Asset securitization management system and method

Abstract

The invention discloses an asset securitization management system and method, and belongs to the field of financial science and technology. The system comprises a creditor's right data access module used for accessing creditor's right data from a plurality of data sources and sorting, classifying and analyzing the accessed creditor's right data; the fund gap calculation module is used for calculating the total amount to be collected in the next period based on the circulation plan of the ABS and creditor's right data in the system, and estimating the fund gap during the circulation purchase in the next period; the compliance checking module is used for automatically checking the creditor and the concentration degree of the associated creditor according to the supervision requirement, and screening out the creditor's right meeting the supervision requirement; the optimal creditor's right list generation module is used for generating an optimal creditor's right list conforming to the next cycle purchase; and the creditor's right monitoring module is used for monitoring the creditor's right state in the ABS plan in real time. The system can automatically adapt to the supervision rule, optimizes the cycle purchase process, finally improves the management efficiency, reduces the operation risk, and guarantees the compliance and safety of the ABS plan.

Description

Technical Field

[0001] This invention relates to the field of financial technology, and in particular to an asset securitization management system and method. Background Technology

[0002] Asset-backed securities (ABS) are financial instruments that convert the future cash flows of underlying assets (such as loans and accounts receivable) into tradable securities. When issuing ABS, a matching asset list must be selected according to regulatory compliance requirements and the relevant rules for this issuance. After successful issuance, regular redemption and repurchase are required according to a "revolving plan." On the one hand, maturing assets need to be redeemed periodically to the brokerage firm; on the other hand, new matching assets will be selected and incorporated into the plan according to the revolving plan agreement to obtain new funds. As the application of ABS in the financial market becomes more widespread, its operational complexity is also increasing, especially in areas such as asset pool management, cash flow collection, bond issuance, and repayment. Therefore, ABS plans place higher demands on management tools and systems.

[0003] For example, Chinese invention patent CN116402614B discloses an asset package management method, system, computer, and readable storage medium. The method includes: acquiring asset securitization project information at preset time points, where each asset securitization project is associated with screening rules and unique account information; if the account information meets preset conditions, creating an additional package for the asset securitization project; screening all asset information in the asset pool based on the screening rules to obtain the screened asset information, where the screening rules include basic rule information, as well as one or more of the following: current creditor conditions, screening order rules, basic filtering conditions, and complex filtering conditions; obtaining a packaged additional package based on the screened asset information and the additional package; and performing a transfer operation on the packaged additional package based on the asset securitization project information. This application can automatically and periodically create, screen, and transfer asset packages, resulting in low labor costs and high accuracy.

[0004] However, the aforementioned ABS management system still has the following shortcomings: 1. Insufficient level of intelligence: Existing processing methods rely heavily on fixed rules, lacking dynamic analysis and intelligent decision-making capabilities. For example, during revolving purchases, they cannot automatically and intelligently select assets suitable for future purchases based on the latest debt data, asset pool information, and regulatory rules. In this situation, the efficiency of key operations such as fund collection and revolving purchases is limited.

[0005] 2. High compliance management risks: Due to the complexity of manual operation and the influence of human factors, traditional methods have certain risks in ensuring compliance. They are prone to operational errors or data omissions, which may lead to non-compliant operations or the selection of incorrect asset lists.

[0006] 3. Inadequate risk management: Due to the lack of in-depth analysis and intelligent prediction of debt data, especially in terms of funding gap prediction and the rationality of debt allocation, the safety and yield of ABS plans are not adequately supported, thus affecting the safety and yield of ABS plans. Summary of the Invention

[0007] The purpose of this invention is to at least solve one of the technical problems existing in the prior art, and to propose an asset securitization management system and method that integrates multiple intelligent modules to realize automated processing of debt data, cyclical purchase decision support, and debt monitoring and analysis.

[0008] To achieve the above-mentioned objectives, the present invention provides the following technical solution: Firstly, an asset securitization management system is provided, the system comprising: The debt data access module is used to access debt data from multiple data sources and to organize, classify, and analyze the accessed debt data. The funding gap calculation module is used to calculate the total amount to be collected in the next period based on the ABS revolving plan and the debt data in the system, and to estimate the funding gap when purchasing in the next revolving period. The compliance check module is used to automatically check the concentration of creditors and related creditors according to regulatory requirements, and to screen out claims that meet regulatory requirements. The optimal debt list generation module is used to generate an optimal debt list that is suitable for the next revolving purchase, based on factors such as the interest rate spread, maturity, and credit rating of the debt. The debt monitoring module is used to monitor the status of debts in the ABS plan in real time, including information such as debt maturity, early repayment, debtor distribution, and regional distribution, and provides real-time funding gap calculation and debt list suggestions.

