AI-driven enterprise financial statement intelligent compilation and compliance verification method and system

Abstract

The invention discloses an AI-driven enterprise financial statement intelligent compilation and compliance verification method and system, and relates to the technical field of financial intelligent analysis and compliance management, and the method comprises the steps: carrying out the logic dependence and semantic matching calculation of an enterprise financial compliance logic embedded vector, and generating a logic activation value; performing enterprise financial compliance logic type division according to the logic activation threshold, establishing a logic set, and allocating a priority sequence for execution; and fusing the enterprise financial compliance logic activation result and the financial data, generating a joint representation vector, and calculating a compliance score to finish judgment. According to the method, enterprise financial compliance logic is converted into a computable logic activation value, accurate matching of rules and data is ensured, compliance verification efficiency is improved, computing resource waste is effectively avoided, high-quality input is provided for compliance score judgment, accuracy and automation of the judgment process are ensured, and the method is suitable for popularization and application. Therefore, intelligent, automatic and efficient enterprise financial compliance verification is realized.

Description

Technical Field

[0001] This invention relates to the field of financial intelligent analysis and compliance management technology, specifically to an AI-driven method and system for intelligent preparation and compliance verification of corporate financial statements. Background Technology

[0002] With the rapid development of artificial intelligence technology in enterprise management and financial analysis, the intelligent preparation and automatic compliance verification of financial statements have become an important direction for enterprise digital governance. Existing financial intelligence systems mostly rely on rule engines, template matching, and machine learning classification models to perform structured analysis and risk identification of financial data. At the same time, the development of natural language processing technology has made semantic understanding and logical parsing of regulatory texts feasible to a certain extent. AI systems can automatically extract and semantically annotate enterprise financial accounting rules to a certain extent. However, existing technologies generally remain at the level of "text understanding" or "rule comparison", making it difficult to achieve deep mapping and logical reasoning between semantic logic and financial data, thus limiting the intelligent decision-making capabilities of automatic financial compliance systems.

[0003] Existing technologies for intelligent preparation and compliance verification of corporate finance have the following shortcomings: Traditional methods mostly rely on static rule templates or manually labeled regulatory mapping tables, which can only perform surface condition matching and cannot identify the complex logical relationships contained in regulatory provisions, such as condition reversal, exception clauses, or multi-layered constraint structures, resulting in insufficient accuracy of regulatory interpretation results. Although some studies have attempted to introduce deep learning models to generate regulatory semantic embeddings, such models usually cannot effectively integrate semantic structure with logical reasoning and lack a logical dependency modeling mechanism between "condition to behavior to result," making it difficult to achieve dynamic execution of regulatory logic at the AI ​​level. In terms of the integration of financial data and regulatory semantics, existing technologies generally adopt a simple splicing method of numerical features and textual features, without establishing a unified semantic-numerical joint representation space, and cannot achieve dynamic correlation between the strength of regulatory logic and changes in financial indicators. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is that existing financial intelligent analysis and compliance management technologies have poor ability to analyze corporate financial compliance logic, low correlation between semantic and numerical integration, lack of dynamic execution mechanism in compliance reasoning process, and how to enable AI systems to achieve unified modeling and automatic compliance judgment between corporate financial compliance semantic logic and financial data.

[0006] To address the aforementioned technical issues, this invention provides the following technical solution: an AI-driven intelligent preparation and compliance verification method for enterprise financial statements, comprising: performing logical dependency and semantic matching calculations on the embedded vector of enterprise financial compliance logic to generate a logical activation value; classifying enterprise financial compliance logic types based on logical activation thresholds, establishing a logic set and assigning priority order for execution; and integrating the enterprise financial compliance logic activation results with financial data to generate a joint representation vector and calculate a compliance score to complete the judgment.

[0007] As a preferred embodiment of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements described in this invention, the step of performing logical dependency and semantic matching calculation includes: performing logical dependency calculation on the condition part and the result part of the enterprise financial compliance logical embedding vector; and calculating the logical strength of the conditional semantics leading to the behavioral semantics based on the symbolic logical implication relationship.

[0008] As a preferred embodiment of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements described in this invention, the generation of logical activation values ​​includes determining the matching degree between each financial compliance item and the logical template based on the semantic similarity measurement results, and generating logical activation values ​​by integrating logical dependency strength and semantic similarity.

