CN117575825A - Financial centralized management system for industry and financial fusion - Google Patents

Abstract

The invention discloses a financial centralized management system for industry and financial integration, which comprises a financial core module, a cost accounting module, a human resource and compensation module, a payment and settlement module, a risk prediction and management module, a financial compliance and audit module, a multi-level authority control and data security module, a supply chain financial module, a real-time data integration and overview module, a fixed asset management module, a financial analysis and reporting module, an intelligent prediction and planning module and a high-level artificial intelligence and machine learning module, wherein the real-time data integration and overview module enables enterprises to obtain real-time business data and make decisions rapidly; the real-time performance is beneficial to enterprises to rapidly cope with market change and adjust strategies, and flexibility and strain capacity are improved; meanwhile, the overview module can provide an overall enterprise condition overview for the management layer and support higher-level strategic planning and decision-making.

Description

Financial centralized management system for industry and financial fusion

Technical Field

The invention relates to the technical field of machinery, in particular to a financial centralized management system for industry and financial integration.

Background

In many institutions and enterprises, common management modes are decentralized, namely, each branch institution or subordinate institution is provided with an independent financial department, so that financial work presents the characteristics of data decentralization, numerous personnel and complex management of post accounting, and the decentralized financial management mode leads to poor synchronism of financial data and business data, poor systematicness and obvious information island risk. To generate the overall financial analysis report, data must be collected from individual accounting unit hands and then a cumbersome aggregation effort is performed. Under such circumstances, the data resource sharing performance is poor, so that the timeliness of the financial analysis is poor, the accuracy of the financial report is affected, the decision support for the units is insufficient, the overall working efficiency is low, the labor cost is high, and the high-school and younger personnel do not fully exert the advantages of the system due to the fact that tedious accounting work is performed. Under the condition, more advanced technical means are needed to be adopted, the information island is broken, the data synchronism and the systematicness are improved, and the centralized management and sharing of the financial data are realized, so that the overall management efficiency and the decision support level are improved. An advanced financial centralized management system needs to be introduced, a data integration and sharing mechanism is enhanced, and automation and optimization of financial flows are realized by using technical means so as to reduce redundancy of manual work.

Disclosure of Invention

The invention aims to provide a financial centralized management system for industry and finance integration, which aims to solve the problems in the background technology.

In order to achieve the above purpose, the present invention provides the following technical solutions: a financial centralized management system for industry and financial fusion comprises a financial core module, a cost accounting module, a human resource and compensation module, a payment and settlement module, a risk prediction and management module, a financial compliance and audit module, a multi-level authority control and data security module, a supply chain financial module, a real-time data integration and overview module, a fixed asset management module, a financial analysis and reporting module, an intelligent prediction and planning module and a high-level artificial intelligence and machine learning module;

the financial core module is used for managing the general ledger accounts of enterprises and managing the business accounts with clients and suppliers;

the cost accounting module is used for tracking and analyzing the cost structure of the enterprise and supporting real-time monitoring and management of the cost;

the human resources and payroll module is used for managing staff payroll, welfare and related human resources matters;

the payment and settlement module is used for managing the payment flow of enterprises and ensuring the accuracy and timeliness of financial transactions;

a risk prediction and management module for providing analysis tools and identifying potential financial risks;

the financial compliance and audit module is used for providing financial reports and carrying out deep analysis on key financial indexes;

the multi-level authority control and data security module is used for providing multi-level authority control and enhancing data security;

the supply chain financial module is used for managing the purchasing process and the supply chain financing and ensuring the smooth operation of the supply chain;

the real-time data integration and overview module is used for integrating the real-time data of each module into a centralized overview page, so that the overall business and financial conditions can be conveniently known;

the fixed asset management module is used for managing fixed assets of an enterprise;

the financial analysis and reporting module is used for carrying out comprehensive performance analysis and further analysis on key financial indexes by combining business data;

the intelligent prediction and planning module is used for predicting future financial trends and assisting enterprises in making strategic plans;

the advanced artificial intelligence and machine learning module is used for providing intelligent decision support based on artificial intelligence technology, and has the training and optimizing functions of a machine learning model so as to continuously improve the prediction accuracy of the system.

Preferably, the fixed asset management module and the cost accounting module are both in bidirectional connection with the financial core module, and the supply chain financial module, the payment and settlement module, the human resource and compensation module and the tax management module are all in bidirectional connection with the financial core module.

