Financial cost accurate measurement and calculation and risk dynamic prevention and control system based on AI

Abstract

The invention relates to an AI-based financial cost accurate measurement and calculation and risk dynamic prevention and control system, which comprises a hardware layer, a data layer, an AI engine layer and an application layer, and is characterized in that each layer is cooperatively linked with a communication protocol through a standardized data interface; the hardware layer provides computing power, storage and data acquisition support for the system; and the data layer realizes collection, analysis and standardization processing of total financial data. The AI model fusing XGBoost and LSTM not only can accurately analyze cost influence factors and realize fine accounting of real-time cost, but also can predict a future cost fluctuation trend, the limitation of post accounting of a traditional system is solved, an enterprise is helped to formulate a cost control strategy in advance, and through a mode of combining association rule mining and an anomaly detection algorithm, the cost control strategy is optimized. The comprehensive identification of dominant and hidden financial risks is realized, the timeliness and pertinence of risk prevention and control are ensured by a three-level early warning mechanism and a risk tracing function, and the financial risk loss of an enterprise is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of financial information technology and artificial intelligence technology, specifically to an AI-based system for accurate calculation of financial costs and dynamic risk control. Background Technology

[0002] In business management, the accuracy of financial cost calculation and the timeliness of financial risk prevention are core elements for ensuring the sound operation of an enterprise. Traditional financial systems face significant technical bottlenecks in practical applications. 1. Limited data processing scope: Traditional systems can only efficiently process structured data, and have weak integration capabilities for data such as audio and video, resulting in incomplete data sources for cost calculation and a high error rate; 2. Poor business adaptability: The accounting and risk assessment logic based on fixed rules cannot cope with dynamic scenarios such as market fluctuations and business adjustments. Model updates rely on manual coding, and the response cycle can be as long as several days, making it difficult to meet the needs of real-time decision-making. 3. Insufficient decision support capabilities; it can only output historical financial data and lacks cost forecasting and risk early warning functions. Remedial measures are often only taken after risks are exposed or costs are exceeded, resulting in irreversible losses. While some existing financial systems have introduced basic automation functions, they have not achieved deep integration of AI technology with core financial scenarios: cost calculation still relies on manually setting fixed parameters, failing to dynamically adapt to the influence of multiple factors; risk control often uses single threshold warnings, lacking multi-dimensional risk correlation analysis capabilities. According to Gartner data, in 2024, more than 40% of enterprises worldwide made operational decision-making errors due to delayed response and calculation errors in their financial systems. Traditional financial systems can no longer meet the management needs of enterprises in the digital age. Therefore, building a financial system based on AI technology to reconstruct its underlying logic has become key to solving these pain points. Summary of the Invention

[0003] This invention aims to overcome the shortcomings of existing financial systems in terms of comprehensive data processing, dynamic decision-making adaptability, and forward-looking risk warning. It provides an AI-based system for accurate financial cost calculation and dynamic risk prevention and control. Through the synergistic effect of multimodal data governance, integrated AI modeling, and intelligent rule engine, it achieves accurate and predictive calculation of financial costs and real-time and comprehensive prevention and control of financial risks, providing enterprises with proactive financial decision support.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an AI-based system for accurate calculation of financial costs and dynamic risk control, comprising a hardware layer, a data layer, an AI engine layer, and an application layer, wherein each layer works together through standardized data interfaces and communication protocols; The hardware layer provides computing power, storage, and data acquisition support for the system; The data layer enables the collection, parsing, and standardization of all financial data. The AI ​​engine layer provides intelligent algorithm support for cost calculation and risk prevention; The application layer implements the output of measurement results and scenario-based services.

[0005] Furthermore, the hardware layer includes a multi-core processor, a distributed storage module, a data acquisition device, and a display and interaction terminal; The data acquisition device includes a high-definition scanner, an OCR recognition terminal, and a network interface module. The OCR recognition terminal supports the recognition of multiple file formats, including PDF, images, and scanned documents, with an accuracy rate of no less than 98%. The network interface module supports dual-mode communication of 5G and Ethernet.

[0006] Furthermore, the data layer includes a multi-source data acquisition module, an intelligent parsing module, and a data governance module; The intelligent parsing module integrates an OCR optical character recognition unit and an NLP natural language processing unit. The OCR unit extracts key financial data from unstructured documents, and the NLP unit performs semantic analysis on the text data. The data governance module constructs a financial data association model based on knowledge graph technology, completes data standardization processing through AI data cleaning algorithms, and outputs a financial data mart.

