An automated enterprise financial operations management platform
Patent Information
- Application Number
- CN202411023462.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-29
- Publication Date
- 2026-10-30
AI Technical Summary
[0002]随着企业规模的扩大和业务复杂度的提升，传统依赖人工输入和处理的财务管理方式已无法满足企业对数据处理精度和效率的需求，尤其在医疗行业，面对海量的财务数据，手工处理不仅效率低下、容易出错，还难以保证数据的准确性和及时性，这对企业的运营和决策产生了重大影响，为了解决这些问题，现代企业特别是医疗行业的企业，开始转向自动化的数据处理系统
[0041](1)通过集成的自动采集模块，解决了传统财务数据收集过程中的繁琐和耗时问题，利用先进的数据采集技术，自动从企业的多个数据源中实时抓取财务数据，包括主营收入、成本和营销数据等，这一自动化的数据采集过程不仅提高了数据收集的效率，而且确保了数据的准确性和实时性，为企业管理层提供了及时的财务信息，从而加快了决策速度，提升了响应市场变化的能力；
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Figure CN123014422A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of financial management technology, specifically an automated enterprise financial operations management platform. Background Technology
[0002] As businesses grow and their operations become more complex, traditional financial management methods that rely on manual input and processing can no longer meet their demands for data processing accuracy and efficiency. This is especially true in the healthcare industry, where manual processing is not only inefficient and prone to errors when dealing with massive amounts of financial data, but also fails to guarantee the accuracy and timeliness of the data. This has a significant impact on business operations and decision-making. To address these issues, modern enterprises, particularly those in the healthcare industry, are turning to automated data processing systems. Summary of the Invention
[0003] The purpose of this invention is to provide an automated enterprise financial operations management platform that can automatically acquire various types of financial data, perform intelligent processing and calculations, and output the required financial reports, thereby improving the accuracy and efficiency of financial data management.
[0004] The objective of this invention can be achieved through the following technical solutions:
[0005] This application provides an automated enterprise financial operations management platform, including an automatic data collection module, a data processing module, a data output module, a visualization module, a report export module, and an automatic classification module;
[0006] The automatic data acquisition module is used to automatically collect the company's financial data; the data processing module is used to process the financial data; the data output module is used to generate customized financial statements, including profit and loss statements, balance sheets, and cash flow statements; the visualization module is used to provide real-time visualization of financial data; the report export module is used to support report export functions to meet the needs of different formats; and the automatic classification module is used to automatically classify and store the financial data in the corresponding database tables according to the nature of the financial data.
[0007] Furthermore, the processing of financial data includes data cleaning, structured storage, processing of the data, output of the required financial data, and automated classification and storage.
[0008] Data cleaning involves identifying and processing missing values, filling missing values in the dataset by imputing the mean, detecting and correcting outliers, and identifying values outside the normal range using box plot methods.
[0009] The missing value The processing is represented as: Where, x iThese are non-missing data points, and n is the number of non-missing data points.
[0010] The detection and correction of outliers begins by calculating the interquartile range (IQR) by calculating the first quartile (Q1) and the third quartile (Q3), expressed as: IQR = Q3 - Q1. Then, upper and lower bounds are determined, with the upper bound being Q3 + 1.5 × IQR and the lower bound being Q1 - 105 × IQR. Outliers that exceed the upper and lower bounds are then corrected or removed.
[0011] Furthermore, the cleaned data is stored in a data model, including table structure, field types, and indexes. Key financial indicators are calculated using this data, and a Market Feedback Composite Score (MFS) is used to measure the market's overall response to the company's products or services. The MFS is calculated by combining multiple different market feedback indicators and assigning appropriate weights to each indicator, and is expressed as: MFS = ω1×F1 + ω2×F2 + ... + ω n ×F n F1, F2...F n These are different market feedback indicators, ω1, ω2...ω n These are the corresponding weights;
[0012] Integrating information from multiple dimensions such as revenue, costs, customer satisfaction, and market feedback, the Financial Health Index (FHI) is used to measure the overall financial health of a company. Specifically, the FHI is expressed as follows:
[0013]
[0014] Among them, R i and C i ρ1, ρ2, and ρ3 represent the revenue and cost of the i-th item, respectively, CS represents customer satisfaction, and ρ1, ρ2, and ρ3 are the weights of the corresponding indicators. By using FHI to assess the financial health of enterprises, we can analyze their performance in different time periods or under different market conditions.
