CN117670056A - Data analysis system for financial management - Google Patents
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Abstract
The invention discloses a data analysis system for financial management, and relates to the technical field of financial management analysis systems; comprising the following steps: and a data collection module: the data collection module is used for collecting financial data of an enterprise, automatically acquiring the data from a financial system of the enterprise through a web crawler technology, or acquiring the data in a manual input mode; and a data processing module: the data processing module is used for processing the collected financial data, including data cleaning, data conversion and data integration; and a data analysis module: the data analysis module is used for analyzing the processed data, including financial ratio analysis, trend analysis and prediction analysis. The data analysis system can predict based on historical data and a financial model, and accurately predict future indexes such as income, expenditure, assets, liabilities and the like; the time sequence analysis model can utilize characteristics of historical data such as trend, seasonality and periodicity to predict, and is suitable for the problem of prediction of nonlinear relations.
Description
Technical Field
The invention relates to the technical field of financial management analysis systems, in particular to a data analysis system for financial management.
Background
With the development of information technology, enterprises have increasingly demanded financial management. Conventional financial management methods have failed to meet the needs of modern enterprises, and thus, a financial management system capable of complex data analysis is needed.
Through retrieval, the patent with the Chinese patent application number of CN201910778443.6 discloses a financial transaction evaluation system based on big data, which comprises a expenditure statistics module, a income statistics module, a financial storage database, a classification module, a financial statistics module, a cost statistics module, a cloud server and a display terminal; the classified dividing module is respectively connected with the expenditure counting module, the income counting module and the financial counting module, the financial storage database is respectively connected with the classified dividing module, the expenditure counting module and the income counting module, the cloud server is respectively connected with the financial counting module, the display terminal and the cost counting module, and the cost counting module is connected with the expenditure counting module. The financial transaction evaluation system of the above patent suffers from the following disadvantages: although a certain evaluation capability can be provided, future financial data cannot be predicted, and improvement is still needed.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a data analysis system for financial management.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a data analysis system for financial management, comprising:
and a data collection module: the data collection module is used for collecting financial data of an enterprise, automatically acquiring the data from a financial system of the enterprise through a web crawler technology, or acquiring the data in a manual input mode;
and a data processing module: the data processing module is used for processing the collected financial data, including data cleaning, data conversion and data integration;
and a data analysis module: the data analysis module is used for analyzing the processed data, including financial ratio analysis, trend analysis and predictive analysis; evaluating financial conditions of the enterprise by calculating various financial ratios; predicting a future financial condition by analyzing a change trend of the historical data; and build a financial model to predict future revenues, expenditures, assets and liabilities;
and the data display module is used for: the data display module is used for displaying the analysis result in the form of a chart.
Preferably: the data processing module comprises:
a data collection unit: responsible for collecting financial data from different channels and sources;
and a data cleaning unit: cleaning the collected data, removing repeated values, missing values and abnormal values, and ensuring the accuracy and the integrity of the data;
a data conversion unit: converting the raw data into a format suitable for analysis;
a data integration unit: and integrating the data from different data sources to form a unified data view.
Preferably: the data analysis module:
financial ratio analysis unit: calculating various financial ratios, and evaluating the repayment capacity, liquidity and profitability of the enterprise;
trend analysis unit: through statistical analysis of historical data, the change trend of the financial condition is identified, and enterprises are helped to predict the future development trend;
prediction analysis unit: based on the historical data and the financial model, a predictive model is established to predict future revenue, expenditure, assets and liability metrics.
