CN115619511A - Data processing method and device, electronic equipment, storage medium and product - Google Patents

Data processing method and device, electronic equipment, storage medium and product Download PDF

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CN115619511A
CN115619511A CN202211204634.XA CN202211204634A CN115619511A CN 115619511 A CN115619511 A CN 115619511A CN 202211204634 A CN202211204634 A CN 202211204634A CN 115619511 A CN115619511 A CN 115619511A
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debt
escort
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李调华
邱诚
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China Construction Bank Corp
CCB Finetech Co Ltd
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China Construction Bank Corp
CCB Finetech Co Ltd
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Abstract

The embodiment of the invention discloses a data processing method, a data processing device, electronic equipment, a storage medium and a product, and relates to the technical field of big data. The method comprises the following steps: obtaining bond information and security information in an original bond item, determining distributable value of a corresponding security in the original bond item according to the bond information and the security information, splitting the original bond item based on the distributable value and a preset security sorting rule to obtain at least two virtual bond items, and forming a one-to-one mapping relation between the virtual bond items and the security. According to the embodiment of the invention, the one-to-many complex relationship between the debt items in the original debt items and the escort is split according to the assignable value of the escort and the preset escort sequencing rule to form the one-to-one mapping relationship between the virtual debt items and the escort, so that the monitoring reporting and the report statistical analysis of the escort can be conveniently carried out, and meanwhile, the guarantee capability of the escort on the debt items can be accurately identified, so that the risk early warning can be timely carried out when the escort is insufficient.

Description

Data processing method, device, electronic equipment, storage medium and product
Technical Field
The present invention relates to the field of big data technologies, and in particular, to a data processing method and apparatus, an electronic device, a storage medium, and a product.
Background
At present, for a new security supervision standard, a financial institution needs to implement unified management on the security under the mechanism so as to realize full coverage on the security data of various types of security in the financial institution. In the course of the bond pledge type buyback business transaction, the complex association relationship between pledges and bonds is involved, and users can use bonds with different quantities and types to carry out pledge financing under the same bond. In the prior art, data of a credit bond pledge type repurchase business pledge in a financial market is not brought into unified management, a statistical rule of credit collateral article splitting is not determined, in order to meet a supervision requirement, a statistical standard of the credit collateral article splitting needs to be defined urgently, the unified monitoring of a full-organization pledge is realized, and how to form a virtual one-to-one relationship between the credit and the pledge by the complex association relationship between the pledge and the credit becomes a problem to be solved by each financial institution.
Disclosure of Invention
In view of the above, the present invention provides a data processing method, apparatus, electronic device, storage medium and product, which can facilitate the supervision and reporting of the security deposit and the statistical analysis of the report, and at the same time can accurately identify the security capability of the security deposit on the debt, so as to perform the risk early warning in time when the security deposit is insufficient.
According to an aspect of the present invention, an embodiment of the present invention provides a data processing method, including:
acquiring bond information and security information in the original debt item; the original debt items comprise at least two escorts and at least two bonds, and the escorts correspond to the bonds one by one;
determining the assignable value of a corresponding security in the original debt item according to the bond information and the security information;
splitting the original debt items based on the distributable value and a preset escort sequencing rule to obtain at least two virtual debt items, and forming a one-to-one mapping relation between the virtual debt items and the escort.
According to another aspect of the present invention, an embodiment of the present invention further provides a data processing apparatus, including:
the information acquisition module is used for acquiring bond information and security information in the original debt items; the original debt items comprise at least two escorts and at least two bonds, and the escorts and the bonds are in one-to-one correspondence;
the value determining module is used for determining the distributable value of a corresponding security in the original debt item according to the bond information and the security information;
and the splitting module is used for splitting the original debt items based on the distributable value and a preset escort sequencing rule to obtain at least two virtual debt items and form a one-to-one mapping relation between the virtual debt items and the escort.
According to another aspect of the present invention, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the data processing method according to any of the embodiments of the present invention.
According to another aspect of the present invention, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer instructions for causing a processor to implement the data processing method according to any embodiment of the present invention when the computer instructions are executed.
According to another aspect of the present invention, an embodiment of the present invention further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the data processing method according to any embodiment of the present invention.
