Supply chain bill asset securitization full-life-cycle dynamic income measuring and calculating system

Abstract

The invention relates to a supply chain bill asset securitization full life cycle dynamic income measuring and calculating system, and belongs to the technical field of supply chain bill processing, and the system comprises a data access module which is used for supporting the manual input of parameters, the automatic importing of bill asset package details, a measuring and calculating plan, and the acquisition of trust transfer-in bill data through a direct connection bill exchange interface; the double-engine calculation module comprises a role dimension income model which is used for calculating sub-item incomes of an asset side, a security side, an intermediary agency and an agency in parallel operation; the cash flow path tracking model is used for simulating fund inflow / outflow according to a time sequence; the visual output module is used for generating a penetrable analysis report of the role dimension and the cash flow dimension; and the dynamic verification module is used for comparing the predicted value before creation with the actual accounting value after creation, and generating a deviation analysis report. The method has the beneficial effects that the accuracy of income calculation is improved, and the stable and sustainable development of supply chain bill asset securitization business is promoted.

Description

Technical Field

[0001] This application relates to the technical field of supply chain bill processing, and in particular to a dynamic revenue calculation system for the entire lifecycle of supply chain bill asset securitization. Background Technology

[0002] In recent years, with the rapid development of supply chain finance, supply chain bill securitization, as an innovative financial instrument, has provided a new avenue for financing small and medium-sized enterprises (SMEs). It uses undiscounted supply chain bills or related property rights as underlying assets, issuing asset-backed securities through special purpose vehicles (SPVs), effectively revitalizing the bill assets of SMEs and enhancing the convenience of supply chain bill financing. However, supply chain bill securitization involves numerous participants and complex fund flows; therefore, the accuracy and timeliness of its return calculations are crucial for the decision-making of all participants and the stable operation of the business.

[0003] Currently, in calculating the returns of supply chain bill securitization, some companies use traditional manual calculation methods, estimating the returns for participants on the asset and securities sides based on relevant financial data and empirical formulas. Other companies use simple financial software, inputting basic data for return calculation. While these methods can accomplish the basic task of return calculation to some extent, limitations in data acquisition and processing make it difficult to meet the demands of accurate calculation and efficient computation required for business development. These methods often rely on periodic manual data collection and entry, leading to discrepancies between the data used in the calculations and actual conditions. This not only affects the accuracy of return calculations but also makes it difficult for companies to adjust their business strategies in a timely manner according to market changes, increasing the risk of cash flow disruptions and hindering the stable and sustainable development of supply chain bill securitization. Summary of the Invention

[0004] To improve the accuracy of revenue calculation and promote the stable and sustainable development of supply chain bill securitization, this application provides a dynamic revenue calculation system for the entire lifecycle of supply chain bill securitization, employing the following technical solution: A dynamic revenue calculation system for the entire lifecycle of supply chain bill securitization includes: The data access module is used to support manual input of parameters, automatic import of bill asset package details, calculation plans, and direct connection to the Shanghai Commercial Paper Exchange interface to obtain trust-transferred bill data; Dual-engine computing modules, including those running in parallel: Role-based revenue model: used to calculate the revenue of the asset side, securities side, intermediary institutions and agency institutions based on the data accessed by the data access module; Cash flow path tracking model: used to simulate cash inflows / outflows in a time series based on the data accessed by the data access module; The visualization output module is used to generate a penetrable analysis report on the role dimension and cash flow dimension based on the calculation results of the dual-engine calculation module. The dynamic verification module is used to compare the predicted value before creation with the actual calculated value after creation and generate a deviation analysis report.

[0005] By adopting the above technical solutions, the system supports manual parameter input, allowing operators to supplement important information based on the actual situation in special circumstances or when data is missing. The automatic import function of bill asset package details can quickly process large amounts of bill data, greatly improving data entry efficiency. Direct connection to the bill exchange interface to obtain trust-transferred bill data ensures the timeliness and accuracy of the data. Through these multiple data access methods, the system can comprehensively grasp various key data in the supply chain bill asset securitization process. The parallel operation of the dual-engine calculation modules brings the advantages of multi-dimensional accurate measurement and efficient computing. The role-dimensional revenue model can calculate the sub-items of revenue for the asset side, securities side, intermediaries, and agents, allowing each participant to clearly understand their own revenue status in the business and providing a strong basis for decision-making. The cash flow path tracking model simulates the inflow / outflow of funds according to the time series, enabling enterprises to plan the use and allocation of funds in advance, avoid the risk of cash flow disruption, and ensure stable business operation. The parallel operation of the two models greatly shortens the calculation time, improves the overall computing efficiency of the system, and meets the timeliness requirements of actual business. The visualization output module further enhances the system's usability. The generated, penetrating analysis reports across role and cash flow dimensions present complex revenue and cash flow information intuitively, reducing the difficulty for users to interpret the data. This allows all participants to quickly understand business revenue and cash flow, facilitating timely problem identification and corrective action. The dynamic verification module provides strong support for accurate business assessment and process optimization. By comparing pre-creation forecasts with post-creation actual accounting values ​​and generating deviation analysis reports, the system can promptly identify discrepancies between forecasts and actual results, and deeply analyze the reasons for deviations. This helps companies summarize lessons learned, improve the accuracy and reliability of future revenue forecasts, and optimize and adjust various aspects of supply chain bill securitization based on the analysis results, thereby improving the overall quality and efficiency of the business.