[0007] Furthermore, the AI ​​engine layer includes a cost calculation AI module, a risk prevention and control AI module, and an intelligent rule engine; The cost calculation AI module adopts a fusion model of gradient boosting tree (XGBoost) and long short-term memory network (LSTM). The XGBoost model analyzes the correlation between cost influencing factors, and the LSTM model learns the time series characteristics of cost changes, so as to realize real-time cost accounting and future cost prediction.

[0008] Furthermore, the risk prevention and control AI module includes a risk identification unit, a risk assessment unit, and an early warning unit; The risk identification unit identifies explicit risks through association rule mining algorithms and implicit risks through isolation forest algorithms. The risk assessment unit constructs a risk assessment indicator system based on the analytic hierarchy process (AHP) and calculates a comprehensive risk score. The early warning unit sets red, yellow, and blue warning thresholds based on a comprehensive risk score and generates risk warning information.

[0009] Furthermore, the intelligent rule engine configurates and visualizes financial rules, allowing financial personnel to adjust parameters through a drag-and-drop interface without the need for code development. The intelligent rule engine works in two directions with the cost calculation AI module and the risk prevention AI module to provide constraints for the AI ​​model and receive the output results of the AI ​​model to optimize the rule parameters.

[0010] Furthermore, the workflow includes four stages: data collection and analysis, data governance, AI intelligent calculation and evaluation, and result output and service, realizing full-process automation from data access to decision support.

[0011] Furthermore, the data acquisition and parsing phase completes the full access and structured extraction of multi-source data; The data governance phase completes data association and standardization. The AI-powered intelligent calculation and evaluation phase completes cost accounting, cost forecasting, and risk identification and assessment. The results output and service phase enables visual display and interactive operation.

[0012] Furthermore, the application layer includes a cost management module, a risk control module, a report generation module, and a system management module; The cost management module supports detailed cost queries, cost deviation analysis, and generation of cost optimization suggestions. The risk control module supports risk warning display, handling suggestion push and risk tracing; the report generation module supports automatic generation of multiple types of financial reports and export in multiple formats. The system management module supports user permission management, operation log recording, data backup and recovery, and AI model updates.

[0013] Compared with the prior art, the technical solution of this application has the following beneficial effects: 1. This invention achieves automated integration and governance of all financial data, improving data processing efficiency by over 90% and reducing the error rate from 5% in traditional systems to below 0.3%, providing a complete and accurate data foundation for cost estimation and risk control; 2. The AI ​​model of this invention, which integrates XGBoost and LSTM, can accurately analyze cost influencing factors, realize real-time refined cost accounting, and predict future cost fluctuation trends. It solves the limitation of the traditional system's "post-event accounting" and helps enterprises formulate cost control strategies in advance. 3. This invention achieves comprehensive identification of explicit and implicit financial risks by combining association rule mining with anomaly detection algorithms. The three-level early warning mechanism and risk tracing function ensure the timeliness and pertinence of risk prevention and control, effectively reducing corporate financial risk losses. 4. The intelligent rule engine of this invention supports financial personnel to configure rules visually, which can adapt to changes in enterprise business without code development, thus lowering the threshold for system use. At the same time, the multi-dimensional visual reports and proactive early warning services realize the transformation of the financial system from "tool attribute" to "service attribute". Attached Figure Description

