Asset management system and method, computing device, storage medium and program product

Abstract

The invention provides an asset management system, and the system comprises a data management module which is configured to obtain target data related to at least one basic asset in a target asset pool; the big data processing module is configured to store the target data by using the data warehouse, determine intermediate statistical data required in the calculation process of the target business index, and determine the target business index based on the intermediate statistical data, wherein the target data and the intermediate statistical data are respectively stored in an operation type data storage layer and an analysis type data storage layer, and the target business index comprises an index related to a target asset pool or at least one basic asset in the target asset pool; the asset pool monitoring module is configured to present information related to one or more indexes; and the index early warning module is configured to check the core business index in a preset period and send corresponding early warning information to a corresponding user when the index meets a preset condition. The invention further provides an asset management method.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular, to an asset management system. In addition, the present application also relates to an asset management method, a computing device, a computer-readable storage medium, and a computer program product. Background Art

[0002] As the degree of enterprise informatization continues to improve, data, as the core asset of modern enterprise operations, plays a vital role in supporting decision-making, business optimization, and improving market competitiveness. Especially in the context of asset data management in the asset pool corresponding to asset-backed securities, there is a need for efficient use and professional analysis of relevant data.

[0003] In related technologies, when enterprises conduct data management and analysis, they need to invest a lot of manpower and material resources to obtain data from multiple external systems (for example, databases, file servers, application programming interfaces), and then verify, parse, calculate and integrate these data to form structured data assets to support the decision-making and business development of the enterprise. This process is not only cumbersome and time-consuming, but also because each data synchronization and update requires repeating the same steps, especially when the data volume is large and the data format is diverse, manual operation is not only inefficient, but also difficult to ensure the accuracy and consistency of the data. In addition, with the rapid development of corporate business and the intensification of market competition, the requirements for the real-time and accuracy of data processing are becoming higher and higher, and traditional manual operation methods can no longer meet the needs of enterprises. Summary of the invention

[0004] In view of this, the present application provides an asset management system to alleviate, mitigate, or even eliminate the above-mentioned problems.

[0005] According to one aspect of the present application, an asset management system is provided, comprising: a data management module configured to obtain target data related to at least one underlying asset in a target asset pool corresponding to an asset-backed security; a big data processing module configured to use a target data warehouse to store target data and determine intermediate statistical data, and determine at least one target business indicator based on the intermediate statistical data, wherein the acquired target data is stored in an operational data storage layer of the target data warehouse, and the determined intermediate statistical data is stored in an analytical data storage layer of the target data warehouse, the intermediate statistical data includes data required in the calculation process of the at least one target business indicator, and the at least one target business indicator includes indicators related to the target asset pool or at least one underlying asset in the target asset pool; an asset pool monitoring module configured to present information related to one or more indicators in the at least one target business indicator; and an indicator early warning module configured to check core business indicators at a first preset period, and when the core business indicators meet preset conditions, send corresponding early warning information to corresponding users, the core business indicators including one or more indicators in the at least one target business indicator.

[0006] According to some embodiments of the present application, the acquired target data is stored in the operational data storage layer of the target data warehouse in the following manner: the original file corresponding to the target data is split into multiple sub-files and then sent to the operational data storage layer of the target data warehouse in a breakpoint-resume mode, and the multiple sub-files obtained by splitting are merged into the original file in the operational data storage layer of the target data warehouse.

[0007] According to some embodiments of the present application, when an indicator viewing request is received, or when the asset management system uses the asset pool monitoring module to perform indicator monitoring, the asset management system uses the big data processing module to determine at least one target business indicator and presents it to the corresponding user who made the indicator viewing request through the asset pool monitoring module.

[0008] According to some embodiments of the present application, the target data warehouse is a Hive data warehouse, and wherein the big data processing module determines the at least one target business indicator through the following steps: obtaining target data from the operational data storage layer of the Hive data warehouse; determining the intermediate statistical data based on the target data and storing it in the analytical data storage layer of the Hive data warehouse; obtaining the intermediate statistical data from the analytical data storage layer of the Hive data warehouse; and determining the at least one target business indicator based on the intermediate statistical data according to preconfigured calculation rules.

[0009] According to some embodiments of the present application, the big data processing module uses a streaming processing platform to determine the at least one target business indicator, and the streaming processing platform is obtained by deploying a Flink cluster on a Kubernetes cluster, wherein the Flink cluster is used to interact with the Hive data warehouse to achieve the intake, processing and output of corresponding data, and the Kubernetes cluster is used to manage the life cycle of the Flink cluster.

[0010] According to some embodiments of the present application, the above-mentioned asset management system also includes: an asset pool reporting module, configured to generate a first data report with a second preset period and a second data report with a third preset period, wherein the third preset period is greater than the second preset period, and wherein the first data report includes at least one of the following indicators related to the target asset pool: number of assets, total asset size, non-performing rate, yield, idle rate, 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, 90-day cumulative overdue rate; the second data report includes at least one of the following indicators related to the target asset pool: total outstanding principal balance, number of loans, number of borrowers, total contract amount, distribution of outstanding principal balance, borrower age distribution, borrower regional distribution, current loan interest rate distribution, loan contract term distribution, and loan remaining term distribution.

[0011] According to some embodiments of the present application, the above-mentioned asset management system also includes: a data overview module, which is configured to present a data overview page when the asset management system is accessed, and the data overview page includes target information corresponding to the user currently accessing the asset management system, and the target information includes at least one of the following indicators: number of assets, total asset size, cumulative cycle purchases, bad debt rate, idle rate, rate of return, geographical distribution, age distribution, sampled basic asset elements, unearned principal balance, asset type distribution, flow distribution, unearned principal balance change, and bad debt rate change.

[0012] According to some embodiments of the present application, the above-mentioned asset management system also includes: a special plan management module, which is configured to present a form to be supplemented to the target user, and when the target user fills in the target information on the form to be supplemented, the indicator warning module is used to perform corresponding warning operations according to the obtained supplemented form, wherein the form to be supplemented includes the initial information obtained by the special plan management module from other modules of the asset management system, and wherein the target information includes at least one of the following: basic data, stratification information, measurement parameters, threshold parameters, the basic data includes at least one of the following: asset party, issuance scale, asset type, issuance date, maturity date, last benchmark date, issuing institution, management institution, opening bank, custodial account bank, special plan transition status, the stratification information includes at least one of the following: stratification name, issuance scale, scale ratio, expected rate of return, the measurement parameters include at least one of the following: management fee rate, custody fee rate, benchmark default rate, benchmark idle rate, the threshold setting includes at least one of the following: idle rate, overdue rate, annualized overdue rate, rate of return.

[0013] According to some embodiments of the present application, the above-mentioned asset management system also includes: a permission management module, which is configured to perform permission management in the following aspects: project group permissions, notification permissions, and directory permissions, wherein the management of the project group permissions is used to isolate business data of different projects, the management of the notification permissions is used to send different types of notification messages to different users, and the management of the directory permissions is used to present different directories to different users to achieve security control of the corresponding functions of the asset management system.

[0014] According to some embodiments of the present application, the asset pool monitoring module is configured to present information related to the following indicators: overdue rate for different overdue days, annualized overdue rate, migration rate, idle rate, yield, circular purchase multiple, amount distribution, interest rate distribution, loan contract term distribution, loan remaining term distribution, regional distribution, and cash flow coverage ratio, wherein the overdue rate for different overdue days includes at least one of the following: 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, and 90-day cumulative overdue rate.

[0015] According to some embodiments of the present application, the indicator warning module is configured to check at least one of the following indicators in a preset cycle: idle rate, 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, 90-day cumulative overdue rate, and asset-weighted return rate.

[0016] According to some embodiments of the present application, the data management module is configured to obtain target data in at least one of the following ways: obtaining corresponding data from a first data source based on an HTTPS protocol by calling a RESTful interface, obtaining corresponding data from a second data source based on an FTP protocol, and obtaining corresponding data from a third data source based on an SFTP protocol.

[0017] According to another aspect of the present application, there is provided an asset management method, which is applied to an employee client, the employee client is used to access an asset management system, the asset management system includes a data management module, a big data processing module, an asset pool monitoring module and an indicator warning module, the method includes: obtaining an asset management request from an employee, the employee is a user with the right to use the employee client, the asset management request is used to obtain at least one of the following services provided by the asset management system: indicator query, asset pool monitoring, indicator warning; according to the asset management request, calling the corresponding module among the data management module, the big data processing module, the asset pool monitoring module and the indicator warning module of the asset management system to obtain a corresponding request result, wherein, when calling the data management module, the data management module is configured to obtain target data related to at least one underlying asset in the target asset pool corresponding to the asset-backed securities as at least part of the request result, and when calling the big data processing module, the big data processing module is configured to determine the corresponding data as at least part of the request result in the following manner: using a target data warehouse to store target data and generate intermediate statistical data and determining at least one target business indicator based on the intermediate statistical data, wherein the acquired target data is stored in the operational data storage layer of the target data warehouse, the determined intermediate statistical data is stored in the analytical data storage layer of the target data warehouse, the intermediate statistical data includes data required in the calculation process of the at least one target business indicator, the at least one target business indicator includes an indicator related to the target asset pool or at least one underlying asset in the target asset pool, when calling the asset pool monitoring module, the asset pool monitoring module is configured to present corresponding information as at least part of the request result in the following manner: present information related to one or more indicators of the at least one target business indicator corresponding to the asset management request, and when calling the indicator warning module, the indicator warning module is configured to send corresponding warning information to the employee client as at least part of the request result in the following manner: check the core business indicators in a first preset period, and when the core business indicators meet the preset conditions, send corresponding warning information to the employee client, the core business indicators include one or more indicators of the at least one target business indicator.

