A data processing method and device, electronic equipment, storage medium and product

Abstract

The application discloses a data processing method and device, electronic equipment, storage medium and product, and relates to the technical field of data processing. The data processing method comprises the following steps: acquiring a service parameter corresponding to a target service in a preset time period in a to-be-tested object; determining a stress test parameter corresponding to the target service according to the service parameter; and determining a risk stress scenario parameter corresponding to the target service according to the stress test parameter. According to the application, the service parameter corresponding to the target service in the preset time period in the to-be-tested object is acquired, and the stress test parameter corresponding to the target service is determined according to the service parameter, so that the risk stress scenario parameter corresponding to the target service can be dynamically determined according to the stress test parameter, the overall architecture of the stress test is ensured to be reasonable, and the effectiveness of the stress test is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a data processing method and device, electronic equipment, storage medium and product. BACKGROUND

[0002] Commercial banks are special financial institutions that operate risks. In daily business activities, they manage credit risks, operational risks, counterparty default risks, reputation risks, capital risks and liquidity risks through various effective means. Commercial banks should establish a liquidity risk stress testing system to analyze their ability to withstand short-term and medium- and long-term stress scenarios.

[0003] With the development of science and technology and the progress of technology, and the breaking of the data island situation, liquidity risk stress testing is no longer limited to a single bank, and regulatory agencies will also integrate and process data based on the entire banking industry or bank institutions in a certain region. How to more accurately determine the risk stress scenario parameters has become a problem to be solved at present. SUMMARY

[0004] The present application provides a data processing method, device, electronic equipment, storage medium and product to realize dynamic determination of risk stress scenario parameters, thereby improving the effectiveness of stress testing.

[0005] According to an aspect of the present application, a data processing method is provided, wherein the method comprises:

[0006] Obtaining a business parameter of a target business in a predetermined time period corresponding to the target business in a to-be-tested object;

[0007] Determining a stress testing parameter corresponding to the target business according to the business parameter;

[0008] Determining a risk stress scenario parameter corresponding to the target business according to the stress testing parameter.

[0009] According to another aspect of the present application, a data processing device is provided, wherein the method comprises:

[0010] A business parameter acquisition module for obtaining a business parameter of a target business in a predetermined time period corresponding to the target business in a to-be-tested object;

[0011] A testing parameter determination module for determining a stress testing parameter corresponding to the target business according to the business parameter;

[0012] A scenario parameter determination module for determining a risk stress scenario parameter corresponding to the target business according to the stress testing parameter.

[0013] According to another aspect of the present application, an electronic equipment is provided, which comprises:

[0014] at least one processor; and

[0015] a memory in communication with the at least one processor; wherein

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the data processing method of any of the embodiments of the present application.

[0017] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the data processing method of any of the embodiments of the present application when executed by the processor.

[0018] According to another aspect of the present application, the embodiments of the present application further provide a computer program product, which comprises a computer program, and the computer program implements the data processing method of any of the embodiments of the present application when executed by a processor.

[0019] The technical solution of the embodiments of the present application acquires the service parameters of the target service in the to-be-tested object within a preset time length, determines the stress test parameters corresponding to the target service according to the service parameters, determines the risk stress scenario parameters corresponding to the target service according to the stress test parameters, realizes the determination of the stress test parameters according to the service parameters of the to-be-tested object, dynamically determines the risk stress scenario parameters corresponding to the target service according to the stress test parameters, guarantees the rationality of the overall architecture of the stress test, and further improves the effectiveness of the stress test.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is a flowchart of a data processing method according to an embodiment of the present application;

[0023] Figure 2 is a flowchart of another data processing method according to an embodiment of the present application;

[0024] Figure 3is a flow chart of still another data processing method according to an embodiment of the present application;

[0025] Figure 4 is a flow chart of still another data processing method according to an embodiment of the present application;

[0026] Figure 5 is a flow chart of still another data processing method according to an embodiment of the present application;

[0027] Figure 6 is a structural schematic diagram of a data processing apparatus according to an embodiment of the present application;

[0028] Figure 7 is a structural schematic diagram of an electronic device implementing the data processing method according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the personnel in the technical field better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", "third", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] The acquisition, storage, use, processing and the like of data in the technical solutions of the present application all comply with the relevant provisions of national laws and regulations.

[0032] In an embodiment, Figure 1 is a flow chart of a data processing method according to an embodiment of the present application, which can be applicable to dynamically determining the risk pressure scenario parameters of a target service. The method can be executed by a data processing apparatus, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. For example,Figure 1 The method includes:

[0033] S110, acquiring a service parameter corresponding to a target service in a preset time period in a to-be-tested object.

[0034] The to-be-tested object can be a financial institution that needs to be detected, and the financial institution can include but is not limited to a bank. The target service can be a target extracted service type, and examples of the target service can include but are not limited to a loan service, a deposit service, a bond service, and the like. The preset time period can be a time period set in advance for collecting service parameters, and according to the preset time period corresponding to the target service, the service parameters in the preset time range can be collected. The preset time period can be a time set by a user according to a requirement, and examples of the preset time period can be 7 days, 30 days, 1 year, 2 years, and the like. The service parameter can be a service parameter corresponding to the target service, and for different types of services, the corresponding service parameters can be different. In an embodiment, for a loan service, the service parameter can include but is not limited to a repayment amount, a loan status, an amount to be repaid, and the like. For a deposit service, the service parameter can include but is not limited to a deposit amount, a deposit period, a withdrawal time, and the like. For a bond service, the service parameter can include but is not limited to a transaction direction, an expiration date, a bond type, and the like.

[0035] In an embodiment, the target service of the to-be-tested object can be found, and the service parameter corresponding to the target service can be acquired. The service parameter corresponding to the target service can be stored in a data table or a database. The data table can include detailed data representing the to-be-tested object and index summary data representing the to-be-tested object. Examples of the detailed data representing the to-be-tested object can include but are not limited to EAST data, and examples of the index summary data representing the to-be-tested object can include but are not limited to 1104 data. Generally, the EAST data is a set of detailed data messages reported by a banking institution to the State Administration for Market Regulation, including customer, credit, inter-bank, and account categories, and the corresponding data table can be queried in the EAST data according to the service type of the target service. The 1104 data is a set of report systems reported by a banking institution to the State Administration for Market Regulation, including financial, risk, capital, and liquidity data. In actual operation, the data table corresponding to the target object or the corresponding field of the target object in the database can be acquired, the service information related to the target object can be extracted, and the service parameter corresponding to the target service in the preset time period can be acquired. According to different target services, the service parameter corresponding to different preset times can be acquired.

[0036] S120, determining a stress test parameter corresponding to the target service according to the service parameter.

[0037] The stress test parameters can be determined according to the business parameters corresponding to the target business. According to a preset time length, a plurality of stress test parameters can be determined. In an embodiment, when the target business is a loan business, the stress test parameters can include, but are not limited to, a loan default rate and a loan rollover rate. When the target business is a deposit business, the stress test parameters can include, but are not limited to, a fixed deposit withdrawal rate, a deposit rollover rate, and the like. When the target business is a bond business, the stress test parameters can include, but are not limited to, a bond default rate, a bond discount rate, and the like. When the target business is other business, the stress test parameters can further include an interbank business default rate, a newly issued interbank deposit ratio, and a central bank deposit reserve repayment, and the like.

[0038] In an embodiment, different business parameters can determine different stress test parameters corresponding to different target businesses, and the determination of the stress test parameters corresponding to different target businesses can adopt different manners. In actual operation, the stress test parameters can be determined according to the business characteristics of different target businesses. In a preset time length, a plurality of business parameters can be obtained, and a plurality of stress test parameters corresponding to the target business in a prediction time length can be determined according to the business parameters.

[0039] In an embodiment, different business parameters can determine different stress test parameters corresponding to different target businesses, and the determination of the stress test parameters corresponding to different target businesses can adopt different manners. In actual operation, the stress test parameters can be determined according to the business characteristics of different target businesses. In a preset time length, a plurality of business parameters can be obtained, and a plurality of stress test parameters corresponding to the target business in a prediction time length can be determined according to the business parameters.