Preferably: the prediction model of the prediction analysis unit is used for establishing a mathematical model to predict based on the trend, seasonal and periodic characteristics of historical data, and specifically adopts an ARIMA model or a SARIMA model, wherein the ARIMA (p, d, q) model is expressed as:
Xt＝c+φ1*Xt-1+…+φp*Xt-p+μt
μt＝α+θ1*εt-1+…+θq*εt-q+νt
wherein Xt is time series data, c is a constant term, phi is an autoregressive coefficient, mu is a moving average coefficient, alpha is an intercept term after difference, theta is a moving average hysteresis coefficient, and v is an error term; p is the number of autoregressions, the part controlling autoregressions in the ARIMA model; d is a differential order, which controls an order of a difference in which data input to the ARIMA model is performed; if the time sequence is not stable, one, two or more differential steps are needed until the sequence is stable, and the order of the differential is the value of d in the ARIMA model; q is the number of moving average terms, the part controlling the moving average in the ARIMA model.
Preferably: in the prediction model of the prediction analysis unit, the SARIMA (P, D, Q) s model is expressed as:
Xt＝c+φ1*Xt-1+…+φp*Xt-p+θ1*εt-1S+…+θq*εt-qS+μt
μt＝α+θ1*εt-1I(D)+…+θq*εt-qI(D)+νt
where Xt is time series data, c is a constant term, φ is an autoregressive coefficient, θ is a moving average hysteresis coefficient, and ν is an error term; meanwhile, S represents a seasonal period, P and Q represent the order of seasonal autoregressive and seasonal moving average, respectively, D represents the number of times of the differentiating operation, and I (D) represents the differentiating operation.
Preferably: further comprises:
budget management module: the budget management module establishes reasonable financial budgets according to historical data and prediction analysis results, and monitors and analyzes budget execution conditions by comparing actual data with budget data;
and a cost control module: the cost control module identifies main sources and influencing factors of the cost through data analysis, and proposes a corresponding cost control strategy;
and a fund flow module: the fund flow module analyzes the fund inflow and outflow conditions of the enterprise and predicts the fund demand and the fund gap of the enterprise;
risk management module: the risk management module identifies main financial risks facing the enterprise through financial ratio analysis and trend analysis;
performance evaluation module: the performance evaluation module calculates various financial indexes and evaluates the financial performance level of the enterprise.
Preferably: the budget management module comprises:
budget planning unit: formulating reasonable financial budgets including income budgets, expenditure budgets, capital budgets according to historical data and business conditions;
budget execution monitoring unit: comparing and analyzing the actual financial data with the budget, monitoring the execution condition of the budget, finding out deviation in time and taking corresponding adjustment measures;
budget adjustment suggesting unit: and according to the budget execution conditions, market change and other factors, reasonable budget adjustment suggestions are provided.
Preferably: the cost control module includes:
cost analysis unit: analyzing and accounting each cost, including direct cost, indirect cost, fixed cost and variable cost, and finding out main sources and influencing factors of the cost;
cost control policy unit: according to the cost analysis result, a corresponding cost control strategy is formulated, including measures of reducing cost, improving efficiency, optimizing a supply chain and the like;
cost performance evaluation unit: and evaluating and analyzing the cost control effect, and evaluating the cost management level of the enterprise by comparing the difference between the actual cost and the budget cost or the industry standard cost.
Preferably: the risk management module includes:
risk identification unit: identifying financial risks faced by the enterprise through analysis of financial data and analysis of market environment data;
risk assessment unit: quantitative or qualitative assessment of each risk is carried out, and potential influence degree and possibility of each risk are determined;
risk countermeasure unit: and according to the risk assessment result, corresponding risk precautions and countermeasures are formulated.
Preferably: the performance evaluation module includes:
performance index calculation unit: calculating various financial indexes according to the requirements of financial management and the characteristics of enterprises;
performance level assessment unit: the level of financial performance of the enterprise is assessed by comparison to industry average or historical data.
The beneficial effects of the invention are as follows:
1. the data analysis system can predict based on historical data and a financial model, and accurately predict future indexes such as income, expenditure, assets, liabilities and the like; the time sequence analysis model can utilize characteristics of historical data such as trend, seasonality and periodicity to predict, and is suitable for the problem of prediction of nonlinear relations.