[0009] As a further improvement, the debt data access module includes: The data conversion submodule is used to convert the incoming debt data into a standardized format that the system can recognize; The data storage submodule is used to store the organized receivables data in the system's receivables database.

[0010] As a further improvement, the funding gap calculation module includes: The debt extraction submodule is used to extract debts that are eligible for purchase in the next revolving cycle from the debt database; The gap estimation submodule is used to calculate the funding gap based on the difference between the total amount of extracted receivables and the total amount to be collected.

[0011] As a further improvement, the compliance check module includes: The concentration check submodule is used to check whether the concentration of each creditor exceeds a predetermined threshold according to regulatory rules. The correlation check submodule is used to check whether the concentration of related creditors complies with regulatory requirements.

[0012] As a further improvement, the optimal debt list generation module includes: The optimization algorithm submodule is used to generate the best list of debts based on multiple factors such as interest rate spread, maturity, and credit rating of the debts using optimization algorithms. The debt ranking submodule is used to prioritize debt claims based on indicators such as interest rate spread, maturity, and credit rating.

[0013] As a further improvement, the debt monitoring module includes: The real-time monitoring submodule is used to track and record the status of claims in the ABS plan in real time, including maturity dates, early repayment status, etc. The dynamic adjustment submodule is used to dynamically adjust the funding gap and debt list recommendations based on the latest debt data and market changes.

[0014] As a further improvement, the system also includes: The user interface is used to display calculation results, monitoring information and debt list to users, and provides user input and feedback functions.

[0015] Secondly, an asset securitization management method is provided, which includes: The debt data access module receives debt data from multiple data sources and organizes, classifies, and analyzes the received debt data. The funding gap calculation module calculates the total amount to be collected in the next period based on the ABS revolving plan and the debt data in the system, and estimates the funding gap when purchasing in the next revolving period. The compliance check module automatically checks the concentration of creditors and related creditors according to regulatory requirements and filters out claims that meet regulatory requirements. The optimal debt list generation module generates an optimal debt list that is suitable for the next revolving purchase, based on factors such as the interest rate spread, maturity, and credit rating of the debts. The debt monitoring module monitors the status of debts in the ABS plan in real time, including debt maturity, early repayment, debtor distribution, and regional distribution, and provides real-time funding gap calculation and debt list suggestions.

[0016] Beneficial effects: 1. Improve data processing efficiency: Through automated data access and cleaning, the system can quickly process and update large amounts of debt data, significantly shortening the decision-making cycle.

[0017] 2. Enhanced compliance management: The built-in rules engine ensures that all operations comply with regulatory requirements, avoiding compliance risks that may arise from manual operations.

[0018] 3. Optimized Debt Screening and Purchase Decisions: Through its intelligent analysis module, the system can automatically select the optimal debt portfolio based on interest rate differentials, debt maturities, and credit ratings, and provide corresponding purchase recommendations. This optimized decision support maximizes returns while effectively controlling risk.

[0019] 4. Real-time debt monitoring: The system can comprehensively monitor the debt status of the ABS plan, providing real-time repayment details, debtor distribution and regional distribution information to help users better understand and manage the current asset status.

[0020] 5. Simplified operation process: The automated operation module reduces the complexity of user operations, reduces the possibility of manual intervention, and improves overall management efficiency.

[0021] 6. Real-time risk monitoring and early warning: The system can monitor debt gaps and potential risks in real time, provide early warnings and response suggestions in advance, and ensure the safety and robustness of the ABS program.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments; Figure 1 This is a schematic diagram of the structure of an asset securitization management system in one embodiment. Detailed Implementation

[0024] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0025] This embodiment proposes an intelligent ABS asset management tool (i.e., an asset securitization management system) aimed at improving the efficiency, compliance, and digitalization of ABS plan management. This tool integrates multiple intelligent modules to achieve functions such as automated processing of debt data, support for revolving purchase decisions, and debt monitoring and analysis. Figure 1 As shown, the specific technical solution includes the following parts: First, the debt data access module: Data Acquisition: The system integrates existing debt data into the tool via API or batch import, including creditor information, debt amount, maturity date, interest rate, credit rating, etc.

