Data Processing Method, Storage Medium, and Electronic Device

Abstract

An embodiment of the present application provides a data processing method, a storage medium, and an electronic device. The method includes: extracting historical data of homogeneous assets corresponding to the target asset pool of the object to be predicted, where the object to be predicted is an asset securitization product initiated by a target originator, and the historical data of homogeneous assets is the historical data of a homogeneous asset pool; parsing the historical data of homogeneous assets according to a preset pressure parameter; calibrating a pressure parameter benchmark sequence corresponding to the target asset and obtained by parsing based on the parameter true value corresponding to the preset pressure parameter, and by applying the calibrated pressure parameter calibration sequence to the target cash flow collection table of the target asset pool, obtaining a predicted cash flow collection table for redemption; based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator, and according to the cash flow payment mechanism of the object to be predicted, distributing the cash flow of the target asset pool to obtain the expected cash flow of the object to be predicted.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of the Internet. Specifically, the present application relates to a data processing method, a storage medium, and an electronic device. Background Art

[0002] When making an investment decision, a user often needs to predict the cash flow of an asset securitization product as a reference for the decision. However, since the cash flow for redeeming an asset securitization product needs to be processed from the cash flow of the underlying asset pool assets, there are many data sources, a large amount of data, and a high proportion of unstructured data, resulting in great difficulties in data collection and processing. In addition, asset securitization products have complex cash flow payment mechanisms and credit enhancement measures, etc., with a long calculation process and a large amount of calculation. It is very difficult for users to directly perform data calculation and processing to obtain accurate predicted cash flow. Thus, in the data processing method in the related art, there is a problem of low accuracy of the data processing result due to the large difficulty in data acquisition and complex data processing. Summary of the Invention

[0003] Embodiments of the present application provide a data processing method, a storage medium, and an electronic device to at least solve the technical problem in the data processing method in the related art that the accuracy of the data processing result is low due to the large difficulty in data acquisition and complex data processing.

[0004] According to an aspect of the embodiments of the present application, a data processing method is provided, including: extracting historical data of homogeneous assets corresponding to a target asset pool of a target object to be predicted, where the homogeneous asset data is stored in a specified database, the target object to be predicted is an asset securitization product initiated by a target originator, the historical data of homogeneous assets is the historical data of a homogeneous asset pool, the homogeneous asset pool is a set of homogeneous assets of the target assets in the target asset pool, and the asset type of the homogeneous assets is the same as that of the corresponding target assets and does not belong to the target asset pool; parsing the historical data of homogeneous assets according to a preset pressure parameter to obtain a pressure parameter reference sequence corresponding to the target asset, where the preset pressure parameter is a parameter that affects the cash flow of the target asset; calibrating the pressure parameter reference sequence corresponding to the target asset based on the true value of the parameter corresponding to the target asset and the preset pressure parameter to obtain a pressure parameter calibration sequence corresponding to the target asset, and obtaining a predicted cash flow collection table for redemption by applying the pressure parameter calibration sequence to a target cash flow collection table of the target asset pool; and distributing the cash flow of the target asset pool according to the cash flow payment mechanism of the target object to be predicted based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator to obtain the expected cash flow of the target object to be predicted.

[0005] In an exemplary embodiment, the specified database further stores at least one of the following pieces of information: the asset pool data of the target asset pool; the cash flow payment mechanism data for indicating the cash flow payment mechanism; the credit enhancement data for indicating the credit enhancement measures; the historical principal and interest repayment data for indicating the historical principal and interest repayment situation of the object to be predicted; the data used for cash flow prediction of the object to be predicted in the specified database is underlying data, and the underlying data includes the historical data of similar assets; the method further includes: querying in a multi-threaded concurrent manner whether the underlying data stored in the specified database has been updated; in the case where the underlying data has been updated, synchronizing the updated underlying data to the cache of each node among multiple nodes, wherein the underlying data used for cash flow prediction of the object to be predicted is the underlying data cached on one of the multiple nodes.

[0006] In an exemplary embodiment, the data used for cash flow prediction of the object to be predicted in the specified database is underlying data, and the underlying data is synchronized to the cache of each node among multiple nodes; the underlying data includes the historical data of similar assets; the amount of memory data on each node is less than or equal to the memory data amount threshold corresponding to each node; the method further includes: in response to a received target prediction request, allocating a target computing task corresponding to the object to be predicted to a target node among the multiple nodes according to a specified load balancing strategy, so as to execute the target computing task on the target node based on the underlying data cached on the target node, wherein the target prediction request is used to request cash flow prediction of the object to be predicted, the specified load balancing strategy is a strategy for balancing the computing tasks allocated to the multiple nodes, the target computing task is a computing task for cash flow prediction of the object to be predicted, and on the target node, the target computing task is executed in a multi-threaded computing manner.

[0007] In an exemplary embodiment, the method further includes: in the case where there is an asset set of the similar assets initiated by the target originator, determining the asset set of the similar assets initiated by the target originator as the similar asset pool; in the case where there is no similar asset initiated by the target originator, determining the asset set of the similar assets initiated by other originators except the target originator as the similar asset pool; after extracting the historical data of similar assets corresponding to the target asset pool of the object to be predicted, the method further includes: based on the checking relationship between fields, performing standardization processing on the historical data of similar assets according to a set of specified fields to obtain the standardized historical data of similar assets.

[0008] In an exemplary embodiment, parsing the historical data of the homogeneous assets according to the preset pressure parameters to obtain a pressure parameter reference sequence corresponding to the target asset includes one of the following: when the homogeneous asset pool includes a static structure asset pool and the historical data of the homogeneous assets includes the historical data of the static structure asset pool, parsing the historical data of the static structure asset pool according to the first pressure parameters to obtain a first parameter reference sequence corresponding to the target asset, where the first pressure parameters include the prepayment rate, default rate, and recovery rate, and the pressure parameter reference sequence corresponding to the target asset includes the first parameter reference sequence; when the homogeneous asset pool includes a revolving structure asset pool and the historical data of the homogeneous assets includes the historical data of the revolving structure asset pool, parsing the historical data of the revolving structure asset pool according to the second pressure parameters to obtain a second parameter reference sequence corresponding to the target asset, where the second pressure parameters include the repayment rate, default rate, recovery rate, yield rate, and purchase rate, and the pressure parameter reference sequence corresponding to the target asset includes the second parameter reference sequence.

[0009] In an exemplary embodiment, calibrating the pressure parameter reference sequence corresponding to the target asset based on the parameter true value corresponding to the target asset and the preset pressure parameters includes: based on the difference between the parameter true value corresponding to the target asset and the preset pressure parameters and the parameter values of the same pressure parameters in the pressure parameter reference sequence corresponding to the target asset, performing a translation adjustment on the pressure parameter reference sequence corresponding to the target asset to obtain a pressure parameter calibration sequence corresponding to the target asset.

[0010] In an exemplary embodiment, before obtaining the predicted cash flow collection table for redemption by applying the pressure parameter calibration sequence to the cash flow collection table of the target asset pool, the method further includes: calculating the expected cash flow recovery information of each target asset according to the asset pool data of the target asset pool, where the expected cash flow recovery information of each target asset is used to indicate the expected future cash flow recovery situation of each target asset; aggregating each target asset in the target asset pool according to each aggregation date of the object to be predicted, and generating a first candidate cash flow collection table according to the obtained cash flow recovery information of each aggregation date, where the cash flow recovery information of each aggregation date is used to indicate the cash flow recovery situation of each aggregation date; determining the cash flow collection table that best matches the latest balance of the target asset pool among the first candidate cash flow collection table and the second candidate cash flow collection table as the target cash flow collection table, where the second candidate cash flow collection table is a cash flow collection table integrated from the cash flow collection table of the target asset pool configured for the object to be predicted and the actual performance of the target asset pool counted.

[0011] In an exemplary embodiment, the predicted cash flow collection table is cached according to a specified data reading mode, and the specified data reading mode is used to balance the total amount of table data and cache load; the method further includes: calling the cash flow distribution sequence calculation method corresponding to the object to be predicted in the preset method module library under the account type of the target initiator to obtain the cash flow payment mechanism of the object to be predicted.

[0012] In an exemplary embodiment, the method further includes: determining the information to be displayed of the object to be predicted based on the historical principal and interest repayment data of the object to be predicted and the expected cash flow of the object to be predicted, and distributing the information to be displayed to the display interface for display, where the information to be displayed is used to describe the principal and interest repayment cash flow during the complete life cycle of the object to be predicted.

[0013] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0014] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to execute the steps in any one of the above method embodiments through the computer program.

[0015] Through the present application, since historical data of similar assets corresponding to the target asset pool of the object to be predicted can be extracted, and the collection standards, processing principles, and usage methods for a large volume of data can be standardized, the time-consuming of long-process calculations can be effectively reduced, and the errors caused by manual calculations can be reduced; the historical data of similar assets are parsed according to preset pressure parameters to obtain a pressure parameter reference sequence corresponding to the target asset, where the preset pressure parameters are parameters affecting the cash flow of the target asset, and the pressure parameter reference sequence corresponding to the target asset is calibrated based on the true parameter values corresponding to the target asset and the preset pressure parameters to obtain a pressure parameter calibration sequence corresponding to the target asset, and by applying the pressure parameter calibration sequence to the target cash flow collection table of the target asset pool, a predicted cash flow collection table for redemption is obtained, so that different scenarios such as prepayment and default recovery can be set for the underlying asset pool assets, and the risk level that the asset securitization product can withstand can be tested through stress testing; in addition, based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator, the cash flow of the target asset pool can be distributed according to the cash flow payment mechanism of the object to be predicted to obtain the expected cash flow of the object to be predicted, realizing a transparent, repeatable, and highly interpretable modeling process and result display. By adopting a distributed computing framework, the data can be comprehensively and accurately processed. Therefore, the technical problem that the data processing method in the related art has low accuracy of data processing results due to the difficulty of data acquisition and the complexity of data processing can be solved, and the effects of improving the accuracy and reliability of data processing can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a schematic diagram of an application scenario of a data processing method according to an embodiment of the present application.