[0015] Furthermore, the system automatically generates profit and loss statements, balance sheets, and cash flow statements based on the acquired financial data, and dynamically displays the company's financial health and key financial indicators through a visualization module.
[0016] Furthermore, the automatic classification module includes a classification rule unit for storing the financial data in a database table;
[0017] The classification rule unit includes a first classification rule and a second classification rule. The first classification rule is used to classify and store the financial data; the second classification rule is used to store the preliminarily classified financial data in a first database table.
[0018] Furthermore, the first classification rule is used to perform preliminary classification of financial data, dividing the data into different categories according to the nature and source of the data; specifically, after data collection, the nature and source of the data are identified through metadata tags; regular expressions or pattern matching technology are used to match the data with the preset classification rules; after a successful match, the data is divided into preliminary classification buckets according to the rules.
[0019] The second classification rule maps and stores the initially classified data into the first database table.
[0020] The specific stored procedure is as follows:
[0021] The data in the initial classification bins are verified and cleaned to ensure data quality;
[0022] Apply mapping rules to direct data storage to the corresponding core database tables;
[0023] Automate data storage operations using database triggers or stored procedures.
[0024] Furthermore, the classification rule unit also includes a third classification rule and a fourth classification rule, wherein the third classification rule is used to store the business logic or analysis requirements of the financial data in a second database table;
[0025] The second database table, based on the second classification rules, classifies data according to additional business logic or analysis needs, and uses data aggregation and decomposition techniques to support multi-dimensional data analysis.
[0026] Then, aggregate functions are used to summarize the data, the summarized data is broken down into finer dimensions, and moving averages are used to analyze the time series data.
[0027] Furthermore, the fourth classification rule is used to store the analysis results and feature data of the financial data in a third database table.
[0028] The third database table is used to create a "trend analysis data table" and a "prediction result data table" to store analysis results and feature data, and to identify data suitable for trend analysis and prediction from the data processed by the third classification rules.
[0029] Apply data mining and machine learning algorithms to extract features and generate insights;
[0030] The processed data is stored in a table such as "Trend Analysis Data Table" or "Forecast Results Data Table".
[0031] Furthermore, by receiving the cleaned data, an isolation forest model is established to identify anomalies in financial data, and observations are "isolated" by randomly selecting features and split values.
[0032] Specific uses of the Isolation Forest model for anomaly detection include:
[0033] Feature extraction: Selecting a feature set X = {x1, x2, ..., x3} from financial data. n}, where x i Represents the i-th feature;
[0034] Training dataset partitioning: The dataset is divided into training and test sets using a partitioning ratio;
[0035] Training an isolated forest model: Train T isolated trees to form an isolated forest. Each tree t is constructed using the following methods:
[0036] Randomly select a feature x from the dataset. i And a segmentation value;
[0037] The data is divided into two parts based on the splitting value, and this process is repeated recursively for these two parts until the stopping condition is met.
[0038] Anomaly score calculation: For each observation x, calculate the average path length required to reach an isolated state in each tree t. t (x), where the anomaly score S(x) is the reciprocal of the negative average of the path lengths in all trees, expressed as:
[0039] Anomaly detection: Based on the anomaly score distribution on the test set, a threshold of 0 is determined. For each observation in the new data, an anomaly score is calculated, and observations with scores higher than the threshold of 0 are marked as anomalies.
[0040] The beneficial effects of this invention are as follows:
[0041] (1) Through the integrated automatic acquisition module, the cumbersome and time-consuming problems in the traditional financial data collection process are solved. By using advanced data acquisition technology, financial data, including main operating income, cost and marketing data, are automatically captured from multiple data sources of the enterprise in real time. This automated data acquisition process not only improves the efficiency of data collection, but also ensures the accuracy and timeliness of the data, providing timely financial information for the enterprise management, thereby accelerating the decision-making speed and improving the ability to respond to market changes.