[0006] Optionally, the dual-engine calculation module allocates cash flow to each role dimension through a revenue attribution matrix, achieving real-time mapping between role revenue and cash flow paths; wherein: The output expression of the role-dimensional benefit model is: Total revenue of the asset plan = Total distributable cash - ∑Interest expense - Total intermediary fees - Total registration and transfer fees; The output expression of the cash flow path model is: Net cash flow = Underlying asset repayments + Unused funds - Priority principal repayments - Priority interest payable - Subordinated interest payable - Subordinated principal repayments - Total intermediary fees - Total registration and transfer fees.

[0007] By adopting the above technical solutions, the output expression of the role-dimensional revenue model clearly defines the calculation method of the total revenue of the asset plan. It can accurately calculate the actual revenue of the asset side in the entire supply chain securitization business, allowing the asset side to clearly understand the value-added of its assets after deducting various costs. This provides accurate data support for the asset side's investment decisions and asset allocation adjustments. Simultaneously, for other participants such as securities firms, intermediaries, and agents, this precise revenue accounting method helps them clarify their own revenue share in the business, rationally plan their business development strategies, and improve their enthusiasm and initiative in participating in the business. The output expression of the cash flow path model details the calculation elements of net cash flow. By calculating net cash flow in real time, enterprises can clearly understand the dynamic changes in funds, predict surpluses or shortages in advance, and thus rationally arrange the use and allocation of funds. Furthermore, real-time monitoring and accurate calculation of cash flow also help enterprises to promptly identify potential risk factors and reduce the impact of risks on business.

[0008] Optionally, the output expression of the role-dimensional benefit model further includes: The total distributable cash is calculated as follows: (Amount of each note × Purchase price × (Number of interest days / Interest base)). Interest expense includes senior tranche interest and subordinated tranche interest. Senior tranche interest = senior tranche size × senior tranche interest rate × (T / B), where T is the product term and B is the interest calculation basis. Subordinated tranche interest = (issue size - senior tranche size) × subordinated tranche interest rate × (T / B). Total intermediary fees = agency service fee + asset service fee + issuance vehicle management institution fee + other fees; Agency service fee = issuance size × agency service fee rate × (T / B), asset service fee = issuance size × asset service fee rate × (T / B), issuance vehicle management institution fee = issuance size × issuance vehicle management institution fee rate × (T / B).

[0009] Optionally, the output expression of the cash flow path model further includes: Unused funds = Issuance size - (Asset pool size - Total distributable cash).

[0010] Optionally, the dual-engine computing module further includes: When calculating the total intermediary fees, identify the payment methods for underwriting fees, law firm fees, and rating fees; If a certain fee is paid through "trust property payment", the fee will be included in the funds occupied by the issuance scale, triggering a recalculation of the issuance scale. The new issuance scale = the original issuance scale - the fee amount. If a fee is paid "separately", it will be directly included as a cost in the total calculation of intermediary agency fees.

[0011] By adopting the above technical solution, when a payment is made through "trust property," it is included in the issuance scale's capital occupation and the issuance scale is adjusted accordingly. This clearly reflects the actual amount of funds available for business operations. This allows companies to more accurately assess the availability and liquidity of funds when planning business development and fund usage, avoiding funding gaps caused by neglecting to consider the occupation of expenses. For "separately paid" expenses, they are directly included as costs in the total calculation of intermediary fees, enabling companies to accurately account for business costs and clearly understand the proportion of each expense in the total cost, providing a basis for cost control and optimization.

[0012] Optionally, the dual-engine computing module further includes: It supports dynamic switching of the interest calculation basis, with switching options including 360 days and 365 days. The number of days for interest calculation for each note is calculated independently as: maturity date - plan start date.

[0013] By adopting the above technical solution, the system supports dynamic switching between 360-day and 365-day interest calculation bases, enabling it to adapt to different business scenarios and market practices. In supply chain bill securitization, the maturity dates and planned start dates of different bills may vary. Using a uniform method for calculating the number of interest days may lead to inaccurate revenue calculations. However, calculating the interest days for each bill independently, taking into account the actual duration of each bill, makes revenue calculations more accurate.