2. The invention adopts ARIMA model, can build mathematical model to predict, and can optimize model performance by adjusting parameters such as autoregressive coefficient, moving average coefficient, differential order and the like; by adopting the SARIMA model, the seasonal period is added on the basis of the ARIMA model, and the influence of seasonal change on the prediction result can be better captured.
Drawings
FIG. 1 is a flow chart of a data analysis system for financial management according to the present invention.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the specific embodiments.
Example 1:
a data analysis system for financial management, comprising:
and a data collection module: the module is responsible for collecting financial data of the business including, but not limited to, revenue, expense, assets, liabilities, and the like; the data collection module can automatically acquire data from a financial system of an enterprise through a web crawler technology, and can acquire the data in a manual input mode;
and a data processing module: the module is responsible for processing the collected financial data, including data cleaning, data conversion and data integration; the data cleaning is to remove noise and abnormal values in the data, so as to ensure the accuracy of the data; the data conversion means converting the original data into a format suitable for analysis; data integration refers to integrating data from different sources to form a unified data view;
and a data analysis module: the module is responsible for analyzing the processed data, including financial ratio analysis, trend analysis, predictive analysis and the like; financial ratio analysis is the assessment of the financial condition of an enterprise by calculating various financial ratios, such as flow ratio, speed ratio, liability ratio, etc.; trend analysis is to predict future financial conditions by analyzing the change trend of historical data; predictive analysis is to predict future revenues, expenditures, assets, liabilities, etc. by building financial models;
and the data display module is used for: the module is responsible for displaying the analysis result in a chart form, so that the analysis result is convenient for a user to understand and use; the data presentation module may generate various types of charts, such as bar charts, line charts, pie charts, and the like.
Wherein the data processing module comprises:
a data collection unit: is responsible for collecting financial data from different channels and sources, including information on revenue, expense, assets, liabilities, etc.;
and a data cleaning unit: cleaning the collected data, removing repeated values, missing values and abnormal values, and ensuring the accuracy and the integrity of the data;
a data conversion unit: converting the original data into a format suitable for analysis, such as unifying currency units into the same standard, converting date formats and the like;
a data integration unit: and integrating the data from different data sources to form a unified data view, so that subsequent analysis and processing are convenient.
Wherein, the data analysis module:
financial ratio analysis unit: calculating various financial ratios, such as flow ratio, speed ratio, liability ratio, etc., and evaluating the repayment capacity, liquidity, and profitability of the business;
trend analysis unit: through statistical analysis of historical data, the change trend of the financial condition is identified, and enterprises are helped to predict the future development trend;
prediction analysis unit: based on the historical data and the financial model, a prediction model is established to predict future indexes such as income, expenditure, assets, liabilities and the like.
The prediction model of the prediction analysis unit is based on characteristics of historical data, such as trend, seasonality, periodicity and the like, a mathematical model is built for prediction, and an ARIMA model or a SARIMA model is specifically adopted, wherein the ARIMA (p, d, q) model is expressed as:
Xt＝c+φ1*Xt-1+…+φp*Xt-p+μt
μt＝α+θ1*εt-1+…+θq*εt-q+νt
wherein Xt is time series data, c is a constant term, phi is an autoregressive coefficient, mu is a moving average coefficient, alpha is an intercept term after difference, theta is a moving average hysteresis coefficient, and v is an error term; p is the number of autoregressions, the part controlling autoregressions in the ARIMA model; d is a differential order, which controls an order of a difference in which data input to the ARIMA model is performed; if the time sequence is not stable, one, two or more differential steps are needed until the sequence is stable, and the order of the differential is the value of d in the ARIMA model; q is the number of moving average terms, the part controlling the moving average in the ARIMA model;
wherein the SARIMA (P, D, Q) (P, D, Q) s model is expressed as:
Xt＝c+φ1*Xt-1+…+φp*Xt-p+θ1*εt-1S+…+θq*εt-qS+μt
μt＝α+θ1*εt-1I(D)+…+θq*εt-qI(D)+νt
where Xt is time series data, c is a constant term, φ is an autoregressive coefficient, θ is a moving average hysteresis coefficient, and ν is an error term; meanwhile, S represents a seasonal period, P and Q represent the order of seasonal autoregressive and seasonal moving average, respectively, D represents the number of times of the differentiating operation, and I (D) represents the differentiating operation.