[0014] Figure 1 This is a framework diagram of the AI-based accurate financial cost calculation and dynamic risk control system of the present invention; Figure 2 This is a data processing flowchart for the AI-based accurate financial cost calculation and dynamic risk control system of the present invention. Figure 3 This is a flowchart of the AI-based financial cost accurate calculation and risk dynamic prevention and control system of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1-3 This embodiment presents an AI-based system for accurate financial cost calculation and dynamic risk control, comprising a hardware layer, a data layer, an AI engine layer, and an application layer. Each layer collaborates through standardized data interfaces and communication protocols. The specific structure is as follows: 1.1.1 Hardware Layer The hardware layer provides basic computing power support, data storage guarantee, and data acquisition channels for system operation, including: Multi-core processor: It adopts Intel Xeon Gold series processors or equivalent ARM architecture processors, supports multi-threaded parallel computing, and meets the computing power requirements for AI model training, large-scale data processing and real-time measurement. Distributed storage module: A distributed file system consisting of at least 3 independent storage nodes with a total storage capacity of no less than 100TB. It supports the classified storage of structured and unstructured data, and has data redundancy backup and fault self-healing functions to ensure the security and availability of financial data. Data acquisition equipment includes a high-definition scanner, an OCR recognition terminal, and a network interface module; the OCR recognition terminal supports character recognition in multiple file formats such as PDF, images, and scanned documents, with an accuracy rate of no less than 98%; the network interface module supports 5G and Ethernet dual-mode communication, enabling real-time data interaction with external platforms such as ERP systems, OA systems, and bank core systems. Display and Interaction Terminal: Equipped with a touch screen and physical keyboard, supporting functions such as system parameter configuration, visualization of calculation results, manual intervention, and rule adjustment. 1.1.2 Data Layer The data layer is responsible for the full collection, intelligent analysis, and standardized governance of financial data, providing high-quality data input to the AI ​​engine layer, including: Multi-source data acquisition module: It can simultaneously collect transaction data from ERP system, expense reimbursement data from OA system, bank statement data, supplier invoice data, internal budget data and market trend data (such as raw material price index) through three methods: API interface call, direct database connection and file upload. It supports full access of structured data (numerical, date), semi-structured data (Excel spreadsheet, XML file) and unstructured data (PDF invoice, contract scan, financial report text). Intelligent parsing module: integrates OCR optical character recognition unit and NLP natural language processing unit; OCR unit is used to extract key financial data such as invoice number, amount, tax, supplier name, and invoice date from unstructured documents; NLP unit uses BERT model to perform semantic analysis on text data such as contract terms, financial reports, and policy documents to extract structured information such as payment terms, liability for breach of contract, and changes in tax policies; Data Governance Module: Based on knowledge graph technology, a financial data association model is built to automatically link invoices with corresponding contracts, payment slips, transaction records, and warehouse receipts to form a complete data chain. Through AI data cleaning algorithms (including outlier detection, duplicate data removal, and data consistency verification), duplicate data and abnormal data (such as invoice amounts not matching contract stipulations or bank statements not matching reimbursement records) are identified and marked, and data quality levels and correction suggestions are output, ultimately forming a standardized and highly reliable financial data mart. 1.1.3 AI Engine Layer The AI ​​engine layer is the core computing unit of the system, providing intelligent algorithm support for cost calculation and risk control, including: The cost calculation AI module employs a fusion model of Gradient Boosting Tree (XGBoost) and Long Short-Term Memory (LSTM) network, using standardized data output from the data layer as input. The XGBoost model is used to mine the non-linear relationships between historical cost data and influencing factors such as business volume, raw material prices, labor costs, and policy adjustments, constructing an accurate cost allocation and accounting model. The LSTM model learns the time-series characteristics of cost changes, combining market trend data (such as raw material price forecasts and industry cost indices) to predict cost fluctuation trends over the next 1-6 months, outputting real-time cost accounting results and predicted cost ranges. The risk prevention and control AI module consists of a risk identification unit, a risk assessment unit, and an early warning unit. The risk identification unit uses association rule mining algorithms to identify explicit risks such as forged invoices, abnormal expense reimbursements, and overdue accounts receivable, and uses the isolated forest algorithm to detect abnormal patterns in financial data (such as sudden large payments or payments without contractual support) to discover implicit risks. The risk assessment unit constructs a risk assessment indicator system based on the Analytic Hierarchy Process (AHP), including financial, business, and market indicators. It combines this with the probability of risk occurrence and the degree of impact output by a machine learning model to calculate a comprehensive risk score. The early warning unit sets red, yellow, and blue warning thresholds based on the comprehensive risk score and generates risk warning information that includes the risk type, risk level, related data, and scope of impact. Intelligent Rule Engine: It configurates and visualizes deterministic rules such as accounting systems, corporate financial regulations, and tax policies, allowing finance personnel to adjust parameters such as cost accounting rules, reimbursement approval thresholds, and risk warning trigger conditions through a drag-and-drop interface without the need for code development. The rule engine achieves two-way linkage with the cost calculation AI module and the risk control AI module. On the one hand, it provides basic constraints for the AI ​​model (such as statutory tax rates and cost allocation limits), and on the other hand, it receives dynamic results output by the AI ​​model (such as cost deviation analysis and risk identification results) and automatically optimizes rule parameters (such as adjusting the judgment threshold for abnormal reimbursements).