[0040] The risk stress scenario parameters can be parameters used for risk stress scenario testing, and different stress test parameters can correspond to different risk stress scenario parameters of the target business. The risk stress scenario parameters can include a mild risk stress scenario parameter, a moderate risk stress scenario parameter, and a severe risk stress scenario parameter. In an embodiment, the risk stress parameters can include, but are not limited to, a loan default rate, a loan rollover rate, a fixed deposit withdrawal rate, a deposit rollover rate, a bond default rate, an interbank business default rate, a newly issued interbank deposit ratio, a central bank deposit reserve repayment, and a bond discount rate.

[0041] In an embodiment, after obtaining the stress test parameters, the risk stress scenario parameters of the corresponding target service can be determined according to the stress scenario parameters. In actual operation, the stress test parameters obtained within the preset time period can be sorted, and one stress test parameter in the sorted stress scenario parameters can be selected as the risk stress scenario parameter. The sorting manner can not be limited, and for example, it can include a small-to-large order or a large-to-small order. In an embodiment, when the stress test parameters are arranged in a small-to-large order, the fourth quartile in the large-value direction can be taken as the risk stress scenario parameter, and when the stress test parameters are arranged in a large-to-small order, the fourth quartile in the small-value direction can be taken as the risk stress scenario parameter. In an embodiment, the risk stress scenario parameter selected according to the sorting can be used as the mild risk stress scenario parameter of the target service, and accordingly, the moderate risk stress scenario parameter and the severe risk stress scenario parameter of the target service can be determined according to the mild risk stress scenario parameter multiplied by a preset value. For example, the preset value can include 2, 4, 6, etc. In an embodiment, the double value of the mild risk stress scenario parameter can be taken as the moderate risk stress scenario parameter, and the four times value of the mild risk stress scenario parameter can be taken as the severe risk stress scenario parameter.

[0042] In the embodiment of the application, by obtaining the service parameters of the target service in the preset time period of the to-be-tested object, the stress test parameters corresponding to the target service can be determined according to the service parameters, and the risk stress scenario parameters of the corresponding target service can be determined according to the stress test parameters, so as to realize the determination of the stress test parameters according to the service parameters of the to-be-tested object, the dynamic determination of the risk stress scenario parameters of the corresponding target service according to the stress test parameters, the rationality of the overall architecture of the stress test, and the improvement of the effectiveness of the stress test.

[0043] In an embodiment, Figure 2 is a flowchart of another data processing method according to an embodiment of the application. The embodiment is a further description of the data processing method based on the above-mentioned embodiment, as shown in Figure 2 The data processing method further includes:

[0044] S210, obtaining service parameters of a target service in a preset time period of a to-be-tested object.

[0045] S220, determining stress test parameters corresponding to the target service according to the service parameters.

[0046] S230, determining risk stress scenario parameters of the corresponding target service according to the stress test parameters.

[0047] S240, obtaining a funding gap of the to-be-tested object.

[0048] The original maturity funding gap can refer to the original maturity funding gap. When the gap is positive, it means that the assets of the subject subject to the test are sufficient to repay the debts due within the maturity period. When the gap is negative, it means that the assets of the subject subject to the test are insufficient to repay the debts due within the maturity period, and funds need to be raised in other ways to repay the debts due.

[0049] In this embodiment, the original maturity funding gap of the target object can be obtained through a data table. In actual operation, the corresponding field of the original maturity funding gap can be found in the data table, and the original maturity funding gap corresponding to the target object can be extracted from the data table. It should be noted that the data table storing the original maturity funding gap may include, but is not limited to, G21. Generally speaking, G21 is a report describing liquidity gaps in the 1104 reporting system. 1104 is a set of reporting systems submitted by banking institutions to the State Financial Supervision and Administration Bureau, including financial, risk, capital, and liquidity data, etc. G21 belongs to the 1104 liquidity data report. In actual operation, when the report storing the original maturity funding gap is G21, the field information corresponding to the target object can be found in G21, the data information related to the target object can be extracted, and then the corresponding field of the original maturity funding gap can be found in the extracted data to extract the original maturity funding gap corresponding to the target object. In one embodiment, the original maturity funding gap can be divided into periods such as "next day," "2 to 7 days," "8 to 30 days," and "31 to 90 days." The total original maturity funding gap within the specified period can be evenly distributed according to the dates to determine the daily original maturity funding gap for the target object.

[0050] S250. Determine the cash inflow, cash outflow, and risk mitigation cash inflow of the target entity based on risk stress scenario parameters and business parameters.

[0051] In this context, "fund inflow" refers to the inflow of funds into the entity being tested, while "fund outflow" refers to the outflow of funds from the entity. Risk mitigation is the process by which the entity actively seeks funds from the market and passively returns funds when facing liquidity shortages, funding gaps, and declining deposit volumes. Risk mitigation fund inflows can refer to the inflows of funds by the entity through risk control measures to reduce the impact of risk. In practice, fund inflows may include, but are not limited to, the renewal of time deposits and the issuance of new interbank certificates of deposit; fund outflows may include, but are not limited to, loan defaults, loan renewals, early withdrawal of time deposits, bond defaults, and interbank business defaults; and risk mitigation fund inflows may include, but are not limited to, bond sales and the return of required reserves held at the central bank.

[0052] In an embodiment, the different risk stress scenario parameters can represent different risk stress scenario parameters of different fund flow situations, exemplary, the risk stress scenario parameters can include risk stress scenario parameters for characterizing fund inflow, risk stress scenario parameters for characterizing fund outflow, and risk stress scenario parameters for characterizing risk release process, and the fund inflow situation, the fund outflow situation, and the risk release fund inflow situation of the to-be-tested object are determined according to the different risk stress scenario parameters and the business parameters. The risk stress scenario parameters can be divided into risk stress scenario parameters for characterizing fund inflow, risk stress scenario parameters for characterizing fund outflow, and risk stress scenario parameters for characterizing risk release process according to different scenarios. The fund inflow situation of the to-be-tested object is determined by the risk stress scenario parameters for characterizing fund inflow and the corresponding business parameters; the fund outflow situation of the to-be-tested object is determined by the risk stress scenario parameters for characterizing fund outflow and the corresponding business parameters; and the risk release fund inflow situation of the to-be-tested object is determined by the risk stress scenario parameters for characterizing risk release fund inflow and the corresponding business parameters.

[0053] In actual operation, the corresponding fund outflow situation can be determined according to each risk stress scenario parameter for characterizing fund outflow and the corresponding business parameter, and the fund outflow situation of the to-be-tested object is obtained by adding the fund outflow situations determined by each risk stress scenario parameter for characterizing fund outflow. In an embodiment, the risk stress scenario parameters for characterizing fund outflow can include, but are not limited to, loan default rate, loan refinancing rate, fixed deposit withdrawal rate, bond default rate, interbank business default rate, etc.; the risk stress scenario parameters for characterizing fund inflow can include, but are not limited to, fixed deposit renewal rate and newly issued interbank deposit bill ratio; and the risk stress scenario parameters for characterizing risk release fund inflow can include, but are not limited to, central bank deposit reserve repayment and bond sale, etc. Correspondingly, the corresponding fund inflow situation can be determined according to each risk stress scenario parameter for characterizing fund inflow and the corresponding business parameter, and the fund inflow situation of the to-be-tested object is obtained by adding the fund inflow situations determined by each risk stress scenario parameter for characterizing fund outflow. And the corresponding risk release fund inflow situation can be determined according to each risk stress scenario parameter for characterizing risk release fund inflow and the corresponding business parameter, and the risk release fund inflow situation of the to-be-tested object is obtained by adding the risk release fund inflow situations determined by each risk stress scenario parameter for characterizing risk release fund inflow.

[0054] S260, determining the shortest survival period of the to-be-tested object according to the fund inflow situation, the fund outflow situation, the risk release fund inflow situation, and the original maturity fund gap.

[0055] The shortest survival period can refer to the minimum date at which the funding gap after risk mitigation becomes negative, and can be considered as the time period corresponding to the first negative cumulative gap after risk mitigation within a preset time period, indicating the number of days that the object under test can survive under a stress scenario. The shortest survival period can refer to the shortest survival period within a preset time period, which can be set according to the needs of business personnel. For example, it can include 30 days, 90 days, 1 year, etc. That is, the shortest survival period within a time period of 30 days, 90 days, 1 year, etc. can be determined according to the needs of business personnel.