[0017] Figure 2 FIG. is a schematic flow chart of an alternative data processing method according to an embodiment of the present application.

[0018] Figure 3 FIG. is a schematic flow chart of an alternative asset securitization according to an embodiment of the present application.

[0019] Figure 4 FIG. is a schematic diagram of an alternative cash flow payment mechanism according to an embodiment of the present application.

[0020] Figure 5 FIG. is a schematic flow chart of another alternative data processing method according to an embodiment of the present application.

[0021] Figure 6 FIG. is a schematic flow chart of another alternative data processing method according to an embodiment of the present application.

[0022] Figure 7It is a schematic flowchart of another optional data processing method according to an embodiment of the present application.

[0023] Figure 8 It is a schematic flowchart of another optional data processing method according to an embodiment of the present application.

[0024] Figure 9 It is a schematic diagram of the technical framework of an optional data processing method according to an embodiment of the present application.

[0025] Figure 10 It is a schematic diagram of the module logic of an optional data processing method according to an embodiment of the present application.

[0026] Figure 11 It is a structural block diagram of an optional data processing device according to an embodiment of the present application.

[0027] Figure 12 It is a structural block diagram of the computer system of an optional electronic device according to an embodiment of the present application. Detailed implementation manners

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily need to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0030] According to one aspect of the embodiments of the present application, a data processing method is provided. Optionally, in this embodiment, the above data processing method may but is not limited to be applied to, for example Figure 1In the hardware environment including the terminal device 102 and the server 104 shown. The server 104 can be connected to the terminal device 102 through a network and can be used to provide services (such as application services, etc.) for the terminal device 102 or the client installed on the terminal device 102. A database can be set up on the server 104 or independently of the server 104 to provide data storage services for the server 104.

[0031] The above-mentioned network can include but is not limited to at least one of the following: wired network, wireless network. The above-mentioned wired network can include but is not limited to at least one of the following: wide area network, metropolitan area network, local area network. The above-mentioned wireless network can include but is not limited to at least one of the following: WIFI (Wireless Fidelity), Bluetooth. The terminal device 102 can be but is not limited to a PC (Personal Computer), mobile phone, tablet computer, etc. The server 104 can be but is not limited to a cloud server, server cluster or other server types.

[0032] The data processing method of the embodiment of the present application can be executed by the server 104, or can be executed by the terminal device 102, or can also be jointly executed by the server 104 and the terminal device 102. Among them, the terminal device 102 executing the data processing method of the embodiment of the present application can also be executed by the client installed on it.

[0033] Taking the terminal device 102 executing the data processing method in this embodiment as an example, Figure 2 It is a schematic flowchart of an optional data processing method according to an embodiment of the present application, as Figure 2 shown. The process of this method can include the following steps:

[0034] Step S202, extract the historical data of similar assets corresponding to the target asset pool of the object to be predicted. Among them, the similar asset data is stored in a specified database. The object to be predicted is an asset securitization product initiated by a target initiator. The historical data of similar assets is the historical data of a similar asset pool. The similar asset pool is a set of similar assets of the target assets in the target asset pool. The asset type of the similar assets is the same as that of the corresponding target assets and does not belong to the target asset pool;

[0035] Step S204, parse the historical data of similar assets according to a preset pressure parameter to obtain a pressure parameter reference sequence corresponding to the target asset. Among them, the preset pressure parameter is a parameter that affects the cash flow of the target asset;

[0036] Step S206: Calibrate the pressure parameter benchmark sequence corresponding to the target asset based on the true parameter value corresponding to the target asset and the preset pressure parameter to obtain the pressure parameter calibration sequence corresponding to the target asset. Then, by applying the pressure parameter calibration sequence to the target cash flow collection table of the target asset pool, obtain the predicted cash flow collection table for redemption;

[0037] Step S208: Based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator, distribute the cash flow of the target asset pool according to the cash flow payment mechanism of the object to be predicted to obtain the expected cash flow of the object to be predicted.

[0038] The data processing method in this embodiment can be applied to the scenario of cash flow prediction for asset securitization products. Here, asset securitization refers to the process in which an enterprise or financial institution combines its assets that can generate cash income, and then issues securitized products supported by its cash flow to investors for sale. This behavior converts illiquid assets into securities that can be freely traded in the financial market, making them liquid. Asset securitization products ostensibly rely on "assets" as support, but actually rely on the "cash flow" generated by the assets. It is a technology for redistributing and reorganizing the cash flow of the asset pool.

[0039] Taking debt assets as an example, the asset securitization process is as Figure 3 shown, and it may include the following steps:

[0040] (1) The original equity holder grants loans to form underlying debt assets;

[0041] (2) The original equity holder, as the originator, entrusts the trustee to establish a special purpose trust with its debt assets as the trust property;

[0042] (3) The trustee issues the current asset securitization product to investors and acquires the underlying assets of the original equity holder within the limit of the subscription amount of the investors;

[0043] (4) During the subsequent product duration, pay the principal and income of the current securities within the limit of the cash flow generated by the trust property;

[0044] (5) Intermediary institutions provide services such as underwriting, financial advisory, fund custody, registration and custody, etc. and charge certain fees, and also pay the relevant fees within the limit of the cash flow generated by the trust property.

[0045] For the above asset securitization products, the asset pool they own is a combination of the underlying assets. Since the cash flow for the repayment of asset securitization products needs to be processed from the cash flows of the underlying asset pool assets, there are many data sources, a large amount of data, and a high proportion of unstructured data, resulting in great difficulties in data collection and processing; coupled with complex cash flow payment mechanisms and credit enhancement measures, etc., the calculation process is long and the calculation volume is large. It is difficult for investors to obtain the expected cash flow of asset securitization products, leading to difficult investment decisions, resulting in a situation where the issuance volume in the primary market is large, but there is still a lack of liquidity and a deviation between the primary and secondary prices.

[0046] If investors calculate the expected cash flow manually, they will face several challenges: First, the amount of data collected is large, the difficulty of data acquisition is high, and data processing and cleaning are complex. Investors need to prepare hundreds of data fields such as the basic information data of asset securitization products, asset pool data, cash flow payment mechanism data, credit enhancement data, and historical principal and interest repayment data. These data come from multiple documents such as the offering memorandum, trust contract, trustee report, and rating report, and a large amount of unstructured data among them still needs to be further processed. In addition, the amount of asset pool data is usually in the tens of thousands and generally has no publicly available channels, making the possibility of manual calculation of asset pool cash flow zero; Second, there are many calculation steps and it takes a long time to calculate manually. To obtain the expected cash flow, it is necessary to perform multiple calculation steps such as statistical analysis of asset pool stress scenario parameters, calculation of asset pool cash flow under the basic scenario, calculation of asset pool cash flow considering stress scenarios, and allocation of asset pool cash flow to asset securitization products. The calculation process is long and there are many calculation details, and the cash flow payment mechanism of each asset securitization product is unique. It is difficult to apply the manual calculation template of other asset securitization products for manual calculation and it has to be calculated from scratch. Therefore, manual calculation consumes a lot of time; Third, the calculation accuracy is relatively low. Due to rich data, a large amount of unstructured data, a long calculation process, and a large calculation volume, mistakes are inevitable in manual processing, making it difficult to guarantee the accuracy of cash flow prediction.

[0047] In order to at least partially solve the above technical problems, in this embodiment, a data processing method is adopted that standardizes data collection standards and processing principles, effectively reduces calculation time, and reduces errors caused by manual calculation, so as to provide a more accurate expected cash flow of asset securitization products, fill the market gap, provide more convenient relevant services for investors, and further promote the price discovery of asset securitization products, improve market pricing efficiency and the liquidity of securities, and provide more convenient financial service support.

[0048] In this embodiment, based on the cash flow of the underlying asset pool of the asset securitization product, and on the premise of considering credit enhancement measures, according to its cash flow payment mechanism, the cash flow of the asset pool can be split into the asset securitization product to obtain the expected cash flow of the asset securitization product. The systematic calculation reduces the cost for investors to obtain the expected cash flow, providing convenience for investors' further valuation, investment decision-making, cash flow management, and position analysis. In addition, in this embodiment, different scenarios such as prepayment and default recovery can be supported for the assets in the underlying asset pool, and the risk level borne by the asset securitization product can be tested through stress tests, so as to facilitate investors' risk management, investment decision-making, credit rating, etc.

[0049] Here, credit enhancement measures refer to the measures that the issuer can take to strengthen the guarantee of the capital repayment of the securitization product and protect investors when the asset quality of the asset pool deteriorates. The issuer can use various means and methods to ensure the timely and full payment of investors' interest and principal. It can be an important feature that differentiates asset securitization products from other bond products. Among them, credit enhancement measures usually include internal measures and external measures. External measures refer to adding guarantees or insurances of third parties, etc. Internal measures do not introduce external institutions and usually achieve credit enhancement through dozens of ways such as the design of the securitization product's own structure and trigger mechanism, and the excess spread of the asset pool.

[0050] Optionally, credit enhancement measures may include structural design, trigger mechanism, and excess spread of the asset pool. Among them, structural design means that the issued securitization products are divided into different structured products such as senior / intermediate / subordinate, and the cash received from the asset pool is paid in accordance with the agreed cash flow repayment mechanism sequence. The subordinated securities provide credit loss protection for the senior securities; the trigger mechanism means setting acceleration of repayment and default events. Once the relevant events are triggered, it will lead to a rearrangement of the cash flow payment mechanism. The arrangement of the trigger mechanism alleviates the impact of time risk to a certain extent and provides a certain degree of credit support; the excess spread of the asset pool means that the weighted average interest rate of the asset pool is greater than the service fees of relevant participating institutions and the coupon rate of the senior tranche securities of the asset securitization product, providing certain support for the repayment of the principal and interest of the asset securitization product.

