Artificial intelligence-based leasing business process standardization and automation method

Abstract

The invention relates to the technical field of lease business process management, and discloses an artificial intelligence-based lease business process standardization and automation method, which comprises the following steps of: obtaining contract, finance, financing and market data through a multi-source data access module; performing semantic analysis on the unstructured data and constructing a unified data model; intelligently classifying business types, asset types and financing channels by using a classification model; a supervision policy is synchronized in real time through a dynamic rule engine to ensure compliance; generating an optimal financing combination in combination with the prediction model; checking term compliance by using an intelligent rule and marking a risk level; and performing moving point feasible region judgment on the operation behavior of the shielding part. The method can achieve the precise management and efficient cooperation of the whole process of the leasing business, optimizes the data sharing, improves the compliance and financing cost optimization capability, and is higher in real-time performance and expansibility.

Description

Technical Field

[0001] The present invention relates to the technical field of leasing business process management, and specifically to a leasing business process standardization and automation method based on artificial intelligence. Background Art

[0002] With the rapid growth of the leasing business, processes such as contract management, asset placement, financial settlement, and financing matching have become core to operations. However, these processes involve processing large amounts of unstructured data (e.g., contract review, financial statement verification, and financing document compliance checks), posing numerous challenges to traditional manual operations. For example, process fragmentation is prominent, with independent operations for contract, placement, finance, and financing modules, leading to severe data silos. Cross-departmental collaboration relies on manual document transfers, resulting in low efficiency. Furthermore, insufficient standardization leads to inconsistent contract templates, approval rules, and data formats across different business lines, potentially raising compliance risks (e.g., conflicts between lease contract terms and regulatory requirements). Furthermore, decision support capabilities are limited, with financing application approvals relying on manual judgment and inability to integrate real-time market dynamics (e.g., interest rate fluctuations, asset valuation changes), limiting the potential for optimizing financing costs. Manual data entry and report verification are prone to errors. For example, inconsistencies between lease contract amounts and financial records can lead to settlement disputes.

[0003] In recent years, with the expansion of the leasing business and tightening regulatory requirements, the industry urgently needs to standardize and automate the entire process through artificial intelligence (AI) technology to improve operational efficiency and reduce compliance risks. Existing leasing business process management systems primarily utilize a "rules engine + manual intervention" model. In terms of contract management, the system generates contracts using fixed templates and checks compliance through keyword matching. However, complex clauses (such as rent adjustment mechanisms and breach of contract liability) still require manual review. In the asset allocation process, allocation plans are recorded using Excel spreadsheets, with projects manually screened and approval processes triggered manually. This lacks real-time correlation analysis between market demand and asset inventory. Financial settlements are handled through the ERP system, but contract data must be manually imported to generate invoices. Invoice issuance and reconciliation require item-by-item verification, taking up to 3-5 business days. Financing matching relies on manual data compilation and submission to financial institutions by financing specialists. Financing solutions rely on historical experience, making it impossible to identify optimal financing channels (such as bank loans and asset securitization) in real time.

[0004] Therefore, existing technologies have some limitations. First, the level of intelligence is limited. The system can only process structured data (such as contract numbers and amounts) and lacks the ability to understand semantics for unstructured text (such as the contract text and legal clauses). Complex scenarios still require manual intervention. Second, there is a significant problem of process fragmentation. Each module operates independently. For example, contract changes are not automatically synchronized to the financial settlement system, which may lead to inconsistent data. Third, dynamic adaptability is weak. When the market environment (such as interest rate policies and industry regulations) changes, the rule engine needs to be manually updated, making it difficult to adjust business strategies in real time. Finally, there are significant efficiency bottlenecks. Manual processing accounts for more than 60%, the average financing approval cycle is 15-20 days, and contract review takes as long as 2-3 working days. These problems have brought significant obstacles to the efficient operation of the leasing business and urgently need to be resolved through technological innovation. Summary of the Invention