[0017] 1.1.4 Application Layer The application layer provides users with scenario-based financial service functions, enabling the visualization and interactive operation of calculation results and early warning information, including: Cost Management Module: Displays real-time cost calculation results, cost composition analysis charts (pie charts, bar charts), cost trend curves, and predicted cost ranges. It supports detailed cost queries by department, project, product, and time period. It also provides a cost deviation analysis function, which automatically compares the differences between actual costs and budgeted costs and standard costs, analyzes the reasons for the differences (such as rising raw material prices or higher-than-expected business volume), and generates optimization suggestions. Risk control module: Real-time display of risk warning list, risk distribution heat map, and risk handling progress tracking table; pushes corresponding handling suggestions for different levels of risk warning (e.g., automatically generates collection plans for overdue accounts receivable with red warnings, and automatically marks key points for verification for abnormal expense reports with yellow warnings); supports risk tracing function, and reverse queries the source data of risk generation through data links (e.g., invoices, contracts, and approval records corresponding to risk payments). Report generation module: Based on AI calculation results and risk assessment data, it automatically generates cost reports, risk assessment reports, financial analysis reports, budget execution reports, etc. It supports exporting in multiple formats such as PDF, Excel, and Word. The report data can be updated in real time and supports custom report templates. The system management module implements user permission management (assigning data viewing, operation, and approval permissions according to roles), operation log recording (recording all system operations, parameter adjustments, and data modification behaviors), data backup and recovery (supporting manual backup and automatic scheduled backup), and AI model update (supporting model training data updates, model parameter optimization, and model version management). It also allows financial personnel to evaluate and provide feedback on the effectiveness of AI calculation models and optimize model performance. 1.3 Workflow The workflow of the AI-based accurate financial cost calculation and dynamic risk control system of this invention is as follows: Data Acquisition and Analysis: The hardware-level data acquisition device accesses the full amount of financial data through the multi-source data acquisition module. The OCR unit and NLP unit in the intelligent analysis module process the unstructured data and text data respectively, extract the structured information, and then transmit it to the data governance module. Data governance: The data governance module builds a data association model based on knowledge graphs, completes data association, cleaning and standardization processing, identifies and marks abnormal data, generates standardized financial data marts and synchronizes them to the AI ​​engine layer; AI-powered intelligent calculation and assessment: The cost calculation AI module performs real-time cost accounting and future cost prediction based on data mart data, while the risk prevention AI module simultaneously conducts risk identification and risk assessment, and the intelligent rule engine provides constraints for the AI ​​model and optimizes the output results. Results Output and Services: The application layer presents the AI ​​engine layer's calculation results, predicted trends, and risk warning information to users in the form of visual charts, reports, and warning notifications. Users can query, filter, export, and manually intervene (such as confirming abnormal data and adjusting calculation parameters). The system management module monitors the entire process, records operation logs, and backs up data regularly, supporting iterative optimization of the AI ​​model. Example

[0018] 1.1.1 Environment Setup Hardware deployment: Server cluster deployment: The cluster consists of 3 Intel Xeon Gold 6330 servers (master node + 2 standby nodes), each configured with 1TB of memory and 20TB of local SSD. The distributed storage module uses GlusterFS to achieve data synchronization between the 3 nodes, expanding the storage capacity to 300TB and supporting 3-replica redundant backup of data. Data acquisition terminal deployment: Five Hanwang HW-3680OCR terminals are configured in the company's finance department and directly connected to the server cluster via Ethernet. The network interface module is equipped with a 5G backup link to ensure uninterrupted data transmission. Security protection configuration: Deploy a firewall (Huawei USG6000), an intrusion detection system (IDS), and a data encryption gateway. Transmitted data is encrypted with AES-256, and stored data is protected by partition encryption and access permissions. Software environment configuration: Operating system: The server uses CentOS 8.4, and the terminal uses Windows 10 Professional. Development environment: Python 3.9, TensorFlow 2.10, Scikit-learn 1.2.2, Neo4j 5.12, MySQL 8.0.33, Miniorelease.2023-04-12T06-55-51Z; Middleware: Nginx 1.21 is used as a reverse proxy, Redis 6.2 is used as a caching service, and RabbitMQ 3.11 is used to implement the message queue (to handle asynchronous communication between data collection and AI computing). 1.1.2 Data Migration and Adaptation Historical data migration: The ETL tool (TalendDataIntegration) was used to extract nearly three years of historical data from the company's original ERP (SAPS / 4HANA), OA (Fanwei e-cology), and financial software (Yonyou U9), including structured transaction data (1.2TB) and unstructured documents (contracts, invoice scans, etc., 800GB). The migrated data is standardized in format: structured data is converted to UTF-8 encoded CSV format, and unstructured files are uniformly converted to PDF / A format. MD5 checksum is used to ensure data integrity.