[0014] Optionally, the dynamic revenue calculation system further includes: The risk warning and stress testing module has a built-in risk warning indicator system and multi-scenario stress testing model, which are used for real-time monitoring and simulation analysis of risk factors; Risk warning indicators include bill delinquency rate, asset pool diversification, cash flow coverage ratio, and interest rate sensitivity gap, which are used to automatically trigger warning signals when the actual value of the risk warning indicator exceeds the corresponding preset threshold. Stress test model: Supports historical scenario and hypothetical scenario methods to simulate asset-side return fluctuations, securities-side redemption capacity and cash flow disruption risks under extreme conditions, and outputs stress test reports.

[0015] By adopting the above technical solutions, combining real-time monitoring and simulation analysis, we can not only focus on the current risk situation, but also take into account the extreme situations that may occur in the future, forming a complete risk management closed loop. This helps enterprises reduce risks and improve their ability to cope with extreme situations.

[0016] Optionally, the dynamic revenue calculation system further includes: The multi-dimensional sensitivity analysis module, based on the underlying data of the dual-engine calculation module, is used to perform single-factor or multi-factor sensitivity analysis on core variables, quantify the impact of variable fluctuations on the revenue and cash flow of each participant, and generate a sensitivity analysis report. Single-factor sensitivity analysis: With other variables fixed, simulate the impact of a certain variable on total revenue on the asset side, redemption pressure on the securities side, and intermediary fees, and display the sensitivity ranking of the variables using a tornado diagram; Multifactor sensitivity analysis: Simultaneously adjust multiple variables to output the range of return fluctuations and the probability distribution of cash flow gaps under combined scenarios.

[0017] By adopting the above technical solutions, the multi-dimensional sensitivity analysis module can help enterprises identify key variables and sensitive factors, thereby strengthening the monitoring and management of these factors in a targeted manner. Enterprises can optimize business processes based on the analysis results, reduce the risks caused by variable fluctuations, and improve business stability and risk resistance.

[0018] Optionally, the dynamic revenue calculation system further includes: The asset pool monitoring and screening module is used to collect key indicators of the asset pool in real time, and to dynamically screen the asset pool based on the key indicator data and preset rules; the key indicators include changes in the rating of bill assets, overdue status, and acceptor qualifications.

[0019] By adopting the above-mentioned technical solutions, the overall quality of the asset pool can be improved. Continuous monitoring of key indicators allows for the selection of high-quality bill assets for inclusion in the pool, while ineligible assets are removed. Continuously optimizing the asset pool structure enhances its overall creditworthiness and stability, strengthens the business's resilience against risks, and creates more reliable revenue streams for all participants.

[0020] In summary, this application includes at least one of the following beneficial technical effects: The system supports manual parameter input, allowing operators to supplement important information based on the actual situation in special circumstances or when data is missing. The automatic import function of bill asset package details can quickly process large amounts of bill data, greatly improving data entry efficiency. Direct connection to the Shanghai Commercial Paper Exchange interface to obtain real-time trust-transferred bill data ensures data timeliness and accuracy. Through these multiple data access methods, the system can comprehensively grasp various key data in the supply chain bill asset securitization process. The parallel operation of the dual-engine calculation modules brings the advantages of multi-dimensional accurate calculation and efficient computation. The role-based revenue model can calculate the sub-items of revenue for the asset side, securities side, intermediaries, and agents, allowing each participant to clearly understand their own revenue status in the business and providing a strong basis for decision-making. The cash flow path tracking model simulates fund inflows / outflows according to time series, enabling enterprises to plan fund usage and allocation in advance, avoiding the risk of a broken capital chain and ensuring stable business operation. The parallel operation of the two models greatly shortens the calculation time, improves the overall system efficiency, and meets the timeliness requirements of actual business operations. The visualization output module further enhances the system's usability. The generated, penetrating analysis reports across role and cash flow dimensions present complex revenue and cash flow information intuitively, reducing the difficulty for users to interpret the data. This allows all participants to quickly understand business revenue and cash flow, facilitating timely problem identification and corrective action. The dynamic verification module provides strong support for accurate business assessment and process optimization. By comparing pre-creation forecasts with post-creation actual accounting values ​​and generating deviation analysis reports, the system can promptly identify discrepancies between forecasts and actual results, and deeply analyze the reasons for deviations. This helps companies summarize lessons learned, improve the accuracy and reliability of future revenue forecasts, and optimize and adjust various aspects of supply chain bill securitization based on the analysis results, thereby improving the overall quality and efficiency of the business. Attached Figure Description

[0021] Figure 1 This is a first structural block diagram of an embodiment of this application; Figure 2 This is a second structural block diagram of an embodiment of this application.

