Financial data analysis report generation method

Abstract

The invention belongs to the technical field of report generation, and particularly relates to a financial data analysis report generation method, which comprises the following steps of: constructing a data access system with wide compatibility, acquiring data from a multivariate and heterogeneous data source of an enterprise by applying an automatic data interface technology, and generating a financial data analysis report. For various databases, whether a relational database or a non-relational database, adaptive data acquisition scripts are developed according to data structure characteristics of the databases, and the data acquisition efficiency is improved through common cooperation of multiple steps of multi-source data access, real-time data synchronization, standardized processing, data cleaning, analysis model construction and report generation. The problems of complicated data sources, non-uniform formats and data delay are solved, so that the difficulty of data integration is reduced, the latest financial condition of an enterprise is reflected through real-time enterprise data, and the timeliness of decision making is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of report generation, and in particular to a method for generating a financial data analysis report. Background Art

[0002] With the development of science and technology, the construction and development of informatization are becoming increasingly mature. Building a scientific, rational, standardized and efficient financial bank-enterprise interconnected management platform will help enterprises standardize the group's fund management process with modern auxiliary means, improve the overall level of group fund management, strengthen fund control, prevent fund use risks, enhance the group's core competitiveness, give full play to the group's fund advantages, and ensure the healthy and orderly development of the group's overall capital chain.

[0003] However, the existing financial data analysis report generation method still has the following technical problems when used:

[0004] An enterprise's financial data comes from multiple different systems, such as financial software, business systems, and databases. The formats and standards of these data are inconsistent, making integration difficult and consuming a lot of time and effort. Some data acquisition may be delayed, resulting in the data based on the analysis report not being able to promptly reflect the company's latest financial status, affecting the timeliness of decision-making.

[0005] To this end, a financial data analysis report generation method is proposed to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for generating a financial data analysis report to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for generating a financial data analysis report, the specific contents of which are as follows:

[0008] Step 1: Multi-source data access: Build a data access system with broad compatibility, using automated data interface technology to acquire data from the enterprise's diverse and heterogeneous data sources. Develop adaptive data collection scripts for various databases, both relational and non-relational, based on their data structure characteristics. For external public data sources, such as industry financial data statistics websites and government economic data release platforms, use data crawler technology and Python's Scrapy framework tool to write crawler programs that simulate human web browsing behavior, accurately locate and extract the required macroeconomic data and industry financial indicator data, and provide external references for comparative analysis of enterprise financial data.

[0009] Step 2: Real-time data synchronization: On the source system side, database log parsing technology is utilized. In business systems and financial software, if a message queue mechanism is supported, messages are triggered at key business nodes where data is generated or updated. The data is encapsulated into a message format and sent to a message queue server. The data collection system monitors the message queue in real time. Once a new message is received, the data is immediately extracted and processed to ensure that the data can enter the analysis process as quickly as possible, so that the data used in the analysis report can reflect the company's financial status in real time.

[0010] Step 3: Standardization: Develop unified standards for data formats. During the data integration process, develop a data standardization conversion program to perform batch format conversion and code mapping on the data collected from various systems according to standardized rules. This ensures data consistency at the format and coding levels, laying the foundation for subsequent data integration and analysis.

[0011] Step 4: Data Cleaning: Utilize a deduplication algorithm to calculate hash values ​​for the collected financial data according to key business fields. Identify and delete duplicate data records by comparing hash values. For missing value processing, if the missing value ratio of a field in the dataset is low, use mean filling, median filling, or interpolation filling based on the correlation between the data. If the missing value ratio is high and the field is not critical to the analysis, delete the field directly. For outlier detection, use statistical methods to calculate the mean and standard deviation of the data. Data that deviates from the mean by more than three times the standard deviation is considered an outlier and further manually verified. If the outlier is caused by a data entry error, it is corrected to the correct data. If it is true abnormal business data, it is separately stated in the analysis report.

[0012] Step 5: Analytical model construction: For financial indicator forecasting, a time series forecasting model is used. Historical financial data is input and the model is trained to predict the trend of financial indicators over a period of time. For financial risk diagnosis, a risk assessment model based on a decision tree algorithm is constructed. Multiple financial ratios such as debt-to-asset ratio, current ratio, and accounts receivable turnover rate are used as input features. Through the branching decision rules of the decision tree, the company's financial risk status is classified and assessed to determine whether the company has potential financial risks and the risk level.