[0051] Here, the cash flow payment mechanism refers to the cash repayment arrangement sequence for using the cash flow recovered from the asset pool to pay relevant fees and redeem the principal and interest of the asset securitization product. Taking the principal and income sub-accounts as an example (that is, the income and principal of the asset pool pay the interest and principal of the asset securitization product respectively. If a single account is not sufficient to pay the corresponding amount, it can be transferred between accounts to make up), the cash flow payment mechanism of the asset securitization product is as Figure 4 shown, and it may include the following steps:

[0052] (1)In terms of the income account, in the case of no default, taxes and trust fees are in the de facto top priority, followed by the service agency remuneration paid preferentially (some products stipulate that the loan service agency can only obtain the remaining service remuneration after paying interest at all levels and making up the principal account), and then the interest of the highest priority and the sub - highest priority respectively;

[0053] (2)Subsequently, if there has been a loan default or a transfer from the principal account to the income account, the income account first transfers funds of the above - mentioned amount to the principal account. After that, the remaining service agency remuneration and the income within the sub - senior limit can be obtained in sequence;

[0054] (3)If there is still a surplus in the income account, it is transferred to the principal account;

[0055] (4)In terms of the principal account, after making up the income account, the principal of each series at each level is repaid in sequence. If there is a surplus, it becomes the income exceeding the sub - senior level.

[0056] Exemplarily, in this embodiment, historical data of similar assets corresponding to the target asset pool of the object to be predicted can be extracted. Among them, the similar asset data is stored in a specified database. The object to be predicted is an asset securitization product initiated by a target originator. The historical data of similar assets is the historical data of a similar asset pool, and the similar asset pool is a set of similar assets of the target assets in the target asset pool.

[0057] Optionally, in this embodiment, the specified database can be directly docked. For example, it can be directly docked with the specified database through a data access and processing module to obtain the required data from the specified database.

[0058] Optionally, historical data of similar assets corresponding to the target asset pool of the asset securitization product initiated by the target originator can be obtained from the specified database, including but not limited to: asset pool data of the asset securitization product, cash - flow payment mechanism data, credit enhancement data, historical principal and interest repayment data, historical data of similar assets of the originator, etc.

[0059] Optionally, after extracting the historical data of similar assets corresponding to the target asset pool of the object to be predicted, data caching can be performed, so that the cached data can be directly called during subsequent data processing, reducing the calculation time consumption.

[0060] Optionally, the data obtained from the specified database can be pre - processed, including but not limited to: data cleaning, conversion, and formatting.

[0061] In this embodiment, after extracting the historical data of similar assets corresponding to the target asset pool of the object to be predicted, the historical data of similar assets can be analyzed according to a preset pressure parameter to obtain a pressure parameter benchmark sequence corresponding to the target asset, where the preset pressure parameter is a parameter that affects the cash flow of the target asset.

[0062] Optionally, by analyzing the historical data of similar assets according to the preset pressure parameter, the cash flow prediction of the object to be predicted under different pressure scenarios can be realized to evaluate its risk tolerance.

[0063] Optionally, the preset pressure parameter can be selected according to market analysis or risk management strategies. For example, for scenarios such as an increase in the prepayment rate, an increase in the default rate, and a decrease in the recovery rate, the prepayment rate, default rate, and recovery rate can be selected as the preset pressure parameters. Another example is that for pressure events that can occur within the asset pool, such as large-scale early repayment, the corresponding preset pressure parameters can be selected.

[0064] Optionally, by analyzing the extracted historical data of similar assets, the expected cash flow of the similar asset pool under different pressure scenarios can be calculated.

[0065] Optionally, based on the above-analyzed data, a pressure parameter benchmark sequence under each preset pressure parameter can be generated. Here, since the cash flow performance of the underlying assets in asset securitization products includes a dynamic process that changes over time, a sequence (i.e., the pressure parameter benchmark sequence) can be used to represent the above parameters to show the changes in the above parameters at different time points during the life cycle of asset securitization products.

[0066] Affected by factors such as the economic environment, the actual performance of the asset pool may be somewhat different from the history. Therefore, it is necessary to calibrate the pressure parameter according to the actual performance. In this embodiment, the pressure parameter benchmark sequence corresponding to the target asset can be calibrated based on the true value of the parameter corresponding to the target asset and the preset pressure parameter to obtain a pressure parameter calibration sequence corresponding to the target asset, that is, align the above pressure parameter benchmark sequence with the actual data, consider the influence of the actual market situation, and generate a pressure parameter calibration sequence of the target asset pool that is closer to the actual situation. For example, if the prepayment rate of the target asset pool in the actual performance is significantly higher than the historical average level, the predicted prepayment rate value shown in the pressure parameter calibration sequence can also be adjusted to a higher level.

[0067] Optionally, after obtaining the pressure parameter calibration sequence corresponding to the target asset, the predicted cash flow collection table for redemption can be obtained by applying the pressure parameter calibration sequence to the target cash flow collection table of the target asset pool.

[0068] Here, the target cash flow aggregation table refers to a cash flow prediction table calculated based on the current state of the target asset pool and the underlying assumptions, which may include, but are not limited to, data such as expected principal recovery, interest payments, and possible credit losses. Each item can correspond to a specific aggregation period.

[0069] The asset pool of an asset securitization product consists of a large number of assets with the same asset type that can generate cash income. The term, recovery amount, and time point of each asset are different. Therefore, it is necessary to calculate the expected future cash flow recovery of each underlying asset, and then aggregate the cash flows of the asset pool assets according to the aggregation date of the asset securitization product to obtain the cash flow recovery situation of each aggregation period, and then it can be used for the cash flow payment of the asset securitization product. The data of the aggregated cash flows of the asset pool assets can be recorded in the form of a cash flow aggregation table, as shown in Table 1.

[0070] Table 1 Cash Flow Aggregation Table

[0071]

[0072] Due to the possibility of prepayment and default recovery of the assets in the asset pool of asset securitization products, there is great uncertainty in generating the continuous and stable cash flows expected by investors. Currently, only a small number of asset securitization products will disclose the cash flow aggregation table of the asset pool under certain stress scenarios in their offering memorandums, and some revolving structure asset securitization products do not even disclose the cash flow aggregation prediction of the asset pool after considering the continuous purchase of new assets.

[0073] In order to obtain a predicted cash flow aggregation situation that is closer to the actual performance of the asset pool and ultimately used for payment, in this embodiment, the cash flow of each aggregation period can be recalculated according to the calibrated stress parameters, that is, the stress parameter calibration sequence, to obtain a predicted cash flow aggregation table for payment.

[0074] Furthermore, asset securitization products have the characteristic of being structured, and each tranche of the product shares the recovery funds of the same asset pool. Therefore, when determining the respective cash flows of each tranche of the product, it is necessary to allocate the cash flows of the entire asset pool. In this embodiment, the flowing water mode can be adopted. Based on the cash flow aggregation table formed by aggregating the cash flows of the underlying asset pool assets (that is, the predicted cash flow aggregation table for payment), and on the premise of considering the credit enhancement measures, according to its cash flow payment mechanism, the cash flows of the asset pool are split into each tranche of the asset securitization product to obtain the expected cash flows of the asset securitization product.

[0075] Optionally, based on the predicted cash flow collection table, the cash flow can be redistributed in combination with the credit enhancement measures adopted by the target originator. For example, for asset securitization products with a structured design, the principal and interest of the senior securities can be ensured to be paid first, and then the subordinated securities, so as to ensure that the senior securities are protected from the credit losses of the asset pool.

[0076] Through the embodiments provided in this application, historical data of similar assets corresponding to the target asset pool of the object to be predicted is extracted, where the similar asset data is stored in a specified database. The object to be predicted is an asset securitization product initiated by the target originator. The historical data of similar assets is the historical data of the similar asset pool, and the similar asset pool is a set of similar assets of the target assets in the target asset pool. The asset type of the similar assets is the same as that of the corresponding target assets and does not belong to the target asset pool; the historical data of similar assets is parsed according to a preset pressure parameter to obtain a pressure parameter benchmark sequence corresponding to the target assets, where the preset pressure parameter is a parameter that affects the cash flow of the target assets; the pressure parameter benchmark sequence corresponding to the target assets is calibrated based on the true value of the parameter corresponding to the target assets and the preset pressure parameter to obtain a pressure parameter calibration sequence corresponding to the target assets, and the predicted cash flow collection table for redemption is obtained by applying the pressure parameter calibration sequence to the target cash flow collection table of the target asset pool; based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator, the cash flow of the target asset pool is distributed according to the cash flow payment mechanism of the object to be predicted to obtain the expected cash flow of the object to be predicted, which can solve the problem that the data processing method in the related technology has low accuracy of the data processing result due to the difficulty of data acquisition and the complexity of data processing, and improve the accuracy and reliability of data processing.

[0077] In an exemplary embodiment, the above method further includes: calling the cash flow distribution order calculation method corresponding to the object to be predicted in the preset method module library under the account type corresponding to the target originator to obtain the cash flow payment mechanism of the object to be predicted.

[0078] Since the trust account includes a single trust account that does not distinguish between principal and interest (hereinafter referred to as a single account) and a sub-trust account that distinguishes between principal and interest (hereinafter referred to as a sub-account), etc., and the repayment order of the cash flow distribution mechanism of different asset securitization products is relatively diverse and may change according to the status of the account, the key to processing cash flow distribution is to flexibly adapt to various repayment orders. To enhance adaptability and scalability, in this embodiment, a trust account framework and a cash flow distribution order calculation method module library can be built, the calculation methods of each unit are agreed, and then the formulas of each link are called in sequence according to the cash flow distribution order of the asset securitization product itself for calculation.

[0079] Correspondingly, for the object to be predicted, the cash flow allocation order calculation method corresponding to the object to be predicted in the preset method module library under the account type corresponding to the target initiator can be called, and the cash flow allocation order corresponding to the object to be predicted can be determined according to the called cash flow allocation order calculation method, so as to obtain the cash flow payment mechanism of the object to be predicted. That is, the cash flow payment mechanism of the object to be predicted indicates the cash flow allocation order corresponding to the object to be predicted. Among them, the account type corresponding to the target initiator may be a single account or a sub-account. The cash flow allocation order calculation method module library can be configured under the trust account framework, so that the cash flow allocation order calculation method in the cash flow allocation order calculation method module library can be called to calculate the cash flow allocation order of different asset securitization products.