According to the technical scheme of the embodiment of the invention, the original debt items are split according to the distributable value of the escort and the preset escort sequencing rule by one-to-many complex relationship between the debt item information and the escort item information in the original debt items, so as to form one-to-one mapping relationship between the virtual debt items and the escort items, so that the monitoring reporting and report of the escort items and the statistical analysis of reports can be conveniently carried out, and meanwhile, the guarantee capability of the escort on the debt items can be accurately identified, so that the risk early warning can be timely carried out when the escort is insufficient.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention;
fig. 2 is a diagram illustrating a relationship between an original debt item and an escort and a bond according to an embodiment of the present invention;
fig. 3 is a mapping relationship diagram of virtual debt items and an escort after splitting an original debt item according to an embodiment of the present invention;
FIG. 4 is a flowchart of another data processing method according to an embodiment of the present invention;
fig. 5 is a block diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It is to be understood that the terms "target" and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet the relevant regulations of national laws and regulations.
In an embodiment, fig. 1 is a flowchart of a data processing method according to an embodiment of the present invention, where the present embodiment is applicable to a case where a financial institution performs a splitting process on escort data, and the method may be executed by a data processing apparatus, where the data processing apparatus may be implemented in a form of hardware and/or software, and the data processing apparatus may be configured in an electronic device.
As shown in fig. 1, the method specifically comprises the following steps:
s110, acquiring bond information and escort information in the original debt items; the original debt items comprise at least two escorts and at least two bonds, and the escorts and the bonds are in one-to-one correspondence.
In this embodiment, the original debt can be understood as the total financial debt with credit risk assumed by the debtor. The original debt item may contain two or more escorts and two or more bonds. The deposit may be referred to as a mortgage, and may include assets in the form of valuable objects such as houses, land, automobiles, and machines. The bond refers to a kind of securities issued by governments, enterprises, banks, etc. for raising funds, which pay a certain proportion of interest at an appointed time and pay back principal when due, and may be a financial bond issued by each bank, a bill issued by a bank, a voucher-type national bond issued by a financial department, etc.
In the present embodiment, there is a one-to-one correspondence between each collateral and bond in the original debt. It is understood that each deposit in the original debt has a corresponding bond. It should be noted that each security in the original debt item corresponds to a corresponding security code, a security category and a corresponding bond code, and the security code and the bond code have a one-to-one correspondence relationship. For example, the deposit 1 corresponds to the bond code 1, and the deposit 2 corresponds to the bond code 2, which is not limited herein.
In this embodiment, the original debt includes bond information corresponding to a bond and security information corresponding to a security. Note that, the bond information may include a bond face value, a bond issued value, a bond net value, a bond number, and the like corresponding to the bond, and the deposit information may include an offset rate, a deposit value, a deposit type, and the like corresponding to each deposit. Of course, each security in the original debt item corresponds to corresponding security information, and each bond has corresponding bond information.
In one embodiment, the bond information includes at least one of: bond face value; bond issuance value; bond net value; the number of bonds.
In this embodiment, the bond face value refers to the face value of issued bond, and the bond face value includes two contents: one is currency, and the other is the sum of the ticket. The denomination may be used in the home currency, or in the foreign currency, depending on the needs of the issuer and the type of bond. The bond issue value may be determined by the sum of the product of face value and market rate and the product of interest per term and market rate. The bond net worth value refers to the bond price after deduction of accrued interest calculated as the face interest rate of the bond.
In this embodiment, the original debt items correspond to corresponding original debt item numbers, and a unique original debt item number, which may also be referred to as a transaction record number, may be generated for each original debt item by the serial number generation tool. The original debt item includes information such as a corresponding transaction amount, a due settlement amount, an interest date, and a due delivery date. The one-to-many association relationship between the original debt item and the deposit can be formed by the type of the bond corresponding to each deposit in the original debt item, the bond code and the deposit value corresponding to the deposit. Fig. 2 is a diagram illustrating a relationship between an original debt item and an escort and a bond according to an embodiment of the present invention. This embodiment will be described by taking an example in which the original debt includes 4 deposits. As shown in fig. 2, the original debt item includes 4 escorts, each escort has a corresponding bond code, escort type and escort value, and the original debt item and the escort are in a one-to-many relationship.
And S120, determining the assignable value of the corresponding deposit in the original debt item according to the bond information and the deposit information.
The assignable value refers to the value of assignable debt corresponding to each security in the original debt.
In this embodiment, the assignable value of the corresponding collateral in the original debt item can be determined according to the collateral information corresponding to each collateral in the original debt item and the debt item information. In some embodiments, the actual value of the deposit corresponding to each deposit can be determined according to the bond face value, the bond issuance value, the bond net value and the bond number in the bond information, and the assignable value of the corresponding deposit in the original bond item can be determined according to the actual value corresponding to the deposit and the pledge rate contained in the deposit information.
S130, splitting the original debt items based on the distributable value and a preset security sorting rule to obtain at least two virtual debt items, and forming a one-to-one mapping relation between the virtual debt items and the security.