[0056] In an embodiment, the cumulative gap after risk mitigation can be determined according to the funding inflow, the funding outflow, the risk mitigation funding inflow, and the original maturity funding gap. The shortest survival period of the object under test can be determined according to the cumulative gap after risk mitigation. In actual operation, the cumulative period gap can be determined by the original maturity funding gap, the funding inflow, and the funding outflow. The cumulative gap after risk mitigation can be determined according to the cumulative period gap and the risk mitigation funding inflow. The shortest survival period of the object under test can be determined according to the cumulative gap after risk mitigation. The cumulative period gap of the first day can be obtained by adding the original maturity funding gap, the funding inflow, and the funding outflow. The cumulative period gap of the second day can be obtained by adding the original maturity funding gap, the funding inflow, the funding outflow, and the cumulative period gap of the previous day. For example, when the original maturity funding gap of the first day is 200 million, the funding inflow is 100 million, and the funding outflow is 200 million, the cumulative period gap of the first day is 2+1-2=1 million. When the original maturity funding gap of the second day is 200 million, the funding inflow is 300 million, and the funding outflow is 500 million, the cumulative period gap of the second day is 2+3-5+1=1 million. Accordingly, the cumulative period gap of each day within the preset time period can be calculated in this way. After determining the cumulative period gap, the shortest survival period of the object under test can be determined according to the cumulative period gap and the risk mitigation funding inflow. The cumulative period gap plus the risk mitigation funding inflow of each day can be determined. When the cumulative period gap plus the risk mitigation funding inflow is negative, the day corresponding to the negative value is determined as the shortest survival period.

[0057] In the embodiment of the present application, the original maturity funding gap of the object under test is obtained. The funding inflow, the funding outflow, and the risk mitigation funding inflow of the object under test are determined according to the risk stress scenario parameters and the business parameters. The shortest survival period of the object under test is determined according to the funding inflow, the funding outflow, the risk mitigation funding inflow, and the original maturity funding gap. The determination of the shortest survival period of the object under test is realized. The operating strategy of the object under test can be adjusted according to the shortest survival period.

[0058] In an embodiment, Figure 3is a flowchart of another data processing method according to an embodiment of the present application, which is a further description of the data processing method based on the above-mentioned embodiment, as shown in Figure 3 The data processing method further comprises:

[0059] S301, obtaining a service parameter in a preset time period corresponding to a target service in a to-be-tested object.

[0060] S302, determining a stress test parameter corresponding to the target service according to the service parameter.

[0061] S303, determining a risk stress scenario parameter corresponding to the target service according to the stress test parameter.

[0062] S304, obtaining a fund gap of the to-be-tested object.

[0063] S305, identifying and extracting, from the risk stress scenario parameter, a risk stress scenario parameter for representing a fund inflow, a risk stress scenario parameter for representing a fund outflow, and a risk stress scenario parameter for representing a risk release process, as a first type risk stress scenario parameter set, a second type risk stress scenario parameter set, and a third type risk stress scenario parameter set.

[0064] The first type risk stress scenario parameter set can be a set of risk stress scenario parameters for representing a fund inflow; the second risk stress scenario parameter set can be a set of risk stress scenario parameters for representing a fund outflow; and the third risk stress scenario parameter set can be a set of risk stress scenario parameters for representing a risk release process.

[0065] In an embodiment, each risk stress scenario parameter can be identified from the risk stress scenario parameter, and the risk stress scenario parameter for representing a fund inflow can be extracted as the first type risk stress scenario parameter set, the risk stress scenario parameter for representing a fund outflow can be extracted as the second type risk stress scenario parameter set, and the risk stress scenario parameter for representing a risk release process can be extracted as the third type risk stress scenario parameter set. In actual operation, the meaning represented by each risk stress scenario parameter can be identified. For example, the risk stress scenario parameter for representing a fund outflow can include, but is not limited to, a loan default rate, a loan refinancing rate, a fixed deposit withdrawal rate, a bond default rate, and an interbank business default rate; the risk stress scenario parameter for representing a fund outflow can include, but is not limited to, a fixed deposit renewal rate and a new issuance of interbank deposit bill ratio; and the risk stress scenario parameter for representing a risk release process can include, but is not limited to, a return of a deposit reserve in the central bank.

[0066] S306, determine the fund inflow of the to-be-tested object according to each first-type risk pressure scenario parameter in the first-type risk pressure scenario parameter set and the corresponding business parameter, and determine the fund outflow of the to-be-tested object according to each second-type risk pressure scenario parameter in the second-type risk pressure scenario parameter set and the corresponding business parameter.

[0067] In an embodiment, each first-type risk pressure scenario parameter can be acquired respectively with the corresponding business parameter to determine the fund inflow of the to-be-tested object. Each second-type risk pressure scenario parameter can be acquired respectively with the corresponding business parameter to determine the fund outflow of the to-be-tested object. Each first-type risk pressure scenario parameter can be brought into a preset formula to determine the fund inflow corresponding to each risk pressure scenario parameter, and the sum of the fund inflows determined by all the first-type risk pressure scenario parameters can be taken as the fund inflow of the to-be-tested object. Each second-type risk pressure scenario parameter can be brought into a preset formula to determine the fund outflow corresponding to each risk pressure scenario parameter, and the sum of the fund outflows determined by all the second-type risk pressure scenario parameters can be taken as the fund outflow of the to-be-tested object. In an embodiment, the business parameter can be divided into “next day”, “2-7 days”, “8-30 days”, “31-90 days” and the like according to the term, the business parameter in the term can be determined, and the single-day fund inflow and fund outflow can be determined according to the business parameter and the corresponding risk pressure scenario parameter.

[0068] S307, determine the risk relief fund inflow of the to-be-tested object according to each third-type risk pressure scenario parameter in the third-type risk pressure scenario parameter set and the corresponding business parameter.

[0069] In an embodiment, each third-type risk pressure scenario parameter can be acquired respectively with the corresponding business parameter to determine the fund inflow of the to-be-tested object. In actual operation, the risk relief fund inflow of the to-be-tested object can be determined according to the preset formula according to the acquired business parameter. In actual operation, the business parameter in 30 days can be acquired to determine the risk relief fund inflow of the to-be-tested object in 30 days, and the single-day risk relief fund inflow of the to-be-tested object can be determined according to the equal division of the risk relief fund inflow.

[0070] S308, determine the cumulative term fund gap of the to-be-tested object according to the original due term fund gap, the fund inflow and the fund outflow.

[0071] The cumulative term fund gap can be the cumulative term fund gap, which can be obtained by adding the original due term fund gap, the fund inflow, subtracting the fund outflow and adding the cumulative term fund gap of the previous day.

[0072] In an embodiment, the cumulative term funding gap of the to-be-tested object can be determined according to the original term funding gap, the funding inflow and the funding outflow. In actual operation, the cumulative term funding gap on the first day can be obtained by adding the original term funding gap, the funding inflow and subtracting the funding outflow; the cumulative term funding gap on the second day can be obtained by adding the original term funding gap, the funding inflow and subtracting the funding outflow and the cumulative term funding gap on the previous day. For example, when the original term funding gap on the first day is 200 million, the funding inflow is 100 million and the funding outflow is 200 million, the cumulative term funding gap on the first day is 200+100-200=100 million; when the original term funding gap on the second day is 200 million, the funding inflow is 300 million and the funding outflow is 500 million, the cumulative term funding gap on the second day is 200+300-500+100=100 million. Correspondingly, the cumulative term funding gap on each day in the preset time period of the shortest survival period can be calculated in this way.

[0073] S309, determining the risk-relief cumulative funding gap of the to-be-tested object according to the cumulative term funding gap and the risk-relief funding inflow.

[0074] The risk-relief funding inflow can refer to the cumulative funding gap obtained by adding the risk-relief funding inflow.

[0075] In an embodiment, the risk-relief cumulative funding gap of the to-be-tested object can be determined according to the cumulative term funding gap and the risk-relief funding inflow. The risk-relief cumulative funding gap of the to-be-tested object can be determined by adding the cumulative term funding gap and the risk-relief funding inflow.

[0076] S310, searching for the date corresponding to the first negative number of the risk-relief cumulative funding gap in the preset time period as the shortest survival period of the to-be-tested object.