[0005] To address significant deficiencies in existing leasing business process management systems in contract management, asset placement, financial settlement, and financing matching, such as the reliance on manual review of complex contract terms, the lack of real-time correlation analysis of asset placement plans, the long and error-prone financial settlement cycle, and the inability to match financing plan design with the optimal channel in real time, the present invention provides the following technical solution: an AI-based full-process leasing business standardization and automation method, including: Through the multi-source data access module, scanned copies of lease contracts, ERP financial data, financing institution API data and external market data are obtained from different sources; semantic analysis of unstructured data is performed to build a unified data model; invalid data is automatically filtered out based on preset rules and anomaly detection algorithms; classification models are used to intelligently classify business types, asset categories and financing channels, and trigger corresponding approval rules; regulatory policies are synchronized in real time through a dynamic rule engine to ensure that business processes meet the latest requirements; the optimal financing portfolio is generated by combining predictive models and optimization algorithms; when operations occur, intelligent rules are used to verify the compliance of terms, identify conflicting terms and mark the risk level; for operations with obscured parts, the feasible domain of moving points is judged, and the optimal output is selected as the recognition result.

[0006] As a preferred solution for the AI-based full-process standardization and automation method for leasing business described in the present invention, the multi-source data access module includes real-time acquisition of scanned copies of leasing contracts, ERP financial data, financing institution API data, and external market data through an API interface; using OCR technology to extract contract text content, and identifying key information through dependency syntax analysis and entity relationship extraction technology; establishing a unified data model to map contract terms, financial subjects, and financing parameters to standardized fields.

[0007] As a preferred solution for the AI-based full-process standardization and automation method for leasing business described in the present invention, the unstructured data processing module includes detecting abnormal data through the isolation forest algorithm, with an identification accuracy rate of 98%; training the classification model to automatically classify the process according to business type, asset category, and financing channel, with a classification accuracy rate of 95% on the test set; example: when the system recognizes that the subject matter of the contract is an "industrial robot", it is automatically classified as an "equipment leasing" process and triggers the corresponding approval rules.

[0008] As a preferred solution for the AI-based full-process standardization and automation method for leasing business described in the present invention, the dynamic rule engine includes integrating regulatory requirements and internal corporate systems to establish a dynamic rule base; rule examples: "The rent payment date in the lease contract shall not be later than the 5th of each month" and "The financing cost must be lower than the LPR+200BP of the same period"; support for automatic rule updates: crawling the latest policy documents through web crawlers, using NLP named entity recognition and rule clause extraction technology to extract key clauses and synchronize them to the rule base.

[0009] As a preferred solution of the AI-based full-process standardization and automation method for leasing business described in the present invention, the prediction model includes establishing a rent recovery prediction model, the input features of which include historical rent data, customer credit scores, industry default rates, and market interest rate fluctuations, and the output is the probability of overdue payment in the next three months, with the warning threshold set to ≥15%; designing a financing plan optimization algorithm, using a multi-objective genetic algorithm, with the objective functions of minimizing financing costs, shortening arrival time, and maximizing credit limit utilization, and generating an optimal financing combination based on multi-objective constraints such as financing costs, arrival time, and credit limit limits.

[0010] As a preferred solution of the AI-based full-process standardization and automation method for leasing business described in the present invention, the intelligent rule verification module includes verifying the compliance of terms through a rule engine, identifying conflicting terms using semantic similarity calculation and conflict logic detection algorithm, and marking the risk level; connecting to the electronic signature system, automatically initiating the contract signing process based on the approval results, and supporting mobile signature confirmation.

[0011] As a preferred solution of the full-process standardization and automation method of leasing business based on artificial intelligence described in the present invention, the moving point feasible domain judgment module includes: judging the number of virtual moving points in the obscured part according to the three-dimensional model of each control branch, obtaining the virtual node directly connected to the obscured virtual moving point as a reference node; setting the reference node A, determining the position A1 of an obscured virtual moving point according to the angle between the reference node A and the connecting part and the fixed distance from the adjacent node; determining the feasible domain a2 of the next obscured virtual moving point position according to the rotatable angle of the connecting part of A1 and the fixed distance from the adjacent node; in a2, randomly selecting a point as the position A2 of the obscured virtual moving point, and determining the next virtual moving point until the positions of all obscured virtual moving points are determined, and a position combination of obscured virtual moving points is obtained; and continuously repeating the operation of randomly selecting the virtual moving point positions in the feasible domain until no new virtual moving point position combination is generated.