[0080] For example, as Figure 5 shown, for an asset securitization product, based on its cash flow collection table, the corresponding fund account framework can be called according to its trust account type (for example, a single trust account without distinguishing principal and interest and a sub-trust account distinguishing principal and interest, etc.), and the corresponding allocation order calculation method can be called according to its allocation order (for example, it can be preferentially allocated to taxes / fees, or it can be preferentially allocated to income within the limit, etc.) to complete the cash flow allocation calculation.

[0081] Optionally, the above-mentioned trust account type and allocation order can be obtained when docking with a specified database and obtaining relevant data of the object to be predicted.

[0082] Through this embodiment, by calling the configured method module library to calculate the cash flow allocation order, the flexibility and reliability of the cash flow allocation order calculation can be improved, and thus the efficiency of data processing can be improved.

[0083] In an exemplary embodiment, the specified database further stores at least one of the following information: the asset pool data of the target asset pool; the cash flow payment mechanism data for indicating the cash flow payment mechanism; the credit enhancement data for indicating the credit enhancement measures; the historical principal and interest repayment data for indicating the historical principal and interest repayment situation of the object to be predicted; the data used for cash flow prediction of the object to be predicted in the specified database is the underlying data, and the underlying data includes historical data of similar assets.

[0084] Correspondingly, the above method further includes: querying in a streaming manner whether the underlying data stored in the specified database has been updated by using a multi-threaded concurrent method; in the case where the underlying data has been updated, synchronizing the updated underlying data to the cache of each node among multiple nodes, where the underlying data used for cash flow prediction of the object to be predicted is the underlying data cached on one of the multiple nodes.

[0085] Similar to the foregoing embodiments, it is possible to dock with a specified database and obtain data in the specified database, including but not limited to: asset pool data of the target asset pool; cash flow payment mechanism data for indicating the cash flow payment mechanism; credit enhancement data for indicating credit enhancement measures.

[0086] Optionally, it is possible to obtain historical principal and interest repayment data in the specified database for indicating the historical principal and interest repayment situation of the object to be predicted, that is, the principal recovery and interest payment situation of each past period of the object to be predicted, including the deviation between the actual payment and the prediction. The above historical principal and interest repayment data can be used to provide a reference for the historical performance of the asset securitization product.

[0087] Optionally, the data used for cash flow prediction of the object to be predicted in the specified database is underlying data, that is, data collected directly from the original source without any processing. It can include historical data of similar assets. Exemplarily, it can include the historical performance of similar assets in the asset pool.

[0088] Similar to the foregoing embodiments, after obtaining the required data in the specified database, data caching can be performed.

[0089] Optionally, in this embodiment, it is possible to periodically or on demand check whether the underlying data stored in the specified database has been updated. It is possible to use a multi-threaded concurrent method to stream query whether the underlying data stored in the specified database has been updated, that is, it is possible to use multi-threaded technology so that each thread is responsible for querying a part of the data. Thus, multiple queries can be processed concurrently, improving the query speed and efficiency. At the same time, the data stream can be processed in a continuous manner instead of loading all data into memory at one time, avoiding the memory overflow problem, and the data updates in the database can be received and processed in real time.

[0090] Optionally, once it is detected that the underlying data has been updated, data synchronization can be performed, that is, the updated underlying data is synchronized to the cache to ensure the consistency of the database and cache data during calculation.

[0091] Optionally, it is possible to use distributed cache technology to achieve full data copy among multiple nodes, that is, save a complete copy of all data in the database on each of multiple nodes. Thus, data reading can be localized during data processing, without the need to read data across nodes, facilitating the processing of a large amount of data and the cash flow integration logic.

[0092] Optionally, when synchronizing the underlying data, version control can be performed through sequence numbers, that is, a sequence number can be set for a specified database. When the underlying data in the specified database is updated, its sequence number changes. Correspondingly, the sequence number of the data cache changes with the sequence number of the underlying database. By ensuring that the sequence numbers of the specified database and the data cache are consistent and performing data update synchronization when the sequence numbers are detected to be inconsistent, it can be ensured that the cached data on each node is the latest and consistent, preventing data conflicts.

[0093] Optionally, the trigger for data synchronization can be through a scheduling system, that is, when the underlying data in the specified database is updated, a data synchronization request can be initiated through an external scheduling system to synchronize the updated underlying data to the cache of each node, thereby reducing the access pressure on the database and improving the efficiency of data processing.

[0094] Through this embodiment, by obtaining the underlying data in the specified database and performing real-time updates, the accuracy of the extracted data can be improved, thereby improving the accuracy of cash flow prediction.

[0095] In an exemplary embodiment, the data used by the specified database for cash flow prediction of the object to be predicted is the underlying data, and the underlying data is synchronized to the cache of each node among multiple nodes; the underlying data includes historical data of similar assets; the amount of memory data on each node is less than or equal to the memory data amount threshold corresponding to each node;

[0096] Correspondingly, the above method further includes: in response to the received target prediction request, according to the specified load balancing policy, allocating the target calculation task corresponding to the object to be predicted to the target node among multiple nodes, so as to execute the target calculation task on the target node based on the underlying data cached on the target node, where the target prediction request is used to request a cash flow prediction for the object to be predicted, the specified load balancing policy is a policy for balancing the calculation tasks allocated to multiple nodes, the target calculation task is a calculation task for performing a cash flow prediction on the object to be predicted, and on the target node, the target calculation task is executed in a multi-threaded calculation manner.

[0097] Similar to the foregoing embodiment, the underlying data of the object to be predicted in the specified database can be extracted, and the distributed cache technology can be used to synchronize the above underlying data completely to the cache of each node among multiple nodes, and the underlying data includes historical data of similar assets.

[0098] Here, since the memory resources of each node are limited, in order to prevent memory overflow problems, when synchronizing the underlying data cache, memory data volume threshold control can be performed, that is, the memory data volume threshold corresponding to each node can be obtained to ensure that the amount of data loaded into the cache of each node does not exceed this threshold, which can effectively solve the problem of business data jitter.

[0099] Optionally, during the data reading process, for a single node, if it is found that the node reads too much data, the data reading of this node can be paused. After the data reading and processing of other nodes are completed, the data of another node is cleared and the unread data of this node is continued to be read through another node whose data has been cleared.

[0100] Optionally, methods such as partitioning or sharding the underlying data or compressing the underlying data can also be adopted to ensure that the data memory volume is within a safe range, thereby ensuring the stability and efficiency of data processing.

[0101] Correspondingly, in this embodiment, in response to the received target prediction request, according to the specified load balancing policy, the target calculation task corresponding to the object to be predicted is assigned to the target node among multiple nodes, so that on the target node, the target calculation task is executed based on the underlying data cached on the target node, where the target prediction request is used to request a cash flow prediction for the object to be predicted.

[0102] Here, the specified load balancing policy is a policy for balancing the calculation tasks assigned to multiple nodes, which can evenly distribute the calculation tasks to multiple computing nodes to prevent a specific node from being overloaded, thereby improving the overall data processing ability and response speed.

[0103] Optionally, the calculation tasks can be assigned based on the computing power of each node among multiple nodes (including the configuration of the node, the current load situation, etc.), or they can be assigned to the node most suitable for processing this type of task based on the complexity of the calculation tasks to be executed. It is also possible to preferentially assign tasks to nodes that have already cached relevant underlying data. This embodiment does not make any limitations in this regard.

[0104] Optionally, the specified load balancing policy can be preset and stored in advance, so that data can be quickly allocated when needed, improving the speed and accuracy of data processing.

[0105] Optionally, for computing acceleration, further exploration is made on the cache performance of the distributed cache, balancing between the total amount of table data and the cache load to obtain the data reading mode with the shortest time consumption, that is, specifying the data reading mode. The specified data reading mode can be used to balance the total amount of table data and the cache load, and it can be selected from a group of data reading modes through experiments. The selection basis can be the total amount of table data and the cache load. Correspondingly, the predicted cash flow aggregation table can be cached according to the specified data reading mode.

[0106] Optionally, on the target node, the target computing task can be executed in a multi-threaded computing manner, that is, the computing task can be decomposed into multiple sub-tasks that can be processed in parallel, and the multiple sub-tasks are assigned to different threads for simultaneous processing, thereby reducing the computing time and improving the efficiency of data processing. For example, when calculating the expected cash flow of assets in the asset pool in each future time period, the computing task can be decomposed into the calculation of each asset, and the calculations of different assets are processed in parallel.

[0107] Optionally, in multi-threaded computing, the computing task can be dynamically assigned to different threads according to the state of each thread to ensure load balancing of each thread. Here, it can be implemented through a pre-set scheduling algorithm, and the scheduling algorithm is not specifically limited in this embodiment.

[0108] Through this embodiment, by allocating and managing the computing tasks on multiple nodes according to the specified load balancing strategy, the computing efficiency and resource utilization rate of data processing can be improved.

[0109] In an exemplary embodiment, the above method further includes: in the case of the asset set of the same type of assets initiated by the target originator, determining the asset set of the same type of assets initiated by the target originator as the same type of asset pool; in the case of no asset set of the same type of assets initiated by the target originator, determining the asset set of the same type of assets initiated by other originators except the target originator as the same type of asset pool;

[0110] Correspondingly, after extracting the historical data of the same type of assets corresponding to the target asset pool of the object to be predicted, the above method further includes: based on the checking relationship between fields, standardizing the historical data of the same type of assets according to a group of specified fields to obtain the standardized historical data of the same type of assets.

[0111] In this embodiment, the same type of asset pool of the asset securitization product can be the remaining asset set initiated by the asset securitization product originator, with the same underlying asset type as the asset pool of the asset securitization product, that is, the same type of asset pool of the asset securitization product can be the remaining asset set that has the same originator as the asset securitization product and has the same underlying asset type as the asset pool of the asset securitization product but is not the asset pool.

[0112] Optionally, if the target originator has not originated homogeneous assets, the homogeneous assets of other originators can be determined as the homogeneous asset pool. That is, in the absence of homogeneous assets originated by the target originator, the asset set of homogeneous assets originated by other originators except the target originator can be determined as the homogeneous asset pool.