[0026] Data cleaning and preprocessing: The incoming data undergoes cleaning and preprocessing to ensure its consistency, integrity, and accuracy.

[0027] The following is a breakdown of each rule for data cleaning and preprocessing: Data cleaning rules include: 1. Remove duplicate data: Duplicate detection: Use the creditor's unique identifier (such as creditor ID or debt contract number) to detect whether there are duplicate debt records in the dataset.

[0028] Merging process: If records with identical attributes such as the same creditor, the same amount, and the same due date are found, they are considered duplicate data. For completely duplicate records, keep one and delete the rest.

[0029] Handling partial duplicates: If some attributes are inconsistent (such as slightly different amounts), use a weighted average or select the optimal value based on a highly reliable data source to merge the records.

[0030] 2. Fill in missing data: Identify missing data types: Identify missing fields in the dataset, such as debt amount, interest rate, maturity date, etc.

[0031] Constant filling: For certain fixed-value fields (such as debt categories), default values ​​are used for filling.

[0032] Mean filling: For numerical data (such as interest rates), the mean is calculated and filled based on historical data of similar claims or creditors.

[0033] Interpolation for data completion: For time series data (such as installment payment amounts), linear interpolation or other suitable interpolation methods can be used for completion.

[0034] Modeling imputation: Using machine learning models (such as regression models) to predict and impute missing values, especially for complex fields such as credit ratings or interest rates.

[0035] 3. Error data correction: Logical checks: Set rules to detect unreasonable data values. For example, interest rates cannot be negative, and debt amounts cannot be zero. If data violates these logical rules, it is marked as anomalous.

[0036] Data range correction: For numerical data, set a reasonable range (such as debt ratio between 0% and 20%), and correct or mark data that exceeds the range.

[0037] Cross-table validation: Perform cross-validation with other data tables (such as the debtor information table) to ensure data consistency. For example, the creditor ID should match the ID in the debtor information table; otherwise, it may need to be corrected.

[0038] Data preprocessing rules include: 1. Data formatting: Date format standardization: Convert all date fields to a uniform date format (such as YYYY-MM-DD) to ensure no errors occur during processing.

[0039] Numerical format standardization: For numerical data such as amounts and interest rates, remove currency symbols, percentage signs, etc., and convert them into a pure numerical format.

[0040] Text field standardization: Standardize text fields such as claim type and debtor name (e.g., remove extra spaces and unify capitalization).

[0041] 2. Data standardization: Range standardization: This involves standardizing numerical data (such as amounts or interest rates) across different ranges to bring them to the same scale, enabling fair comparisons in subsequent analyses. Common methods include Z-score standardization or Min-Max standardization.

[0042] Category coding: Encoding categorical data (such as debt types) converts text into numerical values ​​for system processing. One-hot coding or label coding can be used.

[0043] Time series processing: For time series data (such as repayment records), perform uniform processing of time steps to ensure that the intervals of each data point are consistent.

[0044] 3. Data Validation: Consistency check: A comprehensive check is performed on the cleaned and preprocessed data to ensure data consistency and integrity. This includes re-verifying for no duplicates, no missing values, and no outliers.

[0045] Rule verification: Substitute the cleaned and preprocessed data into the system rules, perform trial calculations and verifications to ensure that subsequent modules can use these data normally.

[0046] Second, the rules engine module: Regulatory rule setting: Users can flexibly set the rule engine parameters at any time according to the local and latest regulatory rules of the issuance plan, such as the proportion of a single creditor not exceeding 40% and the proportion of related creditors not exceeding 50%.

[0047] Dynamic rule application: The rule engine can apply these dynamic rules in real time to filter and classify debt data, automatically updating the results when the debt data changes to ensure that each operation complies with the latest regulatory requirements. For example, by setting regional concentration rules, the system can monitor the regional distribution of relevant debt within the plan, and verify compliance with the rules in real time when making payments and adding new assets to the pool.

[0048] Third, the intelligent analysis module: 1. Fund Collection and Debt Gap Calculation: Based on the ABS plan's revolving schedule, the maturity dates of debts in the asset pool, and early repayment records, the total amount and detailed list of funds to be collected next are automatically calculated. Combined with the current debt data in the system's inventory, a list of debts that meet the requirements of the rule engine and the asset gap for the next revolving purchase are calculated.

