Revenue deviation analysis method and equipment based on assets and securities, medium and product

Abstract

The invention relates to the technical field of revenue analysis, in particular to a revenue deviation analysis method and device based on assets and securities, a medium and a product. The method comprises the steps of receiving asset plan parameters input by a user, and performing simulation based on the asset plan parameters to obtain a predicted cash flow sequence, predicted cost end data and predicted revenue data; after the asset plan enters the duration, collecting actual business data, and generating an actual cash flow sequence, actual cost end data and actual revenue data based on the actual business data; comparing the predicted revenue data with the actual revenue data, and determining the total revenue deviation at the current proofreading moment; and when the total revenue deviation exceeds a preset revenue deviation threshold value, determining a comprehensive deviation sequence and factors causing the revenue deviation based on the predicted cash flow sequence, the actual cash flow sequence, the predicted cost end data and the actual cost end data. The revenue deviation can be automatically calculated, and the deviation source can be positioned.

Description

Technical Field

[0001] This application relates to the field of revenue analysis technology, and in particular to a method, device, medium and product for revenue deviation analysis based on asset securities. Background Technology

[0002] In the asset securitization sector, with the Shanghai Commercial Paper Exchange's "China Commercial Paper Business System Direct Connection Specification V1.3" adding asset securitization-related functions, companies holding commercial paper can initiate supply chain commercial paper trust transfer applications to trust companies through the supply chain commercial paper platform. The scale and complexity of this type of business continue to increase, and the demand for accurate calculation and dynamic monitoring by asset plan participants in the issuance pricing and ongoing management stages is becoming increasingly urgent. Currently, the industry generally relies on manual Excel operations for processing asset securitization revenue, and during the ongoing period, actual business data needs to be collected manually and actual revenue calculated. This makes it difficult to automate the comparison and analysis between predicted and actual revenue. When discrepancies occur, it is impossible to quickly pinpoint the root cause of the discrepancy, resulting in limited business scalability and insufficient risk management capabilities. Summary of the Invention

[0003] To address the problem that existing technologies struggle to automatically calculate revenue deviations and pinpoint their root causes, this application provides a method, device, medium, and product for revenue deviation analysis based on asset securities.

[0004] Firstly, this application provides a revenue deviation analysis method based on asset securities, employing the following technical solution: A revenue deviation analysis method based on asset securities includes: The system receives asset plan parameters input by the user through a parameterized configuration interface, and performs simulations based on these parameters to obtain predicted cash flow sequences, predicted expense data, and predicted revenue data. After the asset plan enters its term, actual business data is collected, and actual cash flow sequence, actual expense data and actual revenue data are generated based on the actual business data. The predicted revenue data and the actual revenue data are compared to determine the total revenue deviation at the current calibration time. When the total revenue deviation exceeds a preset revenue deviation threshold, a comprehensive deviation sequence is determined based on the predicted cash flow sequence, the actual cash flow sequence, the predicted expense data, and the actual expense data. Based on the total revenue deviation and the comprehensive deviation sequence, the factors leading to the revenue deviation are determined.

[0005] By adopting the above technical solution, asset plan parameters are first obtained through a parameterized configuration interface and predicted data is generated through simulation. Then, actual business data during the duration of the asset plan is collected to generate actual data. The total revenue deviation is determined by comparison. When the deviation exceeds the threshold, a comprehensive deviation sequence is constructed and the deviation factors are located. This achieves closed-loop management of asset securities revenue from prediction to actual revenue. It not only solves the problems of low efficiency and large error in traditional manual calculation, but also accurately traces the root cause when the deviation exceeds the limit. This effectively improves the automation level and risk management capability of asset plan revenue calculation and provides a reliable basis for business decision-making.

[0006] In a preferred embodiment, this application can be further configured such that the asset planning parameters include asset-side information, securities-side information, and fee-side information; The predicted cash flow series is obtained by simulation based on the asset planning parameters, including: Based on the asset-side information, the securities-side information, and the fee-side information, the size of the purchasable asset pool is estimated; Construct a sequence of predictive nodes covering the entire product lifecycle, using calendar days as the unit of time. For each prediction node in the prediction node sequence, the expected return cash flow of the asset is simulated based on the asset pool size and the asset interest calculation rules in the asset information to obtain the asset cash flow sequence. Based on the securities-side information, the expected cash outflows of the securities are simulated on the prediction node sequence to obtain the securities-side cash flow sequence; The predicted cash flow sequence is obtained by overlaying the asset-side cash flow sequence and the securities-side cash flow sequence at the corresponding prediction nodes.

[0007] By adopting the above technical solution, the composition of asset planning parameters is clarified. By estimating the size of the asset pool and constructing a daily-level prediction node sequence, the cash flow sequences of the asset side and the securities side are simulated and superimposed to obtain the predicted cash flow sequence. With fine time granularity and clear data flow decomposition, it is ensured that the predicted cash flow sequence fits the actual business logic of the entire life cycle of asset securities. This avoids prediction deviations caused by coarse time dimensions and ambiguous cash flow decomposition, laying a high-quality data foundation for accurate comparison between subsequent predictions and actual data.

[0008] In a preferred embodiment, this application can be further configured such that: the calculation of the purchasable asset pool size based on the asset-side information, the securities-side information, and the fee-side information includes: The payment items configured as trust property payments are selected from the expense information, and the amounts of the expense items are summed to obtain the total amount paid by the trust property. The issuance size is obtained from the securities information, and the difference between the issuance size and the total amount paid by the trust property is calculated as the net investment principal for purchasing the underlying assets. The asset purchase price is extracted from the asset-side information, and the asset pool size is calculated based on the net investment principal and the asset purchase price.

[0009] By adopting the above technical solution, the total amount of payments for trust property is first obtained by screening and summarizing the payments. Then, the difference between the issuance scale and the total amount is calculated as the net investment principal. Finally, the asset pool size is estimated by combining the asset purchase price. This accurately considers the impact of trust property payments on investable funds, eliminates the use of funds for non-asset purchases, and makes the estimated asset pool size more consistent with the actual amount of funds available for investment in the underlying assets. This effectively avoids deviations in the estimated asset pool size due to omissions or errors in expense accounting and ensures the accuracy of subsequent cash flow simulation.