[0018] According to another aspect of the present application, a computing device is provided, comprising: a memory configured to store computer-executable instructions; and a processor configured to execute the asset management method provided according to the aforementioned aspects of the present application when the computer-executable instructions are executed by the processor.

[0019] According to another aspect of the present application, a computer-readable storage medium is provided, which stores computer-executable instructions. When the computer-executable instructions are executed, the asset management method provided according to the aforementioned aspects of the present application is executed.

[0020] According to another aspect of the present application, a computer program product is provided, comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, execute the asset management method provided according to the aforementioned aspects of the present application.

[0021] According to the asset management system provided by the present application, which includes a data management module, a big data processing module, an asset pool monitoring module, and an indicator warning module, the target data can be obtained from the corresponding data source in at least one of the following automated ways: calling a RESTful interface based on the HTTPS protocol, based on the FTP protocol, and based on the SFTP protocol, wherein the target data obtained includes data related to at least one underlying asset in the target asset pool corresponding to the asset-backed securities, and using the target data warehouse to store the target data and determine intermediate statistical data, thereby determining at least one target business indicator based on the intermediate statistical data, and then realizing the monitoring of the asset pool by presenting information related to one or more of these target business indicators, and by checking the core business indicators in a first preset period, when the core business indicators meet the preset conditions, sending corresponding warning information to the corresponding users, thereby realizing automated and customized indicator warning. The above-mentioned asset management system can process asset-related data efficiently, accurately, and automatically, and then can realize real-time, accurate, and customized asset management, so as to better meet user needs.

[0022] These and other aspects of the application will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In the following description of exemplary embodiments in conjunction with the accompanying drawings, more details, features and advantages of the technical solution of the present application are disclosed, in which:

[0024] Figure 1 Schematically illustrates example scenarios in which the technical solutions provided according to some embodiments of the present application can be applied;

[0025] Figure 2 Schematically shows an example architecture diagram of an asset management system according to some embodiments of the present application;

[0026] Figure 3 Schematically shows Figure 2 Example working principle diagram of asset management system in ;

[0027] Figure 4 Schematically shows an example architecture diagram of an asset management system according to some other embodiments of the present application;

[0028] Figure 5 Schematically shows an example architecture diagram of an asset management system according to some further embodiments of the present application;

[0029] Figure 6 Schematically shows an example architecture diagram of an asset management system according to some further embodiments of the present application;

[0030] Figure 7 Schematically shows an example architecture diagram of an asset management system according to some further embodiments of the present application;

[0031] Figure 8 Schematically shows an example architecture diagram of an asset management system according to some further embodiments of the present application;

[0032] Fig. 9 An example principle diagram of an asset management method according to some embodiments of the present application is schematically shown;

[0033] Fig.10 An example system is illustrated that includes an example computing device that is representative of one or more systems and / or devices that may implement the various techniques described herein. DETAILED DESCRIPTION

[0034] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the technical solution of the present application. The technical solution of the present application can be embodied in many different forms and purposes, and should not be limited to the embodiments described herein. These embodiments are provided to make the technical solution of the present application clear and complete, but the described embodiments do not limit the scope of protection of the present application.

[0035] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and / or this specification, and will not be interpreted in an idealized or overly formal sense, unless explicitly defined as such herein.

[0036] Before introducing the embodiments of the present application in detail, some related concepts are first explained.

[0037] 1. Big data computing technology: refers to a series of technologies and methods for processing and analyzing large-scale data sets. It not only involves data collection, storage, management, analysis and interpretation, but also includes data cleaning, preprocessing, data mining, machine learning and other processes. Big data computing technology usually uses distributed computing frameworks (for example, Hadoop, Spark) to process PB or even EB-level data, which can help users extract valuable information from massive data for use in business intelligence, market forecasting, customer behavior analysis and other fields.

[0038] 2. Asset-backed securities (ABS): refers to a financial instrument that is based on assets that can generate cash flow, such as home mortgages, car loans, credit card receivables, etc., and is combined in a structured manner. The issuer of ABS packages these assets into an asset pool and issues securities to investors based on the cash flow generated by this asset pool. This type of security can be divided into different levels (or "layers"), each with different risk and return characteristics to meet the needs of investors with different risk preferences. ABS is an important part of the financial market, which increases market liquidity and provides opportunities to diversify risks.

[0039] 3. Investment Bank: referred to as "investment bank", refers to a type of financial institution corresponding to commercial banks. It is a non-bank financial institution mainly engaged in securities issuance, underwriting, trading, corporate restructuring, mergers and acquisitions, investment analysis, venture capital, project financing and other businesses. It is the main financial intermediary in the capital market.

[0040] 4. Data warehouse: refers to a centralized storage system designed to support enterprise decision-making, data analysis and reporting needs. Data warehouses usually contain cleaned and transformed data to ensure data quality and consistency, and optimize query performance so that users can quickly obtain the information they need.

[0041] 5. Operational Data Store (ODS): refers to a layer in the data warehouse architecture, usually used as a data integration area to store raw data from multiple data sources. This data may not be cleaned or converted and remains in its original state.

[0042] 6. Analytics Data Store (ADS): refers to a layer in the data warehouse architecture that is specifically used to store data that has been processed through the extraction, conversion, and loading processes. These data have been cleaned, integrated, and aggregated to be suitable for complex analysis and decision-making.

[0043] 7. Kubernetes: K8s for short, is an open source automated container orchestration platform for automating the deployment, expansion, and management of containerized applications. Kubernetes provides a way to manage containerized applications across host clusters to ensure that these applications run as expected. It abstracts the underlying hardware resources, allowing developers to focus on the development of the application itself without having to worry about the specific infrastructure details.

[0044] Figure 1 An example scenario 100 is schematically shown in which the technical solutions provided in some embodiments of the present application can be applied. Figure 1 As shown, scenario 100 may include user 110 (e.g., investment bank employee), terminal device 120 (e.g., computer), terminal device 130 (e.g., tablet computer), network 140, and remote facility 150. As an example, remote facility 150 includes server 151 and optionally also includes database device 152 for storing asset-related data, and these servers or devices may communicate via network 140.

[0045] Exemplarily, the asset management system is deployed in a remote facility 150, specifically, it can be deployed on a server 151. The user 110 can use the terminal device 120 (on which the employee client corresponding to the asset management system is deployed) to obtain asset-related information from the remote facility 150 via the network 140. It should be noted that in the example scenario 100, the user 110 can also use the terminal device 130 to obtain asset-related information from the remote facility 150 via the network 140.

[0046] Regarding the architecture of the asset management system deployed in the remote facility 150, for example, Figure 2 As shown, the asset management system 200 may include a data management module 210 , a big data processing module 220 , an asset pool monitoring module 230 , and an indicator warning module 240 .

[0047] Exemplarily, the data management module 210 may be configured to acquire corresponding data as target data from an external first data source by calling a RESTful interface based on the HTTPS protocol, wherein the acquired target data includes data related to at least one underlying asset in a target asset pool corresponding to the asset-backed securities.

[0048] It should be noted that in this application, the term "asset" and its related terms (for example, "underlying assets") may refer to various resources formed by past transactions or events of the enterprise, owned or controlled by the enterprise, and expected to bring economic benefits to the enterprise. Assets can be divided into current assets, long-term investments, fixed assets, intangible assets and other assets according to liquidity. Among them, current assets refer to assets that can be converted into cash or consumed within 1 year or within an operating cycle of more than 1 year, including cash, bank deposits, short-term investments, receivables and prepaid payments, deferred expenses, inventory, etc. Long-term investments refer to investments other than short-term investments, including various equity investments with a holding period of more than 1 year (excluding 1 year), bonds that cannot be converted into cash or are not prepared to be converted into cash, other debt investments and other long-term investments. Fixed assets refer to houses, buildings, machines, machinery, transportation vehicles, and other equipment, appliances, tools, etc. related to production and operation that are used by the enterprise for more than 1 year. Intangible assets refer to non-monetary long-term assets without physical form that are rented to others for the production of goods or provision of services, or held for management purposes. Other assets refer to assets other than current assets, long-term investments, fixed assets, and intangible assets, such as long-term deferred expenses formed by fixed assets, repairs, and reconstruction expenses.