[0049] 2. Debt Screening and Analysis: The system uses intelligent algorithms to compare debt interest rates with ABS issuance rates, prioritizing debts with larger spreads, longer maturities, and higher internal credit ratings. Based on this, the system generates a list of optimal debts for the next revolving purchase; simultaneously, it generates an expected return analysis report based on information such as the interest rate, maturity date, and issuance rate of the assets to be included in the pool.

[0050] Objective: The goal of the intelligent algorithm is to select the best portfolio of debt that meets the requirements of the ABS program from a large number of debt claims, in order to maximize the spread (i.e. the difference between the debt interest rate and the ABS issuance rate), ensure a longer debt maturity and a higher credit rating, while complying with the concentration limits stipulated by regulations.

[0051] Algorithm Framework: In the debt scoring model, the algorithm first calculates a comprehensive score for each debt, mainly considering the following factors: ; Where: Si: Comprehensive score of debt i; ri: Interest rate of debt i; R: ABS issuance rate; Ti: Remaining maturity of debt i (in years); Tmax: The debt with the longest remaining maturity among the currently available debts; CRi: Credit rating of debt i (represented by a score between 0 and 1, with 1 being the highest rating).

[0052] coefficient This is an adjustment factor that can be set according to actual business needs. Its specific value can be obtained through historical data backtracking analysis and optimization algorithms (such as linear programming and genetic algorithms).

[0053] The debt screening process includes: 1. Calculate the interest rate spread: Calculate the interest rate spread for each claim. And interest rate spread is used as the priority screening factor.

[0054] 2. Calculate the overall score: Calculate the overall score Si for all candidate claims and rank the scores.

[0055] 3. Compliance Screening: In accordance with regulatory rules, high-scoring claims are combined with the details of existing claims in the current ABS plan. If a newly added claim does not comply with relevant regulations after being added to the combination, it will be removed.

[0056] 4. Generate the optimal debt list: Among compliant debts, select a certain number of debts with the highest scores to form the optimal debt list. This process also needs to consider fundraising needs, ensuring that the total amount of the selected debts meets the fundraising requirements.

[0057] Detailed explanation of calculation steps: Step 1: Calculate the interest rate spread; Calculate the difference between the debt interest rate and the ABS issuance rate: Interest rate spread ; This difference measures the relative rate of return brought by the debt. The larger the spread, the more attractive the debt is, and therefore it is the main positive factor in the overall score.

[0058] Step 2: Calculate the term factor; Assess the relative position of the remaining maturity of the claim among all available claims: Ti / Tmax; Here, Tmax is the maximum remaining maturity among all optional debts. A longer remaining maturity generally implies higher risk, but provided it complies with compliance requirements, it can also make the cash flow of the ABS program more sustainable, thus being a positive factor.

[0059] Step 3: Calculate the credit rating factor CRi; Credit ratings reflect the safety of debt; higher values ​​indicate lower risk and higher priority, making it another important positive scoring factor.

[0060] Step 4: Calculate the overall score; Taking into account the above factors By combining their respective weights, the final total score for each claim is obtained: ; The overall score Si can serve as a key indicator for subsequent ranking and screening.

[0061] Step 6: Compliance Screening; The details of the newly added claims are sorted from high to low based on the comprehensive score. Then, the details of the existing claims in the current ABS plan are added to the current existing details one by one for combination. Claims that do not meet the compliance requirements (such as claims that exceed the concentration limit of a single or related creditor) are excluded from the combination. For example, in the current debtor distribution, A accounts for 39%, which is stipulated to be no more than 40%. When a new claim N(A) is added to the portfolio, A's debt accounts for 40% of the total debt amount of the ABS plan. Therefore, the high-scoring claim N(A) needs to be removed.

[0062] Step 7: Generate the optimal list of claims; Based on the fundraising needs, the optimal combination is selected to form the best list of debt for the next round of revolving purchase.

[0063] For example, if the planned fundraising amount for this period is 5 million, the corresponding total amount of new debt needs to be at least 5 million and no more than 6 million. Then, the debts selected in the previous steps are ranked A (3 million), B (3.5 million), C (1.5 million), and D (1 million) according to their scores. The optimal combination is {A, C, D}, where the total amount of debt is greater than 5 million and less than 6 million.

[0064] Debt monitoring: The system automatically generates digital reports based on the current debt situation in the ABS plan (such as maturity date and early repayment status), showing the current distribution of debtors (including the proportion of each debtor, the proportion of the top five or top ten debtors) and the monitoring status of the geographical distribution of debts.