[0022] Figure labeling: 101, Data access module; 102, Dual-engine computing module; 103, Visualization output module; 104, Dynamic verification module; 201, Risk warning and stress testing module; 202, Multi-dimensional sensitivity analysis module; 203, Asset pool monitoring and screening module. Detailed Implementation

[0023] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-2The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0024] This application discloses a dynamic revenue calculation system for the entire lifecycle of supply chain bill securitization. (Refer to...) Figure 1 The dynamic revenue calculation system may include a data access module 101, a dual-engine calculation module 102, a visualization output module 103, and a dynamic verification module 104.

[0025] The data access module 101 adopts a "multi-source heterogeneous data integration + real-time verification" architecture to support manual parameter input, automatic import of bill asset package details, calculation plans, and direct connection to the Shanghai Commercial Paper Exchange interface to obtain real-time trust-transferred bill data. The specific implementation steps are as follows: Standardization of manual parameter input: A visual configuration interface has been developed, allowing users to enter core parameters such as issuance size, priority / subordinate interest rates, and fees of various intermediaries through forms. The system has built-in parameter validation rules (such as fee range restrictions and validity checks of interest calculation basis), and provides real-time alerts for abnormal values ​​during input.

[0026] Automatic import of bill asset packages: Design an Excel template import function. The template is preset with required fields such as "bill number, amount, maturity date, and acceptor information". After import, the data is cleaned using the Python Pandas library, and the system automatically queries the acceptors of bills in the bill asset package to check for any risk anomalies and generates an anomaly report.

[0027] Calculation plan association: Establish a calculation plan management module to support users in creating multiple versions of calculation plans (such as "optimistic / baseline / pessimistic" scenarios). Each plan is bound to an independent parameter set and asset package, and historical plans can be traced through version numbers.

[0028] Direct connection to the Shanghai Commercial Paper Exchange (SCPE) API: Connects to the SCPE API interface, using HTTPS encrypted transmission, and obtains real-time data of trust-transferred bills through the OAuth2.0 authentication mechanism, including face value, remaining term, acceptor rating, etc. After the data is stored, it triggers a Redis cache update to ensure that the asset pool data is synchronized with the SCPE in real time.

[0029] The dual-engine computing module 102 adopts a "parallel computing + revenue attribution matrix" architecture, and the core implementation steps are as follows: The role-based revenue model calculates the revenue for the asset side, securities side, intermediaries, and agents based on the data accessed by data access module 101. The output expression is as follows: Total revenue of the asset plan = Total distributable cash - ∑Interest expense - Total intermediary fees - Total registration and transfer fees.

[0030] Total distributable cash calculation: Based on the maturity date and the start date of the plan, the number of interest-bearing days for each bill is calculated using the Python datetime library, accurate to the day. The calculation logic is dynamically switched according to the interest base selected by the user (360 days / 365 days). The formula is "Interest per bill = Amount × Purchase price × (Number of interest-bearing days / Interest base)". After batch calculation, the total distributable cash is summarized.

[0031] Interest expense breakdown: Based on the size of the senior tranche and the subordinated tranche, the subordinated tranche size = issuance size - senior tranche size. The interest of the senior tranche and the subordinated tranche are calculated separately. The senior tranche interest = senior tranche size × senior tranche interest rate × (product term / interest calculation basis). The subordinated tranche interest = subordinated tranche size × subordinated interest rate × (product term / interest calculation basis). It supports calculation in two modes: "fixed interest rate" or "floating interest rate (linked to LPR)". The floating interest rate is obtained by accessing the central bank's LPR historical data interface.

[0032] Total intermediary fees are calculated as follows: Intermediary fees are broken down into agency service fees, asset service fees, issuance vehicle management institution fees, and other sub-items. Agency service fee = Issuance size × Agency service fee rate × (T / B), where T represents the actual number of days the product is outstanding (i.e., the product term), and B represents the interest calculation basis. For the "Agency Service Fee," T and B are automatically calculated based on the start and end dates of the plan, supporting both "daily accrual" and "one-time payment" methods, and linked to the payment date (e.g., issuance date, redemption date). Asset service fee = Issuance size × Asset service fee rate × (T / B), Issuance vehicle management institution fees = Issuance size × Issuance vehicle management institution fee rate × (T / B).

[0033] Localized registration and transfer fees: Taking Shanghai as an example, it has a built-in fee rate database of "Shanghai Clearing House + Beijing Financial Assets Exchange + Shanghai Commercial Paper Exchange". The total registration and transfer fees = Shanghai Clearing House redemption and interest handling fees + Shanghai Clearing House issuance registration fees + Beijing Financial Assets Exchange book-building fees + Shanghai Commercial Paper Exchange fees + taxes and surcharges.