[0042] (2) The data processing and automatic classification modules further enhance the platform's capabilities. Through data cleaning, outlier detection and correction, and structured storage, they solve data quality problems and improve data reliability. Utilizing algorithms such as Isolation Forest for intelligent anomaly detection, the platform can automatically identify and process abnormal data, reducing the need for manual review and mitigating decision-making risks caused by data errors. The automatic classification and storage mechanism automatically categorizes and stores data in corresponding database tables based on its nature and source. This not only optimizes the data organization structure but also improves the efficiency of data retrieval and analysis, providing enterprises with a more accurate and detailed financial management view.
[0043] (3) The data output module, visualization module and report export module work together to provide customized financial statement generation and multi-dimensional data analysis and display, which solves the enterprise's need for in-depth financial data insight. Through these modules, enterprises can generate key financial statements such as profit and loss statements, balance sheets and cash flow statements, and display the dynamic changes of financial data in real time through charts, dashboards and other forms. The report export function supports multiple formats, which further improves the availability and shareability of data. It not only enables enterprises to quickly grasp the financial situation, but also helps management to carry out strategic planning and business optimization, thereby maintaining a competitive advantage in the fierce market competition. Attached Figure Description
[0044] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.
[0045] Figure 1 This is a schematic diagram of the structure of an automated enterprise financial operations management platform provided in Embodiment 1 of this application;
[0046] Figure 2 This is a schematic diagram of the automatic classification module of an automated enterprise financial operations management platform provided in Embodiment 1 of this application. Detailed Implementation
[0047] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.
[0048] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.
[0049] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.
[0050] Example 1
[0051] Please see Figures 1-2 This embodiment provides an automated enterprise financial operations management platform. By automating the processing and calculation of financial data, it improves the efficiency and accuracy of financial management, while reducing the error rate of manual operations, and provides strong data support for enterprise decision-making.
[0052] This invention provides an automated enterprise financial operations management platform, including an automatic data collection module, a data processing module, a data output module, a visualization module, a report export module, and an automatic classification module;
[0053] The automatic data acquisition module is used to automatically collect the company's financial data; the data processing module is used to process the financial data; the data output module is used to generate customized financial reports, including: profit and loss statement, balance sheet, cash flow statement, etc.; the visualization display module is used to provide real-time visualization of financial data; the report export module is used to support the export function of reports to meet the needs of different formats (such as PDF, Excel, etc.); the automatic classification module is used to automatically classify the financial data according to its nature and store it in the corresponding database table.
[0054] Specifically, the financial data includes: the company's main operating revenue, operating revenue, and revenue from various examination items (such as Holter monitoring, resting blood pressure monitoring, and ambulatory blood pressure monitoring); cost data, including equipment and system amortization, report pre-analysis fees, quality control fees, etc.; and marketing data, involving sales volume, market feedback, customer satisfaction, etc. The collected financial data will be integrated to form a comprehensive financial dataset.
[0055] The processing of financial data includes: data cleaning, structured storage, processing of the data, output of the required financial data, and automated classification and storage.
[0056] Data cleaning involves identifying and processing missing values, filling missing values in the dataset by imputing the mean, detecting and correcting outliers, and identifying values outside the normal range using box plot methods.
[0057] The missing value The processing is represented as: Where, x i These are non-missing data points, and n is the number of non-missing data points.
[0058] The detection and correction of outliers begins by calculating the interquartile range (IQR) by calculating the first quartile (Q1) and the third quartile (Q3), expressed as: IQR = Q3 - Q1. Then, upper and lower bounds are determined, with the upper bound being Q3 + 1.5 × IQR and the lower bound being Q1 - 105 × IQR. Outliers that exceed the upper and lower bounds are then corrected or removed.
[0059] The cleaned data is stored in the data model, including table structure, field types, and indexes. Key financial indicators are calculated using this data; the calculation process is existing and will not be elaborated further. The Market Feedback Composite Score (MFS) is used to measure the overall market response to the company's products or services. The MFS is calculated by combining multiple different market feedback indicators and assigning appropriate weights to each indicator, expressed as: MFS = ω1×F1 + ω2×F2 + ... + ω n ×F n F1, F2...F n These are different market feedback indicators, ω1, ω2...ω n These are the corresponding weights.