[0065] Fourth, the debt monitoring module mainly includes the following functions: Monitoring of maturing receivables: Tracking receivables that are about to mature to ensure timely collection.

[0066] Debtor distribution analysis: Analyze the debtor structure, monitor concentration and potential risks.

[0067] Regional distribution analysis: Assess the distribution of claims in different regions to prevent excessive regional risk.

[0068] Early repayment monitoring: Identify debts that are repaid early and adjust the revolving purchase strategy accordingly.

[0069] Dynamic early warning: Provides real-time early warnings for potential risks such as funding gaps and excessive debt concentration.

[0070] The generated monitoring report contains: Monitoring reports should be generated regularly and should cover the following key information: Monitoring of debt maturity: List of soon-to-mature claims: Lists claims that will mature within a certain period of time (e.g., 1 month, 3 months), showing their maturity date, amount, debtor, and other information.

[0071] Summary of Maturity Amounts: Displays the total amount due in various future time periods (e.g., monthly, quarterly, and annual summaries).

[0072] Chart style: Stacked bar chart: Shows the amount due and its composition for each time period.

[0073] Line chart: Shows the trend of the time distribution of debt maturity, helping users understand future cash flow.

[0074] Debtor distribution analysis: Debtor Concentration Report: List the percentage of claims owed by the top five or top ten debtors, as well as the distribution of concentration among all debtors, ensuring that the percentage of a single debtor and related debtors does not exceed the prescribed threshold.

[0075] Risk concentration analysis: Identifies potential risks in the debtor structure, such as situations where a particular debtor accounts for an excessively high proportion.

[0076] Chart style: Pie chart: Displays the percentage of debtors, intuitively reflecting the degree of debt concentration.

[0077] Heat map: Displays the relationship network and concentration of debtors and their related debtors.

[0078] Regional distribution analysis: Regional Concentration Report: Displays the distribution of claims by geographical region and assesses regional risks.

[0079] Regional risk warning: Issue warnings for situations where there is excessive concentration of debt in a specific region or where the regional economic risk is high.

[0080] Chart style: Geographic heat map: Shows the distribution density of claims in various regions, highlighting areas of high concentration.

[0081] Bar chart: Displays the proportion of debt amount in each region, making it easier to compare the risk levels of different regions.

[0082] Early repayment monitoring: Early repayment list: Lists debts eligible for early repayment, displaying information such as the original due date, repayment amount, and debtor.

[0083] Early repayment trend analysis: Analyze the frequency and amount of early repayments to adjust future debt purchase strategies.

[0084] Chart style: Bar chart: Shows the amount and frequency of early repayments, distributed over time.

[0085] Trend line chart: Indicates the growth or decline trend of early repayment amount.

[0086] Dynamic early warning includes: Funding Gap Warning: Based on the maturity and collection of debt claims, predict potential future funding gaps.

[0087] Concentration exceeding the limit warning: Real-time monitoring of the concentration of debtors and regions; if it exceeds the set threshold, an early warning will be generated immediately.

[0088] Chart style: Dashboard: Displays key indicators in real time, such as debtor concentration and funding gap.

[0089] Warning signal diagram: It uses a red, yellow and green light pattern to visually display the warning status.

[0090] Monitoring report style and design: The report cover includes: Title: Includes "Debt Monitoring Report" and the date it was generated.

[0091] Abstract: Briefly describe the main findings and warnings in this report.

[0092] The main body of the report includes: Each section (such as debt maturity monitoring, debtor distribution, etc.) includes detailed tabular data and chart explanations. Explanatory notes are provided below the charts to help users understand the meaning of the data and its potential impact on the ABS program.

[0093] The report appendix includes: Data source description: List the data sources and processing methods used in the monitoring report.

[0094] Technical Description: Briefly introduce the implementation details of the monitoring model and algorithm to enhance the credibility of the report.

[0095] Chart Design: Color scheme: A simple palette of blue, gray, and white is used to ensure visual clarity.

[0096] Chart interactivity: When displayed within the system, charts support interactive functions, such as clicking on chart sections to view detailed data and dynamically adjusting time periods.

[0097] Debt absorption recommendations: If current assets are insufficient to meet the revolving purchase needs, the system will automatically provide users with a feasible debt absorption recommendation report based on the debt gap, rule setting requirements, and debt screening and return analysis, in order to optimize the composition of the asset pool.