Preferably, the intelligent prediction and planning module is in bidirectional connection with the financial analysis and reporting module, the advanced artificial intelligence and machine learning module is in bidirectional connection with the intelligent prediction and planning module, the financial core module and the cost accounting module are in bidirectional connection with the risk prediction and management module, the payment and settlement module, the human resource and compensation module, the risk prediction and management module and the supply chain financial module are in bidirectional connection with the cost accounting module, and the payment and settlement module is in bidirectional connection with the supply chain financial module.

Preferably, the financial risk includes market risk, credit risk, liquidity risk, operational risk, legal and compliance risk, strategic risk, aggregate risk, and financial counterfeiting risk.

Preferably, the market risk includes stock market, risk exchange rate risk, interest rate risk; the liquidity risks include liquidity deficiency, which is a risk caused by enterprises failing to meet short-term debt or business needs, and market liquidity risks, which is a difficulty in trading due to changes in market conditions.

Preferably, the intelligent prediction and planning module comprises a data collection and integration unit, a time sequence analysis unit, a machine learning prediction model unit, a risk influence analysis unit, a business scene simulation unit, an optimization and strategy planning unit, a real-time monitoring and adjustment unit and a reporting and visualization unit;

the data collection and integration unit is used for acquiring related data from the financial analysis module and the risk prediction module and possibly integrating other key data sources;

the time sequence analysis unit is used for carrying out trend analysis on the historical data and identifying periodic changes, seasonal changes and long-term modes of the trend;

the machine learning prediction model unit is used for establishing a prediction model based on a machine learning algorithm, and training by utilizing historical data so as to predict possible future trend and change;

the risk influence analysis unit is used for analyzing the influence of various potential risks on future financial conditions by combining the data of the risk prediction module;

the business scene simulation unit is used for carrying out business scene simulation based on different assumptions and scenes and evaluating the influence of different decisions on the future;

the optimizing and strategy planning unit is used for utilizing an optimizing algorithm and a decision support system to formulate an optimal strategy plan according to the predicted trend and the risk analysis result;

the real-time monitoring and adjusting unit is used for monitoring financial performance and actual conditions in real time according to external environment and internal changes, adjusting a prediction model and a planning scheme according to requirements and ensuring continuous effectiveness of the prediction model and the planning scheme;

the report and visualization unit is used for visually presenting the prediction results, the planning scheme and the related financial analysis and risk prediction data to a decision maker.

Preferably, the risk prediction and management module comprises a risk identification and classification unit, a risk measurement and evaluation unit, a risk monitoring and reporting unit, a risk coping strategy unit, a risk tracing and influence analysis unit, a risk communication and training unit, a technical risk evaluation and management unit and a legal compliance risk management unit.

Preferably, the risk identification and classification unit is in bidirectional connection with the risk measurement and evaluation unit, the risk measurement and evaluation unit is in bidirectional connection with the risk monitoring and reporting unit, the risk monitoring and reporting unit is in bidirectional connection with the risk coping strategy unit, the risk coping strategy unit is in bidirectional connection with the risk tracing and influence analysis unit, the risk tracing and influence analysis unit is in bidirectional connection with the risk communication and training unit, the risk communication and training unit is in bidirectional connection with the technical risk evaluation and management unit, and the legal compliance risk management unit is in bidirectional connection with the technical risk evaluation and management unit;

the risk identification and classification unit is used for identifying potential risks and classifying the potential risks;

a risk measurement and evaluation unit for quantifying and evaluating the probability and influence degree of various risks;

the risk monitoring and reporting unit is used for monitoring the risk condition in real time and periodically generating and distributing a risk report;

the risk coping strategy unit is used for making strategies and plans for coping with various risks;

the risk tracing and influence analysis unit is used for tracing and analyzing risks and knowing the reasons and possible influences of occurrence of risk events;

the risk communication and training unit is used for communicating risk related information with each level of management layers and staff, so that the awareness and understanding of the organization on risk management are improved;

the technical risk assessment and management unit is used for carrying out special assessment and management aiming at risks in the aspects of information technology, data security and the like;

and the legal compliance risk management unit is used for ensuring that the risks of organizations in legal and compliance aspects are effectively managed.