[0019] External system interface adaptation: Interface agreements have been signed with the core banking system (ICBC Corporate Online Banking API), the tax system (Kingdee Tax Cloud Interface), and the industry data platform (Commodity Price Index API). Real-time data synchronization is achieved using RESTful API, with the synchronization frequency set as follows: transaction data every 15 minutes, market trend data every hour, and policy and regulatory data every 24 hours. The interface adaptation layer adopts the adapter pattern design, which supports rapid expansion when adding external systems (such as subsequent integration of supply chain management system SCM, only the corresponding adapter module needs to be added). 1.1.3 Personnel Training Tiered training program: Operations and maintenance personnel training (2 days): covering server cluster management, database maintenance, AI model deployment and monitoring, troubleshooting, etc. A system operations and maintenance qualification certificate will be issued upon passing the assessment. Financial operations training (3 days): Includes practical courses such as data collection operations, rule engine configuration, calculation result analysis, and risk warning handling, with a simulation system for hands-on practice; Management Training (1 day): Focusing on the interpretation of system visualization reports and application scenarios of decision-making suggestions, the training will explain the system's supporting value for business decision-making through case demonstrations. 1.2 Implementation of Multi-Industry Application Scenarios 1.2.1 Manufacturing Implementation Case (Optimized and Supplemented) Company Overview: A large equipment manufacturing enterprise (5,000 employees, annual revenue of 5 billion yuan, product line covering construction machinery and new energy equipment), whose core needs are accurate production cost accounting and risk control of raw material price fluctuations. Implementation process: Data acquisition and analysis phase: Structured data: Synchronized ERP production material requisition data (32,000 records per month), workshop working hour data (18,000 records per month), OA expense reimbursement data (25,000 records per month), and bank statements (12,000 transactions per month); Unstructured data: Purchase invoices (average 1200 per month), purchase contracts (average 320 per month), and technical agreements (average 80 per month) are scanned using an OCR terminal. The NLP unit parses key information in the contracts, such as "raw material price fluctuation clauses" and "delivery cycle," with a parsing accuracy of 98.3%. Market data: Access to price indices for bulk commodities such as steel, copper, and aluminum (Shanghai Metals Market API), and synchronized industry cost trend reports (an average of 20 reports per month). Data governance phase: Knowledge graph association: A five-dimensional association model of "purchase contract - invoice - warehouse receipt - payment order - production order" was established, with an automatic matching rate of 97.2%; Abnormal data handling: Three invoices with amounts inconsistent with the contract (deviation rate exceeding 5%), two duplicate reimbursement records, and one payment application without contract support were identified. After the system pushed correction suggestions, the finance staff completed the data correction within 2 hours. AI-powered intelligent calculation and evaluation phase: Cost Calculation: The XGBoost model identified "raw material price (weight 0.35)," "production man-hours (weight 0.28)," and "equipment depreciation (weight 0.17)" as the core influencing factors. The unit cost of product A for the current month was calculated to be 12,860 yuan / unit, with a deviation rate of 1.23% compared to the traditional manual calculation result (13,020 yuan / unit). The LSTM model predicts that the raw material price will increase by 3%-5% next month, and the unit cost of product A will increase by 4%-6%, with the prediction error rate controlled within 2%. Risk control: Two accounts receivable overdue for more than 30 days (totaling RMB 8.6 million, corresponding to a downgrade of the customer's credit rating) and one large payment application (RMB 1.2 million) without contract support were identified. The risk assessment unit calculated comprehensive scores of 89 points (red warning) and 72 points (yellow warning) respectively. Results output and application stage: Cost Management: The system pushed suggestions such as "Optimize supplier structure (add 2 aluminum profile suppliers)" and "Sign a 3-month price-locked procurement agreement". After management adjusted the production plan, the risk of cost overrun for product A next month was reduced by 70%. Risk Management: For accounts receivable under red alert, a collection plan was generated, and RMB 6.2 million was recovered within 15 days; for payment applications under yellow alert, approval was completed after supplementing the contract to avoid the risk of payment without basis. Implementation results: Monthly cost accounting time was shortened from 3 days to 8 hours, the cost accounting error rate was reduced from the traditional 5.8% to 0.2%, the overdue accounts receivable identification time was shortened from 1 week to real time, the risk handling efficiency was improved by 60%, and the annual financial management cost was saved by approximately RMB 1.2 million. 1.2.2 Service Industry Implementation Case (New) Company Overview: A chain hotel group (80 stores nationwide, annual revenue of 1.8 billion yuan), whose core needs are the sharing of store operating costs and the prevention and control of cash flow risks. Implementation process: Data Acquisition and Analysis: Structured data: Synchronize room sales data, food and beverage consumption data, and labor cost data from the PMS (Hotel Management System), material procurement data from the POS system, and bank transaction data; Unstructured data: OCR is used to identify store lease contracts, supplier service agreements, and energy bills, while NLP is used to analyze information such as "rent increase clauses" and "service quality penalty" in the contracts; Market data: Access to local tourism indices, hotel pricing data from the same industry, and energy consumption price (water, electricity, gas) fluctuation data. AI-powered intelligent calculation and evaluation: Cost Calculation: The XGBoost model constructs a dynamic accounting model of "number of rooms × occupancy rate × unit energy consumption + labor cost + rent allocation" to achieve accurate cost allocation for single stores and single room types (e.g., the operating cost allocation ratio of luxury suites is adjusted from the traditional fixed 25% to a dynamic 18%-28%); the LSTM model predicts that labor costs will increase by 15%-20% during holidays (such as the Spring Festival) and pushes cost optimization suggestions for recruiting temporary workers in advance. Risk control: Through association rule mining, three stores were identified as having "abnormal growth in energy consumption costs (more than 30% month-on-month)". The isolated forest algorithm detected a "large cash expenditure outside of business hours" (a hidden risk, which was later verified to be embezzlement of public funds by an employee). After the system triggered a yellow warning, the group's finance department completed the verification and rectification within three days. Implementation results: The accuracy of store operating cost allocation has increased to 96%, the response time for cash flow risk identification has been shortened from 7 days to 24 hours, and the annual reduction in ineffective cost expenditures is approximately RMB 850,000. 1.2.3 Implementation Cases in the Financial Industry (New) Company Overview: A city commercial bank (32 branches, assets of 80 billion yuan), whose core needs are credit business cost calculation and dynamic credit risk control. Implementation process: Data Acquisition and Analysis: Structured data: loan disbursement data, customer credit data, and funding cost data (deposit interest rates, interbank lending rates) from the synchronized credit system, and transaction flow data from the core system; Unstructured data: OCR identifies loan contracts, guarantee agreements, and customer financial statements (audit reports), while NLP analyzes the "repayment period," "liability for breach of contract," and "collateral valuation clauses" in the contracts, and extracts key indicators such as "debt-to-equity ratio" and "current ratio" from the financial statements; External data: Access to the central bank's credit reporting system, the enterprise credit information disclosure system, and industry risk rating reports (such as the real estate industry risk index). AI-powered intelligent calculation and evaluation: Cost Calculation: The XGBoost model analyzes the relationship between factors such as loan amount, term, customer credit rating, and funding cost and credit cost, accurately calculating the expected return of a single loan (e.g., the credit cost for a 10 million yuan, 3-year term, AA-rated customer is 4.25%); the LSTM model, combined with market interest rate trends, predicts that the LPR rate will decline by 0.25 basis points in the next 6 months, and pushes a suggestion to "adjust loan pricing strategy (reduce the floating range)". Risk control: The risk identification unit identified a high-risk combination of "customer credit delinquency + overvalued collateral" (2 loans totaling 50 million yuan) through association rules, with a comprehensive risk assessment score of 92 (red alert); the isolated forest algorithm detected the hidden risk of "cross-guarantee by multiple companies under the same actual controller" (3 related loans), and pushed the risk mitigation suggestion of "adding collateral".