[0049] In addition, asset securitization refers to the process of issuing asset-backed securities (ABS) based on the future cash flow generated by the underlying assets, credit enhancement through structured design, and on this basis. Asset securitization is a form of financing that issues tradable securities supported by a specific asset portfolio or specific cash flow. Multiple underlying assets constitute the asset pool corresponding to asset-backed securities.

[0050] It should be noted that, considering the update, modification and other operations of the external data source on the data in the actual scenario, and / or in order to obtain more data, as an example, the process of obtaining external data from the external data source (for example, any one or more of the first data source, the second data source, and the third data source) can be executed regularly (for example, every hour, every two hours, every day, every week, etc.). As another example, it is also possible to automatically obtain the latest data from the external data source in response to the corresponding data update generated by the external data source. This data acquisition strategy can be determined when the data management module 210 is started. For example, a configuration file is loaded when the data management module 210 is started, which can include access information (for example, URL, user name, password, etc.) of the external data source (for example, any one or more of the first data source, the second data source, and the third data source) and a data acquisition strategy (for example, a scheduled task, a trigger condition, etc.).

[0051] As an example, the data management module 210 can automatically try to obtain historical data marked as "missing" in the asset management system 200 on or before T-1 day through a scheduled task at a certain frequency (for example, every two hours), and calculate the hash value of the existing data in the asset management system 200, and then compare it with the hash value corresponding to the data obtained from the external data source, for example, compare it with the data hash value in the asset management system 200 (if it already exists) to detect the consistency of the data, if it already exists in the asset management system 200 and the consistency test is correct, it is skipped, if it is inconsistent, it is re-stored in the asset management system 200 to overwrite the original file, if it is not in the asset management system 200 and the integrity test is correct, it is newly stored in the asset management system 200. In addition, if there is a problem with the integrity, an alarm can be issued. In addition to the automatic acquisition of data by the asset management system 200 (specifically the data management module 210) described in this example, the asset management system 200 can also be configured to support the user 110 to operate on the corresponding interactive page (for example, click the corresponding button), so that the task of data synchronization can be manually initiated and the data of the specified date and data source can be updated.

[0052] The data management module 210 can realize automatic matching of upstream system data and automatic acquisition of remote files, thereby reducing manual operations (for example, manual filling of numbers by investment bank employees and logging into FTP to obtain files) and improving work efficiency.

[0053] In addition, the big data processing module 220 can be configured to use the target data warehouse to store target data and determine intermediate statistical data, and to determine at least one target business indicator based on the intermediate statistical data, wherein the acquired target data is stored in the operational data storage layer of the target data warehouse, and the determined intermediate statistical data is stored in the analytical data storage layer of the target data warehouse, and the intermediate statistical data includes data required in the calculation process of the at least one target business indicator, and the at least one target business indicator includes an indicator related to the target asset pool or at least one underlying asset in the target asset pool.

[0054] Exemplarily, data obtained from external data sources may be categorized and sorted by type (eg, funding elements, asset elements, borrower information, repayment reconciliation elements, etc.) and date, and then loaded into the target data warehouse. An exemplary logical structure of the target data warehouse is provided below: the target data warehouse is logically divided into five layers: the ODS data access layer, in which the original data obtained from the external data source is stored; the DWD (DataWarehouse Detail) data detail layer, which performs preliminary cleaning, integration, and normalization on the data in the ODS layer and stores the corresponding result data in this layer; the DWM (DataWarehouse Middle) data middle layer, which performs light aggregation operations on the data in the DWD layer according to different dimensions (for example, borrowers, etc.) to reduce repeated processing; the DWS (DataWarehouse Summary) data service layer, which performs light aggregation operations on the data in the DWM layer to form coarse-grained data that is closer to the final result; the ADS layer, which performs fine processing on the data in the DWS layer to generate the final result data, and pushes the data in this layer to the relational database of the asset management system for the indicator display of the corresponding display page or the generation of quarterly reports, etc.

[0055] Exemplarily, the target data warehouse may be a Hive data warehouse, which is a data warehouse solution built on Hadoop, and allows users to use a language similar to SQL to query, summarize and analyze large-scale data sets stored in Hadoop, which helps investment bank employees use such data warehouses more conveniently and reduces the corresponding learning costs. The target data obtained from an external data source (e.g., any one or more of the first data source, the second data source, and the third data source) may be stored in the ODS layer of the Hive data warehouse, and may be partitioned according to date (e.g., the generation time, storage time, and other related time of the target data) or other dimensions (e.g., the source of the target data, the size of the target data, and the type of the target data) as needed, so as to facilitate subsequent management, query, and other operations. It should be noted here that the target data may undergo further data cleaning in the ODS layer, including but not limited to: standardizing fields, filling missing values, removing duplicate records, and the like. In addition, according to business needs, the target data may be subjected to necessary conversions, such as unit conversion, data format unification, and the like.

[0056] The determined intermediate statistical data is stored in the ADS layer of the Hive data warehouse, wherein the intermediate statistical data can be determined by writing a HiveQL script or by other data analysis tools (such as Spark, Presto, etc.).

[0057] Based on the intermediate statistical data determined by the big data processing module 220, the asset management system 200 can complete the real-time calculation of the data of each indicator according to different business requests. In addition, in conjunction with other modules of the asset management system 200 (see the description of the asset pool monitoring module 230 and the indicator early warning module 240 below), for specific indicators configured in the asset management system 200 (for example, certain core business indicators), periodic automatic verification and active alarms can also be achieved.

[0058] Exemplarily, the at least one target business indicator includes at least one of the following indicators: number of assets, total asset size, non-performing rate, rate of return, idle rate, 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, 90-day cumulative overdue rate, revolving purchase multiples, total outstanding principal balance, number of loans, number of borrowers, total contract amount, distribution of outstanding principal balance, age distribution of borrowers, regional distribution of borrowers, current loan interest rate distribution, loan contract term distribution, remaining loan term distribution, total asset size, cumulative revolving purchases, regional distribution, age distribution, sampling basic asset elements, unearned principal balance, asset type distribution, flow distribution, unearned principal balance change, non-performing rate change, asset weighted rate of return, annualized overdue rate, migration rate, amount distribution, interest rate distribution, and cash flow coverage.

[0059] The asset pool monitoring module 230 may be configured to present information related to one or more indicators of the at least one target business indicator. Exemplarily, the asset pool monitoring module 230 may present this information in a graphical display, so as to facilitate users to quickly understand the relevant business indicators (or their changing trends), and may support indicator comparisons between multiple special plans. Exemplarily, a user may select multiple special plans to be compared, and display the selected multiple special plans in the same line chart used to display, for example, the overdue rate, so as to facilitate and intuitively perform horizontal comparisons among different special plans.

[0060] The asset pool monitoring module 230 can be used to visualize various indicators. In addition, the user can be provided with a function of comparing data based on the same indicator for multiple asset pools, thereby meeting the user's need to evaluate the differences between assets in the asset pools, allowing the user to conveniently manage business.

[0061] For example, the asset pool monitoring module 230 can be configured to present information related to the following indicators: overdue rate for different overdue days, annualized overdue rate, migration rate, idle rate, yield, revolving purchase multiple, amount distribution, interest rate distribution, loan contract term distribution, loan remaining term distribution, regional distribution, and cash flow coverage ratio, wherein the overdue rate for different overdue days includes at least one of the following: 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, and 90-day cumulative overdue rate.

[0062] The indicator warning module 240 can be configured to check the core business indicators at a first preset period, and when the core business indicators meet the preset conditions, send corresponding warning information to the corresponding users, and the core business indicators include one or more indicators of the at least one target business indicator.

[0063] For example, the indicator warning module 240 can be configured to check at least one of the following indicators at a first preset period (for example, weekly, monthly, quarterly, semi-annual, etc.): idle rate, 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, 90-day cumulative overdue rate, and asset-weighted return rate.

[0064] In the present application, the server 151 in the remote facility 150 can be a single server or a server cluster, and the database device 152 in the remote facility 150 can store various data required by the asset management system. Exemplarily, the user 110 can access the remote facility 150 in the form of a web page through the terminal device 120 or the terminal device 130. Alternatively, the user can communicate with the remote facility 150 through the client installed on the terminal device 120 or the terminal device 130 to obtain various required data. Optionally, the server 151 can also run other applications and store other data. For example, the server 151 can include multiple virtual hosts, which are respectively used to run different applications and provide different services.

[0065] In the present application, the terminal devices 120 and 130 may be various types of devices, such as mobile phones, tablet computers, laptop computers, vehicle-mounted devices, etc. A client may be deployed on the terminal devices 120 and 130, which may be used to perform indicator-related operations (e.g., initiate an indicator query task) and optionally provide other services, and may take any of the following forms: locally installed applications, applets accessed via other applications, web programs accessed via browsers, etc. The user 110 may view the information presented by the client and perform corresponding interactive operations through the input / output interfaces of the terminal devices 120 and 130. Optionally, the terminal devices 120 and 130 may be integrated with the server 151.