Compared with the prior art, the invention has the beneficial effects that:

comprehensively and integrally: the system comprises modules covering various aspects of the enterprise, including financial cores, cost accounting, human resources and supply chains, so that the enterprise can complete various services on an integrated platform; the comprehensiveness and the integration are beneficial to eliminating information islands and improving the consistency and the accuracy of data, so that enterprises can know the whole business condition more comprehensively and accurately;

advantages of the real-time data integration and overview module: the real-time data integration and overview module enables enterprises to obtain real-time business data and make decisions rapidly; the real-time performance is beneficial to enterprises to rapidly cope with market change and adjust strategies, and flexibility and strain capacity are improved; meanwhile, the overview module can provide an overall enterprise condition overview for the management layer and support higher-level strategic planning and decision-making;

advantages of the risk prediction and management module: the risk prediction and management module provides the enterprise with the capability of predicting and effectively managing the risk which may occur in the future; by utilizing the technical means of data analysis and model establishment, the system can help enterprises identify potential risks and provide corresponding coping strategies; this helps the enterprise to be more predictive in the face of market uncertainty, reducing potential losses;

advantages of the intelligent prediction and planning module: the intelligent prediction and planning module can more accurately predict future market trend and demand change by applying artificial intelligence and machine learning technology; the method is beneficial to enterprises to plan production, inventory and purchasing business activities more effectively, improves the utilization efficiency of resources, reduces the inventory cost and meets the requirements of clients better;

advantages of advanced artificial intelligence and machine learning modules: advanced artificial intelligence and machine learning modules provide enterprises with advanced data analysis and decision support capabilities; by learning historical data and a pattern recognition method, the system can automatically find rules hidden behind the data and provide more accurate prediction and planning for enterprises; the method is beneficial to enterprises to make business decisions more intelligently, optimize operation efficiency and improve competitiveness.

Drawings

FIG. 1 is a system diagram of the present invention;

FIG. 2 is a block diagram of an intelligent prediction and planning module according to the present invention;

FIG. 3 is a block diagram of risk prediction and management according to the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1-3, the present invention provides a technical solution: the financial core module is used for managing general account accounts of enterprises and business accounts with clients and suppliers; the cost accounting module is used for tracking and analyzing the cost structure of the enterprise and supporting real-time monitoring and management of the cost; the human resources and payroll module is used for managing staff payroll, welfare and related human resources matters; the payment and settlement module is used for managing the payment flow of enterprises and ensuring the accuracy and timeliness of financial transactions; the risk prediction and management module is used for providing analysis tools and identifying potential financial risks; the financial compliance and audit module is used for providing financial reports and carrying out deep analysis on key financial indexes; the multi-level authority control and data security module is used for providing multi-level authority control and strengthening data security; the supply chain financial module is used for managing purchasing process and supply chain financing and ensuring smooth operation of the supply chain; the real-time data integration and overview module is used for integrating the real-time data of each module into a centralized overview page, so that the whole business and financial conditions can be conveniently known; the fixed asset management module is used for managing fixed assets of an enterprise; the financial analysis and report module is used for carrying out comprehensive performance analysis and further analysis on key financial indexes by combining business data; the intelligent prediction and planning module is used for predicting future financial trends and assisting enterprises in making strategic plans; the advanced artificial intelligence and machine learning module is used for providing intelligent decision support based on artificial intelligence technology, and has training and optimizing functions of a machine learning model so as to continuously improve the prediction accuracy of the system.

