a reasonable business explanation, the system will mark them as 'pending review' and push them to the manual review process), and duplicate records (deduplicate based on unique ID or key field combinations).

[0046] (5) Format Conversion and Standardization: Convert data from different data sources into a unified internal storage and analysis format. For example, all dates are standardized to ISO 8601 format, currency amounts are uniformly converted to RMB and the original currency and exchange rate are recorded, and accounting subjects are mapped and standardized according to the company's unified subject system. For invoice images, call OCR services (built-in or third-party) to extract key fields (such as invoice code, number, date, amount, tax, and information of the buyer and seller).

[0047] (6) Data integration and construction of a unified view: Based on business logic (such as order number, customer ID, supplier ID, project code, etc.), data from different systems are associated and aggregated to build a unified data view or data cube for the analysis topic, which is stored in a data warehouse or data lake to provide a high-quality, consistent standard dataset for subsequent intelligent analysis.

[0048] 2. Artificial Intelligence Analysis Engine 102

[0049] This module 102 is the system's "brain," responsible for executing core intelligent analysis tasks. (See reference...) Figure 3 Its internal components may include:

[0050] (1) Model Library 301: A series of artificial intelligence models optimized for enterprise financial analysis scenarios are pre-installed or loaded on demand.

[0051] Machine learning models:

[0052] Predictive models: such as using time series models like ARIMA, SARIMA, Prophet, and LSTM to predict future trends in sales revenue, profit, cash flow, and major cost items, and using regression models (linear regression, logistic regression, and support vector machine regression) to analyze the impact of key drivers on financial indicators.

[0053] Classification models: such as decision trees, random forests, and gradient boosting machines (e.g., XGBoost, LightGBM) are used to classify customers for credit rating, identify fraud risk categories in transactions, and automatically categorize expenses.

[0054] Clustering models: such as K-Means and hierarchical clustering, can be used to classify customer consumption behavior, supplier performance characteristics, and product profitability to discover hidden group patterns.

[0055] Natural Language Processing (NLP) models: such as pre-trained language models like BERT and ERNIE, combined with domain-specific fine-tuning, are used for:

[0056] Information extraction: Automatically extract key clauses, financial commitments, risk descriptions, related party relationships, and public opinion information from financial statement notes, contracts, legal documents, news and public opinion reports, and audit working papers.

[0057] Intelligent question answering and querying: It understands users' natural language questions and retrieves and generates answers from financial databases.

[0058] Knowledge Graph Engine: Constructs a financial knowledge graph containing entities such as enterprises, departments, personnel, accounts, products, suppliers, customers, contracts, regulations, and financial accounts, as well as their interrelationships. Based on the knowledge graph, it performs correlation analysis (e.g., discovering hidden related-party transactions), path mining (e.g., tracing financial chains), and complex reasoning (e.g., determining whether a transaction simultaneously violates multiple internal regulations).

[0059] (2) Analysis Task Scheduler 302: Receives analysis requests from other modules, selects appropriate models or model combinations according to the request type and data characteristics, and allocates computing resources to perform analysis.

[0060] (3) Feature Engineering Component 303: Based on the characteristics of financial analysis, automatically or semi-automatically generate more effective derived features from the original data, such as financial ratios, growth rates, volatility, and behavioral statistics within a specific event window.

[0061] (4) Model Management and Iteration Platform 304: Supports version control, performance monitoring, automatic retraining and deployment updates of models in the model library to ensure continuous optimization of model performance.

[0062] Specific applications of intelligent analysis include, but are not limited to:

[0063] Intelligent financial status assessment: By comprehensively utilizing financial ratio analysis, DuPont analysis system, etc., and combining AI models to dynamically interpret and judge the trends of various indicators, it generates financial health scores and diagnostic reports for the whole enterprise and each business unit.

[0064] Dynamic analysis of operating performance: Real-time tracking of core operating indicators such as sales revenue, costs and expenses, profits, and cash flow; multi-dimensional comparison with budget targets, historical data, and industry benchmarks; analysis of the causes of performance fluctuations using AI models (such as changes in sales volume, price adjustments, cost control, market factors, etc.); and prediction of future performance.