The deposit sorting rule can be understood as a deposit splitting and sorting rule when a deposit in the original debt item is split. In this embodiment, the escort sorting rule may be customized manually, or the sorting rule may be set according to experience, which is not limited herein.
In this embodiment, the deposit sorting rule may be a sorting rule for separating the deposit according to the priorities corresponding to the deposit risk level, the rating of the country or region where the issuer is registered, the issuer standard credit risk assessment result, the deposit amount, and the deposit number. It should be noted that, when one debt item corresponds to a plurality of escorts, the debt items may be sequentially covered according to the priority corresponding to the escort sorting rule, so as to form a one-to-one mapping relationship between the virtual debt item and the escorts. Illustratively, according to the priority corresponding to the escort sorting rule, the escorts in the original debt item are split according to the sorting of the escorts from small to large in risk level, from good to bad in the rating of the country or region where the issuer is registered, from good to bad in the standard credit risk evaluation result of the issuer, from large to small in the amount of the escorts and from small to large in the number of the escorts.
In the present embodiment, the virtual debt item may be understood as a debt item corresponding to an escort, which is obtained by splitting a plurality of escorts in the original debt item. Certainly, the virtual debt items obtained by splitting are at least two virtual debt items, and each virtual debt item corresponds to a corresponding virtual debt item number.
In this embodiment, the escortions in the original debt items may be subjected to corresponding priority ranking according to a preconfigured escorting ranking rule to obtain a corresponding escorting list after the priority ranking, each escortion in the escorting list corresponds to a corresponding assignable value, the original debt items may be split based on the assignable value corresponding to each escorting to obtain at least two corresponding virtual debt items, and the debt item balances are sequentially covered according to the priority ranking to form a one-to-one mapping relationship between the virtual debt items and the corresponding escortions. It should be noted that each split virtual debt corresponds to a virtual debt number.
For example, to facilitate clearer understanding of overlapping debt balances in sequence according to the priority order to form a one-to-one mapping relationship between virtual debt and corresponding deposit, fig. 3 is a mapping relationship diagram of virtual debt and deposit after splitting an original debt according to an embodiment of the present invention, as shown in fig. 3, the original debt is split into 4 virtual debts, each virtual debt corresponds to a corresponding debt amount and a covered amount, each debt corresponds to a corresponding deposit, and each deposit includes a corresponding bond code, a deposit category, and a deposit value.
According to the technical scheme, one-to-many complex relationships between the debt information and the escort information in the original debt item are split according to the assignable value of the escort and the preset escort sequencing rule so as to form one-to-one mapping relationship between the virtual debt item and the escort, so that supervision reporting and report statistical analysis of the escort can be conveniently carried out, meanwhile, the guarantee capacity of the escort on the debt item can be accurately identified, and when the escort is insufficient, risk early warning can be timely carried out.
In an embodiment, after splitting the original debt item based on the assignable value and the preconfigured deposit sorting rule to obtain at least two virtual debt items, the method further includes:
determining a current risk exposure of the original debt;
and executing a corresponding processing strategy according to the current risk exposure and a preset risk exposure threshold.
The current risk exposure refers to that after the debt balances are covered in sequence, the remaining debt partial balances to be covered are used as the current risk exposure, and the current risk exposure can represent the risk part possibly causing loss. The risk exposure threshold may be understood as a threshold corresponding to a pre-configured risk exposure, and the risk exposure threshold may be set manually or according to expert experience, which is not limited in this embodiment.
In this embodiment, the current risk exposure corresponding to the original debt item is determined according to the balance of the debt item corresponding to the original debt item and the total value corresponding to each escort contained in the original debt item, and according to the comparison between the current risk exposure and the pre-configured risk exposure threshold, the corresponding processing strategy can be executed according to the comparison result. It should be noted that the processing strategy can be adjusted at any time according to actual conditions, and the processing strategy can be one or more of a plurality of strategies such as timely reminding business personnel of supplementing the number of corresponding bond escorts, strengthening monitoring measures of the bond escorts, supplementing bond escorts with high priority in an additional escort sequencing rule, and the like.
In one embodiment, determining the current risk exposure of the original debt comprises:
acquiring debt balance of an original debt;
determining a current risk exposure for the original debt based on the debt balance and a total value of an collateral contained in the original debt.