[0013] Step 6. Report generation: Develop a report generation engine that obtains cleaned and analyzed financial data in real time through a data interface, fills and renders the data according to the preset format of the template, and generates financial analysis reports in various formats; at the same time, set up a scheduled report generation task. According to the needs of the enterprise, the report generation process can be triggered on a daily, weekly, monthly or other cycle basis, and the generated report can be automatically sent to the target audience such as relevant financial personnel and management through the email system or the internal enterprise message push platform.

[0014] Preferably, in the step 1, in the process of acquiring data, for financial software, if it provides an API interface, use standardized interface specifications to write a data extraction program to directly extract financial transaction data and account balance data from the database of the financial software according to established rules; for business systems, such as sales management systems and procurement management systems, execute customized SQL query statements through database connection to extract financial-related business data, such as sales order amounts and procurement cost data.

[0015] Preferably, in the real-time data synchronization of step 2, for example, for an Oracle database, the database transaction log is captured in real time by an OGG tool, and the data change information is transmitted to the target data storage platform in real time.

[0016] Preferably, in the standardization processing of step three, the date format is unified as "YYYY-MM-DD", the amount data is uniformly formatted with two decimal places, and the currency unit is clearly defined; for data coding, an enterprise-level data coding dictionary is established, such as customer code, supplier code, and accounting subject code, and all relevant data in all data sources are coded and converted according to the dictionary.

[0017] Preferably, in the data cleaning of step 4, the deduplication algorithm used is, for example, a data deduplication model constructed based on a hash algorithm; the key business fields used include invoice number and transaction serial number.

[0018] Preferably, in the construction of the analysis model in step five, the time series prediction model adopted includes a long short-term memory network neural network model.

[0019] Preferably, in the step 6 of generating the report, the generated financial analysis reports are in multiple formats including PDF, Excel, and Word.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] This application solves the problems of complex data sources, inconsistent formats and data delays through the coordination of multiple steps such as multi-source data access, real-time data synchronization, standardization processing, data cleaning, analysis model construction and report generation, thereby reducing the difficulty of data integration, reflecting the latest financial status of the enterprise through real-time enterprise data, and increasing the timeliness of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of the steps in the method for generating this financial data analysis report. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0025] Example:

[0026] See also Figure 1 , the present invention provides a technical solution:

[0027] A method for generating a financial data analysis report, the specific contents of which are as follows:

[0028] Step 1: Multi-source data access: Build a data access system with broad compatibility, using automated data interface technology to acquire data from the enterprise's diverse and heterogeneous data sources. Develop adaptive data collection scripts for various databases, both relational and non-relational, based on their data structure characteristics. For external public data sources, such as industry financial data statistics websites and government economic data release platforms, use data crawler technology and Python's Scrapy framework tool to write crawler programs that simulate human web browsing behavior, accurately locate and extract the required macroeconomic data and industry financial indicator data, and provide external references for comparative analysis of enterprise financial data.

[0029] Step 2: Real-time data synchronization: On the source system side, database log parsing technology is utilized. In business systems and financial software, if a message queue mechanism is supported, messages are triggered at key business nodes where data is generated or updated. The data is encapsulated into a message format and sent to a message queue server. The data collection system monitors the message queue in real time. Once a new message is received, the data is immediately extracted and processed to ensure that the data can enter the analysis process as quickly as possible, so that the data used in the analysis report can reflect the company's financial status in real time.

[0030] Step 3: Standardization: Develop unified standards for data formats. During the data integration process, develop a data standardization conversion program to perform batch format conversion and code mapping on the data collected from various systems according to standardized rules. This ensures data consistency at the format and coding levels, laying the foundation for subsequent data integration and analysis.

[0031] Step 4: Data Cleaning: Utilize a deduplication algorithm to calculate hash values ​​for the collected financial data according to key business fields. Identify and delete duplicate data records by comparing hash values. For missing value processing, if the missing value ratio of a field in the dataset is low, use mean filling, median filling, or interpolation filling based on the correlation between the data. If the missing value ratio is high and the field is not critical to the analysis, delete the field directly. For outlier detection, use statistical methods to calculate the mean and standard deviation of the data. Data that deviates from the mean by more than three times the standard deviation is considered an outlier and further manually verified. If the outlier is caused by a data entry error, it is corrected to the correct data. If it is true abnormal business data, it is separately stated in the analysis report.

[0032] Step 5: Analytical model construction: For financial indicator forecasting, a time series forecasting model is used. Historical financial data is input and the model is trained to predict the trend of financial indicators over a period of time. For financial risk diagnosis, a risk assessment model based on a decision tree algorithm is constructed. Multiple financial ratios such as debt-to-asset ratio, current ratio, and accounts receivable turnover rate are used as input features. Through the branching decision rules of the decision tree, the company's financial risk status is classified and assessed to determine whether the company has potential financial risks and the risk level.