[0077] In an embodiment, after determining the risk-relief cumulative funding gap of the to-be-tested object, the shortest survival period of the to-be-tested object can be determined according to the risk-relief cumulative funding gap of the to-be-tested object. The value of the risk-relief cumulative funding gap in the preset market can be determined, and when the risk-relief cumulative funding gap first appears negative, the corresponding date can be determined as the shortest survival period of the to-be-tested object.

[0078] According to the embodiment of the present application, the service parameter in the preset time period corresponding to the target service of the to-be-tested object is acquired, the stress test parameter corresponding to the target service is determined according to the service parameter, the risk stress scenario parameter corresponding to the target service is determined according to the stress test parameter, the original due date fund gap of the to-be-tested object is acquired, the risk stress scenario parameter for representing the fund inflow, the risk stress scenario parameter for representing the fund outflow and the risk stress scenario parameter for representing the risk release process are identified and extracted from the risk stress scenario parameter as the first type risk stress scenario parameter set, the second type risk stress scenario parameter set and the third type risk stress scenario parameter set, the fund inflow condition of the to-be-tested object is determined according to each first type risk stress scenario parameter in the first type risk stress scenario parameter set and the corresponding service parameter; the fund outflow condition of the to-be-tested object is determined according to each second type risk stress scenario parameter in the second type risk stress scenario parameter set and the corresponding service parameter, the risk release fund inflow condition of the to-be-tested object is determined according to each third type risk stress scenario parameter in the third type risk stress scenario parameter set and the corresponding service parameter, the cumulative due date fund gap of the to-be-tested object is determined according to the original due date fund gap, the fund inflow condition and the fund outflow condition, the risk release cumulative fund gap of the to-be-tested object is determined according to the cumulative due date fund gap and the risk release fund inflow condition, the date corresponding to the first negative number of the risk release cumulative fund gap in the preset time period is searched as the shortest survival period of the to-be-tested object, the accurate prediction of the shortest survival period of the to-be-tested object is realized, and then the strategy of the to-be-tested object can be adjusted according to the shortest survival period, and the potential liquidity risk is reduced.

[0079] In an embodiment, the risk stress scenario of the to-be-tested object includes one of the following: mild risk stress scenario; moderate risk stress scenario; severe risk stress scenario; the data processing method further includes:

[0080] The survival period threshold of each risk stress scenario corresponding to the to-be-tested object is preconfigured;

[0081] The stress test result of the to-be-tested object is determined according to the shortest survival period and the survival period threshold under the corresponding risk stress scenario.

[0082] The survival period threshold can be a critical value for determining whether the survival period of the risk stress scenario passes the test. The survival period threshold can be set by the business personnel according to the needs, and the survival period threshold can be different for different risk stress scenarios. In an embodiment, the survival period threshold of the mild risk stress scenario and the moderate risk stress scenario can include, but is not limited to, 30 days, and more than 30 days is considered to be qualified, and less than 30 days is considered to be unqualified. The survival period threshold of the severe risk stress scenario can include, but is not limited to, 20 days, that is, more than 20 days is considered to be qualified in the severe risk stress scenario, and less than 20 days is considered to be unqualified. The minimum survival period can refer to the minimum date when the cumulative gap becomes negative after the risk release, indicating the number of days that the subject can survive under stress.

[0083] In an embodiment, the survival period threshold of the subject under the mild risk stress scenario, the moderate risk stress scenario, and the severe risk stress scenario can be pre-configured, and the stress test result of the subject can be determined according to the minimum survival period and the survival period threshold under the corresponding risk stress scenario. In actual operation, the survival period threshold of the mild risk stress scenario and the moderate risk stress scenario can be configured as 30 days, and the survival period threshold under the severe risk stress scenario can be configured as 20 days. Comparing the minimum survival period with the survival period threshold under the corresponding risk stress scenario, when the minimum survival period exceeds the survival period threshold under the corresponding risk stress scenario, it can be determined that the stress test result of the subject passes, and when the minimum survival period does not exceed the survival period threshold under the corresponding risk stress scenario, it can be determined that the stress test result of the subject fails.

[0084] In an embodiment, the data processing method further comprises:

[0085] The stress test result of the subject is reported to the financial management platform, so that the financial management platform adjusts the management strategy of the subject according to the stress test result.

[0086] The financial management platform can refer to a platform for managing financial units, and the financial management platform can adjust the management strategy of the subject according to the stress test result.

[0087] In an embodiment, the stress test result of the subject can be reported to the financial management platform, and the financial management platform can adjust the management strategy of the subject according to the stress test result. In actual operation, when the test result is passed, the financial management platform can not adjust the management strategy of the subject; when the test result is failed, the financial management platform can adjust the management strategy of the subject according to the stress test result.

[0088] In an embodiment, Figure 4is a flowchart of still another data processing method according to an embodiment of the present application, which is a further description of a data processing method based on the above-mentioned embodiments. As shown in Figure 4 The method comprises the following steps.

[0089] S410, acquiring a first type data table and a second type data table corresponding to the to-be-tested object; the first type data table is used to represent detailed data of the to-be-tested object; and the second type data table is used to represent index summary data of the to-be-tested object.

[0090] The first type data table can include but is not limited to an EAST data table, and the second type data table can include but is not limited to a 1104 data table.

[0091] In an embodiment, after the to-be-tested object completes a job service, a report corresponding to the to-be-tested object can be generated and reported to a regulatory unit. The first type data table and the second type data table corresponding to the to-be-tested object can be acquired at the regulatory unit or the to-be-tested object. In actual operation, the corresponding fields of the to-be-tested object can be searched at the regulatory unit or the to-be-tested object, and the first type data table and the second type data table corresponding to the to-be-tested object can be acquired.

[0092] S420, identifying and extracting a service parameter of a target service within a preset time length in the first type data table and / or the second type data table.

[0093] In an embodiment, the service parameter of the target service within the preset time length can be extracted in the first type data table and the second type data table respectively. For different target services, the corresponding service parameters can be stored in the first type data table and / or the second type data table. The corresponding fields of the service data can be extracted in the first type data table and the second type data table respectively, and the service parameter of the target service within the preset time length can be acquired.

[0094] S430, acquiring a parameter statistical strategy corresponding to the target service; the parameter statistical strategy corresponds to the target service in a one-to-one manner.

[0095] The parameter statistical strategy can be a strategy for statistical service parameters. The parameter statistical strategy can be determined according to the service type of the target service. The parameter statistical strategy can correspond to the target service in a one-to-one manner, that is, each target service contains a corresponding parameter statistical strategy.

[0096] In an embodiment, the parameter statistical strategy can be pre-set, and the pre-set parameter statistical strategy corresponding to the target service can be searched at the electronic device. In an embodiment, each parameter statistical strategy can correspond to an identifier of a target service. When the stress test parameter of the target service is determined, the parameter statistical strategy can be extracted according to the identifier of the target service.

[0097] S440, determining the stress test parameter of the target service according to the parameter statistical strategy and the service parameter.

[0098] In an embodiment, when the reference statistical strategy and the service parameter are obtained, the stress test parameter of the target service can be determined according to the parameter statistical strategy and the service parameter. In actual operation, the service parameter can be calculated according to the corresponding parameter statistical strategy to determine the stress test parameter of the target service. The plurality of service parameters obtained within the preset time period can correspond to the stress test parameters of a plurality of target services.

[0099] S450, sorting the stress test parameters within the preset time period according to the sorting algorithm to obtain the sorted stress test parameters.

[0100] The sorting algorithm can include increasing sorting and decreasing sorting, that is, arranging in descending order or ascending order according to size.

[0101] In an embodiment, the stress test parameters within the preset time period can include a plurality of stress test parameters, which can be sorted according to the sorting algorithm to obtain the sorted stress test parameters. In actual operation, the stress test parameters within the preset time period can be sorted in the order of small to large, or in the order of large to small to obtain the sorted stress test parameters.

[0102] S460, selecting one stress test parameter from the sorted stress test parameters as the risk stress scenario parameter of the target service according to the preset selection algorithm.

[0103] In an embodiment, any stress test parameter can be selected from the sorted stress test parameters as the risk stress scenario parameter of the target service, or a fixed position stress test parameter can be selected as the risk stress scenario parameter. In an embodiment, when a fixed position stress test parameter is selected as the risk stress scenario parameter, a stress test parameter at a position of three quarters of the sorted stress test parameters can be selected as the risk stress scenario parameter; or a stress test parameter at a position of one quarter of the sorted stress test parameters can be selected as the risk stress scenario parameter.