Wherein, the data display module:
a chart generation unit: according to the demands of users, various charts such as bar charts, line charts, pie charts and the like are automatically generated, and financial data and analysis results are intuitively displayed;
a data filtering unit: according to the conditions and parameters specified by the user, filtering and screening the data, and only displaying the data and information meeting the requirements of the user;
interactive report unit: the interactive report function is provided, and the user is allowed to perform custom query and display of data according to own requirements.
Example 2:
a data analysis system for financial management, the present embodiment further includes, on the basis of embodiment 1:
budget management module: the module is responsible for setting and executing the financial budget of the enterprise; according to the historical data and the prediction analysis result, reasonable financial budget is established, and the budget execution condition is monitored and analyzed by comparing the actual data with the budget data;
and a cost control module: the module is responsible for managing and controlling the cost of enterprises; the method can identify main sources and influencing factors of the cost through data analysis, and propose corresponding cost control strategies to help enterprises reduce the cost and improve the profitability;
and a fund flow module: the module is responsible for monitoring and managing the fund flow of enterprises; the method can analyze the inflow and outflow conditions of the funds of the enterprise, forecast the funds demand and the funds gap of the enterprise, and provide corresponding funds raising and investment advice;
risk management module: the module is responsible for evaluating and managing the financial risk of the enterprise; the method can identify main financial risks faced by enterprises through financial ratio analysis and trend analysis, and propose corresponding risk precautions and countermeasures;
performance evaluation module: the module is responsible for evaluating and analyzing the financial performance of enterprises; it can calculate various financial indicators, such as net profit margin, asset return, etc., evaluate the financial performance level of the enterprise, and provide corresponding improvement suggestions.
Wherein the budget management module comprises:
budget planning unit: formulating reasonable financial budgets including income budgets, expenditure budgets, capital budgets and the like according to historical data and business conditions;
budget execution monitoring unit: comparing and analyzing the actual financial data with the budget, monitoring the execution condition of the budget, finding out deviation in time and taking corresponding adjustment measures;
budget adjustment suggesting unit: and according to factors such as budget execution conditions, market change and the like, reasonable budget adjustment suggestions are provided, and enterprises are helped to optimize budget management and resource allocation.
Wherein the cost control module comprises:
cost analysis unit: analyzing and accounting each cost, including direct cost, indirect cost, fixed cost, variable cost and the like, and finding out main sources and influencing factors of the cost;
cost control policy unit: according to the cost analysis result, a corresponding cost control strategy is formulated, including measures of reducing cost, improving efficiency, optimizing a supply chain and the like;
cost performance evaluation unit: and evaluating and analyzing the cost control effect, and evaluating the cost management level of the enterprise by comparing the difference between the actual cost and the budget cost or the industry standard cost.
Wherein the fund flow module comprises:
a funds inflow and outflow monitoring unit: monitoring the inflow and outflow of funds of an enterprise in real time, including cash, bank deposit, accounts receivable, accounts payable and the like;
fund demand prediction unit: predicting the fund demand and the fund gap of the enterprise in a future period by combining the historical data and market trend;
a fund raising and investment suggesting unit: and providing a corresponding fund raising scheme and investment advice according to the fund demand prediction result, and helping enterprises to reasonably arrange the fund operation.
Wherein the risk management module comprises:
risk identification unit: identifying major financial risks faced by the enterprise, such as market risks, credit risks, liquidity risks and the like, through analysis of the financial data and analysis of the market environment data;
risk assessment unit: quantitative or qualitative assessment is carried out on each risk, the potential influence degree and the potential influence possibility of each risk are determined, and a basis is provided for an enterprise to formulate a risk management strategy;
risk countermeasure unit: and according to the risk assessment result, corresponding risk precaution and countermeasure measures are formulated, including internal control, insurance purchase, diversified investment and the like.