It should be noted that, the task of the financial core module of the present invention is to manage general ledger accounts, including assets, liabilities, equity, and accounts of clients and suppliers, specifically: recording daily financial transactions of the enterprise; generating a general account, and ensuring accurate account; processing accounts payable between the customer and the provider; the cost accounting module is used for tracking and analyzing the cost structure of an enterprise, supporting real-time cost monitoring and management, and specifically comprises the following steps: collecting cost data of each department; analyzing the cost structure and determining key cost driving factors; monitoring the cost in real time and providing management decision support; the task of the human resources and payroll module is to manage staff payroll, welfare and other human resources matters, specifically: maintaining employee profiles, including payroll information and benefits; processing payroll and related tax transactions; providing staff self-service and training management; the task of the payment and settlement module is to manage the enterprise payment flow, ensure the accuracy and timeliness, and specifically comprises the following steps: processing the supplier invoice and the payment request; managing a customer settlement flow; implementing audit controls to ensure compliance with the transaction; the task of the risk prediction and management module is to provide analysis tools, identifying potential financial risks, in particular: analyzing historical data and market trends; identifying possible risk factors; providing decision support and making a risk mitigation strategy; the financial compliance and audit module is used for providing financial reports and deeply analyzing key financial indexes, and specifically comprises the following steps: generating a financial report to ensure compliance with regulations; implementing internal audit and audit control; supporting an external audit flow; the task of the multi-level authority control and data security module is to provide multi-level authority control, strengthen data security, specifically: configuring user rights and roles; monitoring user activity, detecting potential security threats; implementing data encryption and access control; the task of the supply chain finance module is to manage purchasing process and supply chain financing, ensure smooth operation of the supply chain, and specifically comprises the following steps: tracking purchase orders and deliveries; managing vendor payment and supply chain financing; optimizing inventory management to reduce costs; the task of the real-time data integration and overview module is to integrate the real-time data of each module into one overview page, specifically: collecting real-time data of each module; data cleaning and integration are carried out; providing a concentrated overview page, and supporting real-time business decisions; the fixed asset management module is used for managing fixed assets of enterprises, and specifically comprises the following steps: recording fixed asset purchase and depreciation; performing asset inventory and maintenance planning; support asset rejection and transfer; the financial analysis and reporting module is used for carrying out comprehensive performance analysis by combining business data, and specifically comprises the following steps: collecting business and financial data; performing data analysis and modeling; generating a detailed financial analysis report; the task of the intelligent prediction and planning module is to predict future financial trends and assist strategic planning, and specifically comprises the following steps: trend analysis is performed by using the historical data; predicting by applying a machine learning algorithm; providing intelligent planning suggestions to help enterprises to make strategies; the task of the advanced artificial intelligence and machine learning module is to provide intelligent decision support, and the advanced artificial intelligence and machine learning module has the training and optimizing functions of a machine learning model, and specifically comprises the following steps: collecting a large amount of data for model training; training a machine learning model, and optimizing prediction accuracy; real-time decision support is provided, and model parameters are automatically adjusted.

The system comprises a fixed asset management module, a financial core module, a supply chain financial module, a payment and settlement module, a human resource and compensation module and a tax management module, wherein the fixed asset management module and the cost accounting module are both in bidirectional connection with the financial core module, the supply chain financial module, the payment and settlement module, the human resource and compensation module and the tax management module are all in bidirectional connection with the supply chain financial module, the cost accounting module, the risk prediction and management module, the real-time data integration and overview module, the fixed asset management module, the financial compliance and audit module, the multi-level authority control and data security module and the financial analysis and reporting module are all in bidirectional connection with the financial core module, the intelligent prediction and planning module and the financial analysis and reporting module are both in bidirectional connection with the high-level artificial intelligence and machine learning module and the intelligent prediction and planning module, the payment and settlement module, the human resource and compensation module, the risk prediction and management module and the supply chain financial module are all in bidirectional connection with the cost accounting module, and the payment and settlement module and the supply chain financial module are both in bidirectional connection with the supply chain financial module.

It should be noted that, the fixed asset management module and the cost accounting module of the present invention are connected with the financial core module in two directions: the related cost of the fixed asset and the financial running water are synchronized in real time through the connection, so that accurate metering and accounting of the fixed asset are ensured; the system comprises a supply chain financial module, a payment and settlement module, a human resource and compensation module, a tax management module, a cost accounting module, a risk prediction and management module, a real-time data integration and overview module, a fixed asset management module, a financial compliance and audit module, a multi-level authority control and data security module and a financial analysis and reporting module which are in bidirectional connection with a financial core module: the connection of the modules and the financial core module ensures the synchronization of the financial data and the general ledger accounts of all business links, so that the input, processing and analysis of the financial data are more comprehensive and accurate; the intelligent prediction and planning module is in bidirectional connection with the financial analysis and reporting module: the connection enables the data obtained by intelligent prediction to be directly used for financial analysis, so that future financial trends can be better understood, and powerful support is provided for enterprise planning strategy planning; the advanced artificial intelligence and machine learning module is connected with the intelligent prediction and planning module in a bidirectional way: the connection enables the results obtained by training and optimizing the machine learning model to directly influence intelligent prediction, and the continuous improvement of the prediction accuracy of the system is realized; the financial core module and the cost accounting module are connected with the risk prediction and management module in a bidirectional manner: through this connection, the data of the financial core module and the cost accounting module can provide basic data for the risk prediction and management module to better identify potential financial risks; the payment and settlement module, the human resource and compensation module, the risk prediction and management module and the supply chain financial module are in bidirectional connection with the cost accounting module: the connection ensures that the cost accounting module can acquire detailed cost information of each business link, so that cost analysis and monitoring can be better performed; the payment and settlement module is connected with the supply chain financial module in a bidirectional way: the connection enables payment and settlement information to directly influence the finance of the supply chain, and guarantees normal operation and timely settlement of the supply chain.