[0010] In a preferred embodiment, this application can be further configured as follows: the step of simulating the expected cash outflows of securities on the prediction node sequence based on the securities-side information to obtain a securities-side cash flow sequence includes: Based on the product size, product term, and product interest calculation rules in the securities information, the accrued securities interest expense at each prediction node is calculated to obtain the securities interest expense sequence. Based on the product size, product term, and product interest calculation rules, and combined with the repayment priority order in the securities information, a principal repayment expenditure sequence is generated. The securities interest expense sequence and the principal repayment expense sequence are integrated to obtain the securities-side cash flow sequence; Based on the cash balance simulated by the cash flow sequence on the asset side, the principal repayment ability of each product at the corresponding product term is dynamically determined, and risk warnings are marked for products whose cash balance is insufficient to cover the principal repayment obligation in the current period.

[0011] By employing the aforementioned technical solution, the accrued securities interest expense sequence is calculated, combined with the principal repayment expense sequence generated from the priority repayment, to obtain the securities-side cash flow sequence. Simultaneously, the principal repayment capacity is dynamically assessed and early warnings are issued based on the asset-side cash balance. This process not only complies with the business rule of lump-sum payment of principal and interest upon maturity of asset-backed securities, ensuring the authenticity of the securities-side cash flow sequence, but also enables proactive prediction of principal repayment risks, identifying maturity redemption risks in advance, enhancing risk management capabilities throughout the asset plan's lifespan, and reducing the risk of default.

[0012] In a preferred embodiment, this application can be further configured to: obtain the predicted revenue data through simulation based on the asset planning parameters, including: Define the revenue forecast node sequence; For each revenue forecast node in the revenue forecast node sequence, the cash flow values ​​of all historical nodes preceding the revenue forecast node in the forecast cash flow sequence are summed to obtain the net forecast cash flow of the revenue forecast node. Filter the expense items whose payment methods are configured for separate payment from the expense information, and integrate them into a separate payment expense sequence according to the corresponding planned payment time points; For each revenue forecast node in the revenue forecast node sequence, the cost amounts of all historical nodes preceding the revenue forecast node in the individual payment cost sequence are summed to obtain the total predicted individual payment cost of the revenue forecast node. For each revenue forecast node in the revenue forecast node sequence, the difference between the forecast net cash flow and the forecast total amount of individual expenses is calculated as the forecast revenue value. The predicted revenue values ​​of all revenue forecast nodes are arranged sequentially to form the predicted revenue data.

[0013] By adopting the above technical solution, a revenue forecast node sequence is defined, historical cash flows are accumulated to obtain the predicted net cash flow, the sequence of separately paid expenses is integrated and the total amount of expenses is accumulated, and finally the predicted revenue value is calculated and constituted as the predicted revenue data. The impact of separately paid expenses on revenue is removed, and the predicted revenue data formed by nodes can clearly reflect the expected revenue status at different stages. This solves the problem of inaccurate forecasting caused by the confusion between expenses and cash flow in traditional revenue calculation, and provides a refined forecast benchmark for subsequent revenue deviation analysis.

[0014] In a preferred embodiment, this application can be further configured such that determining the comprehensive deviation sequence based on the predicted cash flow sequence, the actual cash flow sequence, the predicted expense data, and the actual expense data includes: Based on the predicted cash flow series, we screen the cash flow series on the securities side and the cash flow series on the asset side; Based on the actual cash flow sequence, the actual securities-side cash flow sequence and the actual asset-side cash flow sequence are selected; By comparing the actual cash flow sequence at the securities end with the cash flow sequence at the securities end, a securities-end deviation sequence is obtained; By comparing the actual asset-side cash flow sequence with the asset-side cash flow sequence, an asset-side deviation sequence is obtained; By comparing the predicted cost data and the actual cost data, a cost deviation sequence is obtained. The securities-side deviation sequence, the asset-side deviation sequence, and the expense-side deviation sequence constitute the comprehensive deviation sequence.

[0015] By adopting the above technical solution, cash flow sequences from the securities and asset sides are screened from the predicted and actual cash flow sequences respectively. After comparison, the deviation sequences from the securities and asset sides are obtained. At the same time, the deviation sequence from the expense side is obtained by comparing the expense side data, and a comprehensive deviation sequence is formed. The total revenue deviation is decomposed into three core dimensions of detailed deviation, realizing the structured decomposition of deviation, avoiding the vagueness of deviation factors, and providing a clear analytical framework for subsequent quantitative analysis of the impact of each link on the total deviation.

[0016] In a preferred embodiment, this application can be further configured such that the comprehensive deviation sequence includes a securities-side deviation sequence, an asset-side deviation sequence, and a cost-side deviation sequence; The factors contributing to the revenue deviation, determined based on the total revenue deviation and the overall deviation sequence, include: The total deviation on the securities side is calculated based on the deviation sequence on the securities side; the total deviation on the asset side is calculated based on the deviation sequence on the asset side; and the total deviation on the expense side is calculated based on the deviation sequence on the expense side. Using the total revenue deviation as the root node, and the total deviation of securities, the total deviation of assets, and the total deviation of expenses as child nodes of the root node, a contribution analysis tree is constructed. Quantify the contribution of each child node to the root node; The child node with the highest contribution is recursively decomposed to obtain the factors that cause the revenue deviation.

[0017] By adopting the above technical solution, the total deviation value of the three types of deviations is calculated, and a contribution analysis tree with the total revenue deviation as the root node and the three types of deviations as child nodes is constructed. The contribution is quantified and the child node with the highest contribution is recursively decomposed. The influence weight of each link deviation on the total revenue deviation is clarified in a visual and quantitative way, the core deviation source is quickly identified and traced back to specific factors, and the shortcomings of traditional deviation analysis, such as the inability to quantify the degree of influence and the difficulty in locating key issues, are solved, thereby improving the pertinence and efficiency of deviation rectification.

[0018] Secondly, this application provides an electronic device that adopts the following technical solution: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the asset-backed revenue deviation analysis method as described in any of the first aspects.

[0019] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform a revenue deviation analysis method based on asset securities as described in any of the first aspects.