[0060] In addition, by combining information from multiple dimensions such as revenue, costs, customer satisfaction, and market feedback, the Financial Health Index (FHI) is used to measure the overall financial health of a company. Specifically, the FHI is expressed as follows:
[0061]
[0062] Among them, R i and C i ρ1, ρ2, and ρ3 represent the revenue and cost of the i-th item, respectively, CS represents customer satisfaction, and ρ1, ρ2, and ρ3 are the weights of the corresponding indicators. By using FHI to assess the financial health of enterprises, we can analyze their performance in different time periods or under different market conditions.
[0063] Furthermore, the system automatically generates profit and loss statements, balance sheets, and cash flow statements based on the acquired financial data, and dynamically displays the company's financial health and key financial indicators through a visualization module.
[0064] Furthermore, the automatic classification module includes a classification rule unit for storing the financial data in a database table;
[0065] The classification rule unit includes a first classification rule and a second classification rule. The first classification rule is used to classify and store the financial data; the second classification rule is used to store the preliminarily classified financial data in a first database table.
[0066] The first classification rule is used to initially classify financial data. Based on the nature of the data (such as revenue, cost, marketing data, etc.) and its source (such as different departments, projects, or time periods), the data is divided into different categories. Specifically, after data collection, the nature and source of the data are identified through metadata tags. Regular expressions or pattern matching technology are used to match the data with the preset classification rules. After a successful match, the data is divided into preliminary classification buckets according to the rules.
[0067] The second classification rule maps and stores the initially classified data into the first database table, such as the "Revenue Data Table" and the "Cost Data Table".
[0068] The specific stored procedure is as follows:
[0069] The data in the initial classification bins are verified and cleaned to ensure data quality;
[0070] Apply mapping rules to direct data storage to the corresponding core database tables;
[0071] Automate data storage operations using database triggers or stored procedures.
[0072] The classification rule unit also includes a third classification rule and a fourth classification rule. The third classification rule is used to store the business logic or analysis requirements of the financial data in a second database table.
[0073] Specifically, by creating a second database table, based on the second classification rules, further classification is carried out according to additional business logic or analysis needs, and the data is stored in more specific database tables such as "Project Cost Details Table" or "Time Series Revenue Table";
[0074] It leverages data aggregation and decomposition techniques to support multi-dimensional data analysis.
[0075] More specifically, aggregate functions are used to summarize the data, breaking it down into finer dimensions, such as by project or by time period. Moving averages are then used to analyze the time series data, as shown below: Where t represents a specific time point in the time series; i represents an index in a specific time series, indicating a specific observation; and l represents the interval or number of periods in the moving average calculation.
[0076] The aggregated and decomposed data is stored in the corresponding database tables to ensure data consistency and accessibility, and to perform complex data analysis by leveraging the multidimensional query capabilities of the database.
[0077] The fourth classification rule is used to store the analysis results and feature data of the financial data in the third database table.
[0078] The third database table is used to create a "Trend Analysis Data Table" and a "Prediction Result Data Table" to store analysis results and feature data.
[0079] Specifically, this involves identifying data suitable for trend analysis and prediction from the data processed by the third classification rule.
[0080] Apply data mining and machine learning algorithms to extract features and generate insights;
[0081] The processed data is stored in tables such as "Trend Analysis Data Table" or "Forecast Results Data Table" to support complex data analysis and decision support.
[0082] Specifically, the process involves receiving cleaned data and establishing an isolated forest model to identify anomalies in financial data. By randomly selecting features and split values, observations are "isolated." Outliers are usually easier to isolate because they differ significantly from normal data points.
[0083] Specific uses of the Isolation Forest model for anomaly detection include:
[0084] Feature extraction: Selecting a feature set X = {x1, x2, ..., x3} from financial data. n}, where x i Represents the i-th feature;
[0085] Training dataset partitioning: The dataset is divided into a training set Dtrain and a test set Dtest using a partitioning ratio;
[0086] Training an isolated forest model: Train T isolated trees to form an isolated forest. Each tree t is constructed using the following methods:
[0087] Randomly select a feature x from the dataset. i And a segmentation value;
[0088] The data is divided into two parts based on the splitting value, and this process is repeated recursively for both parts until the stopping condition is met.
