Enterprise risk assessment method and device, computer equipment and storage medium

Abstract

This application belongs to the field of risk assessment and relates to a method for enterprise risk assessment, including: extracting key indicators from the enterprise's operating data; calculating the first weight of each key indicator based on the entropy method; calculating the correlation coefficient between each key indicator and adjusting the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator; for each key indicator, decomposing the key indicator into preset time intervals based on a preset time decay algorithm and the second weight of the key indicator to obtain the sub-indicator weight of the key indicator in each time interval; calculating the indicator score of the key indicator based on the sub-indicator weight of the key indicator in each time interval and its corresponding sub-indicator value, and calculating the comprehensive score of the enterprise based on the indicator scores of each key indicator; and generating the enterprise risk assessment result based on the comprehensive score. This application improves the accuracy and efficiency of enterprise risk assessment.

Description

Technical Field

[0001] This application relates to the field of risk assessment technology, and in particular to a method, apparatus, computer equipment and storage medium for enterprise risk assessment. Background Technology

[0002] In the field of supply chain finance, accounts receivable management and financing risk assessment for micro and small enterprises mainly rely on expert experience for indicator selection and weighting. This approach is highly subjective and easily influenced by individual expert judgment, resulting in insufficient objectivity, accuracy, and comprehensiveness in enterprise risk assessment, making it difficult to meet the needs of modern finance for efficient and precise risk management. Furthermore, manually processing and analyzing large amounts of data is inefficient and hinders rapid responses to market changes and enterprise financing needs. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, computer equipment, and storage medium for enterprise risk assessment, in order to solve the problem of low accuracy and efficiency in enterprise risk assessment.

[0004] To address the aforementioned technical problems, this application provides a method for enterprise risk assessment, employing the following technical solution:

[0005] Acquire the enterprise's business data and extract key indicators from the business data according to preset indicator dimensions;

[0006] The first weight of each key indicator is calculated based on the entropy method;

[0007] Calculate the correlation coefficient between the key indicators, and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator.

[0008] For each key indicator, based on a preset time decay algorithm and the second weight of the key indicator, the key indicator is decomposed into preset time intervals to obtain the sub-indicator weights of the key indicator in each time interval.

[0009] Based on the sub-indicator weights and corresponding sub-indicator values ​​of the key indicators in each time interval, the indicator scores of the key indicators are calculated, and the comprehensive score of the enterprise is calculated based on the indicator scores of each key indicator.

[0010] The enterprise risk assessment result is generated based on the comprehensive score.

[0011] To address the aforementioned technical problems, this application also provides an enterprise risk assessment device, which employs the following technical solution:

[0012] The data acquisition module is used to acquire the enterprise's business data and extract key indicators from the business data according to preset indicator dimensions.

[0013] The first calculation module is used to calculate the first weight of each key indicator based on the entropy method.

[0014] The first adjustment module is used to calculate the correlation coefficient between the key indicators and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator.

[0015] The indicator decomposition module is used to decompose each key indicator into preset time intervals based on a preset time decay algorithm and the second weight of the key indicator, so as to obtain the sub-indicator weight of the key indicator in each time interval.

[0016] The scoring calculation module is used to calculate the index score of the key indicator based on the sub-index weights and corresponding sub-index values ​​of the key indicator in each time interval, and to calculate the comprehensive score of the enterprise based on the index scores of each key indicator.

[0017] The risk assessment module is used to generate the enterprise risk assessment result of the enterprise based on the comprehensive score.

[0018] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0019] Acquire the enterprise's business data and extract key indicators from the business data according to preset indicator dimensions;

[0020] The first weight of each key indicator is calculated based on the entropy method;

[0021] Calculate the correlation coefficient between the key indicators, and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator.

[0022] For each key indicator, based on a preset time decay algorithm and the second weight of the key indicator, the key indicator is decomposed into preset time intervals to obtain the sub-indicator weights of the key indicator in each time interval.

[0023] Based on the sub-indicator weights and corresponding sub-indicator values ​​of the key indicators in each time interval, the indicator scores of the key indicators are calculated, and the comprehensive score of the enterprise is calculated based on the indicator scores of each key indicator.

[0024] The enterprise risk assessment result is generated based on the comprehensive score.

[0025] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0026] Acquire the enterprise's business data and extract key indicators from the business data according to preset indicator dimensions;

[0027] The first weight of each key indicator is calculated based on the entropy method;

[0028] Calculate the correlation coefficient between the key indicators, and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator.

[0029] For each key indicator, based on a preset time decay algorithm and the second weight of the key indicator, the key indicator is decomposed into preset time intervals to obtain the sub-indicator weights of the key indicator in each time interval.