The financial risks include market risks, credit risks, liquidity risks, operation risks, legal and compliance risks, strategic risks, convergence risks, and financial faking risks, and the market risks include stock market risks, risk exchange rate risks, and interest rate risks; the liquidity risks include liquidity deficiency, which is a risk caused by enterprises failing to meet short-term debt or business needs, and market liquidity risks, which is a difficulty in trading due to changes in market conditions.

It should be noted that the stock market risk of the present invention: the stock price fluctuation is related, and the stock price fluctuation can be caused by a plurality of factors such as macro economic factors, industry trends and company performance; exchange rate risk: related to exchange rate fluctuations between different currencies, which may affect their financial status, especially for international business and transnational enterprises; credit risk: involves the inability of the enterprise client or partner to fulfill contractual obligations on time, resulting in a loss of funds; which is related to the repayment capacity of the customer, credit records, market condition factors; liquidity risk: loss of fluidity: enterprises may be at risk of failing to meet short-term debt or daily operational requirements in time, which results in financial tension; market mobility risk: concerning the marketability of assets in the market, if market conditions become unfavorable, it may cause the enterprise to have difficulty in marketing the assets, affecting the mobility of the enterprise; operational risk: losses possibly caused by internal processes, systems and human errors of enterprises are involved; this includes technical failure, human negligence, fraud; legal and compliance risk: to legal liabilities and fines that enterprises may face due to non-compliance with regulations, legal changes, contract disputes; strategic risk: losses that may be incurred in relation to enterprise strategic decisions, including risks in terms of market location, product development, expansion planning; aggregation risk: the simultaneous influence of multiple risk factors on enterprises can lead to accumulation and amplification of risks; financial counterfeiting risk: with respect to the authenticity and transparency of corporate financial reports, investors can be misled and lost if corporate financial fraud occurs.

The intelligent prediction and planning module comprises a data collection and integration unit, a time sequence analysis unit, a machine learning prediction model unit, a risk influence analysis unit, a business scene simulation unit, an optimization and strategy planning unit, a real-time monitoring and adjustment unit and a reporting and visualization unit; the data collection and integration unit is used for acquiring related data from the financial analysis module and the risk prediction module and possibly integrating other key data sources; the time sequence analysis unit is used for carrying out trend analysis on the historical data and identifying periodic changes, seasonal changes and long-term modes of the trend; the machine learning prediction model unit is used for establishing a prediction model based on a machine learning algorithm, and training by utilizing historical data so as to predict possible future trend and change; the risk influence analysis unit is used for analyzing the influence of various potential risks on future financial conditions by combining the data of the risk prediction module; the business scene simulation unit is used for carrying out business scene simulation based on different assumptions and scenes and evaluating the influence of different decisions on the future; the optimizing and strategy planning unit is used for utilizing an optimizing algorithm and a decision support system to formulate an optimal strategy plan according to the predicted trend and the risk analysis result; the real-time monitoring and adjusting unit is used for monitoring financial performance and actual conditions in real time according to external environment and internal changes, adjusting a prediction model and a planning scheme according to requirements and ensuring continuous effectiveness of the prediction model and the planning scheme; the report and visualization unit is used for visually presenting the prediction results, the planning scheme and the related financial analysis and risk prediction data to a decision maker.