[0065] Intelligent financial risk identification: (Intelligent risk early warning module)

[0066] Future Financial Trend Forecasting: Based on historical data and external environmental variables (such as macroeconomic forecasts, industry development trends, policy change expectations, etc.), AI prediction models are used to make rolling forecasts of key financial statement items for enterprises over the next 1-5 years, supporting strategic planning and long-term resource allocation.

[0067] Intelligent Invoice and Expense Analysis: Utilizing OCR and NLP technologies, this system automatically identifies various invoice information, performing authenticity checks, duplicate verification, and intelligent matching with orders and contracts. It conducts compliance checks on expense reimbursement data (including automatic comparison with the "five-step method" requirements for revenue recognition under the Enterprise Accounting Standards and the normative provisions of the Tax Collection and Administration Law regarding invoice management, such as whether travel standards are met and whether entertainment expenses exceed limits), reasonableness analysis (e.g., whether expenses match business activities), and trend and structure analysis to identify cost waste and fraud risks, and provides expense optimization suggestions.

[0068] Supply chain finance and customer credit analysis: Integrating upstream and downstream data of the supply chain, analyzing the accounts receivable and payable turnover of core enterprises, the financial stability and performance capabilities of suppliers, and the payment behavior and credit status of customers, and using AI models to predict bad debt risk and supply chain disruption risk.

[0069] 3. Financial Indicators and Performance Calculation Module 103

[0070] Based on the output of the artificial intelligence analysis engine 102 and the standardized financial dataset, this module 103 automatically calculates various key financial indicators (KFIs) and key performance indicators (KPIs) required for enterprise operation.

[0071] (1) Indicator Library Management: The system has a built-in library of standard indicators covering solvency, profitability, operational efficiency, development capability, cash flow health, cost control, budget execution, and risk level. At the same time, it supports enterprises in customizing new indicators, their calculation formulas, data sources, and dimensions based on their industry characteristics, strategic goals, and management sophistication requirements.

[0072] (2) Real-time / periodic calculation: Indicator calculation can be configured to be triggered in real time (such as updating relevant indicators immediately after a transaction occurs) or calculated in batches according to a predetermined period (such as daily, weekly, monthly).

[0073] (3) Multi-dimensional decomposition: Supports the decomposition of total KFI / KPI into different dimensions (such as department, product line, regional market, customer group, time granularity, etc.) to enable more detailed performance tracking and accountability assessment.

[0074] 4. Risk Intelligent Early Warning and Compliance Monitoring Module 104

[0075] Module 104 represents a proactive defense against corporate financial risks. (Refer to...) Figure 4 Its main workflow is as follows:

[0076] (1) Risk Identification and Modeling 401: This combines the output of the artificial intelligence analysis engine 102 (such as abnormal transaction detection results, fraud scoring, and high-risk pattern recognition) with the output of the financial indicators and performance calculation module 103 (such as key indicators exceeding warning thresholds). Simultaneously, the module maintains an extensible financial risk knowledge base and rule base, including:

[0077] (2) Compliance rules: hard rules defined by national tax and financial regulations, corporate accounting standards, industry regulatory requirements, and company internal control systems (such as authorization and approval authority, procurement process, and expense reimbursement standards).

[0078] (3) Risk Characteristic Model: By studying historical financial fraud cases, audit findings, industry risk reports, etc., the typical characteristics and leading indicators of various risk events (such as false income, misappropriation of assets, off-balance-sheet financing, and related party benefit transfer) are summarized.

[0079] (4) Industry risk benchmark: Refer to data such as the risk occurrence rate and loss extent of companies in the same industry.

[0080] (5) Real-time monitoring and triggering 402: Continuously monitor the data flow and analysis results entering the system. Once financial behavior that meets the preset risk rules is found, or the AI ​​model identifies a high-risk pattern, or the key risk indicators reach the triggering conditions, the early warning process will be initiated.