[0030] Based on the sub-indicator weights and corresponding sub-indicator values ​​of the key indicators in each time interval, the indicator scores of the key indicators are calculated, and the comprehensive score of the enterprise is calculated based on the indicator scores of each key indicator.

[0031] The enterprise risk assessment result is generated based on the comprehensive score.

[0032] Compared with existing technologies, the embodiments of this application have the following main advantages: Key indicators are extracted from the enterprise's operational data, and weights are assigned to these indicators using the entropy method, eliminating the subjectivity of traditional risk assessments that rely on expert experience and ensuring the objectivity and rationality of weight allocation; Through correlation adjustment, the weights are further optimized, allowing the interrelationships between key indicators to be fully considered, thus improving the accuracy of the assessment; A time decay algorithm is introduced to decompose key indicators into multiple time intervals, emphasizing the different impacts of data from different periods, making the risk assessment results more reflective of the enterprise's latest risk status; Based on the sub-indicator weights and values ​​of the key indicators in each time interval, the indicator scores of the key indicators are calculated, and the enterprise's comprehensive score is calculated based on the indicator scores of each key indicator. The enterprise risk assessment results are generated based on the comprehensive score, which can reflect changes in the enterprise's operating status in real time, improving the accuracy and efficiency of enterprise risk assessment. Attached Figure Description

[0033] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0035] Figure 2 This is a flowchart of an embodiment of the enterprise risk assessment method according to this application;

[0036] Figure 3 This is a schematic diagram of the structure of one embodiment of the enterprise risk assessment device according to this application;

[0037] Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0039] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0040] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0041] like Figure 1As shown, system architecture 100 may include terminal device 101, network 102, and server 103. Terminal device 101 may be a laptop 1011, tablet 1012, or mobile phone 1013. Network 102 is used as a medium to provide a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0042] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0043] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to laptops 1011, tablets 1012, or mobile phones 1013, terminal device 101 can also be e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers.

[0044] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0045] It should be noted that the enterprise risk assessment method provided in this application embodiment is generally executed by a server, and correspondingly, the enterprise risk assessment device is generally set in the server.

[0046] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0047] Continue to refer to Figure 2 A flowchart illustrating an embodiment of the enterprise risk assessment method according to this application is shown. The enterprise risk assessment method includes the following steps:

[0048] Step S201: Obtain the enterprise's business data and extract key indicators from the business data according to the preset indicator dimensions.

[0049] In this embodiment, the enterprise risk assessment method operates on electronic devices (e.g., Figure 1 The server shown can communicate with the terminal device via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra wideband) connections, and other currently known or future known wireless connection methods.

[0050] Specifically, key indicators are extracted from a company's operational data according to pre-defined indicator dimensions. This operational data can include data from various aspects of the company's operations. The extraction process relies on pre-defined rules for specific indicator dimensions to ensure that the extracted key indicators are relevant to the company's risk assessment.

[0051] Furthermore, step S201 above may include: acquiring the enterprise's business data, which includes the enterprise's cash flow data, credit data, and supply chain data; preprocessing the business data; and extracting a preset number of indicators from the business data as key indicators according to preset indicator dimensions.

[0052] Specifically, business operation data may include multiple sources, such as the company's cash flow data, credit data, and supply chain data. This data provides information on the company's daily operations, credit status, and relationships with upstream and downstream companies in the supply chain.

[0053] After obtaining the company's operational data, preprocessing is required. This may include data cleaning (removing noise or incomplete data), standardization (using uniform units to ensure comparability across different data dimensions), filtering out irrelevant information, filling out outliers with the median, and filling missing values ​​with constants.

[0054] Next, according to the preset indicator dimensions, a certain number of indicators are extracted from the preprocessed enterprise operating data as key indicators for enterprise risk assessment. Typically, key indicators are extracted from cash flow data, credit data, and supply chain data. These key indicators can effectively reflect the overall operating status of the enterprise and provide basic data support for subsequent risk assessment.

[0055] In this embodiment, multi-dimensional data sources from enterprises are acquired and preprocessed to ensure the comprehensiveness and consistency of the data, providing a reliable data foundation for enterprise risk assessment. Preprocessing can eliminate outliers and noise, reduce the impact of data inconsistency on the assessment, and improve the accuracy of the assessment. Key indicators are extracted based on preset indicator dimensions to directly extract the most representative operational characteristics of the enterprise, which facilitates more accurate weight calculation and risk assessment in the future.

[0056] Step S202: Calculate the first weight of each key indicator based on the entropy method.

[0057] Specifically, the entropy method is used to calculate the first weight W of each key indicator. orig Entropy method is an objective weighting method that assigns higher weights to indicators with greater volatility by measuring the dispersion of each indicator. Entropy method can reduce subjective interference and improve the objectivity of weighting.