[0009] As a preferred embodiment of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements described in this invention, the step of classifying enterprise financial compliance logic types includes classifying enterprise financial compliance logic types according to a preset logic activation threshold. When the logic activation value is greater than the first logic activation threshold, it is determined that the current financial compliance logic relationship is clear and is directly executable logic.

[0010] As a preferred embodiment of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements described in this invention, the step of establishing a logical set and assigning priority for execution includes: assigning priority parameters to each logical set to form an execution order table; during logical reasoning, loading logical sets in descending order of priority, and executing financial compliance items in order of logical strength; when the input financial data meets specific compliance logic triggering conditions, automatically calling the rules in the corresponding logical set to perform compliance verification.

[0011] As a preferred embodiment of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements described in this invention, the generation of the joint representation vector includes: inputting the enterprise financial compliance logic activation vector and the enterprise financial indicator vector into the semantic numerical fusion layer, and performing mapping and fusion in a unified representation space; generating a joint representation vector that represents the correlation between the enterprise financial compliance logic features and the financial numerical features; when the logic response value corresponding to the target financial indicator in the joint representation vector is greater than the first logic trigger threshold, it is determined that the enterprise financial compliance logic is triggered on the current financial indicator.

[0012] As a preferred embodiment of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements described in this invention, the calculation of the compliance score includes: calculating the matching value between the enterprise financial compliance logic and financial data based on the joint representation vector to generate a compliance score; performing a judgment based on the numerical range of the compliance score; and determining that the corresponding financial data item is compliant when the compliance score is greater than the first compliance judgment threshold.

[0013] Another objective of this invention is to provide an AI-driven intelligent preparation and compliance verification system for corporate financial statements. This system can generate a joint representation vector and calculate a compliance score by integrating the activation results of corporate financial compliance logic with financial data, thereby solving the problem that current AI financial compliance technologies contain a disconnect between semantic logic and numerical features and cannot achieve cross-modal logical reasoning.

[0014] As a preferred embodiment of the AI-driven intelligent preparation and compliance verification system for enterprise financial statements described in this invention, it includes: a logical dependency calculation module, a logical type classification module, and a compliance score generation module; the logical dependency calculation module is used to perform logical dependency and semantic matching calculations on the embedded vector of enterprise financial compliance logic to generate logical activation values; the logical type classification module is used to classify enterprise financial compliance logic types according to logical activation thresholds, establish logical sets, and assign priority for execution; the compliance score generation module is used to integrate the enterprise financial compliance logic activation results with financial data, generate a joint representation vector, and calculate a compliance score to complete the judgment.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement steps of an AI-driven intelligent preparation and compliance verification method for enterprise financial statements.

[0016] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of an AI-driven intelligent preparation and compliance verification method for enterprise financial statements are disclosed.

[0017] The beneficial effects of this invention are as follows: The AI-driven intelligent preparation and compliance verification method for enterprise financial statements provided by this invention performs logical dependency and semantic matching calculations on the embedded vector of enterprise financial compliance logic to generate logical activation values. This realizes the structured and computable understanding of enterprise financial compliance logic, reduces logical ambiguity judgments under human intervention, and can independently complete the identification and weight calculation of enterprise financial compliance logic. Based on the logical activation threshold, the method classifies enterprise financial compliance logic types, establishes logic sets, and assigns priority order for execution, improving the logical accuracy and controllability of AI in the process of enterprise financial compliance processing. It realizes hierarchical modeling and automatic scheduling execution of regulatory logic, laying the foundation for accurate compliance judgment of financial data. By integrating the activation results of enterprise financial compliance logic with financial data, a joint representation vector is generated and a compliance score is calculated to complete the judgment, improving the intelligent analysis capability in complex financial environments. This makes the compliance verification process interpretable and traceable. This invention achieves better results in terms of the accuracy of understanding enterprise financial compliance logic, the intelligence of financial data compliance judgment, and the synergy of cross-modal semantic and numerical fusion. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 The overall flowchart of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements provided in Embodiment 1 of the present invention is shown. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1 As one embodiment of the present invention, an AI-driven intelligent preparation and compliance verification method for enterprise financial statements is provided, comprising: S1: Perform logical dependency and semantic matching calculations on the corporate financial compliance logic embedding vector to generate logical activation values.