[0033] Step 6. Report generation: Develop a report generation engine that obtains cleaned and analyzed financial data in real time through a data interface, fills and renders the data according to the preset format of the template, and generates financial analysis reports in various formats; at the same time, set up a scheduled report generation task. According to the needs of the enterprise, the report generation process can be triggered on a daily, weekly, monthly or other cycle basis, and the generated report can be automatically sent to the target audience such as relevant financial personnel and management through the email system or the internal enterprise message push platform.

[0034] This application targets data from different systems such as financial software, business systems, and databases in an enterprise, with inconsistent formats and standards. Through specific data collection and preprocessing steps, this method can efficiently integrate this data, greatly reducing the time and effort cost of data integration, and uniformly converting data in various formats into the standard format required for analysis, avoiding the tediousness and errors of manual processing;

[0035] It can solve the problem of data acquisition delay and ensure that the data used in the analysis report can timely reflect the latest financial status of the enterprise. By establishing a real-time data transmission and update mechanism, or adopting data caching and pre-fetching technologies, the latest data can be obtained when generating reports, thus providing a timely and accurate basis for decision-making, improving the timeliness and scientificity of decision-making;

[0036] A series of data verification and quality control measures are applied to ensure the accuracy and reliability of the data. Data consistency checks and outlier processing are carried out to avoid deviations in analysis results caused by data errors or inconsistencies, so that the financial data analysis report can truly and accurately reflect the financial status of the enterprise.

[0037] Automated processes for data collection, processing, analysis, and report generation reduce manual intervention, lowering the risk of human error while also improving work efficiency. For example, by writing scripts or using professional data analysis software, data processing and analysis can be automatically completed according to pre-set rules, and formatted reports can be generated, saving labor and time costs.

[0038] In the step 1, during the data acquisition process, for financial software, if it provides an API interface, use standardized interface specifications to write a data extraction program to directly extract financial transaction data and account balance data from the database of the financial software according to established rules; for business systems, such as sales management systems and procurement management systems, execute customized SQL query statements through database connection to extract financial-related business data, such as sales order amounts and procurement cost data.

[0039] In the real-time data synchronization of step 2, for example, for an Oracle database, the database transaction log is captured in real time by the OGG tool, and the data change information is transmitted to the target data storage platform in real time.

[0040] In the standardization process of step three, the date format is unified as "YYYY-MM-DD", the amount data is uniformly formatted with two decimal places, and the currency unit is clearly defined; for data coding, an enterprise-level data coding dictionary is established, such as customer code, supplier code, and accounting subject code, and the relevant data in all data sources are coded and converted according to the dictionary.

[0041] In the data cleaning of step 4, the deduplication algorithm used is, for example, a data deduplication model built based on a hash algorithm; the key business fields used include invoice number and transaction serial number.

[0042] In the construction of the analysis model in step five, the time series prediction model used includes a long short-term memory network neural network model.

[0043] In the step 6 of generating the report, the generated financial analysis reports are in various formats including PDF, Excel, and Word.

[0044] This method of generating financial data analysis reports is applicable not only to large and medium-sized enterprises, but also to small enterprises;

[0045] Large enterprises have vast amounts of data from complex sources. This method's efficient data integration capabilities allow it to handle massive amounts of data from numerous different systems. Through automated data collection and preprocessing, it significantly improves data processing efficiency, saving both manpower and time. Its flexibility and scalability also meet the ever-changing business needs of large enterprises. For example, as an enterprise expands and its business diversifies, it can easily add new data sources and analytical metrics.

[0046] Medium-sized enterprises need to balance costs and benefits during their development. This method has a high degree of automation, which can reduce the workload of manual data processing and lower labor costs, while improving work efficiency and data accuracy, helping medium-sized enterprises achieve efficient financial management with limited resources.

[0047] Small businesses usually have limited resources and their staff may lack professional skills. This method may be relatively simple and easy to use. Even without a professional data analysis team, the financial personnel of small businesses can easily master and apply it, quickly generate financial analysis reports, and meet the basic financial management needs of the enterprise.

[0048] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the appended claims rather than the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any figure signs in the claims should not be regarded as limiting the claims involved.