[0020] Implementation results: The accuracy rate of credit cost calculation has increased to 97.5%, the advance warning period for non-performing loans has been extended from 3 months to 6 months, the credit risk loss rate has decreased by 0.8 percentage points, and the annual reduction in non-performing loan losses is approximately RMB 32 million. 1.3 Operation and Maintenance Support Mechanism 1.3.1 Daily Operation and Maintenance Hardware maintenance: Zabbix 6.0 is used for hardware monitoring to monitor server CPU utilization (threshold ≤ 85%), memory utilization (threshold ≤ 80%), storage capacity (threshold ≤ 85%), and network bandwidth (threshold ≤ 90%) in real time. When the threshold is triggered, an alarm is automatically sent (SMS + email). Weekly hardware inspections are conducted: check server fans and power supply status, storage node data synchronization, and OCR terminal recognition accuracy (regularly calibrated to ensure no less than 98%). Software maintenance: System log management: The system logs are collected and analyzed using the ELK stack (Elasticsearch + Logstash + Kibana), with a retention period of 1 year, and support for retrieval by module, time, and error type; Patch management: Obtain security patches for operating systems, databases, and middleware from official channels every month. After verifying compatibility in the test environment (testing cycle ≥ 3 days), deploy them to the production environment in batches using automated tools (Ansible). Data Operations and Maintenance: Data backup strategy: A combination of "full backup + incremental backup" is adopted. Incremental backup is performed daily from 00:00 to 02:00, and full backup is performed every Sunday. Backup data is stored in local and off-site dual nodes (the off-site backup center is ≥50 kilometers away from the main data center). Data recovery drills: Conduct a data recovery test once per quarter, simulating scenarios such as "single node failure" and "accidental data deletion". The recovery success rate must reach 100%, and the recovery time must be controlled within 4 hours.