[0058] Entropy analysis is a method used in multi-objective decision analysis. It calculates the information entropy of indicators to determine their dispersion and thus the weight of each indicator. In information theory, entropy is a measure of uncertainty. The greater the amount of information, the lower the uncertainty and the lower the entropy; conversely, the smaller the amount of information, the greater the uncertainty and the greater the entropy. Based on the characteristics of entropy, the randomness and disorder of an event can be determined by calculating its value. Similarly, entropy can be used to determine the dispersion of an indicator; the greater the dispersion, the greater the influence of that indicator on the overall evaluation. Therefore, the weight of each indicator can be calculated using information entropy based on its degree of variation, providing a basis for multi-indicator comprehensive evaluation.

[0059] The application steps of the entropy method typically include the following aspects:

[0060] Data standardization: In order to eliminate the influence of units and dimensions on the evaluation results, the raw data is standardized.

[0061] Calculate the weighting: Form a weighting matrix of the data, that is, calculate the weight of each indicator in all samples.

[0062] Calculate the entropy value: Based on the weight matrix, calculate the entropy value of each indicator to reflect the degree of dispersion of the indicator data.

[0063] Calculate the variance index: The variance index is an indicator that reflects the importance of an indicator, and it can be obtained by subtracting the entropy value from 1.

[0064] Calculate entropy weight: Based on the variation index, calculate the weight of each indicator, i.e., the entropy weight, to reflect the degree of influence of the indicator on the comprehensive evaluation.

[0065] Overall score: Calculate the overall evaluation value of the evaluated object based on the weight and proportion of each indicator.

[0066] In this embodiment, the existing entropy method can be used to calculate the first weight of each key indicator, including calculating the proportion of each key indicator, calculating the information entropy, calculating the entropy redundancy, and calculating the objective weight (first weight). The detailed calculation process will not be described in detail here.

[0067] Step S203: Calculate the correlation coefficient between each key indicator, and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator.

[0068] Specifically, the correlation coefficients between key indicators are calculated, such as the Pearson correlation coefficient, to assess the interrelationships among them. Based on this, the first weights of each key indicator are adjusted to obtain the adjusted second weights W. norm Key indicators with higher correlation coefficients may have a more direct impact on corporate risk; therefore, adjusting for correlation can improve the accuracy of the assessment.

[0069] Furthermore, step S203 may include: calculating the correlation coefficient between each key indicator; calculating the correlation adjustment coefficient between each key indicator based on the obtained correlation coefficient; constructing an adjustment coefficient matrix based on each correlation adjustment coefficient; adjusting the first weight of each key indicator based on the adjustment coefficient matrix, and normalizing the adjusted weight to obtain the second weight of each key indicator.

[0070] Specifically, the correlation coefficients between key indicators are calculated to obtain the degree of association between different indicators. By using correlation coefficients, it can be discovered that certain key indicators may have a high degree of correlation, thus avoiding the repeated consideration of the influence of the same type of information in weight calculations.

[0071] In one embodiment, the Pearson correlation coefficient formula is used to calculate the Pearson correlation coefficient between different key indicators. This application can use existing methods for calculating the Pearson correlation coefficient, which requires observations of each key indicator at multiple time points; the specific calculation process will not be detailed here.

[0072] Based on these correlation coefficients, the adjusted correlation coefficients between the key indicators are calculated using the following formula: a ij =1-|r ij | where i and j represent key metrics, and r ij The correlation coefficient between key indicators i and j is represented by |r. ij |is r ij The absolute value of a ij This represents the correlation adjustment coefficient between key indicators i and j. The correlation adjustment coefficient reflects the relative importance of key indicators in the overall evaluation, especially in cases where there is a strong correlation, some key indicators may be affected by other key indicators.

[0073] Construct an adjustment coefficient matrix based on the correlation adjustment coefficients. Assume we have key indicators X1, X2, and X3. The correlation coefficient between X1 and X2 is r. 12 =0.9, the correlation coefficient between X1 and X3 is r 13=0.8, the correlation coefficient between X2 and X3 is r 23 =0.9. Therefore, the adjustment coefficient matrix A is as follows:

[0074]

[0075] By constructing a correlation adjustment coefficient matrix, the weights of each key indicator are adjusted. This matrix comprehensively considers the interrelationships among the key indicators, ensuring a more reasonable and objective allocation of weights.

[0076] The formula is adjusted as follows:

[0077]

[0078] in, ω represents the weight obtained after adjusting the key indicator i; j ω represents the first weight of the key indicator j. i Indicates the first weight of key indicator i; a ij This represents the correlation adjustment coefficient between key indicators i and j; a kl These are elements in the correlation adjustment coefficient matrix, representing the correlation adjustment coefficient between key indicators k and l; ω l This represents the first weight of the key indicator l. In Formula 1, the numerator Σ(a ij ·ω j () represents the adjustment factor a for key indicator i, taking into account its correlation with other key indicators. ij To adjust its weights, the denominator ∑∑(a kl ·ω l ) is a normalization factor used to ensure that the adjusted weights remain within a reasonable range.