[0081] (6) Risk assessment and classification 403: For identified potential risk events, the system automatically assesses their probability and impact, and classifies them into different risk levels (e.g., low, medium, high, urgent) based on a preset risk matrix.

[0082] (7) Early Warning Information Generation and Push 404: For different levels of risk, generate early warning notifications containing information such as risk description, occurrence time, amount involved, related parties, preliminary evidence, risk level, and recommended handling priority. Early warning information is pushed to pre-defined responsible persons (such as business department managers, financial supervisors, internal audit departments, risk management committees, etc.) through various channels such as system dashboards, in-site messages, emails, SMS, DingTalk / WeChat Work.

[0083] (8) Compliance check: The module can also automatically scan the company’s various financial operations (such as accounting, report preparation, tax declaration, contract execution, etc.) for compliance, find any inconsistencies with the latest laws, regulations or internal systems, and prompt rectification.

[0084] The risk intelligent early warning and compliance monitoring module 104 is further configured for:

[0085] By using a built-in Natural Language Processing (NLP) model, it can capture policy interpretation documents (such as revisions to tax and financial regulations, new regulations on invoice management, etc.) released in real time from official channels such as the State Taxation Administration website and announcements from the Ministry of Finance.

[0086] Automatically parse policy texts, extract key compliance clauses (such as tax rate adjustments, filing deadlines, expense deduction standards, etc.), and generate structured rules;

[0087] The updated rules will be synchronized to the system's built-in accounting and financial rules library to ensure that the compliance monitoring logic is adapted to the latest policy dynamics.

[0088] 5. Interactive Analysis and Decision Support Module 105

[0089] This module 105 serves as the system's user interface and an intelligent decision-making platform, referencing... Figure 5 Its main functions are as follows:

[0090] (1) Visual Dashboard 501: Provides customized financial data dashboards for managers at different levels (such as CEO, CFO, business line director, and finance manager). Through charts (such as KPI dashboards, trend line charts, pie charts, comparison bar charts, related bubble charts, and geographical distribution maps), it intuitively displays core information such as the company's overall financial status, key operating performance, budget execution progress, and major risk points. The dashboard data is updated in real time.

[0091] (2) Self-service Analysis and Reporting 502: Provides flexible report generation tools. Users can select analysis dimensions, indicators, and time ranges as needed, and quickly generate personalized analysis reports through simple drag-and-drop and point-and-click operations. Supports multi-dimensional data extraction, allowing users to delve deeper from summary data to detailed transactions to explore the root causes of problems.

[0092] (3) Intelligent Query and Question Answering 503: Integrates Natural Language Processing (NLP) capabilities, allowing users to request financial data queries and analyses by inputting natural language text or voice (e.g., "Compared to the same period last year, how much did R&D expenses increase this quarter? What are the main reasons?"). After understanding the user's intent, the system automatically performs data retrieval, analysis, and calculation, and returns the results in natural language or chart form.

[0093] (4) Scenario Simulation and Predictive Analysis 504: Allows users to adjust key business parameters (such as sales volume, product price, raw material cost, exchange rate, interest rate, etc.). The system can simulate the impact of different scenarios on the company's future financial situation (such as profit and cash flow) in real time based on AI prediction models, assisting managers in making forward-looking decisions and risk assessments.

[0094] (5) Decision Recommendations and Action Plans 505: Based on the in-depth analysis results of the artificial intelligence analysis engine 102 and the understanding of the company's strategic goals, the system can proactively generate feasible optimization suggestions, improvement measures, or alternative action plans for specific problems (such as cost overruns, tight cash flow, and unmet return on investment), and explain their potential effects and risks for managers' reference. For example, if it is identified that the profit margin of a certain product line is declining continuously, the system may suggest adjusting the pricing strategy, optimizing the cost structure, or considering exiting the market.

[0095] (6) Knowledge sharing and collaboration: Supports users to comment, annotate and share analysis results, reports and dashboards to promote knowledge sharing and collaborative decision-making within the team.