[0113] Here, due to the non-standard data disclosure of the historical data of homogeneous assets of different originators, including different disclosure calibers (i.e., different statistical methods or scopes of data, different originators may use different standards or methods to report the same type of data. For example, for the default rate, the originator may count the proportion of loans overdue for 90 days, or it may be overdue for 30 days, which results in different actual meanings and application scopes of the same indicator in different datasets), different meanings of disclosed fields (i.e., the specific content of each column of data in the dataset may be different, and fields with the same name represent different meanings in different datasets), etc., there are great difficulties in calculation and use. Therefore, based on the checking relationship between fields, the historical data of homogeneous assets can be standardized according to a set of specified fields to obtain the standardized historical data of homogeneous assets.

[0114] Here, the checking relationship between fields refers to the logical relationship that should be satisfied among multiple fields in the data. By checking this logical relationship, the integrity and accuracy of the data can be verified. If the checking relationship between fields does not hold, it can be judged that there are problems in the data.

[0115] Optionally, the field names or meanings used by different originators can be mapped to unified standard field names according to a set of specified fields to ensure data consistency. Here, according to a set of specified fields, it can be preset, and the above set of specified fields can be represented in the form of a table. That is, the standard meanings of the fields and the mapping relationships are stipulated in the form of a table. Furthermore, the data obtained above can be identified, and when it meets the regulations on the standard meanings of the fields, it can be stored in the corresponding position in the specified table.

[0116] Optionally, other standardization processes can also be performed on the data. For example, for the same field, the data obtained may include its total value and its value in the same period, and they can be uniformly converted into the value in the same period.

[0117] In addition, when the historical data of homogeneous assets corresponding to the target asset pool of the object to be predicted is incomplete, the default value can be given or the corrected value input manually can be received by combining the statistical results of relevant fields of homogeneous assets.

[0118] Optionally, after extracting the historical data of homogeneous assets corresponding to the target asset pool of the object to be predicted, the data can also be cleaned. For example, data that does not meet the calculation conditions can be excluded, that is, due to special situations such as too few data samples for some data to perform statistical calculations or the existence of outliers, these small sample combinations and outlier data can be excluded.

[0119] Optionally, after extracting the historical data of homogeneous assets corresponding to the target asset pool of the object to be predicted, the completeness of the data can also be verified. For example, a list of data fields necessary for the distribution and calculation of cash flow can be predefined in advance, and the data extracted from the specified database can be detected. In the case where a field in the above data field list is missing, the missing field information can be identified and the missing data field can be fed back through a feedback mechanism. Thus, the data can be updated and supplemented, and the supplemented data fields can be added to the existing asset pool data to ensure the integrity of all data fields necessary for data processing.

[0120] Through this embodiment, by performing standardized processing on the extracted data to obtain the standardized historical data of homogeneous assets, data standardization can be achieved, improving the accuracy and reliability of data processing.

[0121] In an exemplary embodiment, the homogeneous asset pool of an asset securitization product may include a static structured asset pool or a revolving structured asset pool. Among them, the static structured asset pool (hereinafter referred to as the static pool) may refer to the fact that the assets in the asset pool will not be supplemented once the asset pool is established. The revolving structured asset pool (hereinafter referred to as the dynamic pool) may refer to the fact that after the assets in the asset pool expire, the same type of assets will be purchased continuously to supplement the asset pool. Correspondingly, the historical data of homogeneous assets includes the historical data of the static structured asset pool and the historical data of the revolving structured asset pool (hereinafter referred to as the dynamic-static pool).

[0122] In this embodiment, the true performance of homogeneous assets (including prepayment situation - prepayment rate, default recovery situation - default rate and recovery rate, etc.) can be statistically calculated as the benchmark value of the stress parameter, and then calibrated according to the current true performance of the asset securitization product. The purpose of introducing homogeneous assets is to calculate the benchmark sequence of the asset pool stress parameters of the asset securitization product using their data. The main reasons for doing so are as follows:

[0123] First, the stress parameter performances such as prepayment and default of the same originator and the same type of assets are homogeneous, that is, the stress parameter performances of the dynamic-static pool and the asset pool of the asset securitization product are homogeneous;

[0124] Second, the dynamic-static pool contains more samples and richer historical information;

[0125] Third, the life cycle of the static pool samples is generally longer and can be used to predict the future trend of the asset pool stress parameters.

[0126] Similar to the foregoing embodiments, for the case of an asset collection of homogeneous assets initiated by a target originator, the asset collection of homogeneous assets initiated by the target originator can be determined as a homogeneous asset pool, and the true historical performance of the homogeneous asset pool can be used as its stress parameter; for the case where there are no homogeneous assets initiated by the target originator, the historical true performance of the asset pools of asset securitization products of the same asset type can be statistically analyzed as its stress parameter.

[0127] As an alternative implementation, for the case where the homogeneous asset pool includes a static structured asset pool and the historical data of the homogeneous assets includes the historical data of the static structured asset pool, the historical data of the static structured asset pool can be parsed according to the first stress parameter to obtain a first parameter reference sequence corresponding to the target asset. Parsing the historical data of the homogeneous assets according to a preset stress parameter to obtain a stress parameter reference sequence corresponding to the target asset includes: parsing the historical data of the static structured asset pool according to the first stress parameter to obtain a first parameter reference sequence corresponding to the target asset, and the stress parameter reference sequence corresponding to the target asset includes the first parameter reference sequence. Among them, the first stress parameter includes the prepayment rate, default rate, and recovery rate, and the first stress parameter can be expressed as a time series.

[0128] As another alternative implementation, for the case where the homogeneous asset pool includes a revolving structured asset pool and the historical data of the homogeneous assets includes the historical data of the revolving structured asset pool, the historical data of the revolving structured asset pool can be parsed according to the second stress parameter to obtain a second parameter reference sequence corresponding to the target asset. Parsing the historical data of the homogeneous assets according to a preset stress parameter to obtain a stress parameter reference sequence corresponding to the target asset includes: parsing the historical data of the revolving structured asset pool according to the second stress parameter to obtain a second parameter reference sequence corresponding to the target asset, and the stress parameter reference sequence corresponding to the target asset includes the second parameter reference sequence. Among them, the second stress parameter includes the repayment rate, default rate, recovery rate, yield rate, and purchase rate, and the second stress parameter can be expressed as a single value.

[0129] Optionally, the stress parameters of the static structured asset pool and the revolving structured asset pool can be calculated separately, and their algorithms can be preset and directly called when calculating the corresponding stress parameters.

[0130] Exemplarily, for the above stress parameters, the following calculation algorithms can be adopted:

[0131] (1) Prepayment rate. The prepayment rate can usually be expressed using the single monthly mortality (SMM) and the conditional prepayment rate (CPR), as shown in Formulas (1) and (2). Among them, the CPR can be used to represent the prepayment ratio:

[0132] (1)

[0133] (2)

[0134] (2) Default rate. Loans with an overdue period exceeding 90 days can be regarded as default loans, as shown in Formula (3):

[0135] (3)

[0136] (3) Recovery rate. If the underlying loans in the asset pool can calculate the value of collateral (such as residential mortgage loans, that is, RMBS, etc.), the recovery rate is calculated using the value of the collateral, as shown in Formula (4):

[0137] (4)

[0138] If the value of the collateral cannot be calculated (for example, consumer loans, auto loans, and small and micro enterprise loans, etc.), the recovery rate is calculated through the historical performance of the static pool, as shown in Formulas (5) and (6):

[0139] (5)

[0140] (6)

[0141] Among them, the recovery rate discount coefficient of the collateral can be determined in the following ways: (1) Calculation. The recovery rate discount coefficient is calculated by grouping according to the characteristics of the collateral; (2) Verification and adjustment. The recovery rate discount coefficient is verified and adjusted with reference to the actual recovery.

[0142] (4) Repayment rate. The calculation method of the repayment rate is shown in Formula (7):

[0143] (7)

[0144] (5) Yield rate. The calculation method of the yield rate is shown in Formula (8):

[0145] (8)

[0146] (6) Purchase rate. The calculation method of the purchase rate is shown in Formula (9):

[0147] (9)

[0148] Through this embodiment, by parsing the historical data of similar assets to obtain the corresponding pressure parameter benchmark sequence, the accuracy and effectiveness of data processing can be improved.

[0149] In an exemplary embodiment, calibrating the pressure parameter benchmark sequence corresponding to the target asset based on the true parameter value corresponding to the target asset and the preset pressure parameter includes: based on the difference between the true parameter value corresponding to the target asset and the preset pressure parameter and the parameter values of the same pressure parameter in the pressure parameter benchmark sequence corresponding to the target asset, performing a translation adjustment on the pressure parameter benchmark sequence corresponding to the target asset to obtain the pressure parameter calibration sequence corresponding to the target asset.

[0150] Similar to the foregoing embodiments, affected by various factors, the actual performance of the asset pool may be somewhat different from the history. Therefore, it is necessary to calibrate the pressure parameters according to the actual performance to obtain the pressure parameter calibration sequence. The basic logic of the calibration method is: using the deviation between the actual parameter value and the benchmark sequence to perform a translation adjustment on the benchmark sequence. Exemplarily, the benchmark sequence curve can be compared with the actual parameter value curve. For the non-coincident part, the benchmark sequence is translated and adjusted to make it infinitely close to the actual parameter value.

[0151] Here, for the pressure parameter benchmark sequence, it may have 100 values, while for the actual pressure parameter value, it may only have 20 values. Therefore, the actual parameter value cannot be directly used. Instead, the benchmark sequence needs to be made close to the actual parameter value to obtain the prediction of the remaining values. Thus, on the one hand, the calibrated pressure parameter calibration sequence can reflect the influence of the actual value of the asset pool, and on the other hand, it can retain the trend of the benchmark sequence.