[0104] In an embodiment, the risk stress scenario parameter includes one of the following: a mild risk stress scenario parameter; a moderate risk stress scenario parameter; a severe risk stress scenario parameter;

[0105] Correspondingly, S460 includes:

[0106] S461. Selecting one stress test parameter from the sorted stress test parameters according to a preset selection algorithm as a mild risk stress scenario parameter corresponding to the target service.

[0107] The mild risk stress scenario parameter can refer to a risk stress scenario parameter used when the stress scenario is mild risk.

[0108] In an embodiment, a preset algorithm can be used to select one stress test parameter from the sorted stress test parameters as a mild risk stress scenario parameter corresponding to the target service, that is, the risk stress scenario parameter selected in the stress test is used as the mild risk stress scenario parameter. In actual operation, any stress test parameter from the sorted stress test parameters can be selected as the mild risk stress scenario parameter of the target service after the algorithm, or a stress test parameter at a fixed position can be selected as the mild risk stress scenario parameter. In an embodiment, when a stress test parameter at a fixed position is selected as the mild risk stress scenario parameter, a stress test parameter at a position of three quarters from small to large can be selected as the mild risk stress scenario parameter, or a stress test parameter at a position of one quarter from large to small can be selected as the mild risk stress scenario parameter.

[0109] S462. Determining a moderate risk stress scenario parameter and a severe risk stress scenario parameter corresponding to the target service according to the mild risk stress scenario parameter and a first preset value and a second preset value pre-configured.

[0110] The first preset value can be a preset value corresponding to the moderate stress scenario parameter, and the moderate risk stress scenario parameter can be determined according to the first preset value and the mild risk stress scenario parameter. The second preset value can be a preset value corresponding to the severe stress scenario parameter, and the severe risk stress scenario parameter can be determined according to the second preset value and the mild risk stress scenario parameter. The first preset value and the second preset value can be set in advance according to the demand of the service personnel, and the first preset value should be less than the second preset value. In an embodiment, the first preset value can include but is not limited to 2 and 3, and the second preset value can include but is not limited to 4 and 5. In an embodiment, the first preset value and the second preset value can also be ratio values respectively, and the moderate risk stress scenario parameter and the severe risk stress scenario parameter corresponding to the target service can be directly determined by adding or subtracting the ratio values from the mild risk stress scenario parameter.

[0111] In an embodiment, the moderate risk stress scenario parameter corresponding to the target service can be determined according to the light risk stress scenario parameter and a first preset value, and the severe risk stress scenario parameter corresponding to the target service can be determined according to the light risk stress scenario parameter and a second preset value. In actual operation, the first preset value and the second preset value can be extracted, and the light risk stress scenario parameter is multiplied by the first preset value and the second preset value respectively to obtain the moderate risk stress scenario parameter and the severe risk stress scenario parameter.

[0112] In the embodiment of the application, the first type data table and the second type data table corresponding to the to-be-tested object are obtained, the service parameters of the target service in a preset time length are identified and extracted in the first type data table and / or the second type data table, the parameter statistical strategy corresponding to the target service is obtained, the stress test parameters corresponding to the target service are determined according to the parameter statistical strategy and the service parameters, the stress test parameters in the preset time length are sorted according to a sorting algorithm to obtain sorted stress test parameters, one stress test parameter is selected from the sorted stress test parameters as a risk stress scenario parameter corresponding to the target service according to a preset selection algorithm, the stress test parameters corresponding to the target service are determined according to different parameter statistical strategies, the stress test parameters are accurately determined, one stress test parameter is dynamically selected from the sorted stress test parameters as the risk stress scenario parameter corresponding to the target service through the preset selection algorithm, and the rationality of the overall architecture of the stress test is ensured, thereby improving the effectiveness of the stress test.

[0113] In an embodiment, Figure 5 is a flowchart of another data processing method provided by the embodiment of the application, and the embodiment is a further description of the liquidity risk stress test taking the to-be-tested object as a bank as an example. As shown in Figure 5 the method comprises:

[0114] The risk stress scenario parameters are determined from the EAST data according to dynamic scenarios and static scenarios, the business parameters of the dynamic scenarios, the static scenarios and the liquidity release scenarios are determined according to the G21 liquidity report, the light risk stress scenario test result, the moderate risk stress scenario test result and the severe risk stress scenario test result are determined respectively, and the liquidity risk analysis is performed according to the test results.

[0115] The risk stress scenario parameters can include the loan default rate, the loan renewal rate, the fixed deposit withdrawal rate, the fixed deposit renewal rate, the bond default rate, the interbank business default rate, the newly issued interbank deposit bill ratio, the deposit reserve gold line deposit return and the bond discount rate.

[0116] Wherein, the loan default rate represents the ratio of the amount of loans that should be repaid according to the contract but are not repaid in the next month, and the greater the ratio, the greater the impact on the liquidity of the bank, and the bank should prepare more sufficient funds to deal with the liquidity problem caused by the failure to repay the funds. The index belongs to the static scenario.

[0117] In the embodiment, the first data table and the second data table in the EAST data can be acquired. The first data table can be a loan bill for storing personal loans, and the second data table can be a loan bill for storing public loans. Taking the loan default rate of public loans as an example, the data of January 31 in the second data table can be acquired, and the "loan status" is "normal", the "interest balance in the table" plus the "interest balance outside the table" is equal to 0, and the remaining details filter out the loans with the "next repayment date" in February. At this time, the loans with normal status and repayment plan within one month can be obtained, that is, the loans to be repaid within one month. The data of the second data table at the end of February is acquired, and is internally connected with the above-mentioned loan details to be repaid in January. According to the requirement of the regulatory reporting, the data at the end of February will include the loan data to be repaid in January. After matching and connecting, the loan balance at the end of February is subtracted from the loan balance at the end of January. According to the difference, it is judged that each loan is not repaid, the difference is repaid or the full amount is repaid. The non-full repayment part is regarded as the default part, and the total amount of loans to be repaid at the end of January is divided to obtain the default rate under normal circumstances. The loan default rates of each month in the past two years of the bank can be calculated in turn, and the fourth quantile of the value in the large direction is taken as the mild risk stress scenario parameter, and the moderate risk stress scenario parameter and the severe risk stress scenario parameter are 2 times and 4 times of the mild risk stress scenario parameter respectively. The determination method of the loan default rate of personal loans is the same as that of the loan default rate of public loans.

[0118] Wherein, the loan renewal refers to the original loan after the expiration date, and a new loan is added again, which is classified as a dynamic stress scenario. When measuring, the total value of two time points is directly considered. The loan renewal rate belongs to the dynamic scenario.

[0119] In the embodiment, the first data table and the second data table in the EAST data can be acquired. Taking the EAST data acquired at the end of January as an example, the total balance of loans with the expiration date in January is acquired as the numerator, and the total balance of loans with the issuance date in January is acquired as the denominator, and the loan renewal rate index is obtained by dividing the two. The index does not have mild, moderate and severe.

[0120] Wherein, the periodic deposit pre-withdrawal rate refers to the ratio of the customers who withdraw cash in advance due to temporary fund demand. The index belongs to the static scenario. The risk stress scenario parameter can be determined by using the traditional experience method.

[0121] The deposit renewal rate can be a ratio of deposit placement to withdrawal, and the scenario directly considers the aggregate values at two time points.

[0122] In an embodiment, the third data table, the fourth data table, the fifth data table, and the sixth data table in the EAST data are obtained by taking the data at the end of January as an example. The third data table can be used to store the detailed data of the public deposit sub-account. The fourth data table can be used to store the detailed data of the personal deposit sub-account. The fifth data table can be used to roughly store the non-detailed data of the personal deposit sub-account. The sixth data table can be used to roughly store the non-detailed data of the public personal deposit sub-account. The sixth data table can be used to filter out the current deposit by the account type field in the third data table. The transaction amount of the debit and credit sides is calculated respectively by the transaction debit and credit flag field dimension. The sum of the credit side is divided by the sum of the debit side to obtain the deposit renewal rate in January. Similar to the loan default rate, the minimum value of the quartile of the index value in the past two years is taken as the mild risk stress scenario parameter, and the moderate risk stress scenario parameter and the severe risk stress scenario parameter are reduced by 10% in turn.