[0066] In this application, the database device 152 can be regarded as an electronic file cabinet, that is, a place to store electronic files, and users can add, query, update, delete, etc. data in the files. The so-called "database" is a collection of data that is stored together in a certain way, can be shared with multiple objects, has as little redundancy as possible, and is independent of the application program.

[0067] In addition, in the present application, the network 140 can be a wired network connected via cables, optical fibers, etc., or a wireless network such as 2G, 3G, 4G, 5G, WiFi, Bluetooth, ZigBee, LiFi, etc.

[0068] Below through Figure 3 To further explain Figure 2 The working principle of the asset management system 200 shown in FIG. Figure 3 As shown, the asset management system 320 includes a data management module, a big data processing module, an asset pool monitoring module, and an indicator warning module. Figure 3 The unified figure mark "321" indicates that they cooperate with each other so that the asset management system 320 has the functions of data storage, processing, indicator calculation, indicator monitoring, indicator warning and so on.

[0069] Exemplarily, in some embodiments, the data management module may obtain target data from the external data source 321 in at least one of the following ways: obtaining corresponding data from a first data source (Source_1) by calling a RESTful interface based on the HTTPS protocol, obtaining corresponding data from a second data source (Source_2) based on the FTP protocol, and obtaining corresponding data from a third data source (Source_3) based on the SFTP protocol, wherein the obtained target data includes data related to at least one underlying asset in the target asset pool corresponding to the asset-backed securities.

[0070] The first data source (Source_1) can be any data provider that supports the RESTful interface, for example, it can be an online database or some kind of Web service. The target data can be obtained from the first data source (Source_1) by calling the interface using the Restful interface integrated in the asset management system or by calling the corresponding external Restful interface through the asset management system. Restful is a design style and development method for network applications, based on HTTP, and can be defined in XML format or JSON format. Restful is suitable for scenarios where mobile Internet manufacturers serve as business interfaces, and implements the function of third-party OTT (which is the abbreviation of "Over The Top", referring to Internet companies bypassing operators to develop various video and data service businesses based on the open Internet) to call mobile network resources.

[0071] The second data source (Source_2) can be any data storage server based on the standard FTP protocol. Figure 1 The FTP client library in the remote facility 150 shown is used to establish a connection with such a data storage server. After the connection is successful, the data management module can select the file to be downloaded (i.e., the target data) according to the preset rules based on the file list under the remote directory.

[0072] The third data source (Source_3) can be any secure file transfer server based on the SFTP protocol. The SFTP protocol is often used to transfer sensitive data, which may be particularly suitable for the investment banking field. Similar to the FTP protocol, it can be deployed on, for example, Figure 1 The SFTP client library in the remote facility 150 shown establishes a connection with the third data source (Source_3). After the connection is successful, the data management module can select the file to be downloaded (ie, the target data) according to the preset rules based on the file list in the remote directory.

[0073] It should be noted that, according to actual needs, there can be more data sources, for example, there can be multiple data providers supporting the RESTful interface. In other words, this application does not limit the number of first data sources (Source_1), the number of second data sources (Source_2), and the number of third data sources (Source_3). In addition, the target data obtained from the external data source can have a variety of file formats, including but not limited to: xlsx, csv, pdf and other file formats.

[0074] Regarding the big data processing module, for example, it can extract the corresponding data from the target data warehouse as the internal data source for layer-by-layer calculation by configuring the calculation task, and first store the final calculated result in the table of the ADS layer of the target data warehouse, and then push the result indicator data of the ADS layer to the relational database of the asset management system through the push data task. When the user uses the asset management system to view relevant data, or the asset management system monitors indicators, it triggers the real-time calculation of relevant indicators such as yield, idle rate, overdue rate, etc., and finally pushes the real-time results to the user.

[0075] Here we take the idle rate indicator as an example. Its calculation logic is: take the proportion of the sum of the business account balances on the day in the capital element table of a special plan in the issuance scale of the existing asset pool corresponding to the special plan table. The specific calculation steps and corresponding query logic are as follows:

[0076] 1. The big data processing module or other modules of the asset management system obtain source data (i.e., target data) from the Hive data warehouse. The source data includes the capital element table and the special plan table;

[0077] 2. The big data processing module executes calculation logic based on the source data obtained from the Hive data warehouse:

[0078] 2.1. First, calculate the "Total Cash": filter the records with the status of "CLO" (closed) or "OPN" (open) in the capital element table, take the data date as the latest day and add up the "Business Account Balance" of these records to get the "Total Cash";

[0079] 2.2. Then calculate the "total amount of issuance of the existing asset pool", take the corresponding data in the special plan table according to the special plan number, and obtain the "total amount of issuance of the existing asset pool" of the corresponding special plan;

[0080] 2.3. Finally, divide the "total amount of cash" by the "total amount of issuance of the existing asset pool" to obtain the idle rate of the corresponding special plan;

[0081] 3. When a user (e.g., an investment bank user) requests to view the idle rate indicator, an indicator push task is initiated to store the calculation result in the relational database of the asset management system;

[0082] 4. When the user queries or the asset management system monitors the idle rate indicator, the calculation result stored in the relational database is directly used.

[0083] It should be noted that in the above examples of the calculation steps of the idle rate indicator and the corresponding query logic, since the corresponding calculation results are stored in the relational database of the asset management system, the asset management system can respond to the user's query request more quickly. In addition, since the relational database provides a mature SQL interface, it is convenient for users to perform more complex queries and analyses. Of course, as another example, the corresponding calculation results can also be stored only in the ADS layer of the Hive data warehouse, and when the user queries or the asset management system monitors the idle rate indicator, the asset management system can obtain the corresponding calculation results by accessing the ADS layer of the Hive data warehouse. This processing method is suitable for situations where large-scale data needs to be processed and data consistency requirements are high.

[0084] Those skilled in the art understand that appropriate steps can be taken to calculate the various exemplary indicators listed above regarding the target business indicators, and the calculation steps for these indicators and the corresponding query logic (including the storage method of the corresponding intermediate statistical data) can refer to the above description of the example calculation steps for the idle rate indicator and the corresponding query logic, and will not be repeated here one by one.

[0085] In addition, although in the above examples, the target data warehouse adopts the Hive data warehouse, those skilled in the art understand that other types of data warehouses may also be adopted, including but not limited to Amazon Redshift, Google BigQuery, Snowflake, etc.

[0086] Regarding the asset pool monitoring module, illustratively, the asset pool monitoring module can be built based on ECharts (a JavaScript-based, interactive data visualization chart library) to provide users with intuitive, vivid, interactive, and customizable data visualization charts (e.g., Figure 3 ECharts provides a variety of chart types and styles, including line charts, bar charts, pie charts, scatter charts, etc., and supports dynamic updates and interactive operations. For example, pie charts can be used to show the proportion and distribution of asset types, interest rates, etc., line charts can be used to show the changing trends of yields, overdue rates, etc., and bar charts can be used to show the distribution and comparison of principal balances. In addition, regional maps can be used to show the geographical distribution of borrower dimension data, and data cards can be used to show summary data such as key management scale.

[0087] Exemplarily, the asset pool monitoring module can obtain corresponding data from the ODS layer, ADS layer of the target data warehouse, or the relational database of the asset management system. For example, Node.js or Python can be used as the backend service to pass the obtained data to the front-end asset pool monitoring module integrated with the ECharts component through the corresponding RESTful interface. In addition, in addition to ECharts, the asset pool monitoring module can be built based on other components, including but not limited to: Highcharts, Plotly.js and Chart.js.

[0088] In addition, regarding the indicator warning module, still taking the idle rate as an example, the indicator warning module can check it at a preset period (for example, 1 month, 3 months, 6 months, etc.), and each time it is checked, the big data processing module can be triggered to calculate the indicator and determine whether the calculation result meets the preset conditions. The preset conditions here can be flexibly set as needed, and different indicators can have different preset conditions. For example, when the idle rate is greater than 50%, it can be considered to meet the preset conditions, and the corresponding warning information is sent to the corresponding user (for example, the manager of the corresponding ABS, the person in charge of the relevant department, etc.), and the way of sending the warning information can be diverse, for example, one or more of the methods such as e-mail, text message, and system internal message can be used. Alternatively, the big data processing module can automatically determine the latest indicator data according to the preset period, and store it in the target data warehouse or other storage space of the asset management system (for example, a relational database), and then the indicator warning module can access the storage space storing the latest indicator data (for example, the ADS layer of the target data warehouse) at the same preset period to obtain and check the corresponding indicator.