[0089] Anomaly score calculation: For each observation x, calculate the average path length required to reach an isolated state in each tree t. t(x), where the anomaly score S(x) is the reciprocal of the negative average of the path lengths in all trees, expressed as:
[0090] Anomaly detection: Based on the anomaly score distribution on the test set, a threshold of 0 is determined. For each observation in the new data, an anomaly score is calculated, and observations with scores higher than the threshold of 0 are marked as anomalies.
[0091] This embodiment provides a comprehensive automated enterprise financial operations management platform, which enables efficient collection, processing, analysis, and display of enterprise financial data. The platform's automatic collection module first automatically collects financial data from multiple data sources, including main operating revenue, cost, and marketing data, to ensure the comprehensiveness and real-time nature of the data. Subsequently, the data processing module cleans the collected data, including handling missing values, detecting and correcting outliers, using box plots to determine the upper and lower bounds of the data, and ensuring data quality by filling in the average value and correcting values that exceed the normal range.
[0092] Next, the data output module generates customized financial reports, such as profit and loss statements, balance sheets, and cash flow statements, based on the cleaned data to meet the needs of different users. These reports are displayed in real time through the visualization module in the form of charts, dashboards, etc., providing an intuitive data analysis perspective. The report export module further supports exporting these reports to formats such as PDF and Excel, increasing data accessibility and flexibility.
[0093] The automatic classification module automatically classifies financial data and stores it in the corresponding database tables through classification rule units. The first classification rule is responsible for the initial classification, while the second classification rule maps the data to the core database table. The third and fourth classification rules further subdivide the data and store it in more specific database tables, such as the "Project Cost Details Table" or "Time Series Revenue Table", as well as the "Trend Analysis Data Table" and "Forecast Result Data Table", supporting deeper data analysis and decision support.
[0094] In addition, this invention also uses an isolated forest model for anomaly detection. By randomly selecting features and split values, it identifies outliers that differ significantly from normal data points. This process not only improves the accuracy of anomaly detection but also reduces human intervention and lowers costs through automation.
[0095] The implementation of the platform has yielded remarkable results. It has not only solved the tedious work of data collection and processing in traditional financial management, but also improved the accuracy and efficiency of data processing through automation and intelligent means. More importantly, through real-time data analysis and visualization, the platform can help enterprises quickly identify financial risks and market opportunities, providing strong data support for strategic decision-making, improving the level of financial management, and enhancing the competitiveness of enterprises in complex market environments.
[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. An automated enterprise financial operations management platform, characterized in that: It includes an automatic data acquisition module, a data processing module, a data output module, a visualization module, a report export module, and an automatic classification module; The automatic data acquisition module is used to automatically collect the company's financial data; the data processing module is used to process the financial data; the data output module is used to generate customized financial statements, including profit and loss statements, balance sheets, and cash flow statements; the visualization module is used to provide real-time visualization of financial data; the report export module is used to support report export functions to meet the needs of different formats; and the automatic classification module is used to automatically classify and store the financial data in the corresponding database tables according to the nature of the financial data.
2. The automated enterprise financial operations management platform according to claim 1, characterized in that: Processing financial data includes data cleaning, structured storage, processing of the data, outputting the required financial data, and automated classification and storage. Data cleaning involves identifying and processing missing values, filling missing values in the dataset by imputing the mean, detecting and correcting outliers, and identifying values outside the normal range using box plot methods. The missing value The processing is represented as: Where, x i These are non-missing data points, and n is the number of non-missing data points. The detection and correction of outliers begins by calculating the interquartile range (IQR) by calculating the first quartile (Q1) and the third quartile (Q3), expressed as: IQR = Q3 - Q1. Then, upper and lower bounds are determined, with the upper bound being Q3 + 1.5 × IQR and the lower bound being Q1 - 105 × IQR. Outliers that exceed the upper and lower bounds are then corrected or removed.