[0022] Furthermore, performing logical dependency and semantic matching calculations includes calculating the logical dependencies between the conditional and result parts of the corporate financial compliance logic embedding vector; and calculating the logical strength of the conditional semantics leading to the behavioral semantics based on symbolic logical implication. A preferred approach to calculating the logical dependencies between the conditional and result parts of the corporate financial compliance logic embedding vector specifically includes converting the semantic information in the corporate financial compliance logic text into a vectorized representation using a semantic embedding model, such as BERT or Word2Vec, thereby obtaining the semantic embedding vectors corresponding to the conditional and result parts. Logical dependency calculations are then performed on these two embedding vectors separately to capture the causal and constraint relationships between the conditional and result semantics, and to calculate the logical trigger probability value. , represented as: in, This represents the result layer mapping weight matrix, with values ​​ranging from [-1, 1]. It can be trained using the standard backpropagation process. This represents the semantic embedding vector of the conditional part of the corporate financial compliance logic. This is a logical bias term used to correct the activation threshold of different types of corporate financial rules in the model.

[0023] The final calculated result value , represented as: in, To determine the final calculated value, check if the condition is met. A result of 1 indicates that the condition is true, and a result of 0 indicates that the condition is false. The similarity between the semantic logic output and the financial data can be calculated using common similarity methods, such as cosine similarity and Euclidean distance. The logical trigger indicator function is represented as follows: in, Indicates the logical trigger threshold, if If the value is greater than the set logical trigger threshold, output 1 to indicate that the condition is met; otherwise, output 0 to indicate that the condition is not met.

[0024] It should be noted that generating logical activation values ​​involves determining the degree of matching between each financial compliance item and the logical template based on the semantic similarity measurement results, and then combining the logical dependency strength and semantic similarity to generate logical activation values.

[0025] It should also be noted that by using logical dependency and semantic matching calculation methods, the conditions and results in the enterprise's financial compliance logic clauses are precisely semantically matched, and the logical strength between conditions and results is evaluated based on symbolic logical implication. This process transforms financial compliance rules into computable vector representations, allowing the AI ​​system to automatically identify the dependencies between conditions and results in different financial rules and generate logical activation values. This quantifies the applicability of each financial rule in the current financial data context, enabling the system to accurately judge compliance in subsequent execution processes. This provides accurate computational input for compliance verification and avoids logical judgment errors that may occur during manual review.

[0026] S2: Classify the enterprise financial compliance logic types based on the logic activation threshold, establish a logic set, and assign priority order for execution.

[0027] Furthermore, classifying corporate financial compliance logic types includes classifying corporate financial compliance logic types based on preset logic activation thresholds. When the logic activation value is greater than the first logic activation threshold, it is determined that the current financial compliance logic relationship is clear and can be directly executed.

[0028] It should be noted that establishing logical sets and assigning priority for execution includes: assigning priority parameters to each logical set to form an execution order table; during logical reasoning, loading logical sets in descending order of priority, and executing financial compliance items in order of logical strength; when input financial data meets specific compliance logic triggering conditions, automatically calling the rules in the corresponding logical set to perform compliance verification.

[0029] It should also be noted that a preferred scheme for classifying corporate financial compliance logic types specifically includes setting a logic activation threshold and classifying corporate financial compliance logic types. Taking the sample set of corporate financial compliance logic semantic embedding as the object, statistical calculations are performed on the activation values ​​of all corporate financial compliance logic to obtain their distribution characteristics in the [0,1] interval. Using the median of the activation values ​​as a benchmark, samples with activation values ​​greater than 0.7 are initially marked as high-intensity logic entries, samples with activation values ​​between 0.4 and 0.7 are marked as medium-intensity logic entries, and samples with activation values ​​less than 0.4 are marked as weak logic entries. Subsequently, during the operation phase, based on the above classification results, the first logic activation threshold is set to 0.7. When the input corporate financial compliance logic activation value is greater than 0.7, it indicates that the logical relationship is clear and the semantic matching degree is high, and it is classified into the set of logic that can be directly executed.