It should be noted that, the module of the present invention first collects and sorts related data, which may relate to historical data, market trend, and data of multiple sources required by customers; these data require cleaning, conversion and standardization for subsequent analysis; in the data preparation stage, data preprocessing and feature engineering are carried out, wherein the data preprocessing and feature engineering comprises missing value filling, outlier processing, feature selection and conversion operation so as to extract features useful for prediction and planning; analyzing and predicting the data by using a machine learning algorithm or a statistical model; the method relates to time sequence analysis, regression, classification or clustering technology, and is used for predicting future trend, sales and demand indexes; planning and optimizing on the basis of prediction; including decisions on resource allocation, production planning, inventory management to maximize profits or meet specific goals; verifying and monitoring the prediction and planning results; the module is required to continuously evaluate the prediction accuracy, monitor the execution condition of the planning, and adjust the model or strategy when required; in using the intelligent prediction and planning module, some precautions need to be taken; the data quality is critical to the accuracy of the model; ensuring the accuracy and the integrity of data and carrying out necessary cleaning and preprocessing is key; it is necessary to select the appropriate prediction and planning model; in addition, the model is optimized and verified, so that overfitting or under fitting is avoided, and the robustness and accuracy of the model are ensured; the model developer needs to know the service requirement and background deeply so as to ensure that the model design accords with the actual service scene and can solve the actual problem; the model and planning strategy need to be continuously optimized and updated to adapt to the continuously changing market conditions and demands; the prediction and planning results of the model may have certain uncertainty and risk, and risk assessment is required to be carried out and a corresponding risk management strategy is formulated; when processing data, the method needs to ensure that relevant privacy regulations are met, and measures are taken to protect the data safety and avoid data leakage or abuse.

The risk prediction and management module comprises a risk identification and classification unit, a risk measurement and evaluation unit, a risk monitoring and reporting unit, a risk coping strategy unit, a risk tracing and influence analysis unit, a risk communication and training unit, a technical risk evaluation and management unit and a legal compliance risk management unit, wherein the risk identification and classification unit is in bidirectional connection with the risk measurement and evaluation unit, the risk measurement and evaluation unit is in bidirectional connection with the risk monitoring and reporting unit, the risk monitoring and reporting unit is in bidirectional connection with the risk coping strategy unit, the risk coping strategy unit is in bidirectional connection with the risk tracing and influence analysis unit, the risk tracing and influence analysis unit is in bidirectional connection with the risk communication and training unit, the risk communication and training unit is in bidirectional connection with the technical risk evaluation and management unit, and the legal compliance risk management unit is in bidirectional connection with the technical risk evaluation and management unit; the risk identification and classification unit is used for identifying potential risks and classifying the potential risks; a risk measurement and evaluation unit for quantifying and evaluating the probability and influence degree of various risks; the risk monitoring and reporting unit is used for monitoring the risk condition in real time and periodically generating and distributing a risk report; the risk coping strategy unit is used for making strategies and plans for coping with various risks; the risk tracing and influence analysis unit is used for tracing and analyzing risks and knowing the reasons and possible influences of occurrence of risk events; the risk communication and training unit is used for communicating risk related information with each level of management layers and staff, so that the awareness and understanding of the organization on risk management are improved; the technical risk assessment and management unit is used for carrying out special assessment and management aiming at risks in the aspects of information technology, data security and the like; and the legal compliance risk management unit is used for ensuring that the risks of organizations in legal and compliance aspects are effectively managed.

It should be noted that, first, the present invention explicitly defines risk problems and objectives; it involves determining the type of risk to be predicted, such as market risk, credit risk or operational risk, and specifying the predicted time frame and accuracy requirements; collecting risk-related data including historical risk events, market metrics, corporate financial data; the data may come from multiple sources, internal systems, external databases, news media; ensuring the accuracy and the integrity of data, and cleaning and converting; performing feature engineering on the collected data to extract useful features; the method comprises the steps of calculating risk indexes, constructing factors influencing risks, and performing time sequence analysis operation to better represent the characteristics of risks; selecting an appropriate risk prediction model; common models include statistical models (e.g., regression models), machine learning models (e.g., decision trees, support vector machines, neural networks); the establishment of the model involves training data, verification and tuning to ensure that the model can accurately capture the change and trend of the risk; evaluating and verifying the established model, carrying out back measurement by using historical data, and checking the prediction performance of the model; the evaluation index may include accuracy, precision, recall; predicting and identifying future risks by using the established model; the model can output corresponding risk probability or level according to new data input; integrating the risk prediction result into a decision support system to provide relevant information for a decision maker; including priority of risk, potential impact, suggested countermeasures; according to the prediction result, corresponding risk management strategies and countermeasures are formulated; the method comprises the steps of formulating a strategy for risk avoidance, transfer, reduction or acceptance and a specific operation scheme; continuously monitoring the performance of the risk prediction model, and periodically updating the model to adapt to a changed environment; this also includes real-time monitoring of risk events and assessing the effectiveness of risk management policies.