[0021] 1.3.2 AI Model Iterative Optimization Model monitoring: Real-time monitoring of the XGBoost-LSTM model's accuracy (threshold ≥ 95%) and risk identification recall (threshold ≥ 90%). When the metrics are below the threshold for 3 consecutive days, the model retraining process is triggered. Establish a model performance evaluation system: evaluate the accuracy of cost prediction through MAE (mean absolute error), evaluate the effectiveness of risk identification through F1 score, and generate a model performance report every quarter. Model iteration: The model is retrained every six months, with the addition of nearly six months of actual data (structured data + newly added unstructured data parsing results) to optimize model parameters (such as the learning rate of XGBoost and the number of neurons in LSTM). Supports model version management: Retains historical model versions (the last 3 versions), and can be switched over within 1 hour if a rollback is needed. 1.4 Testing and Verification 1.4.1 Functional Testing Unit testing: Test the core functions of each module, such as the success rate of multi-source data access (≥99.9%), OCR recognition accuracy (≥98%), and cost calculation response time (≤10 minutes / month of data) of the data acquisition module. Integration testing: Verify the collaborative effect of each level, such as the smoothness of the entire process of "data collection → governance → AI calculation → result output", and test the processing of 5,000 concurrent data without any anomalies; Scenario testing: The system can accurately perform calculations and issue warnings for 10 typical business scenarios (such as cost overruns, overdue accounts receivable, and forged invoices), achieving 100% scenario coverage. 1.4.2 Performance Testing Concurrency test: Simulate 1000 users operating simultaneously (querying reports, configuring rules, uploading files) using JMeter. System response time ≤ 2 seconds, server CPU utilization ≤ 75%, no request timeouts; Large data volume test: Import 10TB of historical data (including 500,000 unstructured files), system data governance time ≤ 4 hours, AI model calculation time ≤ 1 hour, meeting the daily business processing needs of enterprises; Stability test: After running at full load for 72 consecutive hours, the system did not crash or lose data, and the core functions had a 100% normal operation rate. 1.4.3 Security Testing Vulnerability Scan: A security vulnerability scan was performed using Nessus 10.1. No high-risk vulnerabilities were found, and there were ≤3 medium-risk vulnerabilities (which have been patched). Access control test: Verify the effectiveness of user access control isolation, ensuring that users with low access privileges cannot access data with high access privileges (e.g., store finance personnel cannot view the group's overall cost data), and that access control change logs are complete and traceable; Data encryption test: Packet capture and analysis of data in transmission confirms that the encryption algorithm is effective (AES-256) and that the stored data cannot be read by unauthorized access. 1.5 System Upgrade and Expansion 1.5.1 Upgrade Process The strategy adopted is "gray-scale upgrade": first deploy the new version on 10% of terminals (such as 1 store or 1 branch), run it for 72 hours without any abnormalities, and then promote it to all terminals in batches; The system automatically backs up its configuration and core data before upgrading, and can roll back to the original version within 30 minutes if the upgrade fails. 1.5.2 Expansion Capabilities Hardware expansion: Distributed storage supports adding storage nodes (up to 10 nodes, with a storage capacity of 1PB), and server clusters support horizontal scaling (adding nodes requires no modification to the core architecture); Functionality expansion: Reserved API interfaces to support the addition of new modules (such as the future expansion of "tax planning" and "financial forecasting" modules), and the intelligent rule engine to support the addition of new rule types (such as the addition of "ESG cost accounting rules" and "cross-border business risk rules"). Industry Expansion: By configuring industry-specific data models and rule bases, it can be quickly adapted to industries such as retail, construction, and pharmaceuticals, with an expansion cycle of ≤15 days.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for accurate calculation of financial costs and dynamic risk control based on AI, characterized in that, It includes a hardware layer, a data layer, an AI engine layer, and an application layer, with each layer working together through standardized data interfaces and communication protocols; The hardware layer provides computing power, storage, and data acquisition support for the system; The data layer enables the collection, parsing, and standardization of all financial data. The AI ​​engine layer provides intelligent algorithm support for cost calculation and risk prevention; The application layer implements the output of measurement results and scenario-based services.