[0096] Example 2: Intelligent Analysis Method and Process for Enterprise Financial Data Based on Artificial Intelligence

[0097] Reference Figure 2 The method flow provided in this embodiment of the invention mainly includes the following steps:

[0098] Step S201: Multi-source heterogeneous data acquisition and standardization preprocessing

[0099] After system startup, the first step is to execute a data collection task. Through configured data interfaces or synchronous jobs, it automatically collects structured, semi-structured, and unstructured financial and operational data from internal enterprise systems such as ERP, CRM, SCM, financial software, expense control systems, and HR systems, as well as external platforms such as banks, tax interfaces, industry databases, and market information platforms. The collection frequency can be set according to the update characteristics of the data source (e.g., real-time, hourly, daily).

[0100] The collected raw data enters the preprocessing stage, including:

[0101] (1) Data cleaning: Remove obviously erroneous data, handle missing values ​​(e.g., mark missing values ​​of non-critical fields as "unknown", and fill a small number of missing values ​​of critical numerical fields with mean, median or regression prediction), identify and smooth or remove outliers (e.g., the amount of a single transaction far exceeds the historical maximum and there is no reasonable explanation).

[0102] (2) Data Conversion and Integration: Data in different formats (such as Excel, CSV, XML, JSON, PDF, and text in images) are uniformly converted into standard data structures within the system (such as relational tables or NoSQL documents). OCR technology is used to extract structured information from scanned invoices, contracts, and other image documents. Through the Master Data Management (MDM) mechanism, key entities (such as customers, suppliers, products, accounting subjects, and cost centers) are coded uniformly and information is matched to eliminate data redundancy and inconsistency, achieving effective integration of cross-system data and forming a unified, high-quality dataset for analysis.

[0103] Step S202: AI-driven deep analysis and modeling

[0104] The standardized dataset to be analyzed is fed into the artificial intelligence analysis engine 102. Based on the preset analysis task or the user's immediate request, the engine invokes the corresponding AI model for deep analysis.

[0105] (1) Descriptive and diagnostic analysis: Utilize statistical methods and machine learning models (such as clustering and association rule mining) to conduct multi-dimensional analysis of existing financial data and answer "What happened?" (such as changes in cost structure and profit source composition) and "Why did it happen?" (such as whether the decline in the profit of a certain product is due to the increase in raw material costs or the decrease in sales).

[0106] (2) Predictive analysis: Using time series models (such as ARIMA, Prophet, LSTM), regression models, etc., based on historical data and influencing factors, predict the trends and specific values ​​of future financial indicators (such as revenue, cost, profit, cash flow). For example, predict cash inflows and outflows for the next three months and assess the risk of a funding gap.

[0107] (3) Prescriptive Analysis: Combining operations research algorithms or reinforcement learning models, based on predictive analysis, it provides optimal or suboptimal solutions for specific financial decision-making problems (such as budget allocation optimization, portfolio selection, and cost control strategy formulation). For example, how to adjust departmental budgets to maximize overall company profits while meeting various business constraints.

[0108] (4) Specialized analysis: such as intelligent auditing (automatically discovering accounting anomalies and potential fraud clues), intelligent taxation (assisting in tax planning and identifying tax risks), intelligent investment and financing (assessing project feasibility and optimizing capital structure), etc.

[0109] Step S203: Real-time calculation of key financial indicators and operating performance

[0110] Based on the intermediate results and basic data generated from the in-depth analysis in step S202, the financial indicators and performance calculation module 103 calculates various KFIs and KPIs that the enterprise is concerned about in real time or on demand. For example, when the AI ​​engine analyzes that there is significant waste in a certain cost, it will automatically update the relevant cost control efficiency indicators.