[0089] In addition, regarding the configuration of preset conditions, a multi-level early warning scheme can be adopted. Taking the idle rate as an example, different, stepped multiple thresholds (or threshold ranges) can be set for it, and different levels of early warning actions can be triggered according to different thresholds. Exemplarily, when the idle rate is greater than 30% and less than 40%, the corresponding user can be warned at a lower frequency (for example, weekly), when the idle rate is greater than 40% and less than 50%, the corresponding user can be warned at a higher frequency (for example, daily), and when the idle rate is greater than 50%, the corresponding user can be warned at a higher frequency (for example, every half day). As another example, according to different situations where the idle rate reaches different threshold ranges, different users can be warned (for example, employees of different levels in the same company or organization). The indicator early warning module can realize the transformation of the working mode from active management by people to management driven by the system, which greatly improves the work efficiency of business personnel and reduces asset management risks.

[0090] In some embodiments, the target data obtained is stored in the operational data storage layer of the target data warehouse in the following manner: the original file corresponding to the target data is split into multiple sub-files and then sent to the operational data storage layer of the target data warehouse in a breakpoint-resume mode, and the multiple sub-files obtained by splitting are merged into the original file in the operational data storage layer of the target data warehouse. Exemplarily, the appropriate sub-file size can be determined according to the size of the original file and the network conditions. For example, each sub-file can be set to 1MB, 5MB, 10MB, 50MB, 100MB, 500MB, etc. The naming rule of each sub-file can be similar and easy to identify, such as "original_file_part_001", "original_file_part_002", etc. After all sub-files have been successfully transferred to the operational data storage layer of the target data warehouse through the breakpoint-resume mode, they can be merged in the order of the sub-files to restore the original file. Exemplarily, a hash check (for example, based on MD5 or SHA-256) can be used to verify the consistency of the merged file with the original file. This method of splitting the original large file into multiple small files and resuming the upload (when the upload is accidentally interrupted, the upload can be continued without re-uploading) not only speeds up the file transfer speed, but also reduces the use of system memory and improves the user experience.

[0091] In some embodiments, the target data warehouse is a Hive data warehouse, and wherein the big data processing module determines the at least one target business indicator by the following steps: obtaining target data from the operational data storage layer of the Hive data warehouse; determining the intermediate statistical data based on the target data and storing it in the analytical data storage layer of the Hive data warehouse; obtaining the intermediate statistical data from the analytical data storage layer of the Hive data warehouse; and determining the at least one target business indicator based on the intermediate statistical data according to preconfigured calculation rules.

[0092] It should be noted that in this application, as defined above, the term "intermediate statistical data" includes data required in the calculation process of the at least one target business indicator. This means that the intermediate statistical data can be various data required in the calculation process of various business indicators. Figure 3The calculation logic is described in detail in the description of , wherein the "total cash amount" calculated in the above step 2.1 and the "total amount of issuance scale of the existing asset pool" calculated in the above step 2.2 can be used as intermediate statistical data and stored in the ADS layer of the Hive data warehouse. In this case, the pre-configured calculation rule is described in the above step 2.3, that is, the "total cash amount" is divided by the "total amount of issuance scale of the existing asset pool" to obtain the corresponding calculation result (i.e., the idle rate of the corresponding special plan), and the calculation result can be stored in the relational database of the asset management system, so that when the user queries or the asset management system monitors the idle rate indicator, the calculation result stored in the relational database can be directly obtained. Of course, this example is only illustrative. If a business indicator is obtained based on other business indicators (such as the idle rate mentioned here), the latter can also be used as the intermediate statistical data required in the determination process of the former. Those skilled in the art should understand that different business indicators may require different intermediate statistical data and have different calculation rules (those skilled in the art can pre-configure these calculation rules according to actual needs).

[0093] In some embodiments, the big data processing module uses a streaming processing platform to determine the at least one target business indicator, and the streaming processing platform is obtained by deploying a Flink cluster on a Kubernetes cluster, wherein the Flink cluster is used to interact with the Hive data warehouse to achieve the intake, processing and output of corresponding data, and the Kubernetes cluster is used to manage the life cycle of the Flink cluster.

[0094] Flink is a high-performance, distributed, event-driven stream processing engine, which is particularly suitable for real-time data processing and analysis in this application, with advantages such as low latency, high throughput, state management and fault tolerance. Combined with the advantages of Kubernetes' automation, elastic scaling and resource management, the life cycle of the Flink cluster is managed, and the scale of the Flink cluster can be dynamically adjusted according to the load, the version of the Flink cluster can be smoothly updated, and the failed Flink task can be automatically restarted. Exemplarily, Flink's Hive connector can be used to read data from the Hive data warehouse (e.g., its ODS layer), and then use Flink's stream processing capabilities for real-time calculation and aggregation, and write the processed results (e.g., intermediate statistical data) back to the Hive data warehouse (e.g., its ADS layer) or other storage space (e.g., relational database). By deploying the Flink cluster on the Kubernetes cluster to realize data intake, processing and output, not only the scalability and reliability of the asset management system are improved, but also powerful monitoring and logging functions can be provided to ensure the stable operation of the asset management system.

[0095] Figure 4 The following schematically shows an example architecture diagram of an asset management system 400 according to some other embodiments of the present application. For example, the asset management system 400 may be deployed in Figure 1 In the remote facility 150 shown. Figure 4 As shown, the asset management system 400 includes a data management module 410, a big data processing module 420, an asset pool monitoring module 430, an indicator warning module 440, and an asset pool reporting module 450. The data management module 410, the big data processing module 420, the asset pool monitoring module 430, and the indicator warning module 440 have the same functions as those described above. Figure 2 The same configurations as the corresponding modules in the described data management module 210, big data processing module 220, asset pool monitoring module 230 and indicator warning module 240 will not be repeated here, but the focus will be placed on the asset pool reporting module 450.

[0096] Specifically, the asset pool reporting module 450 can be configured to generate a first data report with a second preset period and a second data report with a third preset period, wherein the third preset period is greater than the second preset period, and wherein the first data report includes at least one of the following indicators related to the target asset pool: number of assets, total asset size, non-performing rate, yield, idle rate, 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, 90-day cumulative overdue rate; the second data report includes at least one of the following indicators related to the target asset pool: total outstanding principal balance, number of loans, number of borrowers, total contract amount, distribution of outstanding principal balance, borrower age distribution, borrower regional distribution, current loan interest rate distribution, loan contract term distribution, and loan remaining term distribution. Exemplarily, the second preset period may be weekly, monthly, quarterly, semi-annually, etc., and the third preset period may be monthly, quarterly, semi-annually, etc. In one example, the second preset period may be weekly, and the third preset period may be quarterly, so that the asset management system can automatically generate weekly and quarterly reports periodically. Exemplarily, the first data report may be oriented to business personnel, so that they can quickly browse the basic situation of the asset pool; the second data report may be used to meet the management requirements of the regulatory unit in terms of underlying asset management.

[0097] Those skilled in the art should understand that, in addition to the exemplary indicators listed above for the first data report and the second data report, the first data report and the second data report may include other indicators. For example, when used to meet the management requirements of the regulatory unit in the underlying asset management, the second data report may include various indicators required for the underlying asset management (not limited to those listed above).

[0098] Additionally, the asset pool reporting module 450 can be further configured to provide report writing services. Specifically, the asset pool reporting module 450 can process the corresponding business indicators based on the big data processing module 420, and then call the intelligent writing tool (for example, the generative language model) to complete the document generation, and finally the business personnel will review and modify it.

[0099] Figure 5 The example architecture diagram of the asset management system 500 according to some other embodiments of the present application is schematically shown. For example, the asset management system 500 can be deployed in Figure 1 In the remote facility 150 shown. Figure 5As shown, the asset management system 500 includes a data management module 510, a big data processing module 520, an asset pool monitoring module 530, an indicator warning module 540, and a data overview module 550. The data management module 510, the big data processing module 520, the asset pool monitoring module 530, and the indicator warning module 540 have the same functions as those described above. Figure 2 The same configurations as the corresponding modules in the described data management module 210, big data processing module 220, asset pool monitoring module 230 and indicator warning module 240 will not be repeated here, but the focus will be placed on the data overview module 550.

[0100] Specifically, the data overview module 550 can be configured to present a data overview page when the asset management system 500 is accessed, and the data overview page includes target information corresponding to the user currently accessing the asset management system 500, and the target information includes at least one of the following indicators: number of assets, total asset size, cumulative cycle purchases, bad rate, idle rate, rate of return, geographical distribution, age distribution, sampling basic asset elements, unexpended principal balance, asset type distribution, flow distribution, unexpended principal balance change, bad rate change. In addition to these indicators, the data overview page can also include other indicators according to actual needs. Exemplarily, targeted indicator display (including targeted data processing) can be performed according to the special plan actually participated by the user, and multiple indicators can be displayed in a graphical manner. The display methods of different indicators can be various, including but not limited to line charts, bar charts, pie charts, scatter charts and other forms.