[0012] An artificial intelligence-based full-process standardization and automation system for leasing business adopts any method as described in the present invention, wherein: a collection unit obtains scanned copies of leasing contracts, ERP financial data, financing institution API data and external market data through a multi-source data access module; a processing unit performs semantic analysis on unstructured data and builds a unified data model; a classification unit uses the classification model to intelligently classify business types, asset categories and financing channels; a rule unit synchronizes regulatory policies in real time through a dynamic rule engine to ensure that business processes meet the latest requirements; a prediction unit combines the prediction model and optimization algorithm to generate an optimal financing combination; a verification unit uses intelligent rules to verify the compliance of terms when an operation occurs, identifies conflicting terms and marks the risk level; and an output unit performs dynamic point feasible domain judgment on the operation behavior of the occluded part and selects the optimal output as the recognition result.

[0013] This invention provides a method for standardizing and automating leasing business processes based on artificial intelligence. It has the following beneficial effects: This invention achieves precise management and efficient collaboration across the entire leasing business process by building a unified data model, combining multi-source data access with intelligent classification analysis. Addressing the issue of data silos, this invention optimizes data sharing accuracy through multi-source data access and a unified data model. It significantly improves the compliance and adaptability of business processes through a dynamic rule engine and real-time synchronization mechanism. Furthermore, it significantly enhances financing cost optimization and risk early warning capabilities through the combination of predictive models and optimization algorithms, while also offering strong real-time and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0015] Figure 2 It is a structural diagram of the multi-source data access module of the present invention.

[0016] Figure 3 This is a workflow diagram of the unstructured data processing module of the present invention.

[0017] Figure 4 This is the operation logic diagram of the classification model of the present invention.

[0018] Figure 5 This is an architectural diagram of the dynamic rule engine of the present invention.

[0019] Figure 6 This is the input-output relationship diagram of the prediction model of the present invention.

[0020] Figure 7 This is an operational flow chart of the intelligent rule verification module of the present invention.

[0021] Figure 8 Schematic diagram of the moving point feasible domain judgment module of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] The present invention provides an artificial intelligence-based full-process standardization and automation method and system for leasing business, and its specific implementation methods are described in detail with reference to the accompanying drawings. Figure 1 This is a schematic diagram of the overall process of the present invention, illustrating the collaborative relationship between the multi-source data access module, unstructured data processing module, classification model, dynamic rule engine, prediction model, intelligent rule verification module, and dynamic point feasible domain determination module. These modules achieve data interaction and process integration through a unified data model, thus achieving standardization and automation of the entire leasing business process.

[0024] First, the multi-source data access module serves as the data entry point for the system and is responsible for obtaining scanned copies of lease contracts, ERP financial data, financing institution API data, and external market data from different sources. Figure 2As shown, this module acquires data in real time through API interfaces and performs preliminary organization of different types of data. For example, scanned copies of lease contracts, typically in PDF or image format, are extracted using OCR technology. Dependency parsing and entity relationship extraction techniques are also used to identify key information, including the contract subject, lease term, and rent payment method. This information is then mapped to standardized fields in the unified data model, such as "rent adjustment clause" mapped to the "price fluctuation coefficient" field. Furthermore, ERP financial data and financing institution API data are also acquired through corresponding interfaces and integrated into the unified data model to ensure data consistency and operability.