[0079] The adjusted weights are then normalized to eliminate the bias in the total weights caused by the weight adjustments, ensuring that the weights of each key indicator have a uniform standardized range in the overall evaluation, and that the sum of the second weights of all key indicators is 1. After these adjustments and normalization processes, the second weight of key indicator i is obtained. Used for subsequent risk assessment calculations.

[0080] In this embodiment, by calculating the correlation coefficient between key indicators, the correlation between indicators can be effectively identified, avoiding the repeated calculation of redundant information; the correlation adjustment coefficient is calculated based on the correlation coefficient to ensure that the relative importance of each indicator can be reasonably considered; the construction of the adjustment coefficient matrix can comprehensively handle the complex correlation between multiple indicators and achieve a more reasonable weight allocation; the normalization process ensures that the adjusted weights have a consistent standard in the calculation process, avoiding the deviation problem caused by the imbalance of weights.

[0081] Step S204: For each key indicator, based on the preset time decay algorithm and the second weight of the key indicator, the key indicator is decomposed into preset time intervals to obtain the sub-indicator weights of the key indicator in each time interval.

[0082] Specifically, key indicators are decomposed into multiple pre-defined time intervals, and each key indicator is further decomposed into multiple sub-indicators. A time decay algorithm, such as an exponential decay function, is used to ensure that newer data carries a larger weight. The weight of each sub-indicator in each time interval is obtained by decaying the second weight over time using the time decay algorithm, reflecting the impact of dynamic changes on risk assessment.

[0083] Furthermore, step S204 may include: obtaining preset time intervals and obtaining the time interval between each time interval and the target time; calculating the time weight corresponding to each time interval based on the preset time decay algorithm and the obtained time interval; for each key indicator, multiplying the second weight of the key indicator by the time weight of each time interval to decompose the key indicator into each time interval and obtain the sub-indicator weight of the key indicator in each time interval.

[0084] Specifically, a preset time interval is obtained, such as each month, and the time interval between each time interval and the target time is calculated. For example, assuming there is data for four months (months t = 1, 2, 3, 4, where 1 represents the latest month), each month can be considered a time interval. Assuming the current time is the target time, the time intervals would be 1, 2, 3, and 4 respectively. This operation is to reflect the importance of each time interval relative to the current time point in the overall assessment. Based on the time interval, a time weight can be assigned to each time interval. The closer the time interval is to the current time, the greater its time weight may be. This also conforms to the actual assessment needs, that is, more recent operating data may reflect the current risk status of the enterprise better than earlier data.

[0085] The time decay algorithm can be represented by a time decay function, which can be an exponential decay function, expressed as ωt = e^(-t / t). -λt , where ωt represents the time weight, λ is the decay coefficient (e.g., 0.1), and t is the time interval between the time interval and the target time. The t value is smaller for newer months and larger for older months.

[0086] After obtaining the time weight for each time interval, the second weight of the key indicator is multiplied by the time weight of each time interval, thereby dynamically decomposing the second weight of the key indicator over time. This allows the data of the key indicator in different periods to be evaluated and considered separately, maintaining the continuity and dynamism of the evaluation over time. Through dynamic decomposition, the sub-indicator weights of each key indicator in each time interval are obtained. These sub-indicator weights will be used in subsequent steps to calculate the indicator score for each time interval.

[0087] In this embodiment, key indicators are decomposed into different time intervals, enabling a more detailed and dynamic assessment of the company's operating data, making the risk assessment results more continuous and timely. By allocating time weights through a time decay algorithm, the influence of historical data on the assessment results can be reasonably reduced, focusing on recent data that has a significant impact on the current risk situation, thus improving the real-time nature and accuracy of the assessment results. Multiplying the second weight of each key indicator by its time weight reflects the relative importance of each key indicator in each time interval, avoiding excessive influence of long-term or outdated data on the risk assessment results. This more flexible assessment method allows the assessment results to be dynamically adjusted to adapt to changes in the company's operating conditions.

[0088] Step S205: Calculate the indicator score of the key indicator based on the sub-indicator weights and corresponding sub-indicator values ​​of the key indicator in each time interval, and calculate the comprehensive score of the enterprise based on the indicator scores of each key indicator.

[0089] Specifically, indicator scores and comprehensive scores are two key calculation results in the enterprise risk assessment process, and they correspond to different levels of risk assessment data.

[0090] Indicator scoring is the evaluation result for each key indicator. In the previous steps, key indicators were broken down by time intervals, and the indicator score was calculated based on the weights of the sub-indicators and their corresponding values ​​for each time interval. This score reflects the performance of the key indicator across different time intervals and measures its contribution or risk to the overall business operations.