[0152] Exemplarily, the main idea of calculating and calibrating the pressure parameters of asset securitization products is as Figure 6 shown. The static structure asset pool and the revolving structure asset pool follow the same idea, but need to be processed separately, and are divided into three steps as follows:

[0153] Step 1, pressure parameter benchmark calculation. First, perform data processing on the dynamic and static pools (including but not limited to: determining whether there is a matching dynamic and static pool, data standardization processing, etc.); subsequently, calculate the pressure parameters of the dynamic and static pools and the average value of the pressure parameters of the dynamic and static pools of the same underlying asset type. If the asset securitization product has a matching dynamic and static pool, its ABS (Asset-Backed Securities) pressure parameter benchmark is the dynamic and static pool parameters. If there is no matching dynamic and static pool, its ABS pressure parameter benchmark is the average value of the parameters of the same underlying asset type of the dynamic and static pools.

[0154] Step 2, calculating the actual values of the ABS parameters. The actual values can be generated based on the true performance of the asset pool of the asset securitization product and the detailed data of the underlying asset pool (entrusted data parameters). Exemplarily, it can be determined whether there are new default situations, prepayment situations, etc. of the asset securitization product based on the true performance of the asset pool disclosed in the "Trust Entrusted Institution Report".

[0155] Step 3, matching the benchmark sequence of the stress parameters of the asset securitization product with the actual values according to the asset pool of the asset securitization product, performing stress parameter calibration, and finally outputting. Here, if there are periods, corresponding matching can be performed according to the periods.

[0156] Through this embodiment, by calibrating the obtained benchmark sequence of stress parameters according to the actual values, the accuracy and reliability of data processing can be improved.

[0157] In an exemplary embodiment, before obtaining the predicted cash flow collection table for redemption by applying the stress parameter calibration sequence to the cash flow collection table of the target asset pool, the above method further includes: calculating the expected cash flow recovery information of each target asset according to the asset pool data of the target asset pool, where the expected cash flow recovery information of each target asset is used to indicate the expected future cash flow recovery situation of each target asset; aggregating each target asset in the target asset pool according to each collection date of the object to be predicted, and generating a first candidate cash flow collection table according to the obtained cash flow recovery information of each collection date, where the cash flow recovery information of each collection date is used to indicate the cash flow recovery situation of each collection date; determining the cash flow collection table that best matches the latest balance of the target asset pool among the first candidate cash flow collection table and the second candidate cash flow collection table as the target cash flow collection table, where the second candidate cash flow collection table is a cash flow collection table integrated from the cash flow collection table of the target asset pool configured for the object to be predicted and the actual performance of the target asset pool statistically.

[0158] In this embodiment, the expected cash flow recovery information of each target asset can be calculated according to the asset pool data of the target asset pool, and each target asset in the target asset pool can be aggregated according to each collection date of the object to be predicted, and a first candidate cash flow collection table can be generated according to the obtained cash flow recovery information of each collection date.

[0159] Exemplarily, as Figure 7 shown, it can be divided into two steps:

[0160] The first step is to calculate the future repayment plan for each asset in the asset pool:

[0161] Based on the basic loan data such as the underlying asset type, loan repayment method, loan repayment frequency, loan issuance date, loan maturity date, and the current loan status data such as the remaining principal, current execution rate, current overdue days, and overdue principal of each underlying asset in the asset pool, the future loan repayment plan (which may include the repayment of principal and interest in each period) is calculated.

[0162] Different asset securitization products may have different loan repayment methods. Similar to the above-mentioned embodiment, the loan repayment method and the corresponding algorithm corresponding to the asset securitization product may be obtained from a designated database.

[0163] Exemplarily, the loan repayment method algorithm may include:

[0164] (1) Equal principal method: the principal repayment amount for each period is as shown in formula (10):

[0165] (10)

[0166] (2) Lump sum repayment method: the principal repayment amount for each period is as shown in formula (11):

[0167] (11)

[0168] (3) Periodic interest payment method: the amount of interest paid each period is as shown in formula (12):

[0169] (12)

[0170] (4) Lump sum payment method: the amount of interest paid each period is as shown in formula (13):

[0171] (13)

[0172] (5) Equal principal and interest method: the principal and interest payment amount for each period is as shown in formula (14):

[0173] (14)

[0174] The second step is to aggregate cash flows according to the aggregation date:

[0175] According to the collection date of the asset securitization product, the future repayment cash flow of each underlying asset obtained in step 1 is divided into each cash flow collection period, thereby obtaining the basic scenario asset pool cash flow collection table of the asset pool (i.e., the first candidate cash flow collection table).

[0176] Optionally, after obtaining the first candidate cash flow aggregation table, in order to improve the accuracy of prediction and make it closer to the actual cash flow performance of the asset pool, information from other sources can be comprehensively considered. For example, based on the cash flow aggregation table disclosed in the official documents at the time of issuance of the object to be predicted (i.e., the asset securitization product initiated by the target originator) and the latest cash flow aggregation table of the asset pool disclosed in the above-mentioned "Trustee Report", the second candidate cash flow aggregation table can be integrated, and it can be compared with the above-mentioned first candidate cash flow aggregation table. The cash flow aggregation table that best matches the latest balance of the target asset pool is used as the target cash flow aggregation table, and the cash flow aggregation table that is closer to the true situation of the asset pool is obtained as the target cash flow aggregation table.

[0177] For example, as Figure 8 shown, it can be divided into three steps:

[0178] Step 1, update the disclosed cash flow aggregation table: Based on the cash flow aggregation table of the asset pool disclosed in the "Prospectus", update it according to the actual recovery of the asset pool disclosed in the "Trustee Report" and the updated cash flow recovery prediction table of the asset pool to obtain the latest disclosed integrated cash flow aggregation table (i.e., the second candidate cash flow aggregation table);

[0179] Step 2, judge the latest cash flow aggregation table: Compare the latest disclosed integrated cash flow aggregation table (i.e., the second candidate cash flow aggregation table) calculated in Step 1 with the above-mentioned base scenario cash flow aggregation table (i.e., the first candidate cash flow aggregation table), and determine the cash flow aggregation table that is closer to the true situation of the asset pool according to the latest balance of the asset pool disclosed in the "Trustee Report", that is, the target cash flow aggregation table;

[0180] Step 3: Apply pressure to the cash flow aggregation table that is closer to the true situation of the asset pool obtained in Step 2, that is, apply different pressure parameters to the asset pool to simulate the possible pressure scenarios that the asset securitization product may encounter, and obtain the cash flow aggregation table considering the pressure parameters, that is, the predicted cash flow aggregation table for redemption.

[0181] Through this embodiment, calculating the expected cash flow recovery information based on the asset pool data and aggregating it to generate the target cash flow aggregation table can improve the stability and reliability of data processing.

[0182] In an exemplary embodiment, the above method further includes: determining the information to be displayed of the object to be predicted based on the historical principal and interest repayment data of the object to be predicted and the expected cash flow of the object to be predicted, and distributing the information to be displayed to the display interface for display, where the information to be displayed is used to describe the principal and interest repayment cash flow during the entire life cycle of the object to be predicted.

[0183] In this embodiment, the information to be displayed for the object to be predicted can be determined based on the historical principal and interest repayment data of the object to be predicted and the expected cash flow of the object to be predicted. Among them, the information to be displayed is used to describe the principal and interest repayment cash flow during the complete life cycle of the object to be predicted. That is, the predicted future repayment cash flow can be combined with the historical principal repayment cash flow to obtain the complete repayment cash flow table for each tranche of the asset securitization product.

[0184] Here, the expected cash flow of the object to be predicted can be represented in the form of a cash flow collection table, that is, it can be the above-mentioned predicted cash flow collection table for repayment.

[0185] Here, based on the historical principal and interest repayment data of the object to be predicted, the historical principal repayment cash flow can be obtained and can also be represented in the form of a historical principal repayment cash flow table.

[0186] Optionally, by aligning the historical principal repayment data and the expected cash flow data in time to form a complete cash flow time series, thus, by integrating the historical principal and interest repayment data of the object to be predicted with the calculated expected cash flow data, the principal and interest repayment cash flow during the complete life cycle of the object to be predicted can be obtained, including but not limited to: principal recovery, interest income, and possible default losses in each period, etc.

[0187] To facilitate understanding and viewing and provide a more comprehensive and transparent cash flow prediction information, the principal and interest repayment cash flow during the complete life cycle of the above-mentioned object to be predicted can be converted into the information to be displayed, that is, visual data, such as charts, tables, graphs, etc.

[0188] Optionally, the information to be displayed can be distributed to the display interface for display. Thus, users can clearly view the cash flow performance of the asset securitization product during the entire life cycle through the display interface, which helps users complete the decision-making.

[0189] Optionally, the display interface can be a display interface set up for the object to be predicted. It can be an interface that supports user interaction or can be only used for display. This embodiment does not make a limitation on this.

[0190] Optionally, corresponding to the dynamic adjustment that occurs as the object to be predicted changes over time, the information to be displayed can also be dynamically updated.

[0191] Through this embodiment, combining the historical principal and interest repayment data with the predicted cash flow to generate the information to be displayed and distributing it to the display interface for display can improve the transparency of data processing.

[0192] The following explains the data processing method in the embodiment of the present application in combination with optional examples. The data processing method in this optional example can be applied to the Figure 9 technical framework shown asFigure 9 As shown in the figure, the technical framework includes:

[0193] Data service, which is used to implement data scheduling and data synchronization;

[0194] Interface service, which is used to provide interface call services for external scheduling systems or other systems;

[0195] Computing service, which is used for internal system computing;

[0196] Cache service, which is used for data caching;

[0197] Distributed cache technology, which can be used to load the underlying database data into memory for computing;

[0198] Basic framework: It provides distributed computing, can encapsulate the overall business logic, shield the internal logic according to the job, and realize the automation of computing. Among them, the framework support module can use Springboot for the overall framework technology support, the Mybatis-plus can be used in the data synchronization module to connect to the database during data synchronization, and the feign can be used in the external interface call module for external interface calls;

[0199] Database: It is used to provide detailed data of asset securitization products;

[0200] Persistent layer data: It is used for underlying data storage;

[0201] Data adaptation: Through API (Application Programming Interface) calls, synchronize the underlying data (that is, the data in the persistent layer data) to the distributed cache;

[0202] Distributed cache: It is used to load the underlying database data into memory for computing;

[0203] Distributed computing engine: Corresponding to the distributed cache, it is used for corresponding data computing;

[0204] Atomic formula: That is, all data calculation formulas in this application (such as data calculation formulas for recovery rate, prepayment rate, etc.), which are used for the business logic implemented on the basis of the distributed computing engine;

[0205] Indicator calculation: It refers to the key indicators reflecting the performance of asset securitization products and asset pools, including but not limited to prepayment rate, recovery rate, etc.;

[0206] Business ETL (Extract, Transform, Load): Data processing process, which is used to extract data and perform necessary data preprocessing;

[0207] Business encapsulation: used to encapsulate data, including pressure parameters, aggregated cash flow, split cash flow, and individual cash flow;

[0208] Interface layer: used to provide interface call services, including RESTful (Representational State Transfer), Java SDK (Java Software Development Kit), and Python SDK (Python Software Development Kit).