[0123] The bond default rate refers to the ratio of the funds not repaid according to the contract after the bond matures. It has the same meaning as the loan default rate. This index belongs to the static scenario.

[0124] In an embodiment, the seventh data table in the EAST data can be obtained. The seventh data table can be used to store the balance data of the self-operated fund business. By filtering the data of the bond investment and interbank investment in the business category and the data of the bond investment in the business category, the total bond data can be obtained. Taking the data at the end of January as an example, the total amount of bond funds to be recovered in January is calculated by summing up all the bond data with the expiration date in January. Then, the eighth data table is used to filter the data of the bond investment and interbank investment in the business category, the data of the bond investment in the business category, the data of the transaction direction as sell, and the data with the expiration date in the current month, and the amount of bonds recovered in January is obtained by summing up. The eighth data table can be used to store the information of the self-operated fund transaction. The bond default rate is calculated by the obtained recovered amount and the recovered amount. Similar to the loan default rate, the quartile of the maximum value of the index value in the past two years is taken as the mild risk stress scenario parameter, the moderate risk stress scenario parameter, and the severe risk stress scenario parameter is multiplied by 2 and 4 in turn.

[0125] The interbank business default rate refers to the ratio of the funds deposited by the bank in other banks that have defaulted. It has basically the same meaning as the bond default rate. The interbank business includes depositing interbank and placing interbank. This index belongs to the static scenario.

[0126] The seventh data table in the EAST data can be obtained, and through the table, the business major category is inter-bank business, the business medium category is depositing inter-bank and disassembling, and the full amount of inter-bank data can be obtained. Taking the data at the end of January as an example, all the depositing inter-bank and disassembling inter-bank data with the expiration date in January are taken to calculate the total amount of bond expiration funds to be recovered in January. Then, through the eighth data table, the business major category is inter-bank business, the business medium category is depositing inter-bank and disassembling, the transaction direction is selling, and the expiration date is in the current month, the amount of depositing inter-bank and disassembling inter-bank that has been recovered in January is summarized. The default rate of inter-bank business is calculated by the recovered amount and the recovered amount, and the default rate of loan is similar. The maximum value of the index value in the past two years is taken as the light risk stress scenario parameter, and the medium risk stress scenario parameter and the heavy risk stress scenario parameter are multiplied by 2 and 4 in turn. However, the inter-bank business default situation is rare, so it is very likely to get the non-ideal result of 0 through the above calculation, and at the same time, it is likely to evolve into a systemic risk when liquidity risk occurs, which will cause the inter-bank business default rate to increase significantly. Therefore, if the result obtained by the above method is 0, the light risk stress scenario parameter, the medium risk stress scenario parameter and the heavy risk stress scenario parameter are 3%, 5% and 10% respectively by using the empirical method.

[0127] The newly issued inter-bank deposit certificate ratio refers to the bank arranging a certain scale of inter-bank deposit certificate issuance in a proactive liability manner according to the liquidity demand. Since the issuance of inter-bank deposit certificate is a proactive liability behavior of the bank, it seeks funds in the inter-bank market to relieve internal liquidity demand, so the index is a liquidity relief scenario.

[0128] In the embodiment, the eighth data table in the EAST data can be obtained, and through the table, the business major category is inter-bank business, the business medium category is inter-bank deposit certificate, the business sub-category is inter-bank deposit certificate issuance, the transaction direction is selling, and the expiration date is in the current month, the amount of inter-bank deposit certificate issued in January is summarized. Similarly, through the table, the business major category is inter-bank business, the business medium category is inter-bank deposit certificate, the business sub-category is inter-bank deposit certificate issuance, the transaction direction is buying, and the expiration date is in the current month, the amount of inter-bank deposit certificate to be expired in January is summarized. The ratio of newly issued inter-bank deposit certificate to expired inter-bank deposit certificate is obtained by dividing the issuance amount by the expiration amount. The ratio is calculated in the past two years according to the method, and the minimum value of the quartile is taken as the light risk stress scenario parameter, the medium risk stress scenario parameter and the heavy risk stress scenario parameter are reduced by 10% in turn.

[0129] The depositing central bank deposit reserve return can refer to the decrease of the statutory deposit reserve due to the decrease of the deposit scale in the stress scenario, so that the excess deposit reserve available is increased, and the liquidity risk is relieved. The scenario is automatically generated, and the result depends on the bank's own deposit reserve ratio. The final calculation result is used to relieve the liquidity risk, and the index does not set parameters.

[0130] The bond discount rate can refer to the bank voluntarily selling the high-quality bonds held by the bank according to the liquidity demand of the bank to relieve the liquidity in the stress scenario. When the bonds are sold, the value discount is faced due to the priority requirement of the time limit being higher than the bond preservation. The higher the rating is, the stronger the enterprise strength is, and the lower the bond discount rate is.

[0131] In the embodiment, all data of the seventh data table in the EAST data can be obtained, the screening business category is bond investment and interbank investment, the business category is bond investment, and the five-level classification does not belong to the last three categories. According to the bond market data, the external credit rating of the bond is obtained through the name matching. Next, the bank-held bonds are divided into three levels of level 1, 2A and 2B through the bond business subcategory and the external credit rating of the bond. The level 1 bond is all the bonds with the business subcategory equal to national debt, central bank bills and policy financial bonds. The 2A bond is the bond with the business subcategory not being national debt, central bank bills and policy financial bonds and the external credit rating being AA- and above. The 2B bond is the bond with the business subcategory not being national debt, central bank bills and policy financial bonds and the external credit rating being AA- and above. The bank-held bond assets are divided into three parts of level 1, 2A and 2B. The discount rate of the level 1 bond is 3% for the light risk stress scenario parameter, 5% for the moderate risk stress scenario parameter and 7% for the heavy risk stress scenario parameter. The discount rate of the 2A bond is 8% for the light risk stress scenario parameter, 11% for the moderate risk stress scenario parameter and 15% for the heavy risk stress scenario parameter. The discount rate of the 2B bond is 15% for the light risk stress scenario parameter, 20% for the moderate risk stress scenario parameter and 25% for the heavy risk stress scenario parameter.

[0132] In an embodiment, the risk stress scenario parameters can be respectively input into the corresponding index items, and the shortest survival period of the to-be-tested object is determined by calculating the fund inflow, the fund outflow, the risk relief fund inflow and the original maturity fund gap of each index item.

[0133] For example, the preset time length is 30 days, the fund outflow includes the non-periodic repayment of each loan expiring within 30 days, the loan renewal within 30 days, the above-periodic deposit withdrawal within 30 days, the non-periodic repayment rate of the bond expiring within 30 days and the interbank deposit loss within 30 days.

[0134] The non-periodic repayment of each loan expiring within 30 days can obtain the next day, 2 days to 7 days, 8 days to 30 days of each loan in the G21 report, respectively multiply the risk stress scenario parameter loan default rate, and obtain the corresponding cash outflow within 30 days.

[0135] Among them, the loan rollover within 30 days can be calculated based on the non-periodic repayment of the above-mentioned loans expiring within 30 days. Taking the next day index as an example, the calculation methods from the 2nd to the 7th day and from the 8th to the 30th day are consistent with the next day.

[0136] .

[0137] Among them, the above-30-day fixed deposit can obtain the sum of the indexes of all periods of G21 fixed deposit indexes within 30 days, and then allocate the period according to the period weight of the index within 30 days. For example, according to the date average, the calculation method is as follows:

[0138] ;

[0139] ;

[0140] ;

[0141] .

[0142] Among them, the non-periodic repayment rate of bonds expiring within 30 days includes the next day, the 2nd to the 7th day, and the 8th to the 30th day of the bond default rate of the risk stress scenario parameter of G21 report bonds. Get the cash outflow corresponding to each period within 30 days.

[0143] Among them, the 30-day interbank deposit loss includes the next day, the 2nd to the 7th day, and the 8th to the 30th day of the risk stress scenario parameter of interbank business default rate of G21 report deposit interbank and interbank lending. Get the cash outflow corresponding to each period within 30 days.