[0034] Shanghai Clearing House redemption and interest handling fee = Issuance size × Shanghai Clearing House redemption and interest handling fee rate; Shanghai Clearing House issuance registration fee = Issuance size × Shanghai Clearing House issuance registration fee rate; Beijing Financial Assets Exchange book-building fee = Issuance size × Beijing Financial Assets Exchange book-building fee rate; Taxes and surcharges = Value-added tax amount + Urban maintenance and construction tax + Education surcharge + Local education surcharge; Value-added tax amount = Priority interest payable / (1 + Value-added tax amount and rate) × Value-added tax amount and rate; Urban maintenance and construction tax = Value-added tax amount × Urban maintenance and construction tax rate; Education surcharge = Value-added tax amount × Education surcharge rate; Local education surcharge = Value-added tax amount × Local education surcharge rate.

[0035] When calculating taxes and surcharges, the system automatically calls the local tax API to obtain the latest tax rates (such as the urban maintenance and construction tax rate of 7%), and supports switching between tax rate templates for multiple regions (such as Beijing and Shenzhen).

[0036] Cash flow path tracing model: Net cash flow simulation: A cash flow ledger is constructed according to time series (e.g., weekly / monthly). Inflows include repayments of underlying assets (simulated based on the distribution of bill maturity dates) and unused funds (issuance size - (asset pool size - total distributable cash)). Outflows cover senior / subordinate interest, principal repayment, intermediary agency fees, registration and transfer fees, etc. Dynamic calculations are achieved through an Excel formula engine.

[0037] Profit Attribution Matrix Construction: Cash flow is allocated to various role dimensions, enabling real-time mapping between role returns and cash flow paths. A two-dimensional matrix of "role-cash flow type" is designed, such as "priority investor-priority interest" and "intermediary institution-intermediary service fee." Each cash flow is allocated to the corresponding role according to matrix rules. Real-time mapping between cash flow and role returns is achieved through an SQL database, supporting drill-down queries; for example, clicking "subordinate interest" allows tracing back to specific calculation parameters.

[0038] Dynamic processing of fee payment methods: In the process of calculating intermediary fees, the fee payment method is identified through the rule engine. The payment method includes "payment by trust property" or "separate payment". If it is "payment by trust property", the issuance scale is recalculated. The new issuance scale = the original issuance scale - the fee amount, and the priority / subordinate scale ratio is updated simultaneously. If it is "separate payment", it is directly included in the total intermediary fees and an independent cash outflow record is generated.

[0039] The visualization output module 103 adopts a "penetrating analysis + multi-dimensional report" architecture to generate penetrating analysis reports on role and cash flow dimensions based on the calculation results of the dual-engine calculation module 102. The implementation steps are as follows: Role-based report generation: Based on the results of the role-based revenue model, an interactive dashboard is developed. Using ECharts.js, a pie chart showing the revenue share of the asset side / securities side / intermediary institution and a bar chart showing the revenue comparison of the priority / subordinate tranches are drawn. Drill-down analysis is supported. For example, clicking on "intermediary institution fees" can expand to show the detailed composition of agency service fees and asset service fees.

[0040] Cash dimension report generation: The report uses the time axis as the horizontal axis and displays the "weekly net cash flow fluctuation" through a line chart, with a cash flow gap warning line superimposed (marked in red when net cash flow < 0). At the same time, a PDF version of the cash flow calculation table is generated, which includes key indicators such as "beginning cash, cash inflow, cash outflow, and ending cash".

[0041] Drill-down query function: Data traceability links are embedded in the report. Users can click on any data, such as "Total Distributable Cash", to jump to the details page and view the calculation process of interest days for a single bill and the marking of abnormal bills (such as the maturity date being earlier than the plan start date). It also supports exporting detailed data to Excel.

[0042] The dynamic verification module 104 adopts a "prediction-actual deviation analysis + closed-loop feedback" mechanism to compare the predicted value before creation with the actual calculated value after creation, and generate a deviation analysis report. The implementation steps are as follows: Data integration between forecast and actual values: After product creation, the actual accounting data (such as actual interest expenses and handling fees) of the asset management system is integrated. Data is extracted periodically using ETL tools (such as Apache Flink) and stored in the "actual value table" of the data warehouse. This table is then linked to the forecast value table before product creation via the "calculation plan ID".

[0043] Deviation analysis: Calculate the absolute deviation (actual value - predicted value) and relative deviation (deviation rate = (actual value - predicted value) / predicted value) of key indicators, and focus on monitoring highly sensitive indicators such as "total revenue deviation rate > set value (e.g., 5%)" and "priority interest deviation rate > set value (e.g., 3%)", and identify abnormal deviation data through box plots.