[0020] Fourthly, this application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program that, when executed by a processor, implements the revenue deviation analysis method based on asset securities as described in any of the first aspects.

[0021] In summary, this application includes the following beneficial technical effects: This application first obtains asset plan parameters through a parameterized configuration interface and simulates and generates predictive data. Then, it collects actual business data during the duration of the asset plan to generate actual data. After comparison, it determines the total revenue deviation. When the deviation exceeds the threshold, it constructs a comprehensive deviation sequence and locates the deviation factors. This realizes closed-loop management of asset securities revenue from prediction to actual revenue. It not only solves the problems of low efficiency and large error in traditional manual calculation, but also accurately traces the root cause when the deviation exceeds the limit. This effectively improves the automation level and risk management capability of asset plan revenue calculation and provides a reliable basis for business decision-making. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating a revenue deviation analysis method based on asset securities provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] The following is in conjunction with the appendix Figure 1 To be continued Figure 2 This application will be described in further detail.

[0024] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0027] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0028] This application provides a method for revenue deviation analysis based on asset securities, such as... Figure 1 As shown, the method provided in this application embodiment is executed by an electronic device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application embodiment does not impose any limitations on this connection. The method includes steps S101-S105, wherein: S101. Through the parameterized configuration interface, receive the asset planning parameters input by the user, and perform simulation based on the asset planning parameters to obtain the predicted cash flow sequence, predicted expense data and predicted revenue data.

[0029] The asset plan parameters include asset-side information, securities-side information, and fee-side information. Asset-side information includes: asset pool size, asset purchase price, and number of interest-bearing days, with the option to select the interest-bearing basis from a dropdown menu (options: 360, 365 days). Securities-side information includes: issuance size, senior tranche size, subordinated tranche size, senior tranche interest rate, subordinated tranche interest rate, product term, and interest-bearing basis (options: 360, 365 days). The subordinated tranche size is automatically calculated as: subordinated tranche size = issuance size - senior tranche size. Fee-side information includes registration and transfer fees and intermediary fees. Registration and transfer fees include: Shanghai Clearing House redemption and interest payment fee rate, Shanghai Clearing House issuance registration fee rate, Beijing Financial Assets Exchange book-building fee rate, Shanghai Clearing House fees, and taxes and surcharges. Intermediary fees include: underwriting fee rate, issuance vehicle management institution fee, agency service fee, asset service fee, legal firm fees, rating fees, and other fees. The payment method for legal firm fees, rating fees, and underwriting fees can be selected (options: separate payment, trust property payment).

[0030] Total distributable cash = ∑ (Amount of each note × Asset purchase price × Number of days of interest accrual for the note / Interest base). The maturity dates of each note in the actual pool are different. The number of days of interest accrual for each note = Maturity date - Plan start date. When importing the plan, the total distributable cash is calculated by summing the amounts of each note. Distributable cash flow = Total distributable cash - Intermediary fees - Registration and transfer fees. Senior tranche interest payable = Senior tranche size × Senior tranche interest rate × Term / Interest base. Subordinated tranche interest payable = Subordinated tranche total payment × Subordinated tranche interest rate × Product term / Interest base. Agency service fee = Issuance size × Agency service fee rate × Term / Interest base. Asset servicing fee = Issuance size × Asset servicing fee rate × Term / Interest base. Underwriting fee = Issuance size × Underwriting fee rate × Product term / Interest base. Issuance vehicle management fee = Issuance size × Issuance vehicle management fee rate × Product term / Interest base. Intermediary fees = Agency service fee + Asset service fee + Underwriting fee + Law firm fee + Rating fee + Issuing vehicle management institution + Other fees. Underwriting fees, law firm fees, and rating fees are only included in the total intermediary fees when the payment method is configured as separate payment. Beijing Financial Assets Exchange (BFAE) book-building fee = Issuance size × BFAE book-building fee rate. Taxes and surcharges = Value-added tax (VAT) amount + Urban maintenance and construction tax + Education surcharge + Local education surcharge; VAT amount = Priority interest payable / (1 + VAT amount and rate) × VAT amount and rate; Urban maintenance and construction tax = VAT amount × Urban maintenance and construction tax rate; Education surcharge = VAT amount × Education surcharge rate; Local education surcharge = VAT amount × Local education surcharge rate. Registration and transfer fees = Shanghai Clearing House redemption and interest handling fee + Shanghai Clearing House issuance registration fee + BFAE book-building fee + Shanghai Clearing House fees + Taxes and surcharges. Asset plan revenue = Total distributable cash - Priority tranche interest payable - Subordinated tranche interest payable - Total intermediary fees - Total registration and transfer fees.

[0031] S102. After the asset plan enters its term, collect actual business data and generate actual cash flow sequence, actual expense data and actual revenue data based on the actual business data.

[0032] The term "duration period" refers to the stages after the asset plan is created and submitted, including collection, receipt, continuation, collection in transit, and completion. Actual business data includes information on the actual bills included in the pool (such as the amount and maturity date of each bill), actual expense data, actual issuance size, and priority size. The actual cash flow sequence is a collection of actual cash flow data arranged by time nodes, generated based on the actual business data. The actual expense data consists of data related to various expenses actually incurred. Actual revenue data is the actual revenue result calculated based on the actual business data.

[0033] S103. Compare the predicted revenue data with the actual revenue data to determine the total revenue deviation at the current calibration time.

[0034] Specifically, the calibration time refers to the selected point in time for comparing the predicted revenue data with the actual revenue data, such as the end of each month, the end of each quarter, or a key time point in the asset plan. This can be flexibly set by staff based on practical experience. The total revenue deviation refers to the difference between the revenue value in the actual revenue data and the predicted revenue value at the corresponding point in the predicted revenue data at the calibration time.

[0035] S104. When the total revenue deviation exceeds the preset revenue deviation threshold, a comprehensive deviation sequence is determined based on the predicted cash flow sequence, the actual cash flow sequence, the predicted expense data, and the actual expense data.