[0025] After the data enters the unified data model, the unstructured data processing module performs semantic analysis and anomaly detection on the unstructured data. Figure 3 As shown, the module first detects anomalies using the isolation forest algorithm, achieving 98% accuracy. For ambiguous or incomplete information in contract text, the module uses semantic similarity calculation and contextual reasoning techniques to supplement and refine it. For example, if a contract does not specify the rent payment period, the module infers a reasonable default value based on historical data and industry practices. The module then passes the processed data to the classification model for subsequent intelligent classification operations.

[0026] The classification model intelligently classifies business types, asset categories and financing channels based on the BERT neural network. Its operating logic is as follows: Figure 4 As shown. The module receives data input from a unified data model, and performs feature extraction and classification judgment on it through the pre-trained BERT model. For example, when the system recognizes that the subject matter of the contract is an "industrial robot", it automatically classifies it as an "equipment leasing" process and triggers the corresponding approval rules. During this process, the module can also dynamically adjust the classification standards according to business needs. For example, if a certain equipment is a high-value asset, the system will automatically request an additional equipment evaluation report and set the submission deadline to 3 working days after the contract is signed. After the classification is completed, the data is passed to the dynamic rule engine for further compliance verification.

[0027] The core function of the dynamic rule engine is to integrate regulatory requirements, industry standards and internal corporate systems to ensure that business processes comply with the latest policy requirements. Figure 5As shown in the figure, this module uses web crawlers to capture the latest policy documents from designated data sources such as the official website of the State Financial Supervision and Administration Bureau and industry association platforms, and uses NLP named entity recognition and rule clause extraction technology to extract key clauses. For example, the module can automatically identify rules such as "the rent payment date in the lease contract shall not be later than the 5th of each month" or "the financing cost must be lower than the LPR+200BP for the same period" and synchronize them to the dynamic rule base. The design of the dynamic rule base supports automatic rule updates, ensuring that the system is always up to date. In addition, the module can also dynamically adjust the priority of rules based on specific scenarios, such as giving priority to low-risk channels during the financing cost optimization process.

[0028] The forecasting model combines historical data and real-time market information to generate the optimal financing portfolio. The input and output relationship is as follows: Figure 6 As shown. The module receives multiple feature inputs, including historical rental data, customer credit scores, industry default rates, market interest rate fluctuations, etc., and performs optimization calculations through a multi-objective genetic algorithm. The objective function is designed to minimize financing costs, shorten the time to account arrival, and maximize the utilization rate of the credit line, and ultimately outputs the optimal financing plan. For example, in a real case, the system generated a financing combination of "70% bank loans + 30% asset securitization" based on the input features, which significantly reduced the overall financing cost and improved the efficiency of capital use. The prediction model also includes a rent recovery prediction function. By analyzing the probability of overdue payments in the next three months, the warning threshold is set to ≥15%, thereby helping companies identify potential risks in advance.

[0029] The intelligent rule verification module is responsible for checking the compliance of the lease contract terms and identifying conflicting terms. The operation process is as follows: Figure 7 As shown. The module uses a rule engine to verify whether the terms comply with preset rules, such as "the proportion of liquidated damages shall not exceed 30% of the total contract amount." At the same time, the module uses semantic similarity calculation and contradiction logic detection algorithms to identify conflicting content in the terms. For example, when "annual rent payment" and "monthly interest calculation" appear in the contract at the same time, the module will mark them as conflicting terms and take corresponding measures based on the risk level. High-risk terms directly block the process, medium-risk terms trigger a two-person review, and low-risk terms are automatically approved. In addition, the module is also connected to the electronic signature system, automatically initiating the contract signing process based on the approval results, and supports mobile signature confirmation, thereby improving signing efficiency.

[0030] The moving point feasible region judgment module is mainly used to process the operation behavior of the occluded part, such as Figure 8As shown. The module determines the number of virtual moving points in the occluded part based on the three-dimensional model of each control branch, and obtains the virtual nodes directly connected to the occluded virtual moving points as reference nodes. For example, let reference node A determine the position A1 of an occluded virtual moving point based on its angle with the connecting part and the fixed distance from the adjacent node. Subsequently, based on the rotatable angle of the connecting part of A1 and the fixed distance from the adjacent node, the feasible domain a2 of the next occluded virtual moving point position is determined. A point is randomly selected in a2 as the position A2 of the occluded virtual moving point, and the above process is repeated until the positions of all occluded virtual moving points are determined. By repeatedly repeating the operation of randomly selecting virtual moving point positions in the feasible domain, the module finally obtains an optimal virtual moving point position combination as the recognition result.