[0049] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for generating a financial data analysis report, characterized in that: The specific contents of the method for generating the financial data analysis report are as follows: Step 1: Multi-source data access: Build a data access system with broad compatibility, using automated data interface technology to acquire data from the enterprise's diverse and heterogeneous data sources. Develop adaptive data collection scripts for various databases, both relational and non-relational, based on their data structure characteristics. For external public data sources, such as industry financial data statistics websites and government economic data release platforms, use data crawler technology and Python's Scrapy framework tool to write crawler programs that simulate human web browsing behavior, accurately locate and extract the required macroeconomic data and industry financial indicator data, and provide external references for comparative analysis of enterprise financial data. Step 2: Real-time data synchronization: On the source system side, database log parsing technology is utilized. In business systems and financial software, if a message queue mechanism is supported, messages are triggered at key business nodes where data is generated or updated. The data is encapsulated into a message format and sent to a message queue server. The data collection system monitors the message queue in real time. Once a new message is received, the data is immediately extracted and processed to ensure that the data can enter the analysis process as quickly as possible, so that the data used in the analysis report can reflect the company's financial status in real time. Step 3: Standardization: Develop unified standards for data formats. During the data integration process, develop a data standardization conversion program to perform batch format conversion and code mapping on the data collected from various systems according to standardized rules. This ensures data consistency at the format and coding levels, laying the foundation for subsequent data integration and analysis. Step 4: Data Cleaning: Utilize a deduplication algorithm to calculate hash values ​​for the collected financial data based on key business fields. Duplicate data records are identified and deleted by comparing hash values. For missing value processing, if the proportion of missing values ​​in a field in the dataset is low, use mean filling, median filling, or interpolation based on the correlation between the data. If the proportion of missing values ​​is high and the field is not critical to the analysis, the field is deleted directly. For outlier detection, a statistical method is used to calculate the mean and standard deviation of the data. Data that deviates from the mean by more than 3 times the standard deviation is considered an outlier and further manual verification is carried out. If the outlier is caused by data entry errors, it is corrected to correct data. If it is true abnormal business data, it is separately stated in the analysis report. Step 5: Analytical model construction: For financial indicator forecasting, a time series forecasting model is used. Historical financial data is input and the model is trained to predict the trend of financial indicators over a period of time. For financial risk diagnosis, a risk assessment model based on a decision tree algorithm is constructed. Multiple financial ratios such as debt-to-asset ratio, current ratio, and accounts receivable turnover rate are used as input features. Through the branching decision rules of the decision tree, the company's financial risk status is classified and assessed to determine whether the company has potential financial risks and the risk level. Step 6. Report generation: Develop a report generation engine that obtains cleaned and analyzed financial data in real time through a data interface, fills and renders the data according to the preset format of the template, and generates financial analysis reports in various formats; at the same time, set up a scheduled report generation task. According to the needs of the enterprise, the report generation process can be triggered on a daily, weekly, monthly or other cycle basis, and the generated report can be automatically sent to the target audience such as relevant financial personnel and management through the email system or the internal enterprise message push platform.

2. The method for generating a financial data analysis report according to claim 1, wherein: In the step 1, during the data acquisition process, for financial software, if it provides an API interface, use standardized interface specifications to write a data extraction program to directly extract financial transaction data and account balance data from the database of the financial software according to established rules; for business systems, such as sales management systems and procurement management systems, execute customized SQL query statements through database connection to extract financial-related business data, such as sales order amounts and procurement cost data.

3. The method for generating a financial data analysis report according to claim 1, wherein: In the real-time data synchronization of step 2, for example, for an Oracle database, the database transaction log is captured in real time by the OGG tool, and the data change information is transmitted to the target data storage platform in real time.

4. The method for generating a financial data analysis report according to claim 1, wherein: In the standardization process of step three, the date format is unified into "YYYY-MM-DD", the amount data is uniformly formatted with two decimal places, and the currency unit is clearly defined; for data coding, an enterprise-level data coding dictionary is established, such as customer code, supplier code, and accounting subject code, and the relevant data in all data sources are converted according to the dictionary.

5. The method for generating a financial data analysis report according to claim 1, wherein: In the data cleaning of step 4, the deduplication algorithm used is, for example, a data deduplication model built based on a hash algorithm; the key business fields used include invoice number and transaction serial number.

6. The method for generating a financial data analysis report according to claim 1, wherein: In the construction of the analysis model in step five, the time series prediction model used includes a long short-term memory network neural network model.

7. The method for generating a financial data analysis report according to claim 1, wherein: In the step 6 of generating the report, the generated financial analysis reports are in various formats including PDF, Excel, and Word.

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