[0101] Figure 6 The example architecture diagram of the asset management system 600 according to some other embodiments of the present application is schematically shown. For example, the asset management system 600 can be deployed in Figure 1 In the remote facility 150 shown. Figure 6 As shown, the asset management system 600 includes a data management module 610, a big data processing module 620, an asset pool monitoring module 630, an indicator warning module 640, and a special plan management module 650. The data management module 610, the big data processing module 620, the asset pool monitoring module 630, and the indicator warning module 640 have the same functions as those described above. Figure 2 The same configurations as the corresponding modules in the described data management module 210, big data processing module 220, asset pool monitoring module 230 and indicator warning module 240 will not be repeated here, but the focus will be placed on the special plan management module 650.

[0102] Specifically, the special plan management module 650 is configured to present a form to be supplemented to a target user (e.g., an employee in charge of a special plan within an investment bank), and when the target user fills in the target information on the form to be supplemented, the indicator warning module 640 performs a corresponding warning operation based on the obtained supplemented form, wherein the form to be supplemented includes initial information obtained by the special plan management module 650 from other modules of the asset management system 600 (e.g., the data management module 610, the big data processing module 620), and wherein the target information includes at least one of the following: : basic data, layered information, calculation parameters, threshold parameters, the basic data includes at least one of the following: asset side, issuance scale, asset type, issuance date, maturity date, last base date, issuing institution, management institution, account opening bank, custodial account bank, special plan transition, the layered information includes at least one of the following: layer name, issuance scale, scale proportion, expected rate of return, the calculation parameters include at least one of the following: management fee rate, custody fee rate, benchmark default rate, benchmark idle rate, the threshold setting includes at least one of the following: idle rate, overdue rate, annualized overdue rate, rate of return. These rich information configurations can realize precise special plan management. In particular, since the target user fills in the target information including the threshold setting, the indicator warning module 640 can obtain the corresponding preset conditions, so that when the corresponding business indicator meets the preset conditions, the corresponding warning information is sent to the target user.

[0103] Figure 7 The example architecture diagram of the asset management system 700 according to some other embodiments of the present application is schematically shown. For example, the asset management system 700 can be deployed in Figure 1 In the remote facility 150 shown. Figure 7 As shown, the asset management system 700 includes a data management module 710, a big data processing module 720, an asset pool monitoring module 730, an indicator warning module 740, and a permission management module 750. The data management module 710, the big data processing module 720, the asset pool monitoring module 730, and the indicator warning module 740 have the same functions as those described above. Figure 2 The same configurations as the corresponding modules in the described data management module 210, big data processing module 220, asset pool monitoring module 230 and indicator warning module 240 are not repeated here, but the focus will be placed on the permission management module 750.

[0104] Specifically, the authority management module 750 is configured to perform authority management in the following aspects: project group authority, notification authority, and directory authority, wherein the management of the project group authority is used to isolate the business data of different projects, the management of the notification authority is used to send different types of notification messages to different users, and the management of the directory authority is used to present different directories to different users to achieve security control of the corresponding functions of the asset management system 700. Through the management of project group authority, it can be ensured that data from different projects will not interfere with each other, thereby improving data security and management efficiency. Through the management of notification authority, different types of notification messages can be sent to different users to ensure the accuracy and security of information transmission. Through the management of directory authority, the directories and resources that different users can access can be controlled to achieve fine-grained access control.

[0105] It should be noted that, unless there is any contradiction, the above reference Figure 4-Figure 7 Some of the modules described can be freely combined to provide rich extensibility for the asset management system proposed in this application, thereby making the asset management system have diversified functions. Figure 8 An example architecture diagram of an asset management system 800 according to some other embodiments of the present application is shown. The asset management system 800 includes the asset management system described above. Figure 4 The asset pool reporting module described above refers to Figure 5 The data overview module described above refers to Figure 6 The special plan management module described above and the reference Figure 7 Describes the permissions management module.

[0106] Specifically, Figure 8 As shown, the asset management system 800 includes a data management module 810, a big data processing module 820, an asset pool monitoring module 830, an indicator warning module 840, an asset pool reporting module 850, a data overview module 860, a special plan management module 870, and a permission management module 880. Among them, the data management module 810, the big data processing module 820, the asset pool monitoring module 830, and the indicator warning module 840 have the same functions as those described above. Figure 2 The configuration of the corresponding modules in the data management module 210, the big data processing module 220, the asset pool monitoring module 230 and the indicator warning module 240 described above is the same. In addition, the asset pool reporting module 850 has the same configuration as the corresponding modules in the data management module 210, the big data processing module 220, the asset pool monitoring module 230 and the indicator warning module 240 described above. Figure 4 The asset pool reporting module 450 described above has the same configuration, and the data overview module 860 has the same configuration as described above with respect to Figure 5 The data overview module 550 described above has the same configuration, and the special plan management module 870 has the same configuration as that described above. Figure 6The configuration of the special plan management module 650 described above is the same, and the rights management module 880 has the same Figure 7 The same configuration as the authority management module 750 described above will not be described one by one here. Those skilled in the art should understand that in addition to the several modules listed above, other modules can also be set for the asset management system 800 according to actual needs.

[0107] Those skilled in the art should understand that in the present application, the asset management system (for example, the asset management system 200, asset management system 400, asset management system 500, asset management system 600, asset management system 700, asset management system 800 described above with reference to the various figures) can be implemented in software, hardware, or a combination of software and hardware, and multiple different modules in the system can be implemented in the same software or hardware structure, or one module can be implemented by multiple different software or hardware structures.

[0108] In addition, the asset management system can be used to implement the following Fig. 9 Described asset management method 900. In addition, asset management method 900 can have the same features and advantages as described for the corresponding asset management system.

[0109] Fig. 9 The example principle diagram of the asset management method 900 according to some embodiments of the present application is schematically shown. The asset management method 900 is applied to an employee client (for example, deployed in Figure 1 The corresponding application on the terminal device 120 shown, or the corresponding Web page end), accordingly, the user can access the asset management system through the domain name, cloud platform, etc., the asset management system includes a data management module, a big data processing module, an asset pool monitoring module and an indicator early warning module, and the asset management method 900 includes:

[0110] Obtain an asset management request from an employee, the employee being a user with the right to use the employee client, the asset management request being used to obtain at least one of the following services provided by the asset management system: indicator review, asset pool monitoring, and indicator warning. Fig. 9 As shown, illustratively, the user's asset management request can enter the Web service node after passing through the firewall and load balancing service, where static resources will be directly returned to the user, and dynamic requests will be forwarded to various application services provided by the asset management system for dynamic determination, and the corresponding results will be returned to the user. These various application services can rely on the relevant capabilities of the service registration center, configuration management center, and authority management center provided by the basic operation service. In the actual dynamic request link, functions such as service registration and discovery, authorization & authentication, and service operation monitoring may be used.

[0111] Next, according to the asset management request, the corresponding modules among the data management module, the big data processing module, the asset pool monitoring module and the indicator early warning module of the asset management system are called to obtain the corresponding request results, wherein, when calling the data management module, the data management module is configured to obtain target data related to at least one underlying asset in the target asset pool corresponding to the asset-backed securities as at least a part of the request result; when calling the big data processing module, the big data processing module is configured to determine the corresponding data as at least a part of the request result in the following manner: using a target data warehouse to store target data and determine intermediate statistical data, and determining at least one target business indicator based on the intermediate statistical data, wherein the acquired target data is stored in the operational data storage layer of the target data warehouse, and the determined intermediate statistical data is stored in the analytical data storage layer of the target data warehouse. wherein the intermediate statistical data includes data required in the calculation process of the at least one target business indicator, and the at least one target business indicator includes an indicator related to the target asset pool or at least one underlying asset in the target asset pool; when calling the asset pool monitoring module, the asset pool monitoring module is configured to present corresponding information as at least a part of the request result in the following manner: presenting information related to one or more indicators of the at least one target business indicator corresponding to the asset management request; and when calling the indicator early warning module, the indicator early warning module is configured to send corresponding early warning information to the employee client as at least a part of the request result in the following manner: checking core business indicators with a first preset period, and sending corresponding early warning information to the employee client when the core business indicators meet the preset conditions, and the core business indicators include one or more indicators of the at least one target business indicator.

[0112] Exemplarily, in some embodiments, the data management module is configured to obtain corresponding data as at least a part of the request result in at least one of the following ways: obtaining corresponding target data from a first data source based on the HTTPS protocol by calling a RESTful interface, obtaining corresponding target data from a second data source based on the FTP protocol, and obtaining corresponding target data from a third data source based on the SFTP protocol.

[0113] In particular, to quickly respond to asset management requests from employees, at least one of these modules can use Redis as a distributed cache to speed up the response to asset management requests. In addition, XXL-JOB can be used to synchronize source data, calculate indicators, and write reports intelligently (for example, see the above reference Figure 4For example, you can use XXL-JOB to create multiple scheduled tasks, for example, automatically start the pre-made task execution in the asset management system according to the set time and frequency, so as to synchronize source data every two hours, calculate all data of T-1 day at 2 am every day, execute the weekly report intelligent writing task at 10 am every Sunday, and execute the threshold warning monitoring task at 9 am every day.