[0031] The above modules achieve efficient collaboration through a unified data model, jointly completing the standardization and automation of the entire leasing business process. For example, in a specific application scenario, a financial leasing company achieved comprehensive automation of contract management, asset allocation, financial settlement, and financing docking through this system. The system obtains contract scans and ERP financial data from the multi-source data access module. After semantic parsing and anomaly detection by the unstructured data processing module, the classification model automatically identifies the business type and triggers the approval rules. The dynamic rule engine synchronizes the latest regulatory policies in real time to ensure that the contract terms meet compliance requirements, while the predictive model generates the optimal financing plan based on market data. The intelligent rule verification module verifies the compliance of the contract terms and marks the risk level. Finally, the dynamic point feasible domain judgment module processes the occluded operation behavior to ensure the accuracy of the output results. The efficient collaboration of the entire process has significantly improved the company's operational efficiency and risk management capabilities.

[0032] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below with reference to a specific application scenario.

[0033] In the actual operation scenario of a financial leasing company, the system obtains scanned copies of lease contracts, ERP financial data, financing institution API data and external market data through the multi-source data access module. Figure 2 As shown, this module leverages APIs to extract data from multiple sources in real time. Scanned PDFs or images of lease contracts are fed into OCR technology for text extraction. Dependency parsing and entity relationship extraction techniques then identify key fields, such as the contract subject, lease term, and rent payment method. These fields are mapped to standardized fields in a unified data model; for example, "rent adjustment clause" is mapped to "price fluctuation coefficient." Simultaneously, ERP financial data and financing institution API data are integrated into the unified data model through corresponding interfaces, ensuring the consistency and operability of all data.

[0034] After entering the unified data model, the unstructured data processing module begins to perform semantic analysis and anomaly detection on the data. Figure 3 As shown, the Isolation Forest algorithm is used to detect anomalous data with an accuracy rate of 98%. For ambiguous or incomplete information in contracts, the module uses semantic similarity calculation and contextual reasoning techniques to supplement and refine it. For example, if a contract does not specify the rent payment period, the module infers a reasonable default value based on historical contract data and industry practices. This processed data is then fed into a classification model to trigger subsequent intelligent classification operations.

[0035] The classification model uses the BERT neural network to intelligently classify business types, asset categories, and financing channels. Figure 4 As shown, the module receives data input from a unified data model and uses a pre-trained BERT model to extract features and perform classification. For example, if the system identifies the contract subject as an "industrial robot," it automatically classifies it as an "equipment leasing" process and triggers the corresponding approval rules. If a piece of equipment is a high-value asset, the system will require an additional equipment assessment report, with a deadline of three business days after the contract is signed. After classification, the data is passed to the dynamic rule engine for further compliance verification.

[0036] The core function of the dynamic rule engine is to integrate regulatory requirements, industry standards and internal corporate systems to ensure that business processes comply with the latest policy requirements. Figure 5 As shown, this module uses web crawlers to retrieve the latest policy documents from the official website of the State Financial Supervision and Administration Bureau and industry association platforms, and uses NLP named entity recognition and rule clause extraction techniques to extract key terms. For example, rules such as "rent payment date in a lease contract must not be later than the 5th of each month" or "financing costs must be lower than the LPR for the same period + 200 basis points" are automatically identified and synchronized to the dynamic rule base. The dynamic rule base supports automatic rule updates, ensuring that the system is always up to date. Furthermore, the module can dynamically adjust rule priorities based on specific scenarios, for example, prioritizing low-risk channels during financing cost optimization.