[0036] The preset revenue deviation threshold refers to a critical value pre-set in the system to determine whether further analysis is needed for the total revenue deviation; it can be set to ±5% of the predicted revenue value. The comprehensive deviation sequence includes: a securities-side deviation sequence, an asset-side deviation sequence, and an expense-side deviation sequence, a data set used to comprehensively reflect the deviation at each stage. The securities-side deviation sequence is a sequence consisting of the deviation between actual and predicted cash flows at each node; the asset-side deviation sequence is a sequence consisting of the deviation between actual and predicted cash flows at each node; and the expense-side deviation sequence is a sequence consisting of the deviation between actual and predicted expense data.

[0037] S105. Based on the total revenue deviation and comprehensive deviation series, determine the factors that cause revenue deviation.

[0038] Specifically, the contribution analysis tree algorithm can be used to analyze the factors causing revenue deviations. A contribution analysis tree is a tree structure used to analyze the degree of contribution of each factor to the total deviation. The total revenue deviation is the root node, and the deviations on the securities side, assets side, and expenses side are the child nodes. Contribution refers to the proportion of the deviation value of a certain child node to the total revenue deviation value, used to measure the degree of influence of that link on the total deviation. Factors causing revenue deviations refer to links and related influencing items that have a high contribution to the total revenue deviation and whose specific causes can be further broken down, such as changes in the priority interest rate on the securities side and changes in the asset purchase price on the asset side.

[0039] This embodiment first obtains asset plan parameters through a parameterized configuration interface and simulates and generates predictive data. Then, it collects actual business data during the duration of the asset plan to generate actual data. After comparison, it determines the total revenue deviation. When the deviation exceeds the threshold, it constructs a comprehensive deviation sequence and locates the deviation factors. This realizes closed-loop management of asset securities revenue from prediction to actual revenue. It not only solves the problems of low efficiency and large error in traditional manual calculation, but also accurately traces the root cause when the deviation exceeds the limit. This effectively improves the automation level and risk management capability of asset plan revenue calculation and provides a reliable basis for business decision-making.

[0040] One possible implementation of this application embodiment is that the asset plan parameters include asset-side information, securities-side information, and fee-side information; The predicted cash flow series is obtained through simulation based on asset planning parameters, including: Based on asset-side information, securities-side information, and fee-side information, the size of the available asset pool is estimated. Construct a sequence of predictive nodes covering the entire product lifecycle, using calendar days as the unit of time. For each prediction node in the prediction node sequence, the expected return cash flow of the asset is simulated based on the asset pool size and the asset interest calculation rules in the asset information to obtain the asset cash flow sequence. Based on information from the securities, the expected cash outflows of securities are simulated on the prediction node sequence to obtain the cash flow sequence from the securities. The predicted cash flow series is obtained by overlaying the cash flow series from the asset side and the cash flow series from the securities side at the corresponding prediction nodes.

[0041] The asset pool size refers to the total amount of underlying note assets that can be purchased, calculated by combining the issuance scale with parameters such as asset purchase cost and related expenses. The product's entire lifecycle refers to the entire period from the creation of the asset plan to the product's maturity date. The predicted node sequence refers to the set of all time nodes arranged sequentially from the product's start date to its maturity date, with each node corresponding to one calendar day.

[0042] Specifically, the asset interest calculation rules are clarified: Daily expected return of assets = Asset pool size × Asset purchase price / Interest calculation basis. Since the asset purchase price is an annual interest rate, it needs to be allocated to each day according to the interest calculation basis. For each node in the prediction node sequence, the expected cash flow of the asset at that node = Daily expected return.

[0043] The asset-side cash flow sequence includes daily interest inflows and principal repayments at maturity. The expected cash flows from each asset at each node are arranged sequentially to form the asset-side interest inflow sequence. The node corresponding to principal maturity is determined from the predicted node sequence, and the principal amount is used as the principal repayment amount. Based on the predicted node sequence, the node value corresponding to the principal maturity date is set to the corresponding principal repayment amount, while non-principal maturity dates are set to 0, resulting in the principal repayment sequence at maturity. The asset-side interest inflow sequence and the principal repayment sequence at maturity are then superimposed according to the node correspondence to obtain the asset-side cash flow sequence.

[0044] Expected cash outflows from the securities side refer to the amount of interest or principal payable to senior and junior investors at each forecast node. A securities-side cash flow sequence is a data set of expected cash outflows from all forecast nodes, arranged sequentially. Securities-side expenditures are divided into interest expenses and principal expenses. Interest is typically accrued daily and paid in a lump sum at maturity, while principal is repaid in a lump sum on the maturity date. Therefore, it is necessary to simulate the daily accrued interest at each node, excluding it from the actual cash outflow for that day, only accruing it. On the maturity date, the accumulated interest and principal are paid uniformly; that is, the accumulated interest on the maturity date is treated as cash outflow, ensuring that the expenditure simulation complies with the securities redemption rules.

[0045] Cash flow overlay rule: The predicted net cash flow of each prediction node = the cash flow from the asset side of that node - the cash flow from the securities side of that node. Traverse the prediction node sequence and perform overlay calculations for each node to obtain the final predicted cash flow sequence.

[0046] This embodiment clarifies the composition of asset planning parameters, calculates the asset pool size, constructs a daily-level prediction node sequence, simulates the cash flow sequences of the asset side and the securities side respectively, and superimposes them to obtain the predicted cash flow sequence. With fine time granularity and clear data flow decomposition, it ensures that the predicted cash flow sequence fits the actual business logic of the entire life cycle of asset securities, avoiding prediction deviations caused by coarse time dimensions and ambiguous cash flow decomposition, and laying a high-quality data foundation for accurate comparison between subsequent predictions and actual data.

[0047] One possible implementation of this application embodiment involves calculating the size of the purchasable asset pool based on asset-side information, securities-side information, and fee-side information, including: Filter the expense items whose payment method is configured as trust property payment from the expense information, and sum the amounts of the expense items to obtain the total amount paid by the trust property; The issuance size is obtained from securities information, and the difference between the issuance size and the total amount paid by the trust property is used as the net investment principal for purchasing the underlying assets. Extract the asset purchase price from the asset-side information, and calculate the asset pool size based on the net investment principal and the asset purchase price.