Wherein the performance assessment module comprises:
performance index calculation unit: according to the requirements of financial management and the characteristics of enterprises, calculating various financial indexes such as net profit rate, asset return rate, sales growth rate and the like;
performance level assessment unit: the level of financial performance of the business is assessed by comparison with industry average levels or historical data and corresponding improvement advice is provided.
Wherein the report generation module comprises:
report template management unit: providing template selection and management functions of various financial management reports, wherein a user can select a proper template according to own requirements;
report content customizing unit: allowing a user to customize the content and format of the report according to the own requirements, including adding titles, notes, footnotes and the like;
report generation and export unit: according to the templates selected by the user and the customized contents, a financial management report is automatically generated, and a export function is provided to support common file formats such as PDF, excel and the like.
A report generation module: the module is responsible for generating various financial management reports; the system can automatically generate various reports, such as financial reports, financial analysis reports, budget execution condition reports and the like, according to the requirements of users, and is convenient for the users to make decisions and communicate.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (10)
1. A data analysis system for financial management, comprising:
and a data collection module: the data collection module is used for collecting financial data of an enterprise, automatically acquiring the data from a financial system of the enterprise through a web crawler technology, or acquiring the data in a manual input mode;
and a data processing module: the data processing module is used for processing the collected financial data, including data cleaning, data conversion and data integration;
and a data analysis module: the data analysis module is used for analyzing the processed data, including financial ratio analysis, trend analysis and predictive analysis; evaluating financial conditions of the enterprise by calculating various financial ratios; predicting a future financial condition by analyzing a change trend of the historical data; and build a financial model to predict future revenues, expenditures, assets and liabilities;
and the data display module is used for: the data display module is used for displaying the analysis result in the form of a chart.
2. A data analysis system for financial management according to claim 1, wherein the data processing module comprises:
a data collection unit: responsible for collecting financial data from different channels and sources;
and a data cleaning unit: cleaning the collected data, removing repeated values, missing values and abnormal values, and ensuring the accuracy and the integrity of the data;
a data conversion unit: converting the raw data into a format suitable for analysis;
a data integration unit: and integrating the data from different data sources to form a unified data view.
3. A data analysis system for financial management according to claim 1, wherein the data analysis module:
financial ratio analysis unit: calculating various financial ratios, and evaluating the repayment capacity, liquidity and profitability of the enterprise;
trend analysis unit: through statistical analysis of historical data, the change trend of the financial condition is identified, and enterprises are helped to predict the future development trend;
prediction analysis unit: based on the historical data and the financial model, a predictive model is established to predict future revenue, expenditure, assets and liability metrics.
4. A data analysis system for financial management according to claim 3, wherein the prediction model of the prediction analysis unit is configured to build a mathematical model based on the trend, seasonal and periodic characteristics of the historical data to perform prediction, specifically using ARIMA model or SARIMA model, wherein ARIMA (p, d, q) model is expressed as:
Xt＝c+φ1*Xt-1+…+φp*Xt-p+μt
μt＝α+θ1*εt-1+…+θq*εt-q+νt
wherein Xt is time series data, c is a constant term, phi is an autoregressive coefficient, mu is a moving average coefficient, alpha is an intercept term after difference, theta is a moving average hysteresis coefficient, and v is an error term; p is the number of autoregressions, the part controlling autoregressions in the ARIMA model; d is a differential order, which controls an order of a difference in which data input to the ARIMA model is performed; if the time sequence is not stable, one, two or more differential steps are needed until the sequence is stable, and the order of the differential is the value of d in the ARIMA model; q is the number of moving average terms, the part controlling the moving average in the ARIMA model.