[0098] Fifth, Decision Support Module: Revolving Purchase Strategy Generation: Based on the results of the intelligent analysis module, the system generates an optimized revolving purchase strategy that meets regulatory requirements. The strategy includes a list of debt assets that can be used for revolving purchases, a recommended purchase amount, and potential funding gaps.

[0099] This process aims to ensure the effective redistribution of funds by providing corresponding new debt while repaying maturing principal and interest. The following is a detailed breakdown of the revolving purchase strategy: The data foundation includes: The generation of the recurring purchase strategy is based on the following core data points: Current debt pool data: Debt maturity status: including the principal and interest amount of the matured debt, the credit rating of the debt, and debtor information.

[0100] Interest rate of the debt: Compare with the current issuance rate of ABS to assess the spread.

[0101] Distribution of claims: including geographical distribution, industry distribution, and debtor concentration.

[0102] ABS issuance rate: The average issuance rate of ABS in the current market environment serves as a reference point for assessing the spread between new debt and ABS.

[0103] Regulatory requirements: Such as a single creditor accounting for no more than 40%, related creditors accounting for no more than 50%, and other applicable compliance requirements.

[0104] Funding gap: The total amount of principal and interest owed to securities firms in the current debt pool, and the difference between this amount and the amount of new debt to be provided.

[0105] The strategy generation steps include: Assess the funding gap: Calculate the total amount of principal and interest owed on the current debt (A), and determine the total amount of new debt to be provided (B).

[0106] Calculate the funding gap (C): `C=AB`. If C is positive, it indicates that additional debt needs to be absorbed; otherwise, it means that the currently prepared debt is sufficient.

[0107] Claims screening and priority ranking: Interest rate spread analysis: Prioritize bonds with larger interest rate spreads, i.e., bonds with interest rates higher than the ABS issuance rate.

[0108] Debt term: Prioritize debts with longer terms to increase stability.

[0109] Credit rating: Prioritize debt with higher internal credit ratings to reduce risk.

[0110] Distribution: Consider the geographical and industry distribution of claims to ensure compliance with regulatory requirements and reduce concentration risk.

[0111] Generate a list of strategies: Based on the above priorities, generate a list of optimal debt claims for revolving purchase. The list should include: Debt ID.

[0112] Debt amount, debt-to-equity ratio, debt maturity date, debtor information, internal credit rating, and geographical and industry distribution information.

[0113] Simulation results and risk assessment: The generated list is used for simulation to evaluate the effectiveness of the strategy under current market conditions, including potential interest rate spreads, debtor concentration, geographic and industry risks, etc.

[0114] The system automatically identifies potential risks (such as excessive concentration of claims) and provides adjustment suggestions.

[0115] Output the revolving purchase strategy: Output the final revolving purchase strategy, including a detailed list of debts, expected interest rate spreads, potential risks, and the extent to which the funding gap is covered.

[0116] The specific content of the strategy output includes: List of Debts: Lists all debts recommended for purchase and their key attributes.

[0117] Risk Report: Includes identification of potential risks in the strategy and recommendations for mitigation.

[0118] Funding Coverage Analysis: Describe in detail how the funding gap can be filled by purchasing new debt and assess the impact on the overall stability of the ABS program.

[0119] Compliance Report: Confirms that all operations in the strategy comply with current regulatory requirements.

[0120] Through this refined process, the cyclical purchase strategy not only ensures the continuity of the ABS program, but also optimizes asset allocation in a dynamic market environment, increasing returns and reducing risks.

[0121] Risk warning: During the strategy generation process, the system automatically identifies and marks potential risk points (such as funding gaps, excessive debt concentration, etc.) and provides corresponding solution suggestions.

[0122] The following are specific potential risks and corresponding solutions: 1. Risk of concentrated claims: Risk Description: High Concentration on a Single Debtor: If too many claims are concentrated on a single debtor, it may lead to a concentration of default risk. If that debtor defaults, it will severely impact the entire ABS program. High Geographic or Industry Concentration: If claims are too concentrated in a particular region or industry, the program is more susceptible to risks specific to that region or industry (such as economic recessions or industry crises).

[0123] Recommendations: Diversification Strategy: It is recommended to reduce concentration risk by selecting a wider range of debts distributed across different regions and industries, ensuring diversity in the number and type of debtors. Setting Concentration Limits: Set upper limits on the proportion of debts from a single debtor, industry, or region in the strategy (e.g., no more than 20% from a single debtor, no more than 30% from a single industry, etc.), and dynamically adjust the investment portfolio.