[0048] The expense information refers to various expense data related to the asset plan, including legal fees, rating fees, underwriting fees, etc. Each expense item is assigned a payment method, categorized as either separate payment or trust property payment. Trust property payment means the expense is deducted in advance from the raised trust assets, while separate payment means the expense is borne by an external entity and does not utilize trust assets. The total trust property payment amount refers to the sum of all expense items paid using the trust property payment method.

[0049] Specifically, the fee items configured as selectable payment methods in the fee information include: law firm fees, rating fees, and underwriting fees. If the law firm fee payment method is selected as trust property payment, it will be included in the statistics, and the amount will be the law firm fee entered on the interface. If the rating fee payment method is trust property payment, it will be included in the statistics, and the amount will be the rating fee entered. If the underwriting fee payment method is trust property payment, the amount will be calculated as follows: underwriting fee = issuance size × underwriting fee rate × product term / interest calculation basis. The total amount of trust property payment is obtained by adding up the amounts of all fee items that meet the trust property payment conditions.

[0050] The issuance size is the total source of trust assets, and the expenses paid by the trust assets must be deducted from the total size. Only the remaining funds can be used to purchase underlying assets. Net investment principal = issuance size - total amount paid by trust assets. This value directly determines the maximum upper limit of funds that can be used to purchase assets.

[0051] The asset purchase price is the comprehensive cost at which the supplier (bill holder) sells the asset, essentially representing the annualized yield of the underlying asset. The asset pool size refers to the total amount of underlying bill assets that can be purchased with net investment principal; it serves as the foundational data for simulating asset-side cash flow. The yield of the underlying asset is calculated as asset pool size × asset purchase price × number of interest-bearing days / interest base. Since the net investment principal must cover the asset purchase cost (i.e., the asset pool size minus the yield), the asset pool size must be calculated by using the formula: Asset Pool Size = Net Investment Principal × (1 - (Asset Purchase Price × Number of Interest-bearing Days / Interest Base)). This ensures that the net investment principal can adequately purchase the corresponding size of assets and cover the cost.

[0052] Total distributable cash = Asset pool size × Asset purchase price × Number of interest days / Interest base; Distributable cash flow = Total distributable cash - Intermediary fees - Registration and transfer fees.

[0053] This embodiment first filters and summarizes the payment expenses of the trust property to obtain the total payment amount, then calculates the difference between the issuance scale and the total amount as the net investment principal, and finally combines the asset purchase price to estimate the asset pool size. This accurately considers the impact of the payment expenses of the trust property on investable funds, eliminates the use of funds for non-asset purchase, and makes the estimated asset pool size more consistent with the actual amount of funds that can be invested in the underlying assets. It effectively avoids the deviation in the estimated asset pool size due to omissions or errors in expense accounting, and ensures the accuracy of subsequent cash flow simulation.

[0054] One possible implementation of this application embodiment involves simulating the expected cash outflows of securities on a prediction node sequence based on securities-side information to obtain a securities-side cash flow sequence, including: Based on the product size, product term, and product interest calculation rules in the securities information, the accrued securities interest expense at each prediction node is calculated to obtain the securities interest expense sequence. Based on the product size, product term, and product interest calculation rules, and combined with the repayment priority order in the securities information, a sequence of principal repayment expenditures is generated. By integrating the securities interest expense series and the principal repayment expense series, the securities-side cash flow series is obtained; Based on the cash balance simulated by the cash flow sequence on the asset side, the principal repayment ability of each product at the corresponding product term is dynamically judged, and risk warnings are marked for products whose cash balance is insufficient to cover the principal repayment obligation in the current period.

[0055] The securities information includes: senior tranche size, senior tranche interest rate, subordinated tranche size, subordinated tranche interest rate, product term, and interest calculation basis. For each forecast node, accrued senior tranche interest is calculated as: senior tranche size × senior tranche interest rate / interest calculation basis; accrued subordinated tranche interest is calculated as: subordinated tranche size × subordinated tranche interest rate / interest calculation basis. The accrued interest for each forecast node is the sum of senior and subordinated interest. By traversing all forecast nodes in the forecast node sequence, the forecasted interest expense sequence for the securities is obtained.

[0056] Securities interest needs to be accrued daily to accurately calculate the total interest over the entire period, but it is actually paid in a lump sum at maturity. For each product, the maturity interest settlement date is determined based on the product's term. The node corresponding to the interest settlement date is determined in the predicted expenditure sequence of securities interest. At this node, the cumulative predicted expenditure of securities interest is calculated to obtain the product's expenditure interest. The securities interest expenditure sequence has the same format as the predicted expenditure sequence. In the securities interest expenditure sequence, the node corresponding to the interest settlement date is set as the product's expenditure interest, and other non-interest settlement dates are set to 0, indicating no cash flow expenditure.

[0057] The principal repayment expenditure sequence records the actual principal expenditures in the order of the predicted nodes. Since the principal is paid out in a lump sum upon maturity, the principal repayment expenditure sequence has the same format as the predicted expenditure sequence, except that only the principal maturity nodes have monetary values, while the remaining nodes are zero. The repayment priority order refers to the principle that when repaying principal upon maturity, if both senior and junior investors' principal exist at the same node, the senior investors' principal is paid first, followed by the junior investors' principal, thus protecting the rights of senior investors.

[0058] If the principal and interest are to be repaid simultaneously at maturity, then the nodes with non-zero values ​​in the securities interest expense sequence and the principal repayment expense sequence will be the same. If the interest settlement date and the principal settlement date are not exactly the same, then the nodes with non-zero values ​​in the securities interest expense sequence and the principal repayment expense sequence will not be exactly the same. Both the securities interest expense sequence and the principal repayment expense sequence have the same length as the predicted node sequence. By superimposing the securities interest expense sequence and the principal repayment expense sequence according to the node correspondence, the securities-side cash flow sequence is obtained.