[0091] For example, a key indicator might be a company's cash flow data, which can vary across different time periods. By using a time decay algorithm and sub-indicator weights, the cash flow performance from different periods is summed to obtain a comprehensive score of the company's cash flow status.

[0092] The comprehensive score is a summary of the scores for all key indicators. The final comprehensive score is calculated based on the scores of each key indicator. It is a comprehensive reflection of the overall business risk or situation, considering not only the independent performance of each key indicator but also their weight distribution, making the risk assessment of the enterprise more holistic and objective.

[0093] Furthermore, step S205 may include: multiplying the sub-indicator weights and their corresponding sub-indicator values ​​of the key indicator in each time interval to obtain the sub-indicator score in each time interval; summing the sub-indicator scores of the key indicator to obtain the indicator score of the key indicator; and summing the indicator scores of the key indicators to obtain the comprehensive score of the enterprise.

[0094] Specifically, key indicators are statistically analyzed according to time intervals, and the sub-indicator values ​​for each time interval are recorded in the enterprise's operating data. The sub-indicator weights of the key indicators in each time interval are multiplied by the corresponding sub-indicator values ​​to obtain the sub-indicator scores for each time interval. This ensures that each sub-indicator is appropriately reflected in the overall evaluation, so that the evaluation considers both the importance of the indicators (weights) and the actual performance (sub-indicator values).

[0095] In one embodiment, since the sub-indicator values ​​of the key indicator are large in some time intervals, the sub-indicator values ​​of the key indicator in each time interval can be normalized first, and then the sub-indicator score for each time interval can be calculated.

[0096] The overall score for a key indicator is obtained by summing the scores of all its sub-indicators. This process allows for the merging of sub-indicator scores from different time periods, forming a comprehensive assessment of the indicator and reflecting its performance over a specific timeframe. Finally, the scores of all key indicators are summed to generate the company's overall score. This overall score visually demonstrates the company's performance in the overall risk assessment and provides a quantitative basis for decision-making, supporting the formulation and implementation of relevant risk management measures.

[0097] In this embodiment, a refined scoring mechanism provides a comprehensive perspective for enterprise risk assessment. Multiplying the sub-indicator weights and values ​​of key indicators ensures that the assessment results reflect the importance and actual performance of each sub-indicator, improving the accuracy of the scoring. When calculating the key indicator scores, the summation of sub-indicator scores across different time intervals allows the assessment to dynamically adapt to changes in the enterprise's operations, better capturing the enterprise's risk status at different times. The comprehensive score provides the enterprise with a clear risk profile, facilitating risk warning and decision-making, optimizing the enterprise's risk management strategy, and improving the accuracy and timeliness of risk assessment.

[0098] Step S206: Generate the enterprise risk assessment result based on the comprehensive score.

[0099] Specifically, the comprehensive score represents a company's overall performance across multiple key dimensions. It is a comprehensive risk assessment result that reflects the company's overall health and potential risk level. Based on the comprehensive score, a risk assessment result can be generated, reflecting the company's operational stability and potential risks over a specific time period.

[0100] In this embodiment, key indicators are extracted from the enterprise's operational data. Entropy value is used to assign weights to these key indicators, eliminating the subjectivity of traditional risk assessments that rely on expert experience and ensuring the objectivity and rationality of weight allocation. Correlation adjustment further optimizes the weights, fully considering the interrelationships between key indicators and improving the accuracy of the assessment. A time decay algorithm is introduced to decompose key indicators into multiple time intervals, emphasizing the different impacts of data from different periods, making the risk assessment results more reflective of the enterprise's latest risk status. Based on the sub-indicator weights and values ​​of the key indicators in each time interval, indicator scores are calculated for each key indicator. A comprehensive score for the enterprise is then calculated based on these scores, generating the enterprise risk assessment result. This allows for real-time reflection of changes in the enterprise's operational status, improving the accuracy and efficiency of enterprise risk assessment.

[0101] Furthermore, the above-mentioned enterprise risk assessment method may also include: summing the weights of the sub-indicators of each key indicator in each time interval to obtain the final weight of each key indicator; and adding the final weight of each key indicator to the enterprise risk assessment result.

[0102] Specifically, the weights of the sub-indicators of each key indicator in each time interval are summed to obtain the final weight of each key indicator. By integrating the performance of key indicators in each time interval, it is ensured that the key indicators can be reflected with their most authentic weights when conducting comprehensive risk assessments of enterprises.

[0103] After summing the weights of the sub-indicators for each key indicator, a more representative final weight value is obtained, reflecting the overall importance and stability of the key indicator. This final weight can be added to the company's risk assessment results, providing a comprehensive risk assessment perspective. This ensures that the risk assessment results not only demonstrate the company's current risk status but also provide a basis for subsequent risk management strategies. Based on the final weight, high-risk areas can be identified and corresponding preventative measures can be developed to reduce risks in the company's operations.