[0144] Among them, taking 30 days as the preset time length, the fund outflow includes the 30-day fixed deposit renewal and the newly issued interbank deposit single ratio.

[0145] Among them, the 30-day fixed deposit renewal includes the next day, the 2nd to the 7th day, and the 8th to the 30th day of the risk stress scenario parameter of G21 report fixed deposit renewal rate. Get the cash outflow corresponding to each period within 30 days.

[0146] Among them, the newly issued interbank deposit single ratio is similar to the 30-day fixed deposit renewal, which takes the next day, the 2nd to the 7th day, and the 8th to the 30th day of the risk stress scenario parameter of G21 report interbank deposit single ratio. Get the cash outflow corresponding to each period within 30 days.

[0147] Among them, taking 30 days as the preset time length, the risk release fund inflow includes the deposit of the central bank deposit reserve return and the bond sale. The former belongs to the passive scenario, and the latter belongs to the active scenario.

[0148] The deposit of the central bank deposit reserve return can be based on the 30-day current deposit and the loss of the fixed deposit within 30 days, multiplied by the deposit reserve rate applicable to the bank, to obtain the total amount of 30-day statutory deposit reserve return, and then according to the date weight, the next day, 2 to 7 days, 8 to 30 days are divided.

[0149] The bond sale can sell the above-mentioned 30-day bond in advance in the trading market to obtain the inter-day liquidity fund, according to the extraction and classification method of the bond holding corresponding to the bond discount rate, on the basis of which the 30-day bond is removed, and the remaining bond is multiplied by the corresponding discount rate according to the division of first-level, 2A and 2B assets, to obtain the total amount of the bond holding that can be realized. On this basis, a bond realization rate parameter is added, which represents the percentage of bond realization. The preset parameters are 80% for first-level assets, 70% for 2B assets, and 50% for 2B assets. Multiply the total amount of each level of bond obtained in the previous step by the parameter, and finally obtain the 30-day realizable bond assets. Then, the results are divided according to the date weight to the next day, 2 to 7 days, and 8 to 30 days.

[0150] The expiration gap is taken from the G21 report, and the fund inflow, fund outflow, risk mitigation fund inflow, and original expiration gap are divided according to the date weight to the daily cash flow as shown in Table 1:

[0151] Table 1

[0152]

[0153] According to the original expiration gap, the fund inflow and the fund outflow, the cumulative expiration gap of the to-be-tested object is determined, and according to the cumulative expiration gap and the risk mitigation fund inflow, the risk mitigation cumulative fund gap of the to-be-tested object is determined. Find the date corresponding to the first negative number of the risk mitigation cumulative fund gap within the preset time length as the shortest survival period of the to-be-tested object.

[0154] The cumulative expiration gap can be added to the cumulative expiration gap calculation column when the risk mitigation fund inflow is added, which is used to shield the positive and negative interlaced situation of the corresponding expiration cash flow each day. The calculation method is to add the sum from the first day to the calculation day as the cumulative expiration gap of the calculation day. The risk mitigation cumulative fund gap can be calculated by adding the cumulative expiration gap and the risk mitigation fund inflow. The cumulative expiration gap and the risk mitigation cumulative fund gap are added to the cash flow table as shown in Table 2:

[0155] Table 2

[0156]

[0157] The shortest survival period can be the minimum date when the cumulative funding gap becomes negative after the risk mitigation, indicating the number of days the bank can survive under stress scenarios. In the mild risk stress scenario and the moderate risk stress scenario, more than 30 days is considered qualified, and less than 30 days is considered unqualified. In the severe risk stress scenario, more than 20 days is considered qualified, and less than 20 days is considered unqualified. The bank should report the stress test results to the management, and the management should decide to adjust the bank's business strategy, including optimizing the asset-liability structure, increasing the holding of high-quality liquid assets, and reducing the maturity mismatch, to alleviate potential liquidity risks.

[0158] Figure 6 is a structural schematic diagram of a data processing device according to an embodiment of the application. As shown in the figure, the device comprises a business parameter acquisition module 61, a test parameter determination module 62 and a scenario parameter determination module 63. Figure 6

[0159] The business parameter acquisition module 61 is configured to acquire the business parameter of the target business in the preset time period in the to-be-tested object.

[0160] The test parameter determination module 62 is configured to determine the stress test parameter corresponding to the target business according to the business parameter.

[0161] The scenario parameter determination module 63 is configured to determine the risk stress scenario parameter corresponding to the target business according to the stress test parameter.

[0162] In the embodiment of the application, the business parameter acquisition module acquires the business parameter of the target business in the preset time period in the to-be-tested object, the test parameter determination module determines the stress test parameter corresponding to the target business according to the business parameter, and the scenario parameter determination module determines the risk stress scenario parameter corresponding to the target business according to the stress test parameter, so as to determine the stress test parameter according to the business parameter of the to-be-tested object, dynamically determine the risk stress scenario parameter corresponding to the target business according to the stress test parameter, ensure the rationality of the overall architecture of the stress test, and further improve the effectiveness of the stress test. In an embodiment, the data processing device further comprises:

[0163] A funding gap acquisition module is configured to acquire the original to-expire funding gap of the to-be-tested object.

[0164] A funding situation determination module is configured to determine the funding inflow situation, the funding outflow situation and the risk mitigation funding inflow situation of the to-be-tested object according to the risk stress scenario parameter and the business parameter.

[0165] A survival period determination module is configured to determine the shortest survival period of the to-be-tested object according to the funding inflow situation, the funding outflow situation, the risk mitigation funding inflow situation and the original to-expire funding gap. ​

[0166] In an embodiment, the service parameter obtaining module 61 comprises:

[0167] a data table obtaining unit, configured to obtain a first type data table and a second type data table corresponding to the to-be-tested object; the first type data table is used to represent detailed data of the to-be-tested object; the second type data table is used to represent index summary data of the to-be-tested object;

[0168] a parameter extracting unit, configured to identify and extract, in the first type data table and / or the second type data table, a service parameter of the target service within a preset time length.

[0169] In an embodiment, the test parameter determining module 62 comprises:

[0170] a policy obtaining unit, configured to obtain a parameter statistical policy corresponding to the target service; the parameter statistical policy corresponds to the target service in a one-to-one manner;

[0171] a parameter determining unit, configured to determine, according to the parameter statistical policy and the service parameter, a stress test parameter of the corresponding target service.

[0172] In an embodiment, the scenario parameter determining module 63 comprises:

[0173] a parameter sorting unit, configured to sort, according to a sorting algorithm, the stress test parameters within the preset time length to obtain sorted stress test parameters;

[0174] a parameter selecting unit, configured to select, according to a preset selection algorithm, one stress test parameter from the sorted stress test parameters as a risk stress scenario parameter of the corresponding target service.

[0175] In an embodiment, the risk stress scenario parameter in the scenario parameter determining module 63 comprises one of the following: a mild risk stress scenario parameter; a moderate risk stress scenario parameter; a severe risk stress scenario parameter;

[0176] Correspondingly, the parameter selecting unit comprises:

[0177] a first parameter selecting unit, configured to select, according to a preset selection algorithm, one stress test parameter from the sorted stress test parameters as a mild risk stress scenario parameter of the corresponding target service;

[0178] a second parameter selecting unit, configured to determine, according to the mild risk stress scenario parameter and a first preset value and a second preset value configured in advance, a moderate risk stress scenario parameter and a severe risk stress scenario parameter of the corresponding target service.

[0179] In an embodiment, the fund situation determining module comprises:

[0180] The parameter set determination unit is configured to identify and extract, from the risk stress scenario parameters, risk stress scenario parameters for characterizing the fund inflow, risk stress scenario parameters for characterizing the fund outflow, and risk stress scenario parameters for characterizing the risk mitigation process, as a first-type risk stress scenario parameter set, a second-type risk stress scenario parameter set, and a third-type risk stress scenario parameter set, respectively.

[0181] The first fund determination module is configured to determine the fund inflow of the to-be-tested object according to each first-type risk stress scenario parameter and a corresponding business parameter; and determine the fund outflow of the to-be-tested object according to each second-type risk stress scenario parameter and a corresponding business parameter.

[0182] The second fund determination module is configured to determine the risk mitigation fund inflow of the to-be-tested object according to each third-type risk stress scenario parameter and a corresponding business parameter.