[0059] For each product, determine its maturity date corresponding to its term. Identify the first node corresponding to the maturity date from the asset-side cash flow and the second node corresponding to the maturity date from the securities-side cash flow sequence. Calculate the sum of all values ​​before the first node in the asset-side cash flow sequence as the cumulative inflow to the asset side on the maturity date. Calculate the sum of all values ​​before the second node in the securities side as the cumulative outflow to the securities side on the maturity date. Calculate the difference between the cumulative inflow and outflow to obtain the cash balance on the maturity date. Determine the product's principal as the principal repayment amount. Compare the cash balance on the maturity date with the principal repayment amount. If the cash balance is not less than the principal repayment amount, the product is deemed capable of repaying the principal; if the cash balance is less than the principal repayment amount, the cash balance is deemed insufficient to cover the current principal repayment obligation, and a risk warning is issued for the product.

[0060] This embodiment calculates the accrued securities interest expense sequence and combines it with the priority repayment principal repayment expense sequence to obtain the securities-side cash flow sequence. Simultaneously, it dynamically assesses principal repayment capacity and issues early warnings based on the asset-side cash balance. This process not only complies with the business rule of lump-sum payment of principal and interest upon maturity of asset-backed securities, ensuring the authenticity of the securities-side cash flow sequence, but also enables advance prediction of principal repayment risk, identifying maturity redemption risks in advance, enhancing risk management capabilities throughout the asset plan's lifespan, and reducing the risk of default.

[0061] One possible implementation of this application embodiment involves simulating and obtaining predicted revenue data based on asset planning parameters, including: Define the revenue forecast node sequence; For each revenue forecast node in the revenue forecast node sequence, the cash flow values ​​of all historical nodes preceding the revenue forecast node in the forecast cash flow sequence are summed to obtain the net forecast cash flow of the revenue forecast node. Filter the expense items whose payment methods are configured for separate payment from the expense information, and integrate them into a separate payment expense sequence according to the corresponding planned payment time points; For each revenue forecast node in the revenue forecast node sequence, the cost amounts of all historical nodes preceding the revenue forecast node in the separate payment cost sequence are summed to obtain the total predicted separate payment cost of the revenue forecast node. For each revenue forecast node in the revenue forecast node sequence, the difference between the forecast net cash flow and the forecast total amount of individual expenses is calculated as the forecast revenue value. The predicted revenue values ​​for all revenue forecast nodes are arranged sequentially to form the predicted revenue data.

[0062] In this embodiment, the revenue forecast node sequence is a series of predefined time points used to periodically snapshot and cumulatively calculate forecasted revenue throughout the entire asset plan's lifespan. These nodes can be evenly spaced. The forecasted cash flow sequence records the net difference between cash inflows from assets and cash outflows from securities on each calendar day. The forecasted net cash flow represents the sum of all historical net cash flows from the plan's start date to a specific revenue forecast node, indicating the theoretically newly added net cash in the trust property account up to that point in time.

[0063] The length of the individual payment expense sequence corresponds to the revenue forecast node. For expense items in the individual payment expense sequence that have expenditures, the node value corresponding to the planned payment time point is set to the expense value; for nodes without expenditures, the value is set to 0. The total forecast of individual payment expenses represents the total cost incurred for the establishment and operation of the plan up to the current revenue forecast node, but borne externally. Although these expenses do not flow through the trust account, they represent the plan's true cost.

[0064] This embodiment defines a revenue forecast node sequence, accumulates historical cash flows to obtain the predicted net cash flow, integrates the sequence of separately paid expenses and accumulates the total expenses, and finally calculates the predicted revenue value to form the predicted revenue data. It removes the impact of separately paid expenses on revenue. The predicted revenue data formed by nodes can clearly reflect the expected revenue status at different stages, solves the problem of inaccurate prediction caused by the confusion between expenses and cash flow in traditional revenue calculation, and provides a refined prediction benchmark for subsequent revenue deviation analysis.

[0065] One possible implementation of this application embodiment involves determining a comprehensive deviation sequence based on the predicted cash flow sequence, the actual cash flow sequence, the predicted expense data, and the actual expense data, including: Based on screening the cash flow series of securities and the cash flow series of assets from the predicted cash flow series; Based on the actual cash flow series, we screen the actual cash flow series on the securities side and the actual cash flow series on the asset side; By comparing the actual cash flow series on the securities side with the cash flow series on the securities side, a deviation series on the securities side is obtained. By comparing the actual asset-side cash flow series with the asset-side cash flow series, the asset-side deviation series is obtained; By comparing the predicted cost data with the actual cost data, a cost deviation sequence is obtained; Among them, the securities-side deviation sequence, the asset-side deviation sequence, and the expense-side deviation sequence constitute the comprehensive deviation sequence.

[0066] In this embodiment, the cash flow sequence on the securities side, the cash flow sequence on the asset side, the actual cash flow sequence on the securities side and the actual cash flow sequence on the asset side, the predicted expense data and the actual expense data are all numerical sequences. Each value in the sequence represents the data for one natural day, and the length and time span of the sequence are consistent with the predicted node sequence.

[0067] Cash flow consists of two parts: cash flow generated from assets (such as the redemption of bills upon maturity) and cash outflows from securities (such as interest payments to investors and principal repayments). In calculating the predicted cash flow series, we obtain the cash flow series from securities and the cash flow series from assets. From actual business data, we select all cash flow items related to payments from securities (such as senior tranche interest payments, senior tranche principal repayments, subordinated tranche interest payments, and subordinated tranche principal repayments) to form the actual cash flow from securities, and we select all cash flow items related to returns from assets (such as bill redemptions) to form the actual cash flow series from assets.

[0068] The actual cash flow series on the securities side and the predicted cash flow series on the securities side are compared point-by-point at the same time. For each time point (prediction node), the difference between the actual cash flow on the securities side and the predicted cash flow is calculated, and the differences at each time point form the securities side deviation series. Similarly, the actual cash flow series on the asset side and the predicted cash flow series on the asset side are compared point-by-point at the same time. For each time point (prediction node), the difference between the actual cash flow on the asset side and the predicted cash flow is calculated, and the differences at each time point form the asset side deviation series. The expense side deviation reflects the difference between operating costs and budgets. Discrepancies between the actual amount of various intermediary fees and the contract budget directly affect final revenue. For each time point (prediction node), the difference between the actual and predicted expense values ​​is calculated, and the differences at each time point form the expense side deviation series.