[0044] Deviation Attribution Report Generation: The system automatically traces the root cause of deviations. For example, when "Priority Interest Deviation Rate = 8%", the system automatically traces the source of the deviation by comparing the predicted and actual values ​​of variables such as interest calculation days, interest rate parameters, and interest calculation basis to pinpoint the cause of the deviation (e.g., the actual product term is extended due to holidays, resulting in an increase in the number of interest calculation days) and quantifies the impact of the deviation on total revenue (e.g., causing a decrease in total revenue of 230,000 yuan). The report is automatically pushed to product managers and risk control departments via email, and high-risk deviation indicators are displayed with a red warning light on the system dashboard.

[0045] Reference Figure 2 Furthermore, the dynamic return calculation system also includes a risk warning and stress testing module 201, a multi-dimensional sensitivity analysis module 202, and an asset pool monitoring and screening module 203.

[0046] Specifically, the risk warning and stress testing module 201 adopts a "real-time monitoring + scenario simulation" architecture, with a built-in risk warning indicator system and multi-scenario stress testing models, used for real-time monitoring and simulation analysis of risk factors. The implementation steps are as follows: Real-time monitoring of risk warning indicators: The system obtains daily updated information on bill repayment status, borrower performance records, interest rate changes, and asset pool composition details from the underlying asset management system, fund settlement platform, and external credit reporting agencies through API interfaces or ETL processes. It then automatically calculates four core warning indicators: bill delinquency rate (defined as the proportion of overdue bills in the current period to the total asset pool balance), asset pool diversification (using the Herfindahl-Hirschman Index (HHI) to measure industry, region, and entity concentration), cash flow coverage ratio (i.e., the ratio of expected recoverable cash flow to the principal and interest payment needs of senior tranches), and interest rate sensitivity gap (referring to the difference between floating-rate assets and floating-rate liabilities). All of the above indicators have dynamic thresholds. These thresholds are not statically set, but are adaptively adjusted using statistical methods (such as moving average ± 2 standard deviations) or expert rule engines, taking into account historical fluctuation ranges, project rating requirements, and regulatory standards. Once any indicator exceeds the preset threshold, the system immediately triggers a tiered warning signal (yellow, orange, red) and pushes it to the risk control manager and relevant decision-makers via email, SMS, or enterprise IM tools. At the same time, the abnormal items are marked in the visual dashboard and associated with relevant asset details, supporting one-click drill-down analysis.

[0047] Stress test model development: A dual-track scenario generation approach is adopted. The historical scenario method selects major past financial events as benchmark scenarios, mapping the macroeconomic parameters at that time (GDP growth rate, unemployment rate, interest rate path, etc.) onto the current asset pool characteristics to reenact the default transmission path under extreme conditions. The hypothetical scenario method allows users to customize the impact intensity, such as setting a combination of conditions like "a 30% decline in real estate sales in the next 12 months + a 50 basis point reduction in LPR + an upgrade in regional purchase restrictions." The system then calls a Monte Carlo simulation engine to generate thousands of random paths while considering the correlation between variables. This assesses the extent of the reduction in total returns on the asset side, whether there is a redemption gap on the securities side, and the time point of free cash flow disruption under different stress levels, ultimately outputting a structured stress test report. This report not only includes the trajectory graphs of key indicators under various scenarios but also introduces VaR (Value at Risk) and CVaR (Conditional Value at Risk) to measure the expected extreme losses, assisting management in judging whether the capital buffer is sufficient.

[0048] The multi-dimensional sensitivity analysis module 202 adopts a "single-factor / multi-factor variable simulation + visualization attribution" architecture, based on the underlying cash flow engine and valuation engine provided by the built-in "dual-engine calculation module 102". The implementation steps are as follows: Core variable identification and definition: More than 10 key variables, such as "issuance size, priority interest rate, and average maturity date of bills", are included in the sensitivity analysis. Each variable has a set fluctuation range (such as priority interest rate ±20BP), and users can customize variable combinations.

[0049] Single-factor sensitivity analysis: Using the "controlled variable method" as its core idea, all input parameters except the target variable (such as default rate, recovery rate, discount rate, service fee rate, etc.) are kept constant. Only one key variable (such as the base interest rate, prepayment rate, and operating cost growth rate) is allowed to gradually increase or decrease within a reasonable range. Its impact on three core outputs is observed: first, the slope of change in total revenue on the asset side, reflecting revenue stability; second, the principal and interest repayment pressure index at each level of the securities side (defined as the maximum ratio of payable amount to available cash flow), revealing the repayment safety margin; and third, the elasticity of the total intermediary fees, reflecting cost controllability. After each simulation, the system automatically generates a tornado diagram, arranging each variable according to the absolute value of the resulting return fluctuations from largest to smallest, visually demonstrating which factors are the main drivers, helping the project team focus on high-impact variables for key monitoring and mitigation arrangements.