3. The automated enterprise financial operations management platform according to claim 2, characterized in that: The cleaned data is stored in a data model, including table structure, field types, and indexes. Key financial indicators are calculated using this data, and a Market Feedback Composite Score (MFS) is used to measure the market's overall response to the company's products or services. The MFS is calculated by combining multiple different market feedback indicators and assigning appropriate weights to each indicator, and is expressed as: MFS = ω1×F1 + ω2×F2 + ... + ω n ×F n F1, F2...F n These are different market feedback indicators, ω1, ω2...ω n These are the corresponding weights; Integrating information from multiple dimensions such as revenue, costs, customer satisfaction, and market feedback, the Financial Health Index (FHI) is used to measure the overall financial health of a company. Specifically, the FHI is expressed as follows: Among them, R i and C i ρ1, ρ2, and ρ3 represent the revenue and cost of the i-th item, respectively, CS represents customer satisfaction, and ρ1, ρ2, and ρ3 are the weights of the corresponding indicators. By using FHI to assess the financial health of enterprises, we can analyze their performance in different time periods or under different market conditions.
4. The automated enterprise financial operations management platform according to claim 1, characterized in that: The system automatically generates profit and loss statements, balance sheets, and cash flow statements based on the acquired financial data, and dynamically displays the company's financial health and key financial indicators through a visualization module.
5. The automated enterprise financial operations management platform according to claim 1, characterized in that: The automatic classification module includes a classification rule unit for storing the financial data in a database table; The classification rule unit includes a first classification rule and a second classification rule. The first classification rule is used to classify and store the financial data; the second classification rule is used to store the preliminarily classified financial data in a first database table.
6. The automated enterprise financial operations management platform according to claim 5, characterized in that: The first classification rule is used to initially classify financial data, dividing the data into different categories based on its nature and source; Specifically, after data collection, the nature and source of the data are identified through metadata tags; regular expressions or pattern matching techniques are used to match the data with preset classification rules; and if a match is successful, the data is divided into preliminary classification buckets according to the rules. The second classification rule maps and stores the initially classified data into the first database table. The specific stored procedure is as follows: The data in the initial classification bins are verified and cleaned to ensure data quality; Apply mapping rules to direct data storage to the corresponding core database tables; Automate data storage operations using database triggers or stored procedures.
7. An automated enterprise financial operations management platform according to claim 5, characterized in that: The classification rule unit further includes a third classification rule and a fourth classification rule. The third classification rule is used to store the business logic or analysis requirements of the financial data in a second database table. The second database table, based on the second classification rules, classifies data according to additional business logic or analysis needs, and uses data aggregation and decomposition techniques to support multi-dimensional data analysis. Then, aggregate functions are used to summarize the data, the summarized data is broken down into finer dimensions, and moving averages are used to analyze the time series data.
8. The automated enterprise financial operations management platform according to claim 1, characterized in that: The fourth classification rule is used to store the analysis results and feature data of the financial data in the third database table. The third database table is used to create a "trend analysis data table" and a "prediction result data table" to store analysis results and feature data, and to identify data suitable for trend analysis and prediction from the data processed by the third classification rules. Apply data mining and machine learning algorithms to extract features and generate insights; The processed data is stored in a table such as "Trend Analysis Data Table" or "Forecast Results Data Table".
9. An automated enterprise financial operations management platform according to claim 2, characterized in that: By receiving cleaned data, an isolation forest model is built to identify anomalies in financial data, and observations are "isolated" by randomly selecting features and split values. Specific uses of the Isolation Forest model for anomaly detection include: Feature extraction: Selecting a feature set X = {x1, x2, ..., x3} from financial data. n }, where x i Represents the i-th feature; Training dataset partitioning: The dataset is divided into training and test sets using a partitioning ratio; Training an isolated forest model: Train T isolated trees to form an isolated forest. Each tree t is constructed using the following methods: Randomly select a feature x from the dataset. i And a segmentation value; The data is divided into two parts based on the splitting value, and this process is repeated recursively for these two parts until the stopping condition is met. Anomaly score calculation: For each observation x, calculate the average path length required to reach an isolated state in each tree t. t (x), where the anomaly score S(x) is the reciprocal of the negative average of the path lengths in all trees, expressed as: Anomaly detection: Based on the anomaly score distribution on the test set, a threshold θ is determined. For each observation in the new data, an anomaly score is calculated, and observations with scores higher than the threshold 0 are marked as anomalies.