[0114] Fig.10 An example system is illustrated that includes an example computing device 1000 that represents one or more systems and / or devices that can implement the various techniques described herein. The computing device 1000 can be, for example, a server, a device associated with a server, a system on a chip, and / or any other suitable computing device or computing system. The asset management systems described above with reference to the various figures can each take the form of the computing device 1000. Alternatively, the asset management systems described above with reference to the various figures can be implemented as a computer program in the form of an application 1016.

[0115] like Fig.10 The illustrated example computing device 1000 includes a processing system 1011, one or more computer-readable media 1010, and one or more I / O interfaces 1013 that are communicatively coupled to each other. Although not shown, the computing device 1000 may also include a system bus or other data and command transmission system that couples the various components to each other. The system bus may include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and / or a processor or local bus utilizing any of a variety of bus architectures. Various other examples are also contemplated, such as control and data lines.

[0116] Processing system 1011 represents the functionality of performing one or more operations using hardware. Thus, processing system 1011 is illustrated as including hardware elements 1014 that may be configured as processors, functional blocks, and the like. This may include other logic devices implemented in hardware as application specific integrated circuits or formed using one or more semiconductors. Hardware elements 1014 are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, a processor may be composed of (multiple) semiconductors and / or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor executable instructions may be electronic executable instructions.

[0117] Computer readable media 1010 is illustrated as including memory / storage 1015. Memory / storage 1015 represents memory / storage capacity associated with one or more computer readable media. Memory / storage 1015 may include volatile media (such as random access memory (RAM)) and / or non-volatile media (such as read-only memory (ROM), flash memory, optical disks, magnetic disks, etc.). Memory / storage 1015 may include fixed media (e.g., RAM, ROM, fixed hard drives, etc.) and removable media (e.g., flash memory, removable hard drives, optical disks, etc.). Computer readable media 1010 may be configured in various other ways as further described below.

[0118] One or more I / O interfaces 1013 represent functionality that allows a user to input commands and information to the computing device 1000 using various input devices and optionally also allows information to be presented to the user and / or other components or devices using various output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone (e.g., for voice input), a scanner, touch functionality (e.g., a capacitive or other sensor configured to detect physical touch), a camera (e.g., that can detect motion that does not involve touch as gestures using visible or invisible wavelengths (such as infrared frequencies), etc.). Examples of output devices include a display device (e.g., a projector), a speaker, a printer, a network card, a tactile response device, etc. Thus, the computing device 1000 can be configured in various ways as further described below to support user interaction.

[0119] The computing device 1000 also includes an application 1016. The application 1016 may be, for example, a software instance of the asset management system described above with reference to the various figures, and in combination with other elements in the computing device 1000 implements the techniques described herein.

[0120] Various techniques may be described herein in the general context of software hardware elements or program modules. Generally, these modules include routines, programs, elements, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The terms "module", "function" and "component" as used herein generally represent software, firmware, hardware or a combination thereof. The features of the techniques described herein are platform-independent, meaning that these techniques can be implemented on a variety of computing platforms with a variety of processors.

[0121] An implementation of the described modules and techniques may be stored on or transmitted across some form of computer-readable media. Computer-readable media may include various media accessible by computing device 1000. By way of example and not limitation, computer-readable media may include “computer-readable storage media” and “computer-readable signal media”.

[0122] As opposed to a simple signal transmission, carrier wave, or signal itself, "computer-readable storage medium" refers to a medium and / or device capable of persistently storing information, and / or a tangible storage device. Therefore, computer-readable storage media refers to non-signal bearing media. Computer-readable storage media include hardware such as volatile and non-volatile, removable and non-removable media and / or storage devices implemented in a method or technology suitable for storing information (such as computer-readable instructions, data structures, program modules, logic elements / circuits, or other data). Examples of computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CDROM, digital versatile disk (DVD) or other optical storage device, hard disk, cassette, magnetic tape, magnetic disk storage device or other magnetic storage device, or other storage device, tangible medium or article of manufacture suitable for storing desired information and accessible by a computer.

[0123] "Computer-readable signal media" refers to signal-bearing media that is configured to send instructions to the hardware of the computing device 1000, such as via a network. Signal media typically can embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, data signal, or other transport mechanism. Signal media also include any information delivery media. The term "modulated data signal" refers to a signal that has one or more of its characteristics set or changed so as to encode information into the signal. By way of example and not limitation, communication media include wired media such as a wired network or direct connection and wireless media such as acoustic, RF, infrared, and other wireless media.

[0124] As previously described, hardware elements 1014 and computer-readable media 1010 represent instructions, modules, programmable device logic, and / or fixed device logic implemented in hardware form, which in some embodiments can be used to implement at least some aspects of the technology described herein. Hardware elements can include integrated circuits or systems on a chip, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), and other implementations in silicon or components of other hardware devices. In this context, hardware elements can be used as processing devices for executing program tasks defined by instructions, modules, and / or logic embodied by hardware elements, as well as hardware devices for storing instructions for execution, such as the computer-readable storage media described previously.

[0125] The aforementioned combination may also be used to implement the various techniques and modules described herein. Therefore, software, hardware or program modules and other program modules may be implemented as one or more instructions and / or logic embodied on some form of computer-readable storage medium and / or by one or more hardware elements 1014. The computing device 1000 may be configured to implement specific instructions and / or functions corresponding to the software and / or hardware modules. Therefore, for example, by using a computer-readable storage medium and / or hardware element 1014 of a processing system, the module may be implemented as a module executable by the computing device 1000 as software, at least in part, in hardware. Instructions and / or functions may be executable / operable by one or more articles (e.g., one or more computing devices 1000 and / or processing systems 1011) to implement the techniques, modules, and examples described herein.

[0126] In various implementations, the computing device 1000 may adopt various different configurations. For example, the computing device 1000 may be implemented as a computer-type device including a personal computer, a desktop computer, a multi-screen computer, a laptop computer, a netbook, etc. The computing device 1000 may also be implemented as a mobile device-type device including mobile devices such as a mobile phone, a tablet computer, a multi-screen computer, etc.

[0127] The techniques described herein may be supported by these various configurations of computing device 1000 and are not limited to the specific examples of the techniques described herein. Functionality may also be implemented in whole or in part on the "cloud" 1020 by using a distributed system, such as through platform 1022 as described below.

[0128] Cloud 1020 includes and / or represents a platform 1022 for resources 1024. Platform 1022 abstracts the underlying functionality of the hardware (e.g., servers) and software resources of cloud 1020. Resources 1024 may include applications and / or data that may be used when executing computer processing on a server remote from computing device 1000. Resources 1024 may also include services provided over the Internet and / or over a subscriber network such as a cellular or WiFi network.

[0129] The platform 1022 can abstract resources and functionality to connect the computing device 1000 with other computing devices. The platform 1022 can also be used to abstract the hierarchy of resources to provide a hierarchy of corresponding levels of demand encountered for resources 1024 implemented via the platform 1022. Therefore, in an interconnected device embodiment, the implementation of the functionality described herein can be distributed throughout the system 1000. For example, the functionality can be implemented partially on the computing device 1000 and through the platform 1022 that abstracts the functionality of the cloud 1020.

[0130] It should be understood that, for the sake of clarity, embodiments of the present application are described with reference to different functional units. However, it will be apparent that, without departing from the present application, the functionality of each functional unit can be implemented in a single unit, in multiple units or as a part of other functional units. For example, the functionality that is described as being performed by a single unit can be performed by multiple different units. Therefore, reference to a specific functional unit is only considered as a reference to a suitable unit for providing the described functionality, rather than indicating a strict logical or physical structure or organization. Therefore, the present application can be implemented in a single unit, or can be physically and functionally distributed between different units and circuits.

[0131] It will be understood that although the first, second, third, etc. terms may be used to describe various devices, elements, parts or portions in this article, these devices, elements, parts or portions should not be limited by these terms. These terms are only used to distinguish one device, element, part or portion from another device, element, part or portion.

[0132] Although the present application has been described in conjunction with some embodiments, it is not intended to be limited to the specific form set forth herein. On the contrary, the scope of the present application is limited only by the appended claims. Additionally, although individual features may be included in different claims, these may possibly be advantageously combined, and the inclusion in different claims does not imply that the combination of features is not feasible and / or advantageous. The order of features in the claims does not imply any specific order in which the features must work. In addition, in the claims, the word "comprising" does not exclude other elements, and the term "one" or "an" does not exclude a plurality. The figure marks in the claims are provided only as clear examples and should not be interpreted as limiting the scope of the claims in any way.

[0133] It should be understood that, for the sake of clarity, embodiments of the present application are described with reference to different functional units. However, it will be apparent that, without departing from the present application, the functionality of each functional unit can be implemented in a single unit, in multiple units or as a part of other functional units. For example, the functionality that is described as being performed by a single unit can be performed by multiple different units. Therefore, reference to a specific functional unit is only considered as a reference to a suitable unit for providing the described functionality, rather than indicating a strict logical or physical structure or organization. Therefore, the present application can be implemented in a single unit, or can be physically and functionally distributed between different units and circuits.