[0104] In this embodiment, the weights of sub-indicators in each time interval are accumulated, and the final weight obtained can more realistically reflect the overall importance of key indicators, thus enhancing the comprehensiveness of the risk assessment results. Incorporating the final weight into the enterprise risk assessment results enables managers to more intuitively identify the enterprise's risk status, clarify the degree of impact of each key indicator on the overall risk, and provide data support for the enterprise's daily operational decisions.

[0105] Furthermore, after step S206 above, the process may also include: generating enterprise management information based on the enterprise risk assessment results of each enterprise, the enterprise management information including a list of safe enterprises and a list of risky enterprises; and sending the enterprise management information to the terminal logged into by the target account.

[0106] Specifically, enterprise management information is generated based on the enterprise risk assessment results of each enterprise. This information can include a list of safe enterprises and a list of risky enterprises, transforming the risk assessment results into actionable information and providing effective decision-making basis for enterprise management.

[0107] Based on the enterprise risk assessment results, companies with high assessment scores can be included in a "safe enterprise list," commonly known as a whitelist; while companies with low assessment scores and higher risks are included in a "risk enterprise list," commonly known as a blacklist. This helps managers quickly identify and monitor risky enterprises, and take timely measures to mitigate potential losses. The safe enterprise list can also serve as an important basis for enterprise assessment and certification.

[0108] The generated enterprise management information is sent to the terminal logged into the target account, ensuring that relevant decision-makers can receive the latest information in real time, improving the enterprise's response speed to changes in risk, enabling the enterprise management to quickly adjust strategies to deal with risks, and enhancing the practicality and timeliness of risk management.

[0109] In this embodiment, the enterprise risk assessment results are effectively transformed into enterprise management information, providing enterprises with a dual perspective of security and risk. This enables enterprises to clearly understand their own risk status and that of other enterprises in the same industry, which is conducive to formulating more accurate risk management strategies. By generating lists of safe and risky enterprises, enterprises can quickly identify high-risk targets, thereby prioritizing risk control and reducing potential losses.

[0110] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0111] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0112] Further reference Figure 3 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of an enterprise risk assessment device, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0113] like Figure 3 As shown, the enterprise risk assessment device 300 described in this embodiment includes: a data acquisition module 301, a first calculation module 302, a first adjustment module 303, an indicator decomposition module 304, a scoring calculation module 305, and a risk assessment module 306, wherein:

[0114] The data acquisition module 301 is used to acquire the enterprise's business data and extract key indicators from the business data according to preset indicator dimensions.

[0115] The first calculation module 302 is used to calculate the first weight of each key indicator based on the entropy method.

[0116] The first adjustment module 303 is used to calculate the correlation coefficient between each key indicator and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator.

[0117] The indicator decomposition module 304 is used to decompose each key indicator into preset time intervals based on a preset time decay algorithm and the second weight of the key indicator, so as to obtain the sub-indicator weight of the key indicator in each time interval.

[0118] The scoring calculation module 305 is used to calculate the index score of the key index based on the sub-index weights and corresponding sub-index values ​​of the key index in each time interval, and to calculate the comprehensive score of the enterprise based on the index scores of each key index.

[0119] Risk assessment module 306 is used to generate enterprise risk assessment results based on the comprehensive score.

[0120] In this embodiment, key indicators are extracted from the enterprise's operational data. Entropy value is used to assign weights to these key indicators, eliminating the subjectivity of traditional risk assessments that rely on expert experience and ensuring the objectivity and rationality of weight allocation. Correlation adjustment further optimizes the weights, fully considering the interrelationships between key indicators and improving the accuracy of the assessment. A time decay algorithm is introduced to decompose key indicators into multiple time intervals, emphasizing the different impacts of data from different periods, making the risk assessment results more reflective of the enterprise's latest risk status. Based on the sub-indicator weights and values ​​of the key indicators in each time interval, indicator scores are calculated for each key indicator. A comprehensive score for the enterprise is then calculated based on these scores, generating the enterprise risk assessment result. This allows for real-time reflection of changes in the enterprise's operational status, improving the accuracy and efficiency of enterprise risk assessment.

[0121] In some optional implementations of this embodiment, the data acquisition module 301 may include:

[0122] The data acquisition submodule is used to acquire the enterprise's business data, which includes the enterprise's cash flow data, credit data, and supply chain data.

[0123] The preprocessing submodule is used to preprocess enterprise operating data.

[0124] The indicator extraction submodule is used to extract a preset number of indicators as key indicators from the enterprise's operating data according to preset indicator dimensions.