[0050] Multifactor sensitivity analysis: Using orthogonal experimental design, under multivariate combination scenarios (such as "senior interest rate +20BP and issuance size -10%), a random forest model is trained using the Python Scikit-learn library to quantify the interaction effects of variables and output the return fluctuation range (such as "95% confidence interval: [50 million, 80 million]") and the probability distribution of the cash flow gap (displayed by histogram).

[0051] Further integrating the critical point detection algorithm, the system uses a binary search method to continuously approach the critical value at which a certain variable leads to the first default (e.g., when the weighted average duration of the asset pool exceeds 7.2 years, the subordinated tranche protection mechanism fails), and incorporates these key thresholds into the monitoring list of the risk warning system. All analysis results are packaged into a standardized sensitivity analysis report, including a variable impact coefficient matrix (showing the change in returns caused by each unit change in the variable), a nonlinear relationship fitting curve (revealing whether there is an inflection point or saturation effect), and a scenario clustering summary (summarizing three typical patterns: "mild pressure," "severe erosion," and "systemic collapse").

[0052] The asset pool monitoring and filtering module 203 adopts a "real-time indicator collection + dynamic filtering rule engine" architecture. It is used to collect key indicators of the asset pool in real time, and to dynamically filter the asset pool based on key indicator data and preset rules. The implementation steps are as follows: Key indicators are collected in real time: Through the Shanghai Commercial Paper Exchange API and the acceptor's credit information platform (such as the enterprise credit system), the rating changes of the bill assets (such as AAA→AA+), the overdue status (whether it has been overdue for more than 30 days), and the acceptor's qualifications (entity rating, whether it is a dishonest enterprise) are collected in real time. The data update frequency is set to once per hour.

[0053] Dynamic screening rules: A pre-set screening rule library is used, and the screening rules are formulated based on factors such as the risk appetite, investment objectives, and market environment of the asset pool. For example, "removing bills with acceptor ratings lower than AA" is implemented. The system performs a screening task every day at midnight, marking bills that do not meet the rules as "to be replaced" and automatically matching bills that meet the conditions (such as remaining maturity and amount matching) from the alternative asset pool. After replacement, the dual-engine calculation module 102 is triggered to recalculate the yield, ensuring the compliance of the asset pool.

[0054] The implementation principle of this embodiment is as follows: The system first constructs a multi-source data hub through data access module 101: business personnel can manually input key parameters such as issuance scale and priority tier ratio, while automatically importing detailed data such as face value, acceptor information and maturity date of the underlying bill assets, and directly connect to the Shanghai Bill Exchange interface to obtain the ownership status and cash flow data of the bills under the trust plan, ensuring the timeliness and completeness of the basic data.

[0055] Upon entering the calculation phase, the dual-engine calculation module 102 initiates a parallel computing mechanism. In the role-based revenue model, the system calculates the revenue of each bill on a dynamic interest-bearing basis, generating a complete revenue distribution table that includes total revenue on the asset side, priority / subordinate interest payments, four types of intermediary fees (agency service fees, asset service fees, issuance vehicle management fees, and others), and specific registration and transfer fees in Shanghai. Intermediary fees are intelligently identified based on the payment method: fees paid using "trust property" (such as partial underwriting fees) are deducted from the issuance scale in real time, while fees paid separately are directly included in operating costs. Simultaneously, the cash flow path tracking model simulates fund flows based on a timeline: cash inflows are generated from the repayment of underlying assets and unused funds (the difference between the issuance scale and the asset pool), and net cash flow is output after deducting priority / subordinate principal and interest payments, intermediary fees, and handling fees. The two engines achieve dynamic mapping through a revenue attribution matrix, ensuring that each cash flow can be traced back to the specific role's revenue composition.

[0056] After the calculation results are sent to the visualization output module 103, the system generates a two-dimensional, penetrating report: the role-based report displays the profit composition and proportion of each participant, while the cash-based report provides daily cash inflow / outflow forecast curves to help decision-makers identify payment peaks and liquidity gaps. The dynamic verification module 104 continuously compares the pre-creation forecast values ​​with the actual calculated values ​​during the term. When it detects that the deviation of key indicators such as the bill delinquency rate and cash flow coverage ratio exceeds the threshold, it automatically triggers an alert and generates an attribution analysis report.

[0057] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce a good effect.

[0058] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A dynamic revenue calculation system for the entire lifecycle of supply chain bill securitization, characterized in that, include: The data access module (101) is used to support manual input of parameters, automatic import of bill asset package details, calculation plan and direct connection to the bill exchange interface to obtain trust-transferred bill data; The dual-engine computing module (102) includes parallel-running: Role-based revenue model: used to calculate the revenue of the asset side, securities side, intermediary institutions and agency institutions based on the data accessed by the data access module (101); Cash flow path tracking model: used to simulate cash inflows / outflows in a time series based on the data accessed by the data access module (101); The visualization output module (103) is used to generate a penetrable analysis report on the role dimension and cash flow dimension based on the calculation results of the dual-engine calculation module (102); The dynamic verification module (104) is used to compare the predicted value before creation with the actual calculated value after creation and generate a deviation analysis report.