[0069] This embodiment filters the cash flow sequences of the securities and assets sides from the predicted and actual cash flow sequences respectively. After comparison, the deviation sequences of the securities and assets sides are obtained. At the same time, the deviation sequence of the expense side is obtained by comparing the expense side data and forming a comprehensive deviation sequence. The total revenue deviation is decomposed into the subdivided deviations of three core dimensions, realizing the structured decomposition of deviations and avoiding the vagueness of deviation factors. This provides a clear analytical framework for subsequent quantitative analysis of the impact of each link on the total deviation.

[0070] One possible implementation of this application embodiment is that the comprehensive deviation sequence includes a securities-side deviation sequence, an asset-side deviation sequence, and a cost-side deviation sequence; Based on the total revenue deviation and overall deviation series, the factors contributing to the revenue deviation were identified, including: The total deviation on the securities side is calculated based on the deviation sequence on the securities side; the total deviation on the asset side is calculated based on the deviation sequence on the asset side; and the total deviation on the expense side is calculated based on the deviation sequence on the expense side. Contribution analysis tree is constructed with total revenue deviation as the root node and total deviation of securities, total deviation of assets, and total deviation of expenses as child nodes of the root node. Quantify the contribution of each child node to the root node; Recursively decompose the child node with the highest contribution to obtain the factors that cause revenue deviation.

[0071] In this embodiment, the total deviation on the securities side is obtained by summing all values ​​in the deviation sequence on the securities side, the total deviation on the asset side is obtained by summing all values ​​in the deviation sequence on the asset side, and the total deviation on the expense side is obtained by summing all values ​​in the deviation sequence on the expense side. The total revenue deviation is used as the root node of the analysis tree, and the calculated total deviation on the securities side, the total deviation on the asset side, and the total deviation on the expense side are used as the three child nodes of the root node. The absolute value of the deviation of each child node is calculated as the child node deviation, and the sum of the deviations of the three child nodes is calculated to obtain the total deviation. For each child node, the ratio of its child node deviation to the total deviation is calculated as the contribution of that child node. The child node with the highest contribution is selected from the three child nodes. Based on the business nature of the child nodes, the next layer of decomposition dimensions is designed.

[0072] Optionally, if the sub-node with the highest contribution corresponds to the asset side, it can be decomposed according to the list of underlying assets (notes). The deviation of each note can be broken down from the deviation sequence of the asset side, and each note can be arranged in descending order of deviation. The proportion of the deviation of each note to the total deviation of the asset side can be calculated as the deviation contribution of the note. Then, the cause of the deviation can be determined. The multi-dimensional information of the note can be compared with the expected value (including the number of interest days, face value, etc.) to identify the factors that do not meet the expectations as the cause of the deviation.

[0073] If the sub-node with the highest contribution corresponds to the securities side, it can be distinguished according to structure (priority, subordinate) and nature (interest, principal). Payment items for priority interest, priority principal, subordinate interest, and subordinate principal can be screened from the cash flow sequence of the securities side and the actual cash flow sequence of the securities side, respectively. The cumulative deviation of these four categories can be calculated separately, and the deviation values ​​of these four dimensions can be used as the sub-nodes of the total deviation of the securities side of the first-level node, that is, the second-level sub-nodes. From the second-level child nodes, identify the node with the largest deviation contribution. If the node is a principal node, the principal amount is taken as the cause of the deviation. If the node is an interest node, the interest calculation formula involves the principal size, interest rate, number of interest days, and interest basis. Calculate the independent deviation value for factors that change in the principal size, interest rate, number of interest days, and interest basis. The calculation methods are: Size deviation = |(Actual principal size - Predicted principal size) × Predicted interest rate × Predicted number of interest days / Predicted interest basis|, Interest rate deviation = |Predicted principal size × (Actual interest rate - Predicted interest rate) × Predicted number of interest days / Predicted interest basis|, Number of interest days deviation = |Predicted principal size × Predicted interest rate × (Actual number of interest days - Predicted number of interest days) / Predicted interest basis|, Interest basis deviation = |Predicted principal size × Predicted interest rate × Predicted number of interest days / (Actual interest basis - Predicted interest basis)|. Select the node with the largest independent deviation from the four dimensions as the cause of the deviation.

[0074] If the sub-node with the highest contribution is the expense side, then it is broken down according to the expense item name, such as underwriting fees, legal fees, rating fees, etc., and grouped. For each expense item, the absolute value of the difference between the predicted expense and the actual expense is calculated, and the expense item with the largest deviation is identified as the cause of the deviation.

[0075] In order to understand the deviations of each project, the asset side, securities side, and expense side can be decomposed down to the lowest level. The deviation value of each bottom factor can be calculated, and the bottom factors can be arranged from largest to smallest according to the deviation value and displayed on the interactive interface.

[0076] This embodiment calculates the total deviation value of the three types of deviations, constructs a contribution analysis tree with the total revenue deviation as the root node and the three types of deviations as child nodes, quantifies the contribution, and recursively decomposes the child node with the highest contribution. It clarifies the impact weight of each link deviation on the total revenue deviation in a visual and quantitative way, quickly identifies the source of the core deviation and traces it back to the specific factors. It solves the shortcomings of traditional deviation analysis, which cannot quantify the degree of impact and is difficult to locate key issues, and improves the pertinence and efficiency of deviation rectification.

[0077] This application provides an electronic device, such as... Figure 2 As shown, Figure 2The illustrated electronic device 200 includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may also include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one type, and the structure of this electronic device 200 does not constitute a limitation on the embodiments of this application.

[0078] Processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 201 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0079] Bus 202 may include a pathway for transmitting information between the aforementioned components. Bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 202 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0080] The memory 203 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0081] The memory 203 stores the application code that executes the scheme of this application, and its execution is controlled by the processor 201. The processor 201 executes the application code stored in the memory 203 to implement the content shown in the aforementioned embodiment of the revenue deviation analysis method based on asset securities.

[0082] Figure 2 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0083] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the content shown in the aforementioned embodiment of the revenue deviation analysis method based on asset securities.

[0084] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0085] This application provides a computer program product, including a computer program that, when executed by a processor, implements the content shown in the aforementioned embodiment of the revenue deviation analysis method based on asset securities.