[0125] In some optional implementations of this embodiment, the first adjustment module 303 may include:

[0126] The coefficient calculation submodule is used to calculate the correlation coefficients between various key indicators.

[0127] The correlation calculation submodule is used to calculate the correlation adjustment coefficient between key indicators based on the obtained correlation coefficient.

[0128] The matrix construction submodule is used to construct the adjustment coefficient matrix based on each correlation adjustment coefficient.

[0129] The weight adjustment submodule is used to adjust the first weight of each key indicator according to the adjustment coefficient matrix, and then normalize the adjusted weights to obtain the second weight of each key indicator.

[0130] In some optional implementations of this embodiment, the index decomposition module 304 may include:

[0131] The interval acquisition submodule is used to acquire preset time intervals and obtain the time interval between each time interval and the target time.

[0132] The weight calculation submodule is used to calculate the time weight corresponding to each time interval based on the preset time decay algorithm and the obtained time interval.

[0133] The indicator decomposition submodule is used to decompose the key indicator into sub-indicator weights for each time interval by multiplying the second weight of the key indicator by the time weight of each time interval.

[0134] In some optional implementations of this embodiment, the scoring calculation module 305 may include:

[0135] The calculation submodule is used to multiply the sub-indicator weights and their corresponding sub-indicator values ​​for each time interval of the key indicator to obtain the sub-indicator score for each time interval.

[0136] The scoring calculation submodule is used to sum the scores of each sub-indicator of the key indicator to obtain the indicator score of the key indicator.

[0137] The comprehensive calculation submodule is used to sum up the scores of each key indicator to obtain the company's comprehensive score.

[0138] In some optional implementations of this embodiment, the enterprise risk assessment device 300 may further include:

[0139] The weight accumulation module is used to accumulate the weights of the sub-indicators of each key indicator in each time interval to obtain the final weight of each key indicator.

[0140] The weighting module is used to add the final weight of each key indicator to the enterprise risk assessment results.

[0141] In some optional implementations of this embodiment, the enterprise risk assessment device 300 may further include:

[0142] The information generation module is used to generate enterprise management information based on the enterprise risk assessment results of each enterprise. The enterprise management information includes a list of safe enterprises and a list of risky enterprises.

[0143] The information sending module is used to send enterprise management information to the terminal logged into by the target account.

[0144] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0145] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with memory 41, processor 42, and network interface 43 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0146] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0147] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for enterprise risk assessment methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0148] In some embodiments, the processor 42 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, such as executing computer-readable instructions for the enterprise risk assessment method.

[0149] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0150] The computer device provided in this embodiment can execute the above-described enterprise risk assessment method. The enterprise risk assessment method here can be any of the enterprise risk assessment methods described in the various embodiments above.

[0151] In this embodiment, key indicators are extracted from the enterprise's operational data. Entropy value is used to assign weights to these key indicators, eliminating the subjectivity of traditional risk assessments that rely on expert experience and ensuring the objectivity and rationality of weight allocation. Correlation adjustment further optimizes the weights, fully considering the interrelationships between key indicators and improving the accuracy of the assessment. A time decay algorithm is introduced to decompose key indicators into multiple time intervals, emphasizing the different impacts of data from different periods, making the risk assessment results more reflective of the enterprise's latest risk status. Based on the sub-indicator weights and values ​​of the key indicators in each time interval, indicator scores are calculated for each key indicator. A comprehensive score for the enterprise is then calculated based on these scores, generating the enterprise risk assessment result. This allows for real-time reflection of changes in the enterprise's operational status, improving the accuracy and efficiency of enterprise risk assessment.

[0152] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the enterprise risk assessment method described above.

[0153] In this embodiment, key indicators are extracted from the enterprise's operational data. Entropy value is used to assign weights to these key indicators, eliminating the subjectivity of traditional risk assessments that rely on expert experience and ensuring the objectivity and rationality of weight allocation. Correlation adjustment further optimizes the weights, fully considering the interrelationships between key indicators and improving the accuracy of the assessment. A time decay algorithm is introduced to decompose key indicators into multiple time intervals, emphasizing the different impacts of data from different periods, making the risk assessment results more reflective of the enterprise's latest risk status. Based on the sub-indicator weights and values ​​of the key indicators in each time interval, indicator scores are calculated for each key indicator. A comprehensive score for the enterprise is then calculated based on these scores, generating the enterprise risk assessment result. This allows for real-time reflection of changes in the enterprise's operational status, improving the accuracy and efficiency of enterprise risk assessment.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0155] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A method for enterprise risk assessment, characterized in that, Includes the following steps: Acquire the enterprise's business data and extract key indicators from the business data according to preset indicator dimensions; The first weight of each key indicator is calculated based on the entropy method; Calculate the correlation coefficient between the key indicators, and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator. For each key indicator, based on a preset time decay algorithm and the second weight of the key indicator, the key indicator is decomposed into preset time intervals to obtain the sub-indicator weights of the key indicator in each time interval. The time decay algorithm is reflected by a time decay function, which is an exponential decay function. Based on the sub-indicator weights and corresponding sub-indicator values ​​of the key indicators in each time interval, the indicator scores of the key indicators are calculated, and the comprehensive score of the enterprise is calculated based on the indicator scores of each key indicator. The enterprise risk assessment result is generated based on the comprehensive score. The step of decomposing each key indicator into preset time intervals based on a preset time decay algorithm and the second weight of the key indicator, and obtaining the sub-indicator weights of the key indicator in each time interval, includes: Obtain preset time intervals and the time interval between each time interval and the target time; Based on the preset time decay algorithm and the obtained time interval, the time weight corresponding to each time interval is calculated; For each key indicator, the second weight of the key indicator is multiplied by the time weight of each time interval to decompose the key indicator into each time interval and obtain the sub-indicator weight of the key indicator in each time interval.