[0037] The forecasting model combines historical data and real-time market information to generate the optimal financing portfolio. Figure 6As shown, the module receives multiple feature inputs, including historical rental data, customer credit scores, industry default rates, and market interest rate fluctuations. Optimization calculations are performed using a multi-objective genetic algorithm, with the objective function designed to minimize financing costs, minimize payment time, and maximize credit utilization. For example, in one real-world case, the system generated a financing portfolio of "70% bank loans + 30% asset-backed securities" based on the input features, significantly reducing overall financing costs and improving capital utilization efficiency. The predictive model also includes a rent recovery forecast function. By analyzing the probability of overdue payments over the next three months and setting an early warning threshold of ≥15%, it helps companies identify potential risks in advance.

[0038] The intelligent rule verification module is responsible for checking the compliance of the lease contract terms and identifying conflicting terms. Figure 7 As shown, the module uses a rules engine to verify whether the terms comply with preset rules, such as "the percentage of liquidated damages shall not exceed 30% of the total contract amount." At the same time, the module uses semantic similarity calculation and contradiction logic detection algorithms to identify conflicting content in the terms. For example, when "annual rent payment" and "monthly interest calculation" appear simultaneously in a contract, the module will mark them as conflicting terms and take appropriate measures based on the risk level. High-risk terms directly block the process, medium-risk terms trigger a two-person review, and low-risk terms are automatically approved. Furthermore, the module connects to the electronic signature system, automatically initiating the contract signing process based on the approval results, and supports mobile signature confirmation.

[0039] The moving point feasible region judgment module is mainly used to process the operation behavior of the occluded part. Figure 8 As shown, the module determines the number of virtual moving points in the occluded part based on the three-dimensional model of each control branch, and obtains the virtual nodes directly connected to the occluded virtual moving points as reference nodes. For example, let reference node A be used to determine the position A1 of an occluded virtual moving point based on its angle with the connecting part and the fixed distance from the adjacent node. Subsequently, based on the rotatable angle of the connecting part of A1 and the fixed distance from the adjacent node, the feasible domain a2 of the next occluded virtual moving point position is determined. A point in a2 is randomly selected as the position A2 of the occluded virtual moving point, and the above process is repeated until the positions of all occluded virtual moving points are determined. By repeatedly randomly selecting the positions of virtual moving points in the feasible domain, the module ultimately obtains an optimal combination of virtual moving point positions as the recognition result.

[0040] The above modules achieve efficient collaboration through a unified data model, jointly completing the standardization and automation of the entire leasing business process. For example, in a specific application scenario, a financial leasing company achieved comprehensive automation of contract management, asset allocation, financial settlement, and financing docking through this system. The system obtains contract scans and ERP financial data from the multi-source data access module. After semantic parsing and anomaly detection by the unstructured data processing module, the classification model automatically identifies the business type and triggers the approval rules. The dynamic rule engine synchronizes the latest regulatory policies in real time to ensure that the contract terms meet compliance requirements, while the predictive model generates the optimal financing plan based on market data. The intelligent rule verification module verifies the compliance of the contract terms and marks the risk level. Finally, the dynamic point feasible domain judgment module processes the occluded operation behavior to ensure the accuracy of the output results. The efficient collaboration of the entire process has significantly improved the company's operational efficiency and risk management capabilities.

[0041] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The AI-based leasing business process standardization and automation method is characterized by: include: Through the multi-source data access module, scanned copies of lease contracts, ERP financial data, financing institution API data and external market data are obtained from different sources; Perform semantic analysis on unstructured data and build a unified data model; Automatically filter invalid data based on preset rules and anomaly detection algorithms; Use classification models to intelligently classify business types, asset categories, and financing channels, and trigger corresponding approval rules; Synchronize regulatory policies in real time through a dynamic rule engine to ensure that business processes comply with the latest requirements; Combine prediction models and optimization algorithms to generate the optimal financing portfolio; When an operation occurs, the intelligent rule verification module is used to verify the compliance of the terms, identify conflicting terms and mark the risk level; For the operation behavior of the occluded part, the virtual moving point position combination is determined through the moving point feasible domain judgment module, and the optimal output is selected as the recognition result.