[0183] In an embodiment, the survival period determination module comprises:

[0184] The first gap determination unit is configured to determine the cumulative term fund gap of the to-be-tested object according to the original to-be-expired term fund gap, the fund inflow, and the fund outflow.

[0185] The second gap determination unit is configured to determine the cumulative fund gap after risk mitigation of the to-be-tested object according to the cumulative term fund gap and the risk mitigation fund inflow.

[0186] The survival period determination unit is configured to find a date corresponding to a first time when the cumulative fund gap after risk mitigation is negative within a preset time length, as the shortest survival period of the to-be-tested object.

[0187] In an embodiment, the risk stress scenario of the to-be-tested object comprises one of the following: a mild risk stress scenario; a moderate risk stress scenario; and a severe risk stress scenario; and the data processing apparatus further comprises:

[0188] The threshold pre-configuration module is configured to pre-configure a survival period threshold of the to-be-tested object corresponding to each risk stress scenario.

[0189] The test result determination module is configured to determine the stress test result of the to-be-tested object according to the shortest survival period and the survival period threshold under the corresponding risk stress scenario.

[0190] In an embodiment, the data processing apparatus further comprises:

[0191] The test result reporting module is configured to report the stress test result of the to-be-tested object to the financial management platform, so that the financial management platform adjusts the management strategy of the to-be-tested object according to the stress test result.

[0192] The data processing apparatus provided by the embodiments of the present application can execute the data processing method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0193] In an embodiment, Figure 7 is a structural schematic diagram of an electronic device 10 implementing the data processing method of the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0194] As shown in Figure 7 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0195] The plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0196] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, and the like. The processor 11 performs various methods and processes described above, such as a data processing method.

[0197] In some embodiments, the data processing method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the data processing method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the data processing method by any other suitable means, such as by means of firmware.

[0198] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0199] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0200] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0201] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0202] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0203] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of great management difficulty and weak business scalability in traditional physical hosts and VPS services.

[0204] In an embodiment, the present application also includes a computer program product comprising a computer program which, when executed by a processor, implements the data processing method of any embodiment of the present application.

[0205] The computer program product, in its implementation, can be written in one or more programming languages or combinations of languages to implement the computer program code for performing the operations of the present application, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can be executed entirely on a user computer, partially on a user computer, as a standalone software package, partially on a user computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0206] It should be understood that the various forms of flow shown above can be reordered, added to, or deleted from. For example, the steps described in the present application can be executed in parallel, in sequence, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, and this is not limited herein.

[0207] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A data processing method, characterized in that, include: Obtain the business parameters of the target business within a preset time period in the object under test; Determine the stress test parameters corresponding to the target service based on the aforementioned service parameters; Based on the stress test parameters, determine the risk stress scenario parameters for the corresponding target business; The method further includes: Obtain the original funding gap for the target object at the due date; The fund inflow, fund outflow, and risk mitigation fund inflow of the target object are determined based on the risk stress scenario parameters and the business parameters. The shortest lifespan of the target object is determined based on the fund inflow, fund outflow, risk mitigation fund inflow, and the original maturity funding gap. The step of determining the shortest survival period of the target object based on the capital inflow, capital outflow, risk mitigation capital inflow, and the original maturity funding gap includes: The cumulative funding gap for the target entity is determined based on the original funding gap at maturity, the inflow of funds, and the outflow of funds. The cumulative funding gap after risk mitigation for the target entity is determined based on the cumulative funding gap over the specified period and the inflow of risk mitigation funds. Find the date on which the cumulative funding gap after the risk mitigation first becomes negative within a preset time period, and use this date as the shortest survival period of the test object.

2. The method according to claim 1, characterized in that, The acquisition of service parameters within a preset time period corresponding to the target service in the test object includes: Obtain a first type of data table and a second type of data table corresponding to the object to be tested; wherein, the first type of data table is used to represent the detailed data of the object to be tested; and the second type of data table is used to represent the summary data of the indicators of the object to be tested. Identify and extract the service parameters of the target service within a preset time period from the first type of data table and / or the second type of data table.

3. The method according to claim 1, characterized in that, The step of determining the stress test parameters corresponding to the target service based on the service parameters includes: Obtain the parameter statistics strategy corresponding to the target service; wherein, the parameter statistics strategy corresponds one-to-one with the target service; The stress test parameters for the corresponding target business are determined according to the parameter statistics strategy and the business parameters.

4. The method according to claim 1, characterized in that, The step of determining the risk stress scenario parameters for the corresponding target business based on the stress test parameters includes: The stress test parameters within a preset time period are sorted according to the sorting algorithm to obtain the sorted stress test parameters; According to a preset selection algorithm, a stress test parameter is selected from the sorted stress test parameters as the risk stress scenario parameter for the corresponding target business.

5. The method according to claim 4, characterized in that, The risk stress scenario parameters include one of the following: mild risk stress scenario parameters; moderate risk stress scenario parameters; severe risk stress scenario parameters; Accordingly, the step of selecting a stress test parameter from the sorted stress test parameters according to a preset selection algorithm as the risk stress scenario parameter for the corresponding target business includes: According to a preset selection algorithm, a stress test parameter is selected from the sorted stress test parameters as the mild risk stress scenario parameter for the corresponding target business. Based on the mild risk stress scenario parameters and the pre-configured first and second preset values, determine the medium risk stress scenario parameters and severe risk stress scenario parameters for the corresponding target business.

6. The method according to claim 1, characterized in that, The process of determining the cash inflow, cash outflow, and risk mitigation cash inflow of the target entity based on the risk stress scenario parameters and the business parameters includes: From the risk stress scenario parameters, risk stress scenario parameters for characterizing capital inflows, risk stress scenario parameters for characterizing capital outflows, and risk stress scenario parameters for characterizing risk mitigation processes are identified and extracted respectively, and used as the first type of risk stress scenario parameter set, the second type of risk stress scenario parameter set, and the third type of risk stress scenario parameter set. The cash inflow of the target object is determined based on each first-type risk stress scenario parameter in the first-type risk stress scenario parameter set and the corresponding business parameter; the cash outflow of the target object is determined based on each second-type risk stress scenario parameter in the second-type risk stress scenario parameter set and the corresponding business parameter. The risk mitigation funding inflow of the target object is determined based on each third-type risk stress scenario parameter in the third-type risk stress scenario parameter set and the corresponding business parameters.

7. The method according to claim 1, characterized in that, The risk stress scenario of the subject under test includes one of the following: mild risk stress scenario; moderate risk stress scenario; Severe risk and stress scenarios; the method further includes: Pre-configure the survival threshold for each risk stress scenario corresponding to the object under test; The stress test results of the test object are determined based on the shortest survival period and the survival threshold under the corresponding risk stress scenario.

8. The method according to claim 7, characterized in that, The method further includes: The stress test results of the object under test are reported to the financial management platform so that the financial management platform can adjust the management strategy of the object under test based on the stress test results.

9. A data processing apparatus, characterized in that, include: The business parameter acquisition module is used to acquire the business parameters of the target business in the test object within a preset time period; The test parameter determination module is used to determine the stress test parameters corresponding to the target service based on the service parameters. The scenario parameter determination module is used to determine the risk pressure scenario parameters of the corresponding target business based on the stress test parameters. The device further includes: The funding gap acquisition module is used to acquire the funding gap for the original maturity date of the object under test; The funding situation determination module is used to determine the fund inflow, fund outflow, and risk mitigation fund inflow of the target object based on the risk stress scenario parameters and the business parameters. The lifespan determination module is used to determine the shortest lifespan of the test object based on the inflow of funds, the outflow of funds, the inflow of risk mitigation funds, and the funding gap at the original maturity date. The lifespan determination module includes: The first gap determination unit is used to determine the cumulative funding gap of the target object based on the original maturity funding gap, the fund inflow situation, and the fund outflow situation; The second gap determination unit is used to determine the cumulative funding gap after risk mitigation for the test object based on the cumulative funding gap over the cumulative period and the inflow of risk mitigation funds. The survival period determination unit is used to find the date corresponding to the first negative cumulative funding gap after the risk mitigation within a preset time period, which is taken as the shortest survival period of the test object.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data processing method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the data processing method according to any one of claims 1-8.

12. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the data processing method according to any one of claims 1-8.

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