A supply chain company needs to track and manage many aspects of business, from purchasing, inventory management, to sales and financial accounting. They can utilize this system to achieve comprehensive financial management and business integration.

Specific examples:

1. supply chain finance module:

in this module, the company can track the payment and receivables of the provider. For example, through this module they can manage the schedule of payments by suppliers, optimize cash flows, and automatically integrate with inventory and procurement modules to ensure that payments are consistent with delivery times.

2. Cost accounting module:

the company may need to conduct cost analysis and accounting, learn the product cost structure, determine profit margins, and learn the profitability of different product lines. Through the cost accounting module, the cost accounting module can track and analyze the cost of each link in real time, including raw material purchasing cost, production cost and the like, so as to optimize production and purchasing strategies.

3. Risk prediction and management module:

this module can help companies identify potential risks and formulate corresponding risk management policies. For example, identifying the risk of supply chain interruption that may be caused by market fluctuations, predicting the likelihood of supply chain delays using a model, and taking action in advance, such as stock or diversification of supplier sources, to reduce the risk.

4. Intelligent prediction and planning module:

through intelligent forecasting, companies can forecast demand and optimize inventory based on historical sales data, market trends, and other factors. This helps to avoid overstock or backorder and improve supply chain efficiency.

5. Financial analysis and reporting module:

the module can be used by companies to generate financial reports and monitor the financial health condition of the enterprises and the development trend of various indexes in real time. This helps the management layer make informed financial decisions.

6. Data security module and rights control:

the system also comprises a multi-level authority control and data security module, which ensures that sensitive information is protected and only authorized personnel can access key data.

7. Advanced artificial intelligence and machine learning modules:

these modules may be used to optimize supply chain planning, predict market trends, and even automate certain decision processes such as intelligent supply chain management or demand-based production planning.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (8)

1. A financial centralized management system for industry and financial fusion is characterized in that: the system comprises a financial core module, a cost accounting module, a human resource and compensation module, a payment and settlement module, a risk prediction and management module, a financial compliance and audit module, a multi-level authority control and data security module, a supply chain financial module, a real-time data integration and overview module, a fixed asset management module, a financial analysis and reporting module, an intelligent prediction and planning module and an advanced artificial intelligence and machine learning module;

the financial core module is used for managing the general ledger accounts of enterprises and managing the business accounts with clients and suppliers;

the cost accounting module is used for tracking and analyzing the cost structure of the enterprise and supporting real-time monitoring and management of the cost;

the human resources and payroll module is used for managing staff payroll, welfare and related human resources matters;

the payment and settlement module is used for managing the payment flow of enterprises and ensuring the accuracy and timeliness of financial transactions;

a risk prediction and management module for providing analysis tools and identifying potential financial risks;

the financial compliance and audit module is used for providing financial reports and carrying out deep analysis on key financial indexes;

the multi-level authority control and data security module is used for providing multi-level authority control and enhancing data security;

the supply chain financial module is used for managing the purchasing process and the supply chain financing and ensuring the smooth operation of the supply chain;

the real-time data integration and overview module is used for integrating the real-time data of each module into a centralized overview page, so that the overall business and financial conditions can be conveniently known;

the fixed asset management module is used for managing fixed assets of an enterprise;

the financial analysis and reporting module is used for carrying out comprehensive performance analysis and further analysis on key financial indexes by combining business data;

the intelligent prediction and planning module is used for predicting future financial trends and assisting enterprises in making strategic plans;

the advanced artificial intelligence and machine learning module is used for providing intelligent decision support based on artificial intelligence technology, and has the training and optimizing functions of a machine learning model so as to continuously improve the prediction accuracy of the system.

2. A financial centralized management system for industry and finance integration as in claim 1, wherein: the fixed asset management module and the cost accounting module are both in bidirectional connection with the financial core module, and the supply chain financial module, the payment and settlement module, the human resource and compensation module and the tax management module are all in bidirectional connection with the financial core module.