[0124] 2. Debtor's credit risk: Risk Description: Debtor Credit Rating Downgrade: A downgrade in a debtor's credit rating may increase their default risk, impacting the liquidity and yield of ABS. Potential Default Risk: Some debtors may default or delay payments, especially in an unstable economic environment.

[0125] Recommendations: Credit Rating Monitoring: Continuously monitor changes in debtors' credit ratings and promptly identify and address downgraded claims. Dynamically Adjust the Claims List: Regularly adjust the revolving purchase strategy based on credit rating changes, excluding or reducing low-rated claims. Pre-established Default Handling Plans: Establish a rapid response mechanism for defaults, including solutions for handling defaulted claims and alternative plans.

[0126] 3. Interest rate risk: Risk Description: Compressed Interest Rate Spread: Interest rate fluctuations may reduce the spread between the bond yield and the ABS issuance rate, lowering returns. Rising Interest Rate Risk: If the issuance rate rises, existing low-interest bonds may face the risk of reduced returns.

[0127] Recommendations: Interest Rate Forecasting and Analysis: Utilize market data to forecast interest rate trends and adjust bond selection strategies in advance. Select Floating-Rate Bonds: Where possible, prioritize floating-rate bonds to mitigate the risks associated with rising interest rates. Spread Optimization: Regularly analyze the spreads of existing bonds and promptly replace bonds with excessively low spreads.

[0128] 4. Asset liquidity risk: Risk Description: Insufficient debt liquidity: If the selected debt is difficult to liquidate quickly, it may affect the cash flow and repayment ability of the ABS program. Insufficient market liquidity: In the event of increased market volatility, some debt may be difficult to sell or refinance, increasing funding pressure.

[0129] Recommendations: Liquidity Screening Criteria: Add a liquidity factor to debt selection, prioritizing debt with better liquidity (such as debt that is easier to sell or refinance). Diversify Debt Portfolio: Ensure sufficient liquidity support under different market conditions by mixing highly liquid and less liquid debt. Establish Liquidity Reserves: Set aside a portion of funds as a liquidity reserve in the strategy to cope with possible emergency funding needs.

[0130] 5. Market volatility risk: Risk Description: Market Price Fluctuations: Significant fluctuations in market prices may lead to a decline in the market value of debt assets, affecting the stability and yield of the ABS program.

[0131] Recommendations: Regular Market Assessment: Conduct regular assessments of the market environment and the value of debt assets, and adjust investment strategies promptly to cope with market volatility. Hedging Strategies: Consider using hedging tools such as financial derivatives to mitigate the impact of market volatility on the value of debt assets. Risk Premium Analysis: Adjust the risk premium requirements for debt assets in response to potential market volatility to ensure returns are maintained even during periods of fluctuation.

[0132] The above embodiments demonstrate that the intelligent ABS asset management tool of the present invention has significant technical effects in improving ABS management efficiency, ensuring compliance, optimizing returns, and providing transparent monitoring. 1. Improve data processing efficiency: Through automated data access and cleaning, the system can quickly process and update large amounts of debt data, significantly shortening the decision-making cycle.

[0133] 2. Enhanced compliance management: The built-in rules engine ensures that all operations comply with regulatory requirements, avoiding compliance risks that may arise from manual operations.

[0134] 3. Optimized Debt Screening and Purchase Decisions: Through its intelligent analysis module, the system can automatically select the optimal debt portfolio based on interest rate differentials, debt maturities, and credit ratings, and provide corresponding purchase recommendations. This optimized decision support maximizes returns while effectively controlling risk.

[0135] 4. Real-time debt monitoring: The system can comprehensively monitor the debt status of the ABS plan, providing real-time repayment details, debtor distribution and regional distribution information to help users better understand and manage the current asset status.

[0136] 5. Simplified operation process: The automated operation module reduces the complexity of user operations, reduces the possibility of manual intervention, and improves overall management efficiency.

[0137] 6. Real-time risk monitoring and early warning: The system can monitor debt gaps and potential risks in real time, provide early warnings and response suggestions in advance, and ensure the safety and robustness of the ABS program.