5. The data analysis system for financial management according to claim 4, wherein, in the prediction model of the prediction analysis unit, a SARIMA (P, D, Q) s model is expressed as:
Xt＝c+φ1*Xt-1+…+φp*Xt-p+θ1*εt-1S+…+θq*εt-qS+μt
μt＝α+θ1*εt-1I(D)+…+θq*εt-qI(D)+νt
where Xt is time series data, c is a constant term, φ is an autoregressive coefficient, θ is a moving average hysteresis coefficient, and ν is an error term; meanwhile, S represents a seasonal period, P and Q represent the order of seasonal autoregressive and seasonal moving average, respectively, D represents the number of times of the differentiating operation, and I (D) represents the differentiating operation.
6. A data analysis system for financial management according to any one of claims 1 to 5, further comprising:
budget management module: the budget management module establishes reasonable financial budgets according to historical data and prediction analysis results, and monitors and analyzes budget execution conditions by comparing actual data with budget data;
and a cost control module: the cost control module identifies main sources and influencing factors of the cost through data analysis, and proposes a corresponding cost control strategy;
and a fund flow module: the fund flow module analyzes the fund inflow and outflow conditions of the enterprise and predicts the fund demand and the fund gap of the enterprise;
risk management module: the risk management module identifies main financial risks facing the enterprise through financial ratio analysis and trend analysis;
performance evaluation module: the performance evaluation module calculates various financial indexes and evaluates the financial performance level of the enterprise.
7. The data analysis system for financial management of claim 6, wherein the budget management module comprises:
budget planning unit: formulating reasonable financial budgets including income budgets, expenditure budgets, capital budgets according to historical data and business conditions;
budget execution monitoring unit: comparing and analyzing the actual financial data with the budget, monitoring the execution condition of the budget, finding out deviation in time and taking corresponding adjustment measures;
budget adjustment suggesting unit: and according to the budget execution conditions, market change and other factors, reasonable budget adjustment suggestions are provided.
8. A data analysis system for financial management as claimed in claim 6, wherein the cost control module comprises:
cost analysis unit: analyzing and accounting each cost, including direct cost, indirect cost, fixed cost and variable cost, and finding out main sources and influencing factors of the cost;
cost control policy unit: according to the cost analysis result, a corresponding cost control strategy is formulated, including measures of reducing cost, improving efficiency, optimizing a supply chain and the like;
cost performance evaluation unit: and evaluating and analyzing the cost control effect, and evaluating the cost management level of the enterprise by comparing the difference between the actual cost and the budget cost or the industry standard cost.
9. A data analysis system for financial management according to claim 6, wherein the risk management module comprises:
risk identification unit: identifying financial risks faced by the enterprise through analysis of financial data and analysis of market environment data;
risk assessment unit: quantitative or qualitative assessment of each risk is carried out, and potential influence degree and possibility of each risk are determined;
risk countermeasure unit: and according to the risk assessment result, corresponding risk precautions and countermeasures are formulated.
10. The data analysis system for financial management of claim 6, wherein the performance assessment module comprises:
performance index calculation unit: calculating various financial indexes according to the requirements of financial management and the characteristics of enterprises;
performance level assessment unit: the level of financial performance of the enterprise is assessed by comparison to industry average or historical data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118082593A (en) * | 2024-04-26 | 2024-05-28 | 北京基业昌达新能源技术有限公司 | Intelligent charging pile based on energy storage battery power supply and control method thereof |
| CN118096223A (en) * | 2024-04-23 | 2024-05-28 | 紫金诚征信有限公司 | Financial product marketing method and device based on artificial intelligence |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118096223A (en) * | 2024-04-23 | 2024-05-28 | 紫金诚征信有限公司 | Financial product marketing method and device based on artificial intelligence |
| CN118082593A (en) * | 2024-04-26 | 2024-05-28 | 北京基业昌达新能源技术有限公司 | Intelligent charging pile based on energy storage battery power supply and control method thereof |
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