[0030] It should also be noted that by classifying corporate financial compliance logic types based on logical activation values ​​and combining this with a priority-based execution mechanism, logical rules are categorized and sorted according to their importance and applicability, thereby optimizing the execution efficiency of compliance verification. Specifically, when a logical activation value exceeds a preset threshold, it indicates that the financial rule is highly matched with the current financial data, and the rule can be executed directly. If the activation value is low, the rule is processed first to avoid unnecessary computational waste. Through this step, the automated priority sorting and execution of multiple financial rules are achieved, making the compliance verification process more efficient and intelligent, while improving processing speed and reducing the waste of computing resources. This process is particularly suitable for processing complex corporate financial data, and when there are many rules, it can effectively avoid resource conflicts and computational burdens during execution.

[0031] S3: Integrate the activation results of corporate financial compliance logic with financial data, generate a joint representation vector, and calculate the compliance score to complete the judgment.

[0032] Furthermore, generating the joint representation vector includes inputting the corporate financial compliance logic activation vector and the corporate financial indicator vector into the semantic numerical fusion layer, and performing mapping and fusion in a unified representation space; generating a joint representation vector that represents the correlation between the corporate financial compliance logic features and the financial numerical features; and determining that the corporate financial compliance logic is triggered on the current financial indicator when the logic response value corresponding to the target financial indicator in the joint representation vector is greater than the first logic trigger threshold.

[0033] It should be noted that a preferred scheme for generating the joint representation vector specifically includes: standardizing the corporate financial compliance logic activation vector and the financial indicator vector respectively, so that the two types of features are comparable under a unified scale; establishing the correspondence between logical and numerical features, setting a sliding fusion window, calculating the synchronization degree between logical activation changes and financial indicator fluctuations in each window, and dynamically adjusting the fusion ratio of logical features and numerical features according to the synchronization level; inputting the window fusion results into the relevant analysis module, calculating the dependency weight between the logical dimension and the financial dimension, and adaptively updating the fusion ratio according to the weight changes; performing feature aggregation on the multi-round fusion output to generate a joint representation vector in a unified format; when the intensity of any logical response exceeds the set logical trigger threshold, it is determined that the corresponding corporate financial compliance logic is activated, and the vector is used as the input data for compliance scoring.

[0034] Calculate the logical dependency strength between semantic conditions and semantic results. , represented as: in, This value represents the activation value of the financial compliance logic, indicating the strength of the logical condition. A larger value indicates a stronger activation of the rule, and a value closer to 1 represents the degree to which the logic holds true. This represents the conditional mapping weight matrix. This represents the conditional semantic embedding vector.

[0035] By statistically analyzing the training data on corporate financial compliance logic, the distribution range of logic activation values ​​was determined, and the mean of logic activation values ​​was calculated. and standard deviation Approximately 80% of effective corporate financial compliance logic responses fall within the range of [0.5, 0.9]. Therefore, a first logic trigger threshold is set. , represented as: when When the activation level is high, it indicates that the enterprise's financial compliance logic is highly activated and the clear "if...then..." relationship structure in the regulatory text is successfully identified. At this time, the enterprise's financial compliance item is judged as the main logic item, which means that the model can accurately understand the conditions and corresponding behaviors in the regulations, mark it as the core execution rule, and directly apply the logic in the preparation of financial statements and compliance verification, automatically generating the corresponding constraints or calculation conditions.

[0036] It should be noted that the completion of the compliance score calculation includes: calculating the matching value between the enterprise's financial compliance logic and financial data based on the joint representation vector to generate a compliance score; performing a judgment based on the numerical range of the compliance score; when the compliance score is greater than the first compliance judgment threshold, the corresponding financial data item is judged to be compliant; the first compliance judgment threshold is dynamically determined based on the activation intensity of the regulatory logic, the distribution of historical compliance scores and the model confidence level. The initial threshold is set based on the statistical characteristics of compliance sample scores and is automatically corrected during operation based on the feedback misjudgment rate to adapt to the compliance judgment requirements under different financial scenarios.

[0037] It should also be noted that by semantically and numerically fusing the activation results of corporate financial compliance logic with corporate financial indicator data, a unified representation space is constructed. This allows regulatory logic features and numerical features to be expressed collaboratively in the same vector space. Through the semantic-numerical fusion layer, the correspondence between corporate financial clauses and financial indicators is captured, achieving dynamic linkage between logical conditions and numerical changes. When a corporate financial compliance logic is triggered on a specific financial indicator, a significant response is generated in the joint vector, and a compliance score is further generated through matching operations. Financial items with scores greater than a preset threshold are judged as compliant; otherwise, they are marked as pending review or non-compliant. This mechanism enables the AI ​​system not only to "understand regulatory language" but also to "perceive numerical constraints," achieving bidirectional reasoning fusion of linguistic logic and numerical logic. Through this cross-modal compliance judgment method, non-compliance risks can be automatically identified in complex financial scenarios, and traceable compliance decision-making basis can be output, thereby improving the automation level of financial statement preparation and audit transparency.