[0209] Correspondingly, as Figure 10 shown, the data processing system provided in the embodiments of the present application may include the following three modules: a data access and processing module, a model development module, and a data distribution module. Among them, the processing flow of the data access and processing module can be divided into four stages: docking with the database to obtain data in the database, data aggregation, data processing and data cleaning, and data storage; the processing flow of the model development module can be divided into four stages: statistical analysis of asset pool pressure scenario parameters, prediction of basic scenario asset pool cash flow, calculation of asset pool cash flow considering pressure scenarios, and distribution of asset pool cash flow to asset securitization products; the processing flow of the data distribution module can be divided into three stages: generating future cash flow prediction values, converting them into complete repayment cash flows, and data distribution.

[0210] Through this optional example, after obtaining the required data, the data is parsed according to the preset pressure parameters to obtain the pressure parameter reference sequence and calibrated, and finally the calibrated pressure parameter calibration sequence is applied to the corresponding data to obtain the final prediction data, which can improve the accuracy and reliability of data processing.

[0211] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0212] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM (Read-Only Memory), RAM (Random Access Memory), magnetic disk, optical disk), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0213] According to another aspect of the embodiments of the present application, a data processing device is further provided. This data processing device can be used to implement the data processing method provided in the above embodiments, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0214] Figure 11 is a structural block diagram of an optional data processing device according to the embodiments of the present application. As Figure 11 shown in, the data processing device includes:

[0215] An extraction unit 1102, configured to extract historical data of similar assets corresponding to the target asset pool of the object to be predicted. Among them, the similar asset data is stored in a specified database. The object to be predicted is an asset securitization product initiated by a target originator. The historical data of similar assets is the historical data of a similar asset pool. The similar asset pool is a set of similar assets of the target assets in the target asset pool. The asset type of the similar assets is the same as that of the corresponding target assets and does not belong to the target asset pool;

[0216] An analysis unit 1104, configured to analyze the historical data of similar assets according to a preset pressure parameter to obtain a pressure parameter reference sequence corresponding to the target asset. The preset pressure parameter is a parameter that affects the cash flow of the target asset.

[0217] A first execution unit 1106, configured to calibrate the pressure parameter reference sequence corresponding to the target asset based on the true value of the parameter corresponding to the target asset and the preset pressure parameter to obtain a pressure parameter calibration sequence corresponding to the target asset, and obtain a predicted cash flow collection table for redemption by applying the pressure parameter calibration sequence to the target cash flow collection table of the target asset pool.

[0218] A first allocation unit 1108, configured to allocate the cash flow of the target asset pool according to the cash flow payment mechanism of the object to be predicted, based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator, so as to obtain the expected cash flow of the object to be predicted.

[0219] It should be noted that the extraction unit 1102 in this embodiment may be used to execute the above step S202, the parsing unit 1104 in this embodiment may be used to execute the above step S204, the first execution unit 1106 in this embodiment may be used to execute the above step S206, and the first allocation unit 1108 in this embodiment may be used to execute the above step S208.

[0220] Through the embodiment provided by the present application, historical data of similar assets corresponding to the target asset pool of the object to be predicted is extracted, wherein the similar asset data is stored in a specified database, the object to be predicted is an asset securitization product initiated by a target originator, the historical data of similar assets is the historical data of a similar asset pool, the similar asset pool is a set of similar assets of the target assets in the target asset pool, and the asset type of the similar assets is the same as that of the corresponding target assets and does not belong to the target asset pool; the historical data of similar assets is parsed according to a preset pressure parameter to obtain a pressure parameter reference sequence corresponding to the target asset, wherein the preset pressure parameter is a parameter affecting the cash flow of the target asset; the pressure parameter reference sequence corresponding to the target asset is calibrated based on the true value of the parameter corresponding to the target asset and the preset pressure parameter to obtain a pressure parameter calibration sequence corresponding to the target asset, and the predicted cash flow collection table for redemption is obtained by applying the pressure parameter calibration sequence to the target cash flow collection table of the target asset pool; based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator, the cash flow of the target asset pool is allocated according to the cash flow payment mechanism of the object to be predicted, so as to obtain the expected cash flow of the object to be predicted, which solves the technical problem that the data processing method in the related art has low accuracy of the data processing result due to the large difficulty in data acquisition and the complexity of data processing, and improves the accuracy and reliability of data processing.

[0221] In an exemplary embodiment, the specified database further stores at least one of the following information: asset pool data of the target asset pool; cash flow payment mechanism data for indicating the cash flow payment mechanism; credit enhancement data for indicating the credit enhancement measures; historical principal and interest repayment data for indicating the historical principal and interest repayment situation of the object to be predicted; the data used for cash flow prediction of the object to be predicted in the specified database is underlying data, and the underlying data includes historical data of similar assets.

[0222] In this embodiment, the above-mentioned device further includes: a query unit, configured to stream and query whether the underlying data stored in a specified database has been updated in a multi-threaded concurrent manner; a synchronization unit, configured to, in the case where the underlying data has been updated, synchronize the updated underlying data to the cache of each node among multiple nodes, wherein the underlying data used for cash flow prediction of the object to be predicted is the underlying data cached on one of the multiple nodes.

[0223] In an exemplary embodiment, the data used by the specified database for cash flow prediction of the object to be predicted is the underlying data, and the underlying data is synchronized to the cache of each node among multiple nodes; the underlying data includes historical data of similar assets; the amount of memory data on each node is less than or equal to the memory data volume threshold corresponding to each node.

[0224] In this embodiment, the above-mentioned device further includes: a second allocation unit, configured to, in response to a received target prediction request, allocate the target calculation task corresponding to the object to be predicted to the target node among multiple nodes according to a specified load balancing policy, so as to execute the target calculation task on the target node based on the underlying data cached on the target node, wherein the target prediction request is used to request cash flow prediction of the object to be predicted, the specified load balancing policy is a policy for balancing the calculation tasks allocated to multiple nodes, the target calculation task is a calculation task for cash flow prediction of the object to be predicted, and on the target node, the target calculation task is executed in a multi-threaded calculation manner.

[0225] In an exemplary embodiment, the above-mentioned device further includes: a determination unit, configured to, in the case where there is a set of similar assets initiated by a target initiator, determine the set of similar assets initiated by the target initiator as the similar asset pool; in the case where there is no set of similar assets initiated by the target initiator, determine the set of similar assets initiated by other initiators except the target initiator as the similar asset pool; a second execution unit, configured to, after extracting the historical data of similar assets corresponding to the target asset pool of the object to be predicted, perform standardization processing on the historical data of similar assets according to a set of specified fields based on the check relationship between fields, so as to obtain the standardized historical data of similar assets.

[0226] In an exemplary embodiment, the parsing unit includes one of the following: a first parsing module, configured to, when the homogeneous asset pool includes a static structured asset pool and the homogeneous asset historical data includes the historical data of the static structured asset pool, parse the historical data of the static structured asset pool according to a first stress parameter to obtain a first parameter reference sequence corresponding to the target asset, where the first stress parameter includes a prepayment rate, a default rate, and a recovery rate, and the stress parameter reference sequence corresponding to the target asset includes the first parameter reference sequence; a second parsing module, configured to, when the homogeneous asset pool includes a revolving structured asset pool and the homogeneous asset historical data includes the historical data of the revolving structured asset pool, parse the historical data of the revolving structured asset pool according to a second stress parameter to obtain a second parameter reference sequence corresponding to the target asset, where the second stress parameter includes a repayment rate, a default rate, a recovery rate, a yield rate, and a purchase rate, and the stress parameter reference sequence corresponding to the target asset includes the second parameter reference sequence.

[0227] In an exemplary embodiment, the first execution unit includes: a translation module, configured to perform a translation adjustment on the stress parameter reference sequence corresponding to the target asset based on the difference between the parameter true value corresponding to the target asset and the preset stress parameter and the parameter value of the same stress parameter in the stress parameter reference sequence corresponding to the target asset, to obtain a stress parameter calibration sequence corresponding to the target asset.

[0228] In an exemplary embodiment, the above device further includes: a calculation unit, configured to calculate the expected cash flow recovery information of each target asset according to the asset pool data of the target asset pool before obtaining the predicted cash flow collection table for redemption by applying the stress parameter calibration sequence to the cash flow collection table of the target asset pool, where the expected cash flow recovery information of each target asset is used to indicate the expected future cash flow recovery situation of each target asset; a collection unit, configured to collect each target asset in the target asset pool according to each collection date of the object to be predicted, and generate a first candidate cash flow collection table according to the obtained cash flow recovery information of each collection date, where the cash flow recovery information of each collection date is used to indicate the cash flow recovery situation of each collection date; a determination unit, configured to determine the cash flow collection table that best matches the latest balance of the target asset pool among the first candidate cash flow collection table and the second candidate cash flow collection table as the target cash flow collection table, where the second candidate cash flow collection table is a cash flow collection table integrated from the cash flow collection table of the target asset pool configured for the object to be predicted and the actual performance of the target asset pool statistically.