2. The AI-based leasing business process standardization and automation method according to claim 1 is characterized in that: The multi-source data access module includes real-time acquisition of lease contract scans, ERP financial data, financing institution API data and external market data through the API interface; Use OCR technology to extract contract text content, and identify key information through dependency syntax analysis and entity relationship extraction technology; Establish a unified data model to map contract terms, financial accounts, and financing parameters to standardized fields.

3. The AI-based leasing business process standardization and automation method according to claim 1 is characterized in that: The unstructured data processing module includes detecting abnormal data through the isolation forest algorithm; Training classification models to automatically categorize processes based on business type, asset class, and financing channel; When the system identifies the subject matter of the contract as a specific asset, it automatically categorizes it into the corresponding process and triggers the approval rules.

4. The AI-based leasing business process standardization and automation method according to claim 1 is characterized in that: The dynamic rule engine includes integrating regulatory requirements, industry standards and internal enterprise systems to establish a dynamic rule base; The latest policy documents are captured through web crawlers, and key terms are extracted using NLP named entity recognition and rule clause extraction technology and synchronized to the rule base.

5. The AI-based leasing business process standardization and automation method according to claim 1 is characterized in that: The prediction model includes establishing a rent recovery prediction model, the input features of which include historical rent data, customer credit score, industry default rate, and market interest rate fluctuations, and the output is the probability of overdue payment in the next three months; Design a financing plan optimization algorithm using a multi-objective genetic algorithm, with the objective functions being minimizing financing costs, shortening account arrival time, and maximizing credit utilization.

6. The AI-based leasing business process standardization and automation method according to claim 1 is characterized in that: The intelligent rule verification module includes verifying the compliance of clauses through a rule engine and identifying contradictory clauses using semantic similarity calculation and contradiction logic detection algorithm; Take appropriate measures based on the risk level, connect to the electronic signature system, and automatically initiate the contract signing process based on the approval results.

7. The AI-based leasing business process standardization and automation method according to claim 1 is characterized in that: The moving point feasible domain judgment module includes judging the number of virtual moving points in the blocked part according to the three-dimensional model of each control branch; Obtain a virtual node directly connected to the occluded virtual moving point as a reference node; Determine the position of the occluded virtual moving point based on the angle between the reference node and the connected part and the fixed distance from the adjacent node; The positions of virtual moving points are randomly selected in the feasible domain until the position combinations of all occluded virtual moving points are obtained.

8. The AI-based leasing business process standardization and automation system is characterized by: include: The collection unit obtains scanned copies of lease contracts, ERP financial data, financing institution API data, and external market data through the multi-source data access module; The processing unit performs semantic analysis on unstructured data and builds a unified data model; Classification unit, which uses classification models to intelligently classify business types, asset categories and financing channels; The rule unit synchronizes regulatory policies in real time through a dynamic rule engine to ensure that business processes comply with the latest requirements; The forecasting unit combines the forecasting model and optimization algorithm to generate the optimal financing portfolio; The verification unit uses the intelligent rule verification module to verify the compliance of clauses when an operation occurs, identify conflicting clauses and mark the risk level; The output unit selects the optimal output as the recognition result through the moving point feasible domain judgment module for the operation behavior of the occluded part.

9. The AI-based leasing business process standardization and automation system according to claim 8 is characterized in that: The acquisition unit acquires data in real time through the API interface and uses OCR technology to extract the content of the contract text; The processing unit detects abnormal data through the isolation forest algorithm and constructs a unified data model.

10. The AI-based leasing business process standardization and automation system according to claim 8 is characterized in that: The rule unit crawls the latest policy documents through a web crawler and extracts key clauses using NLP technology; The prediction unit generates an optimal financing combination through a multi-objective genetic algorithm and sets an early warning threshold to identify potential risks.

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