[0038] Example 2, an embodiment of the present invention, provides an AI-driven intelligent preparation and compliance verification system for enterprise financial statements, including a logical dependency calculation module, a logical type classification module, and a compliance score generation module.

[0039] The logical dependency calculation module is used to perform logical dependency and semantic matching calculations on the embedded vector of corporate financial compliance logic to generate logical activation values; the logical type classification module is used to classify corporate financial compliance logical types based on logical activation thresholds, establish logical sets, and assign priority for execution; the compliance score generation module is used to integrate the corporate financial compliance logical activation results with financial data, generate a joint representation vector, and calculate a compliance score to complete the judgment.

Claims

1. An AI-driven intelligent preparation and compliance verification method for enterprise financial statements, characterized in that: include: Logical dependency and semantic matching calculations are performed on the embedded vector of corporate financial compliance logic to generate logical activation values; Based on the logic activation threshold, classify the enterprise financial compliance logic types, establish logic sets, and assign priority order for execution; By integrating the activation results of corporate financial compliance logic with financial data, a joint representation vector is generated and a compliance score is calculated to complete the judgment.

2. The AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in claim 1, characterized in that: The calculation of logical dependency and semantic matching includes, Logical dependency calculation is performed on the conditional and result parts of the corporate financial compliance logic embedding vector; Based on the symbolic logical implication relation, calculate the logical strength of the conditional semantics that lead to the behavioral semantics.

3. The AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in claim 2, characterized in that: The generated logic activation values ​​include, Based on the semantic similarity measurement results, the matching degree between each financial compliance item and the logical template is determined, and the logical dependency strength and semantic similarity are combined to generate a logical activation value.

4. The AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in claim 3, characterized in that: The classification of corporate financial compliance logic types includes: The enterprise financial compliance logic type is classified according to the preset logic activation threshold. When the logic activation value is greater than the first logic activation threshold, it is determined that the current financial compliance logic relationship is clear and can be directly executed.

5. The AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in claim 4, characterized in that: The step of establishing a logical set and assigning priority order for execution includes, Assign priority parameters to each logical set to form an execution order table; When performing logical reasoning, logical sets are loaded in descending order of priority, and financial compliance items are executed in order of logical strength. When the input financial data meets the specific compliance logic trigger conditions, the rules in the corresponding logic set are automatically invoked to perform compliance verification.

6. The AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in claim 5, characterized in that: The generated joint representation vector includes, The activation vector of corporate financial compliance logic and the vector of corporate financial indicators are input into the semantic numerical fusion layer and mapped and fused in a unified representation space. Generate a joint representation vector that characterizes the relationship between the logical features of corporate financial compliance and the numerical features of financial data; When the logical response value corresponding to the target financial indicator in the joint representation vector is greater than the first logical trigger threshold, it is determined that the enterprise financial compliance logic has been triggered on the current financial indicator.

7. The AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in claim 6, characterized in that: The completion of the compliance score calculation includes... A compliance score is generated by calculating the matching value between the enterprise's financial compliance logic and financial data based on the joint representation vector. The judgment is made based on the numerical range of the compliance score. When the compliance score is greater than the first compliance judgment threshold, the corresponding financial data item is judged to be compliant.

8. An AI-driven intelligent preparation and compliance verification system for enterprise financial statements, employing the AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in any one of claims 1 to 7, characterized in that: This includes a logical dependency calculation module, a logical type classification module, and a compliance score generation module; The logical dependency calculation module is used to perform logical dependency and semantic matching calculations on the enterprise financial compliance logical embedding vector to generate logical activation values. The logic type classification module is used to classify enterprise financial compliance logic types based on logic activation thresholds, establish logic sets, and assign priority order for execution. The compliance score generation module is used to integrate the activation results of the enterprise's financial compliance logic with financial data, generate a joint representation vector, and calculate the compliance score to complete the judgment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the AI-driven intelligent preparation and compliance verification method for enterprise financial statements as described in any one of claims 1 to 7.

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