[0111] Step S204: Intelligent Identification and Dynamic Early Warning of Financial Risks

[0112] The risk intelligent early warning and compliance monitoring module 104 continues to operate:

[0113] (1) The abnormal patterns identified by the AI ​​analysis engine 102 (such as abnormal transaction behavior, characteristics of high-risk suppliers, unfavorable terms in contracts, etc.) and the indicators exceeding the threshold calculated by the financial indicators and performance calculation module 103 (such as low current ratio, excessive budget execution deviation) are compared with the built-in risk rule library and compliance requirements.

[0114] (2) Use risk assessment models (which can be scorecards based on expert experience or risk prediction models trained by machine learning) to quantitatively assess the identified potential risk events (such as calculating risk scores and predicting loss probabilities).

[0115] (3) When the risk score exceeds the preset threshold or a high-priority compliance rule is triggered, the system automatically generates an early warning message and sends an alarm to the relevant person in charge through the interactive analysis and decision support module 105.

[0116] (4) Dynamic updates of policies and rules: The system regularly scans authoritative information sources such as the official website of the State Taxation Administration and industry supervision platforms through NLP models to automatically capture the latest financial and tax policy documents (such as the "Regulations on the Use of Electronic Special Invoices for Value-Added Tax").

[0117] By using semantic analysis and keyword extraction technologies, core compliance requirements in policies (such as invoice issuance time limits and rules for reversing red-letter invoices) are identified and transformed into executable monitoring rules, which are then updated to the compliance rule library in real time.

[0118] Step S205: Interactive Visualization and Intelligent Decision Support

[0119] All analysis results, calculated indicators, and compliance monitoring information are ultimately aggregated into the interactive analysis and decision support module 105.

[0120] (1) Present the data to users in a user-friendly visual format (such as charts, dashboards, and dynamic reports). Users can view, filter, and extract data according to their permissions and needs.

[0121] (2) Users can ask questions in natural language or make selections on the interface to initiate new analysis requests or explore existing results in depth.

[0122] (3) The system can proactively push relevant decision-making suggestions or action plans to users based on the analysis results. For example, when it is identified that the risk-reward ratio of an investment is not good, the system can suggest re-evaluating the investment or considering alternatives, and provide reasons and data support.

[0123] By implementing the above systems and methods, enterprises can establish an efficient and intelligent financial data analysis system, which can not only greatly improve the efficiency and accuracy of daily financial work, but more importantly, shift from reactive to proactive forecasting and management, extract insights from data, drive scientific decision-making, and thus continuously enhance competitiveness in a complex and ever-changing business environment.

Claims

1. An intelligent analysis system for enterprise financial data based on artificial intelligence, characterized in that, include: The data acquisition and integration module is configured to automatically collect and integrate multi-source heterogeneous financial data and operational data from one or more internal business systems and designated external data sources, and to preprocess the collected data to form a standardized dataset to be analyzed. An artificial intelligence analysis engine, connected to the data acquisition and integration module, is configured to load and execute at least one preset artificial intelligence analysis model to perform in-depth analysis on the standardized dataset to be analyzed. The financial indicators and performance calculation module is connected to the artificial intelligence analysis engine and is configured to calculate key financial indicators and operating performance indicators of the enterprise in real time or periodically based on the results of the deep analysis. The risk intelligent early warning and compliance monitoring module is connected to the artificial intelligence analysis engine and the financial indicator and performance calculation module. It is configured to automatically identify potential financial risks and compliance issues based on the risk characteristics or abnormal fluctuations of the key financial indicators identified by the deep analysis, combined with the preset corporate financial system rule library and industry risk model, and generate early warning information. The interactive analysis and decision support module is connected to the artificial intelligence analysis engine, the financial indicator and performance calculation module, and the risk intelligent early warning and compliance monitoring module. It is configured to present analysis results, financial indicators, performance data, and early warning information in a visual manner, and provide interactive data extraction, intelligent query, and decision support suggestions.

2. The intelligent analysis system for enterprise financial data based on artificial intelligence according to claim 1, characterized in that, The data acquisition and integration module is further configured to connect with the enterprise resource planning system, customer relationship management system, supply chain management system, e-invoice platform, bank interface and other third-party data services to achieve comprehensive coverage and automated aggregation of financial data.