2. The enterprise risk assessment method according to claim 1, characterized in that, The steps of acquiring enterprise operating data and extracting key indicators from the enterprise operating data according to preset indicator dimensions include: Acquire enterprise operational data, including the enterprise's cash flow data, credit data, and supply chain data; The enterprise's operating data is preprocessed; According to the preset indicator dimensions, a preset number of indicators are extracted from the enterprise's operating data as key indicators.

3. The enterprise risk assessment method according to claim 1, characterized in that, The steps of calculating the correlation coefficients among the key indicators and adjusting the first weights of the key indicators based on the obtained correlation coefficients to obtain the second weights of the key indicators include: Calculate the correlation coefficients among the key indicators; Calculate the correlation adjustment coefficients among the key indicators based on the obtained correlation coefficients; Construct an adjustment coefficient matrix based on each correlation adjustment coefficient; The first weights of each key indicator are adjusted according to the adjustment coefficient matrix, and the adjusted weights are normalized to obtain the second weights of each key indicator.

4. The enterprise risk assessment method according to claim 1, characterized in that, The steps of calculating the indicator score of the key indicator based on the sub-indicator weights and corresponding sub-indicator values ​​of the key indicator in each time interval, and calculating the comprehensive score of the enterprise based on the indicator scores of each key indicator, include: The sub-indicator weights and their corresponding sub-indicator values ​​for each time interval are multiplied together to obtain the sub-indicator scores for each time interval. The scores of each sub-indicator of the key indicator are summed to obtain the indicator score of the key indicator. The scores of each key indicator are summed to obtain the company's overall score.

5. The enterprise risk assessment method according to claim 1, characterized in that, The method further includes: The weights of the sub-indicators of each key indicator in each time interval are summed to obtain the final weight of each key indicator. The final weight of each key indicator is added to the enterprise risk assessment result.

6. The enterprise risk assessment method according to claim 1, characterized in that, Following the step of generating the enterprise risk assessment result based on the comprehensive score, the method further includes: Enterprise management information is generated based on the enterprise risk assessment results of each enterprise. The enterprise management information includes a list of safe enterprises and a list of risky enterprises. The enterprise management information is sent to the terminal logged into by the target account.

7. A business risk assessment device, characterized in that, include: The data acquisition module is used to acquire the enterprise's business data and extract key indicators from the business data according to preset indicator dimensions. The first calculation module is used to calculate the first weight of each key indicator based on the entropy method. The first adjustment module is used to calculate the correlation coefficient between the key indicators and adjust the first weight of each key indicator based on the obtained correlation coefficient to obtain the second weight of each key indicator. The indicator decomposition module is used to decompose each key indicator into preset time intervals based on a preset time decay algorithm and the second weight of the key indicator, so as to obtain the sub-indicator weight of the key indicator in each time interval. The time decay algorithm is reflected by a time decay function, which is an exponential decay function. The scoring calculation module is used to calculate the index score of the key indicator based on the sub-index weights and corresponding sub-index values ​​of the key indicator in each time interval, and to calculate the comprehensive score of the enterprise based on the index scores of each key indicator. The risk assessment module is used to generate the enterprise risk assessment result of the enterprise based on the comprehensive score; The index decomposition module includes: The interval acquisition submodule is used to acquire preset time intervals and acquire the time interval between each time interval and the target time. The weight calculation submodule is used to calculate the time weight corresponding to each time interval based on a preset time decay algorithm and the obtained time interval; The indicator decomposition submodule is used to multiply the second weight of each key indicator by the time weight of each time interval to decompose the key indicator into each time interval and obtain the sub-indicator weight of the key indicator in each time interval.

8. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the enterprise risk assessment method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions that, when executed by a processor, implement the steps of the enterprise risk assessment method as described in any one of claims 1 to 6.

Images