[0086] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A revenue deviation analysis method based on asset securities, characterized in that, include: The system receives asset plan parameters input by the user through a parameterized configuration interface, and performs simulations based on these parameters to obtain predicted cash flow sequences, predicted expense data, and predicted revenue data. After the asset plan enters its term, actual business data is collected, and actual cash flow sequence, actual expense data and actual revenue data are generated based on the actual business data. The predicted revenue data and the actual revenue data are compared to determine the total revenue deviation at the current calibration time. When the total revenue deviation exceeds a preset revenue deviation threshold, a comprehensive deviation sequence is determined based on the predicted cash flow sequence, the actual cash flow sequence, the predicted expense data, and the actual expense data. Based on the total revenue deviation and the comprehensive deviation sequence, the factors leading to the revenue deviation are determined.

2. The revenue deviation analysis method based on asset securities according to claim 1, characterized in that, The asset plan parameters include asset-side information, securities-side information, and fee-side information; The predicted cash flow series is obtained by simulation based on the asset planning parameters, including: Based on the asset-side information, the securities-side information, and the fee-side information, the size of the purchasable asset pool is estimated; Construct a sequence of predictive nodes covering the entire product lifecycle, using calendar days as the unit of time. For each prediction node in the prediction node sequence, the expected return cash flow of the asset is simulated based on the asset pool size and the asset interest calculation rules in the asset information to obtain the asset cash flow sequence. Based on the securities-side information, the expected cash outflows of the securities are simulated on the prediction node sequence to obtain the securities-side cash flow sequence; The predicted cash flow sequence is obtained by overlaying the asset-side cash flow sequence and the securities-side cash flow sequence at the corresponding prediction nodes.

3. The revenue deviation analysis method based on asset securities according to claim 2, characterized in that, The calculation of the purchasable asset pool size based on the asset-side information, the securities-side information, and the fee-side information includes: The payment items configured as trust property payments are selected from the expense information, and the amounts of the expense items are summed to obtain the total amount paid by the trust property. The issuance size is obtained from the securities information, and the difference between the issuance size and the total amount paid by the trust property is calculated as the net investment principal for purchasing the underlying assets. The asset purchase price is extracted from the asset-side information, and the asset pool size is calculated based on the net investment principal and the asset purchase price.

4. The revenue deviation analysis method based on asset securities according to claim 2, characterized in that, The process of simulating the expected cash outflows of securities on the prediction node sequence based on the securities-side information to obtain the securities-side cash flow sequence includes: Based on the product size, product term, and product interest calculation rules in the securities information, the accrued securities interest expense at each prediction node is calculated to obtain the securities interest expense sequence. Based on the product size, product term, and product interest calculation rules, and combined with the repayment priority order in the securities information, a principal repayment expenditure sequence is generated. The securities interest expense sequence and the principal repayment expense sequence are integrated to obtain the securities-side cash flow sequence; Based on the cash balance simulated by the cash flow sequence on the asset side, the principal repayment ability of each product at the corresponding product term is dynamically determined, and risk warnings are marked for products whose cash balance is insufficient to cover the principal repayment obligation in the current period.

5. The revenue deviation analysis method based on asset securities according to claim 1, characterized in that, The predicted revenue data is obtained by simulation based on the asset planning parameters, including: Define the revenue forecast node sequence; For each revenue forecast node in the revenue forecast node sequence, the cash flow values ​​of all historical nodes preceding the revenue forecast node in the forecast cash flow sequence are summed to obtain the net forecast cash flow of the revenue forecast node. Filter the expense items whose payment methods are configured for separate payment from the expense information, and integrate them into a separate payment expense sequence according to the corresponding planned payment time points; For each revenue forecast node in the revenue forecast node sequence, the cost amounts of all historical nodes preceding the revenue forecast node in the individual payment cost sequence are summed to obtain the total predicted individual payment cost of the revenue forecast node. For each revenue forecast node in the revenue forecast node sequence, the difference between the forecast net cash flow and the forecast total amount of individual expenses is calculated as the forecast revenue value. The predicted revenue values ​​of all revenue forecast nodes are arranged sequentially to form the predicted revenue data.

6. The revenue deviation analysis method based on asset securities according to claim 1, characterized in that, The determination of the comprehensive deviation sequence based on the predicted cash flow sequence, the actual cash flow sequence, the predicted expense data, and the actual expense data includes: Based on the predicted cash flow series, we screen the cash flow series on the securities side and the cash flow series on the asset side; Based on the actual cash flow sequence, the actual securities-side cash flow sequence and the actual asset-side cash flow sequence are selected; By comparing the actual cash flow sequence at the securities end with the cash flow sequence at the securities end, a securities-end deviation sequence is obtained; By comparing the actual asset-side cash flow sequence with the asset-side cash flow sequence, an asset-side deviation sequence is obtained; By comparing the predicted cost data and the actual cost data, a cost deviation sequence is obtained. The securities-side deviation sequence, the asset-side deviation sequence, and the expense-side deviation sequence constitute the comprehensive deviation sequence.

7. The revenue deviation analysis method based on asset securities according to claim 1, characterized in that, The comprehensive deviation series includes the securities-side deviation series, the asset-side deviation series, and the expense-side deviation series; The factors contributing to the revenue deviation, determined based on the total revenue deviation and the overall deviation sequence, include: The total deviation on the securities side is calculated based on the deviation sequence on the securities side; the total deviation on the asset side is calculated based on the deviation sequence on the asset side; and the total deviation on the expense side is calculated based on the deviation sequence on the expense side. Using the total revenue deviation as the root node, and the total deviation of securities, the total deviation of assets, and the total deviation of expenses as child nodes of the root node, a contribution analysis tree is constructed. Quantify the contribution of each child node to the root node; The child node with the highest contribution is recursively decomposed to obtain the factors that cause the revenue deviation.

8. An electronic device, characterized in that, include: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the revenue deviation analysis method based on asset securities as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in a computer, the computer is instructed to perform the revenue deviation analysis method based on asset securities as described in any one of claims 1-7.

10. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the steps of the revenue deviation analysis method based on asset securities as described in any one of claims 1-7.

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