2. The AI-based system for accurate calculation of financial costs and dynamic risk control as described in claim 1, characterized in that, The hardware layer includes a multi-core processor, a distributed storage module, a data acquisition device, and a display and interaction terminal; The data acquisition device includes a high-definition scanner, an OCR recognition terminal, and a network interface module. The OCR recognition terminal supports the recognition of multiple file formats, including PDF, images, and scanned documents, with an accuracy rate of no less than 98%. The network interface module supports dual-mode communication of 5G and Ethernet.

3. The AI-based system for accurate calculation of financial costs and dynamic risk control as described in claim 1, characterized in that, The data layer includes a multi-source data acquisition module, an intelligent parsing module, and a data governance module; The intelligent parsing module integrates an OCR optical character recognition unit and an NLP natural language processing unit. The OCR unit extracts key financial data from unstructured documents, and the NLP unit performs semantic analysis on the text data. The data governance module constructs a financial data association model based on knowledge graph technology, completes data standardization processing through AI data cleaning algorithms, and outputs a financial data mart.

4. The AI-based system for accurate calculation of financial costs and dynamic risk control as described in claim 1, characterized in that, The AI ​​engine layer includes a cost calculation AI module, a risk prevention and control AI module, and an intelligent rule engine; The cost calculation AI module adopts a fusion model of gradient boosting tree (XGBoost) and long short-term memory network (LSTM). The XGBoost model analyzes the correlation between cost influencing factors, and the LSTM model learns the time series characteristics of cost changes, so as to realize real-time cost accounting and future cost prediction.

5. The AI-based system for accurate calculation of financial costs and dynamic risk control according to claim 1, characterized in that, The risk prevention and control AI module includes a risk identification unit, a risk assessment unit, and an early warning unit; The risk identification unit identifies explicit risks through association rule mining algorithms and implicit risks through isolation forest algorithms. The risk assessment unit constructs a risk assessment indicator system based on the analytic hierarchy process (AHP) and calculates a comprehensive risk score. The early warning unit sets red, yellow, and blue warning thresholds based on a comprehensive risk score and generates risk warning information.

6. The AI-based system for accurate calculation of financial costs and dynamic risk control according to claim 1, characterized in that, The intelligent rule engine configurates and visualizes financial rules, allowing financial personnel to adjust parameters through a drag-and-drop interface without the need for code development. The intelligent rule engine works in two directions with the cost calculation AI module and the risk prevention AI module to provide constraints for the AI ​​model and receive the output results of the AI ​​model to optimize the rule parameters.

7. The AI-based system for accurate calculation of financial costs and dynamic risk control as described in claim 1, characterized in that, The workflow includes four stages: data collection and analysis, data governance, AI intelligent calculation and evaluation, and result output and service, realizing full-process automation from data access to decision support.

8. The AI-based system for accurate calculation of financial costs and dynamic risk control according to claim 7, characterized in that, The data acquisition and analysis phase completes the full access and structured extraction of multi-source data; The data governance phase completes data association and standardization. The AI-powered intelligent calculation and evaluation phase completes cost accounting, cost forecasting, and risk identification and assessment. The results output and service phase enables visual display and interactive operation.

9. The AI-based system for accurate calculation of financial costs and dynamic risk control according to claim 1, characterized in that, The application layer includes a cost management module, a risk control module, a report generation module, and a system management module; The cost management module supports detailed cost queries, cost deviation analysis, and generation of cost optimization suggestions. The risk prevention and control module supports risk warning display, handling suggestion push and risk tracing; The report generation module supports automatic generation of multiple types of financial reports and export in multiple formats; The system management module supports user permission management, operation log recording, data backup and recovery, and AI model updates.

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