[0229] In an exemplary embodiment, the predicted cash flow collection table is cached according to a specified data reading mode, and the specified data reading mode is used to balance the total amount of table data and the cache load. The above device further includes: a calling unit, configured to call the cash flow distribution order calculation method corresponding to the object to be predicted in the preset method module library under the account type corresponding to the target initiator, so as to obtain the cash flow payment mechanism of the object to be predicted.

[0230] In an exemplary embodiment, the above device further includes: a third execution unit, configured to determine the information to be displayed of the object to be predicted based on the historical principal and interest repayment data of the object to be predicted and the expected cash flow of the object to be predicted, and distribute the information to be displayed to a display interface for display, where the information to be displayed is used to describe the principal and interest repayment cash flow during the entire life cycle of the object to be predicted.

[0231] It should be noted that the above-mentioned various modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to this: the above modules are all located in the same processor; or, the above-mentioned various modules are separately located in different processors in any combination form.

[0232] According to another aspect of the embodiments of the present application, there is provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, it executes the steps in any one of the above method embodiments.

[0233] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media such as USB flash drives, ROMs, RAMs, mobile hard disks, magnetic disks, or optical discs that can store computer programs.

[0234] According to another aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor is configured to execute the steps in any one of the above method embodiments through the computer program. In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device, where the transmission device is connected to the above processor, and the input / output device is connected to the above processor.

[0235] The specific examples in this embodiment may refer to the examples described in the above embodiments and exemplary embodiments, and will not be repeated here.

[0236] According to another aspect of the embodiments of the present application, a computer program product is further provided. The computer program product includes computer programs / instructions, and the computer programs / instructions contain program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 1209 and / or installed from the removable medium 1211. When the computer program is executed by the central processing unit 1201, various functions provided by the embodiments of the present application are executed. The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0237] Figure 12 Schematically shown is a block diagram of a computer system of an electronic device for implementing the embodiments of the present application. As Figure 12 shown, the computer system 1200 includes a CPU (Central Processing Unit), the central processing unit 1201, which can execute various appropriate actions and processes according to the programs stored in the ROM 1202 or the programs loaded from the storage part 1208 into the RAM 1203. In the random access memory 1203, various programs and data required for system operations are also stored. The central processing unit 1201, the read-only memory 1202, and the random access memory 1203 are connected to each other through the bus 1204. The I / O (Input / Output) interface 1205 is also connected to the bus 1204.

[0238] The following components are connected to the I / O interface 1205: an input part 1206 including a keyboard, a mouse, etc.; an output part 1207 including such as a CRT (Cathode Ray Tube), an LCD (Liquid Crystal Display), etc. and speakers, etc.; a storage part 1208 including a hard disk, etc.; and a communication part 1209 including a network interface card such as a local area network card, a modem, etc. The communication part 1209 performs communication processing via a network such as the Internet. The drive 1210 is also connected to the input / output interface 1205 as required. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1210 as required so that the computer program read from it can be installed into the storage part 1208 as required.

[0239] In particular, according to an embodiment of the present application, the processes described in each method flow chart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1209, and / or installed from the removable medium 1211. When the computer program is executed by the central processing unit 1201, various functions defined in the system of the present application are executed.

[0240] It should be noted that Figure 12 The computer system 1200 of the electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0241] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program code executable by the computing device. Thus, they can be stored in the storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the present application is not limited to any specific combination of hardware and software.

[0242] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method, characterized in that, Including: extracting historical data of homogeneous assets corresponding to the target asset pool of the object to be predicted, wherein the homogeneous asset data is stored in a specified database, the object to be predicted is an asset securitization product initiated by a target originator, the historical data of homogeneous assets is the historical data of a homogeneous asset pool, the homogeneous asset pool is a set of homogeneous assets of the target assets in the target asset pool, and the asset type of the homogeneous assets is the same as that of the corresponding target assets and does not belong to the target asset pool; analyzing the historical data of homogeneous assets according to a preset pressure parameter to obtain a pressure parameter benchmark sequence corresponding to the target asset, wherein the preset pressure parameter is a parameter affecting the cash flow of the target asset; calibrating the pressure parameter benchmark sequence corresponding to the target asset based on the parameter true value corresponding to the target asset and the preset pressure parameter to obtain a pressure parameter calibration sequence corresponding to the target asset, and obtaining a predicted cash flow collection table for redemption by applying the pressure parameter calibration sequence to the target cash flow collection table of the target asset pool; allocating the cash flow of the target asset pool according to the cash flow payment mechanism of the object to be predicted based on the predicted cash flow collection table and the credit enhancement measures adopted by the target originator to obtain the expected cash flow of the object to be predicted; the specified database further stores at least one of the following information: asset pool data of the target asset pool; cash flow payment mechanism data for indicating the cash flow payment mechanism; credit enhancement data for indicating the credit enhancement measures; historical principal and interest repayment data for indicating the historical principal and interest repayment situation of the object to be predicted; the data used for cash flow prediction of the object to be predicted in the specified database is underlying data, and the underlying data includes the historical data of homogeneous assets; the method further includes: querying in a multi-threaded concurrent manner whether the underlying data stored in the specified database has been updated; in the case where the underlying data has been updated, synchronizing the updated underlying data to the cache of each node among multiple nodes, wherein the underlying data used for cash flow prediction of the object to be predicted is the underlying data cached on one of the multiple nodes; the calibrating the pressure parameter benchmark sequence corresponding to the target asset based on the parameter true value corresponding to the target asset and the preset pressure parameter to obtain a pressure parameter calibration sequence corresponding to the target asset includes: performing a translation adjustment on the pressure parameter benchmark sequence corresponding to the target asset based on the difference between the parameter true value corresponding to the target asset and the preset pressure parameter and the parameter values of the same pressure parameters in the pressure parameter benchmark sequence corresponding to the target asset to obtain a pressure parameter calibration sequence corresponding to the target asset.

2. The method according to claim 1, wherein The data used by the specified database for cash flow prediction of the object to be predicted is underlying data, and the underlying data is synchronized to the cache of each node among multiple nodes; the underlying data includes the historical data of the same type of assets; The amount of in-memory data on each node is less than or equal to the in-memory data volume threshold corresponding to each node; The method further includes: In response to the received target prediction request, according to the specified load balancing strategy, allocate the target calculation task corresponding to the object to be predicted to the target node among the multiple nodes, so as to execute the target calculation task on the target node based on the underlying data cached on the target node, wherein the target prediction request is used to request cash flow prediction for the object to be predicted, the specified load balancing strategy is a strategy for balancing the calculation tasks allocated to the multiple nodes, the target calculation task is a calculation task for cash flow prediction of the object to be predicted, and on the target node, the target calculation task is executed in a multi-threaded calculation manner.

3. The method according to claim 1, wherein: The method further includes: when there is a set of assets of the same type initiated by the target initiator, determine the set of assets of the same type initiated by the target initiator as the pool of assets of the same type; when there is no set of assets of the same type initiated by the target initiator, determine the set of assets of the same type initiated by other initiators except the target initiator as the pool of assets of the same type; After extracting the historical data of the same type of assets corresponding to the target asset pool of the object to be predicted, the method further includes: based on the checking relationship between fields, perform standardization processing on the historical data of the same type of assets according to a set of specified fields to obtain the standardized historical data of the same type of assets.

4. The method according to claim 1, wherein The parsing of the historical data of the same type of assets according to the preset pressure parameter to obtain the pressure parameter reference sequence corresponding to the target asset includes one of the following: When the pool of assets of the same type includes a static structure asset pool and the historical data of the same type of assets includes the historical data of the static structure asset pool, parse the historical data of the static structure asset pool according to the first pressure parameter to obtain the first parameter reference sequence corresponding to the target asset, wherein the first pressure parameter includes the prepayment rate, default rate, and recovery rate, and the pressure parameter reference sequence corresponding to the target asset includes the first parameter reference sequence; When the pool of assets of the same type includes a cyclic structure asset pool and the historical data of the same type of assets includes the historical data of the cyclic structure asset pool, parse the historical data of the cyclic structure asset pool according to the second pressure parameter to obtain the second parameter reference sequence corresponding to the target asset, wherein the second pressure parameter includes the repayment rate, default rate, recovery rate, yield rate, and purchase rate, and the pressure parameter reference sequence corresponding to the target asset includes the second parameter reference sequence.

5. The method according to claim 1, wherein Before obtaining the predicted cash - flow collection table for redemption by applying the pressure - parameter calibration sequence to the cash - flow collection table of the target asset pool, the method further includes: Calculating the expected cash - flow recovery information of each target asset according to the asset - pool data of the target asset pool, where the expected cash - flow recovery information of each target asset is used to indicate the expected future cash - flow recovery situation of each target asset; Aggregating each target asset in the target asset pool according to each aggregation date of the object to be predicted, and generating a first candidate cash - flow collection table based on the cash - flow recovery information obtained for each aggregation date, where the cash - flow recovery information of each aggregation date is used to indicate the cash - flow recovery situation of each aggregation date; Determining the target cash - flow collection table as the cash - flow collection table that best matches the latest balance of the target asset pool among the first candidate cash - flow collection table and the second candidate cash - flow collection table, where the second candidate cash - flow collection table is a cash - flow collection table integrated from the cash - flow collection table of the target asset pool configured for the object to be predicted and the actual performance of the target asset pool statistically analyzed; 6. The method according to any one of claims 1 to 5, characterized in that, The predicted cash - flow collection table is cached according to a specified data - reading mode, and the specified data - reading mode is used to balance the total amount of table data and cache load; The method further includes: Invoking the cash - flow distribution - order calculation method corresponding to the object to be predicted in the preset method - module library under the account type of the target originator to obtain the cash - flow payment mechanism of the object to be predicted.

7. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Determining the information to be displayed of the object to be predicted based on the historical principal - and - interest repayment data and the expected cash - flow of the object to be predicted, and distributing the information to be displayed to a display interface for display, where the information to be displayed is used to describe the principal - and - interest cash - flow during the entire life cycle of the object to be predicted.

8. A computer-readable storage medium, characterized in that, A computer program is stored in the computer - readable storage medium, where when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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