[0134] The present application provides a computer-readable storage medium on which computer-readable instructions are stored. When the computer-readable instructions are executed, the above-mentioned asset management method is implemented.

[0135] The present application provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computing device executes the asset management method provided in the above-mentioned various optional implementations.

[0136] Variations to the disclosed embodiments will be understood and effected by those skilled in the art in practicing the claimed subject matter by studying the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0137] It should be understood that in the specific implementation of this application, data related to the asset management process is involved. When the above embodiments of this application are applied to specific products or technologies, the permission or consent of the relevant users / platforms / institutions needs to be obtained, and the collection, use and processing of relevant data needs to comply with the relevant laws, regulations and standards of the relevant countries and regions.

Claims

1. An asset management system, comprising: A data management module is configured to obtain target data related to at least one underlying asset in a target asset pool corresponding to the asset-backed securities; a big data processing module, configured to use a target data warehouse to store target data and determine intermediate statistical data, and to determine at least one target business indicator based on the intermediate statistical data, wherein the acquired target data is stored in an operational data storage layer of the target data warehouse, and the determined intermediate statistical data is stored in an analytical data storage layer of the target data warehouse, and the intermediate statistical data includes data required in the calculation process of the at least one target business indicator, and the at least one target business indicator includes an indicator related to the target asset pool or at least one underlying asset in the target asset pool; an asset pool monitoring module configured to present information related to one or more indicators of the at least one target business indicator; as well as The indicator warning module is configured to check the core business indicators at a first preset period, and when the core business indicators meet the preset conditions, send corresponding warning information to the corresponding users, and the core business indicators include one or more indicators of the at least one target business indicator.

2. The asset management system according to claim 1, wherein the acquired target data is stored in the operational data storage layer of the target data warehouse in the following manner: the original file corresponding to the target data is split into multiple sub-files and then sent to the operational data storage layer of the target data warehouse in a breakpoint-resume mode, and the multiple sub-files obtained by splitting are merged into the original file in the operational data storage layer of the target data warehouse.

3. The asset management system according to claim 1, wherein when an indicator viewing request is received, or when the asset management system uses the asset pool monitoring module to perform indicator monitoring, the asset management system uses the big data processing module to determine the at least one target business indicator and presents it to the corresponding user who made the indicator viewing request through the asset pool monitoring module.

4. The asset management system according to claim 1, wherein the target data warehouse is a Hive data warehouse, and wherein the big data processing module determines the at least one target business indicator by the following steps: Get the target data from the operational data storage layer of the Hive data warehouse; Determine the intermediate statistical data based on the target data and store it in the analytical data storage layer of the Hive data warehouse; Obtaining the intermediate statistical data from the analytical data storage layer of the Hive data warehouse; and According to a preconfigured calculation rule, the at least one target business indicator is determined based on the intermediate statistical data.

5. The asset management system according to claim 4, wherein the big data processing module uses a streaming processing platform to determine the at least one target business indicator, and the streaming processing platform is obtained by deploying a Flink cluster on a Kubernetes cluster, wherein the Flink cluster is used to interact with the Hive data warehouse to realize the intake, processing and output of corresponding data, and the Kubernetes cluster is used to manage the life cycle of the Flink cluster.

6. The asset management system according to claim 1, further comprising: An asset pool reporting module is configured to generate a first data report at a second preset period and a second data report at a third preset period, wherein the third preset period is greater than the second preset period, and wherein, The first data report includes at least one of the following indicators related to the target asset pool: number of assets, total asset size, non-performing rate, rate of return, idle rate, 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, 90-day cumulative overdue rate; The second data report includes at least one of the following indicators related to the target asset pool: total outstanding principal balance, number of loans, number of borrowers, total contract amount, distribution of outstanding principal balance, borrower age distribution, borrower regional distribution, current loan interest rate distribution, loan contract term distribution, and loan remaining term distribution.

7. The asset management system according to claim 1, further comprising: The data overview module is configured to present a data overview page when the asset management system is accessed, and the data overview page includes target information corresponding to the user currently accessing the asset management system, and the target information includes at least one of the following indicators: number of assets, total asset size, cumulative cycle purchases, bad debt rate, idle rate, rate of return, geographical distribution, age distribution, sampling basic asset elements, unearned principal balance, asset type distribution, flow distribution, unearned principal balance change, and bad debt rate change.

8. The asset management system according to claim 1, further comprising: The special plan management module is configured to present a form to be supplemented to a target user, and when the target user fills in the target information on the form to be supplemented, use the indicator early warning module to perform corresponding early warning operations according to the obtained supplemented form, wherein the form to be supplemented includes initial information obtained by the special plan management module from other modules of the asset management system, and wherein, The target information includes at least one of the following: basic data, stratified information, calculation parameters, and threshold parameters. The basic data includes at least one of the following: asset party, issuance scale, asset type, issuance date, maturity date, last benchmark date, issuing institution, management institution, opening bank, custodial account bank, and special plan transition status. The stratified information includes at least one of the following: stratification name, issuance scale, scale ratio, and expected rate of return. The calculation parameters include at least one of the following: management fee rate, custody fee rate, benchmark default rate, and benchmark idle rate. The threshold setting includes at least one of the following: idle rate, overdue rate, annualized overdue rate, and rate of return.

9. The asset management system according to claim 1, further comprising: The permission management module is configured to perform permission management in the following aspects: project group permissions, notification permissions and directory permissions, wherein the management of the project group permissions is used to isolate the business data of different projects, the management of the notification permissions is used to send different types of notification messages to different users, and the management of the directory permissions is used to present different directories to different users to achieve security control of the corresponding functions of the asset management system.

10. The asset management system according to claim 1, wherein the asset pool monitoring module is configured to present information related to the following indicators: overdue rate of different overdue days, annualized overdue rate, migration rate, idle rate, yield rate, circulation purchase multiple, amount distribution, interest rate distribution, loan contract term distribution, loan remaining term distribution, regional distribution, cash flow coverage ratio, wherein, The overdue rates for different overdue days include at least one of the following: 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, and 90-day cumulative overdue rate.

11. The asset management system according to claim 1, wherein the indicator early warning module is configured to check at least one of the following indicators in a preset cycle: idle rate, 30-day overdue rate, 60-day overdue rate, 90-day overdue rate, 30-day annualized overdue rate, 60-day annualized overdue rate, 90-day annualized overdue rate, 30-day cumulative overdue rate, 60-day cumulative overdue rate, 90-day cumulative overdue rate, and asset-weighted return rate.

12. The asset management system according to claim 1, wherein the data management module is configured to obtain target data in at least one of the following ways: obtaining corresponding data from a first data source based on an HTTPS protocol by calling a RESTful interface, obtaining corresponding data from a second data source based on an FTP protocol, and obtaining corresponding data from a third data source based on an SFTP protocol.

13. An asset management method, applied to an employee client, the employee client is used to access an asset management system, the asset management system includes a data management module, a big data processing module, an asset pool monitoring module and an indicator early warning module, the method includes: Obtaining an asset management request from an employee, the employee being a user with the use authority of the employee client, the asset management request being used to obtain at least one of the following services provided by the asset management system: indicator query, asset pool monitoring, and indicator warning; According to the asset management request, the corresponding modules in the data management module, the big data processing module, the asset pool monitoring module and the indicator warning module of the asset management system are called to obtain the corresponding request result, wherein: When calling the data management module, the data management module is configured to obtain target data related to at least one underlying asset in the target asset pool corresponding to the asset-backed securities as at least part of the request result, When calling the big data processing module, the big data processing module is configured to determine the corresponding data as at least a part of the request result in the following manner: using a target data warehouse to store target data and determine intermediate statistical data, and determining at least one target business indicator based on the intermediate statistical data, wherein the acquired target data is stored in an operational data storage layer of the target data warehouse, the determined intermediate statistical data is stored in an analytical data storage layer of the target data warehouse, the intermediate statistical data includes data required in the calculation process of the at least one target business indicator, and the at least one target business indicator includes an indicator related to the target asset pool or at least one underlying asset in the target asset pool, When calling the asset pool monitoring module, the asset pool monitoring module is configured to present corresponding information as at least a part of the request result by: presenting information related to one or more indicators of the at least one target business indicator corresponding to the asset management request, and When calling the indicator warning module, the indicator warning module is configured to send corresponding warning information to the employee client as at least a part of the request result in the following manner: checking core business indicators at a first preset period, and when the core business indicators meet preset conditions, sending corresponding warning information to the employee client, the core business indicators including one or more indicators of the at least one target business indicator.

14. A computing device comprising: a memory configured to store computer-executable instructions; A processor configured to perform the method according to claim 13 when the computer executable instructions are executed by the processor. 15 . A computer-readable storage medium storing computer-executable instructions, which, when executed, perform the method according to claim 13 .

16. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, perform the method according to claim 13.

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