2. The supply chain bill asset securitization full life cycle dynamic revenue calculation system according to claim 1, characterized in that, The dual-engine calculation module (102) allocates cash flow to each role dimension through a revenue attribution matrix, realizing real-time mapping between role revenue and cash flow path; wherein: The output expression of the role-dimensional benefit model is: Total revenue of the asset plan = Total distributable cash - ∑Interest expense - Total intermediary fees - Total registration and transfer fees; The output expression of the cash flow path model is: Net cash flow = Underlying asset repayments + Unused funds - Priority principal repayments - Priority interest payable - Subordinated interest payable - Subordinated principal repayments - Total intermediary fees - Total registration and transfer fees.

3. The supply chain bill asset securitization full life cycle dynamic revenue calculation system according to claim 2, characterized in that, The output expression of the role-dimensional benefit model also includes: The total distributable cash is calculated as follows: (Amount of each note × Purchase price × (Number of interest days / Interest base)). Interest expense includes senior tranche interest and subordinated tranche interest. Senior tranche interest = senior tranche size × senior tranche interest rate × (T / B), where T is the product term and B is the interest calculation basis. Subordinated tranche interest = (issue size - senior tranche size) × subordinated tranche interest rate × (T / B). Total intermediary fees = agency service fee + asset service fee + issuance vehicle management institution fee + other fees; Agency service fee = issuance size × agency service fee rate × (T / B), asset service fee = issuance size × asset service fee rate × (T / B), issuance vehicle management institution fee = issuance size × issuance vehicle management institution fee rate × (T / B).

4. The supply chain bill asset securitization full life cycle dynamic revenue calculation system according to claim 2, characterized in that, The output expression of the cash flow path model also includes: Unused funds = Issuance size - (Asset pool size - Total distributable cash).

5. The supply chain bill asset securitization full life cycle dynamic revenue calculation system according to claim 3, characterized in that, The dual-engine computing module (102) also includes: When calculating the total intermediary fees, identify the payment methods for underwriting fees, law firm fees, and rating fees; If a certain fee is paid through "trust property payment", the fee will be included in the funds occupied by the issuance scale, triggering a recalculation of the issuance scale. The new issuance scale = the original issuance scale - the fee amount. If a fee is paid "separately", it will be directly included as a cost in the total calculation of intermediary agency fees.

6. The supply chain bill asset securitization full life cycle dynamic revenue calculation system according to claim 3, characterized in that, The dual-engine computing module (102) also includes: It supports dynamic switching of the interest calculation basis, with switching options including 360 days and 365 days. The number of days for interest calculation for each note is calculated independently as: maturity date - plan start date.

7. The supply chain bill asset securitization full life cycle dynamic revenue calculation system according to claim 1, characterized in that, The dynamic revenue calculation system also includes: The risk warning and stress testing module (201) has a built-in risk warning indicator system and a multi-scenario stress testing model, which are used for real-time monitoring and simulation analysis of risk factors; Risk warning indicators include bill delinquency rate, asset pool diversification, cash flow coverage ratio, and interest rate sensitivity gap, which are used to automatically trigger warning signals when the actual value of the risk warning indicator exceeds the corresponding preset threshold. Stress test model: Supports historical scenario and hypothetical scenario methods to simulate asset-side return fluctuations, securities-side redemption capacity and cash flow disruption risks under extreme conditions, and outputs stress test reports.

8. The supply chain bill asset securitization full life cycle dynamic revenue calculation system according to claim 1, characterized in that, The dynamic revenue calculation system also includes: The multi-dimensional sensitivity analysis module (202), based on the underlying data of the dual-engine calculation module (102), is used to perform single-factor or multi-factor sensitivity analysis on core variables, quantify the impact of variable fluctuations on the returns and cash flow of each participant, and generate a sensitivity analysis report. Single-factor sensitivity analysis: With other variables fixed, simulate the impact of a certain variable on total revenue on the asset side, redemption pressure on the securities side, and intermediary fees, and display the sensitivity ranking of the variables using a tornado diagram; Multifactor sensitivity analysis: Simultaneously adjust multiple variables to output the range of return fluctuations and the probability distribution of cash flow gaps under combined scenarios.

9. A dynamic revenue calculation system for the entire lifecycle of supply chain bill securitization according to claim 1, characterized in that, The dynamic revenue calculation system also includes: The asset pool monitoring and screening module (203) is used to collect key indicators of the asset pool in real time, and to dynamically screen the asset pool according to the key indicator data and preset rules; the key indicators include changes in the rating of bill assets, overdue status, and acceptor qualifications.

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