3. A financial centralized management system for industry and finance integration as in claim 1, wherein: the intelligent prediction and planning module is in bidirectional connection with the financial analysis and reporting module, the advanced artificial intelligence and machine learning module is in bidirectional connection with the intelligent prediction and planning module, the financial core module and the cost accounting module are in bidirectional connection with the risk prediction and management module, the payment and settlement module, the human resource and salary module, the risk prediction and management module and the supply chain financial module are in bidirectional connection with the cost accounting module, and the payment and settlement module is in bidirectional connection with the supply chain financial module.

4. A financial centralized management system for industry and finance integration as in claim 1, wherein: the financial risks include market risks, credit risks, liquidity risks, operational risks, legal and compliance risks, strategic risks, aggregate risks, and financial counterfeiting risks.

5. The finance integrated financial centralized management system of claim 4, wherein: the market risk comprises stock market, risk exchange rate risk and interest rate risk; the liquidity risks include liquidity deficiency, which is a risk caused by enterprises failing to meet short-term debt or business needs, and market liquidity risks, which is a difficulty in trading due to changes in market conditions.

6. A financial centralized management system for industry and finance integration as in claim 1, wherein: the intelligent prediction and planning module comprises a data collection and integration unit, a time sequence analysis unit, a machine learning prediction model unit, a risk influence analysis unit, a business scene simulation unit, an optimization and strategy planning unit, a real-time monitoring and adjustment unit and a reporting and visualization unit;

the data collection and integration unit is used for acquiring related data from the financial analysis module and the risk prediction module and possibly integrating other key data sources;

the time sequence analysis unit is used for carrying out trend analysis on the historical data and identifying periodic changes, seasonal changes and long-term modes of the trend;

the machine learning prediction model unit is used for establishing a prediction model based on a machine learning algorithm, and training by utilizing historical data so as to predict possible future trend and change;

the risk influence analysis unit is used for analyzing the influence of various potential risks on future financial conditions by combining the data of the risk prediction module;

the business scene simulation unit is used for carrying out business scene simulation based on different assumptions and scenes and evaluating the influence of different decisions on the future;

the optimizing and strategy planning unit is used for utilizing an optimizing algorithm and a decision support system to formulate an optimal strategy plan according to the predicted trend and the risk analysis result;

the real-time monitoring and adjusting unit is used for monitoring financial performance and actual conditions in real time according to external environment and internal changes, adjusting a prediction model and a planning scheme according to requirements and ensuring continuous effectiveness of the prediction model and the planning scheme;

the report and visualization unit is used for visually presenting the prediction results, the planning scheme and the related financial analysis and risk prediction data to a decision maker.

7. A financial centralized management system for industry and finance integration as in claim 1, wherein: the risk prediction and management module comprises a risk identification and classification unit, a risk measurement and evaluation unit, a risk monitoring and reporting unit, a risk coping strategy unit, a risk tracing and influence analysis unit, a risk communication and training unit, a technical risk evaluation and management unit and a legal compliance risk management unit.

8. A financial centralized management system for industry and finance integration as in claim 7, wherein: the risk identification and classification unit is in bidirectional connection with the risk measurement and evaluation unit, the risk measurement and evaluation unit is in bidirectional connection with the risk monitoring and reporting unit, the risk monitoring and reporting unit is in bidirectional connection with the risk coping strategy unit, the risk coping strategy unit is in bidirectional connection with the risk tracing and influence analysis unit, the risk tracing and influence analysis unit is in bidirectional connection with the risk communication and training unit, the risk communication and training unit is in bidirectional connection with the technical risk evaluation and management unit, and the legal compliance risk management unit is in bidirectional connection with the technical risk evaluation and management unit;

the risk identification and classification unit is used for identifying potential risks and classifying the potential risks;

a risk measurement and evaluation unit for quantifying and evaluating the probability and influence degree of various risks;

the risk monitoring and reporting unit is used for monitoring the risk condition in real time and periodically generating and distributing a risk report;

the risk coping strategy unit is used for making strategies and plans for coping with various risks;

the risk tracing and influence analysis unit is used for tracing and analyzing risks and knowing the reasons and possible influences of occurrence of risk events;

the risk communication and training unit is used for communicating risk related information with each level of management layers and staff, so that the awareness and understanding of the organization on risk management are improved;

the technical risk assessment and management unit is used for carrying out special assessment and management aiming at risks in the aspects of information technology, data security and the like;

and the legal compliance risk management unit is used for ensuring that the risks of organizations in legal and compliance aspects are effectively managed.