[0138] In one embodiment, an asset securitization management method is provided, the method comprising: The debt data access module receives debt data from multiple data sources and organizes, classifies, and analyzes the received debt data. The funding gap calculation module calculates the total amount to be collected in the next period based on the ABS revolving plan and the debt data in the system, and estimates the funding gap when purchasing in the next revolving period. The compliance check module automatically checks the concentration of creditors and related creditors according to regulatory requirements and filters out claims that meet regulatory requirements. The optimal debt list generation module generates an optimal debt list that is suitable for the next revolving purchase, based on factors such as the interest rate spread, maturity, and credit rating of the debts. The debt monitoring module monitors the status of debts in the ABS plan in real time, including debt maturity, early repayment, debtor distribution, and regional distribution, and provides real-time funding gap calculation and debt list suggestions.

[0139] In one embodiment, a computer-readable storage medium is also provided, storing computer-executable instructions for causing a computer to perform the steps of the asset securitization management method described above. The steps of the asset securitization management method here can be steps from the asset securitization management methods of the various embodiments described above.

[0140] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRA), direct RAM via Rambus (RDRA), direct memory bus dynamic RAM (DRDRAM), and dynamic RAM via Rambus (RDRAM), etc.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. An asset securitization management system, characterized in that, The system includes: The debt data access module is used to access debt data from multiple data sources and to organize, classify, and analyze the accessed debt data. The funding gap calculation module is used to calculate the total amount to be collected in the next period based on the ABS revolving plan and the debt data in the system, and to estimate the funding gap when purchasing in the next revolving period. The compliance check module is used to automatically check the concentration of creditors and related creditors according to regulatory requirements, and to screen out claims that meet regulatory requirements. The optimal debt list generation module is used to generate an optimal debt list that is suitable for the next revolving purchase, based on factors such as the interest rate spread, maturity, and credit rating of the debt. The debt monitoring module is used to monitor the status of debts in the ABS plan in real time, including information such as debt maturity, early repayment, debtor distribution, and regional distribution, and provides real-time funding gap calculation and debt list suggestions.

2. The asset securitization management system according to claim 1, characterized in that, The debt data access module includes: The data conversion submodule is used to convert the incoming debt data into a standardized format that the system can recognize; The data storage submodule is used to store the organized receivables data in the system's receivables database.

3. The asset securitization management system according to claim 1, characterized in that, The funding gap calculation module includes: The debt extraction submodule is used to extract debts that are eligible for purchase in the next revolving cycle from the debt database; The gap estimation submodule is used to calculate the funding gap based on the difference between the total amount of extracted receivables and the total amount to be collected.

4. The asset securitization management system according to claim 1, characterized in that, The compliance check module includes: The concentration check submodule is used to check whether the concentration of each creditor exceeds a predetermined threshold according to regulatory rules. The correlation check submodule is used to check whether the concentration of related creditors complies with regulatory requirements.

5. The asset securitization management system according to claim 1, characterized in that, The optimal debt list generation module includes: The optimization algorithm submodule is used to generate the best list of debts based on multiple factors such as interest rate spread, maturity, and credit rating of the debts using optimization algorithms. The debt ranking submodule is used to prioritize debt claims based on indicators such as interest rate spread, maturity, and credit rating.

6. The asset securitization management system according to claim 1, characterized in that, The debt monitoring module includes: The real-time monitoring submodule is used to track and record the status of claims in the ABS plan in real time, including maturity dates, early repayment status, etc. The dynamic adjustment submodule is used to dynamically adjust the funding gap and debt list recommendations based on the latest debt data and market changes.

7. The asset securitization management system according to claim 1, characterized in that, The system also includes: The user interface is used to display calculation results, monitoring information and debt list to users, and provides user input and feedback functions.

8. An asset securitization management method, characterized in that, The method includes: The debt data access module receives debt data from multiple data sources and organizes, classifies, and analyzes the received debt data. The funding gap calculation module calculates the total amount to be collected in the next period based on the ABS revolving plan and the debt data in the system, and estimates the funding gap when purchasing in the next revolving period. The compliance check module automatically checks the concentration of creditors and related creditors according to regulatory requirements and filters out claims that meet regulatory requirements. The optimal debt list generation module generates an optimal debt list that is suitable for the next revolving purchase, based on factors such as the interest rate spread, maturity, and credit rating of the debts. The debt monitoring module monitors the status of debts in the ABS plan in real time, including debt maturity, early repayment, debtor distribution, and regional distribution, and provides real-time funding gap calculation and debt list suggestions.

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