3. The intelligent analysis system for enterprise financial data based on artificial intelligence according to claim 1, characterized in that, The artificial intelligence analysis engine employs an artificial intelligence analysis model that includes any one or a combination of machine learning models, deep learning models, natural language processing models, and knowledge graph models, used to extract patterns, correlations, trends, or anomalies from the dataset to be analyzed.

4. The intelligent analysis system for enterprise financial data based on artificial intelligence according to claim 1 or 3, characterized in that, The in-depth analysis includes at least one or more of the following: intelligent assessment of financial condition, dynamic analysis of operating performance, intelligent identification of financial risks, or prediction of future financial trends.

5. The intelligent analysis system for enterprise financial data based on artificial intelligence according to claim 1, characterized in that, The risk intelligent early warning and compliance monitoring module is further configured to classify and process the early warning information according to the type, impact and probability of the risk event, and provide risk tracing analysis function to assist users in tracing the causes and transmission paths of risks. It also uses natural language processing technology to parse financial and tax policy documents in real time, extract key compliance clauses and update them to the enterprise financial and accounting system rule base, so as to realize the dynamic adaptation of compliance rules.

6. The intelligent analysis system for enterprise financial data based on artificial intelligence according to claim 1, characterized in that, The interactive analysis and decision support module is further configured to receive natural language queries from users, and convert the natural language queries into structured queries on the dataset to be analyzed through the artificial intelligence analysis engine, thereby generating corresponding analysis results.

7. A method for intelligent analysis of enterprise financial data using the system described in claim 1, characterized in that, Includes the following steps: (1) Automatically collect and integrate multi-source heterogeneous financial data and operational data from one or more business systems within the enterprise and designated external data sources, and preprocess the collected data to form a standardized dataset to be analyzed. (2) Using an artificial intelligence analysis engine to load and execute at least one preset artificial intelligence analysis model to perform in-depth analysis on the standardized dataset to be analyzed. The in-depth analysis includes at least one or more of the following: intelligent assessment of financial status, dynamic analysis of operating performance, intelligent identification of financial risks, or prediction of future financial trends. (3) Based on the results of the in-depth analysis, calculate the enterprise’s key financial indicators and operating performance indicators in real time or periodically. (4) Based on the risk characteristics identified by the in-depth analysis or the abnormal fluctuations of the key financial indicators, combined with the preset corporate financial system rule library and industry risk model, potential financial risks and compliance issues are automatically identified and early warning information is generated. (5) Present analysis results, financial indicators, performance data and early warning information in a visual manner, and provide interactive data extraction, intelligent query and decision support suggestions.

8. The method according to claim 7, characterized in that, Step (2) specifically includes the following steps: (2.1) Apply machine learning algorithms, such as classification, regression or clustering algorithms, to identify specific patterns in financial data and predict the probability or value of future financial events. (2.2) Apply natural language processing technology to automatically extract and analyze key financial information and risk points in unstructured data such as financial reports, contract texts, and audit records. Compare the parsing results of unstructured financial statements with the structured general ledger data in the enterprise resource planning system. If the difference exceeds 1%, trigger the manual review process.

9. The method according to claim 7 or 8, characterized in that, The depth analysis further includes: (1) Intelligently evaluate the rationality and effectiveness of various expenses of enterprises, and identify potential cost waste or optimization space; (2) Dynamically monitor and intelligently predict corporate cash flow. The cash flow prediction module models and analyzes the cash inflows / outflows of operating activities, investing activities and financing activities according to the classification standards of cash flow in the Accounting Standards for Business Enterprises; analyzes the efficiency of capital turnover and provides early warning of potential liquidity risks.

10. The method according to claim 7, characterized in that, Step (4) further includes: intelligently comparing real-time financial behavior data with enterprise internal control processes, tax regulations and industry compliance requirements to identify non-compliant operations; and using a risk evolution model based on historical data to predict the development trend and potential impact of specific risk events.

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