In this embodiment, the current risk exposure of the original debt item can be determined according to the balance of the debt item corresponding to the original debt item and the total value of the collateral contained in the original debt item. Illustratively, the balance of the debt corresponding to the original debt is 3000, the value of 3 escorts included in the original debt is 1000, the value of escort 1 corresponds to 1000, the value of escort 2 corresponds to 1000, the value of escort 3 corresponds to 500, the original debt is split according to assignable values and preset escort sorting rules into 3 virtual debts, escort 1 covers virtual debt 1, escort 2 covers virtual debt 2, escort 3 covers virtual debt 3, escort 1 corresponds to 1000 and can cover when virtual debt is covered, escort 2 corresponds to 1000 and can cover, escort 3 corresponds to 500 and covers debt balance 500, the balance of the original debt corresponds to 3000-1000-1000-500, the covered amount of the debt is 2500, and the current risk exposure at this time can be represented as 3000-2500=500.
In one embodiment, executing the corresponding processing policy according to the current risk exposure and the pre-configured risk exposure threshold includes:
determining an exposure comparison result between the current risk exposure and a pre-configured risk exposure threshold;
and executing a corresponding processing strategy according to the exposure comparison result.
In this embodiment, the current risk exposure corresponding to the original debt item may be compared with a preconfigured risk exposure threshold to determine an exposure comparison result between the current risk exposure and the preconfigured risk exposure threshold, and when the current risk exposure is greater than or equal to the preconfigured risk exposure threshold, the service staff is timely reminded to supplement the number of corresponding bond escorts, or to enhance the monitoring measures of the bond escorts, or to supplement bond escorts with high priority in the ordering rules of additional escorts; and under the condition that the current risk exposure is smaller than the preset risk exposure threshold, it indicates that the current risk exposure corresponding to the debt items of the user is in the safety range, and the processing strategy can be to monitor the debt items of the user according to a certain period and the like, so as to timely feed back and carry out a corresponding execution strategy under the condition that the preset risk exposure threshold is exceeded.
In an embodiment, fig. 4 is a flowchart of another data processing method according to an embodiment of the present invention, in this embodiment, on the basis of the foregoing embodiments, the assignable value of a corresponding deposit in the original debt item is determined according to bond information and deposit information, the original debt item is split based on the assignable value and a pre-configured deposit sorting rule, at least two virtual debt items are obtained, and a one-to-one mapping relationship between the virtual debt items and the deposit is formed, which is further refined, as shown in fig. 4, the data processing method in this embodiment may specifically include the following steps:
s410, acquiring bond information and security information in the original bond item; the original debt items comprise at least two escorts and at least two bonds, and the escorts and the bonds are in one-to-one correspondence.
And S420, determining the actual value of the corresponding security product according to the bond information.
Wherein, the actual value can be understood as the minimum value of the valuation of the total value corresponding to all the escorts in the original debt.
In this embodiment, the actual value corresponding to each of the wagers in the original debt item may be determined according to the bond face value, the bond issuance value, the bond net value and the bond number included in the bond information. Specifically, the minimum value of the bond corresponding to the evaluation value of the bond can be determined according to the face value of the bond, the issued value of the bond and the net value of the bond contained in the bond information, and the actual value of each security can be determined according to the minimum value of the bond and the number of the bonds.
In one embodiment, determining the actual value of the corresponding collateral based on the bond information includes:
determining the minimum value of the bond according to the bond information;
and determining the actual value of the corresponding security according to the minimum value of the bonds and the number of the bonds.
Wherein, the minimum value of the bond refers to the minimum value of the valuation of the corresponding total value of each security in the original bond.
In this embodiment, the bond face value, the bond issuance value, the bond net value and the bond number may be utilized to establish a bond valuation model, and the bond face value, the bond issuance value, the bond net value and the bond number are input into the bond valuation model to calculate the actual value of the security corresponding to each security in the original bond item. Specifically, the bond valuation model can be represented by the following formula:
value＝min(faceVal,issuVal,netVal)*n
wherein n is the bond number, faceVal is the bond face value, issuVal bond issue value, netVal represents the bond net value, and value is the actual value.
And S430, determining the assignable value of the corresponding deposit in the original debt item according to the actual value and the offset deposit rate.
In the present embodiment, the mortgage rate refers to a ratio of the secured principal balance to the bond valuation. It should be noted that the pledge rate may be defined manually according to the requirement analysis, or may be defined according to the actual experience of the business department, which is not limited herein in this embodiment.
In this embodiment, the assignable value of the corresponding deposit in the original debt item can be determined by the actual value of each deposit in the original debt item and the mortgage rate corresponding to the deposit. Specifically, the calculation formula of the assignable value corresponding to the escort is as follows:
availableVal＝value*m
wherein availableVal is an allocable value corresponding to the deposit, value is an actual value, and m is a mortgage rate.
In this embodiment, the pledge rate, pledge type, risk level, etc. corresponding to each pledge in the original debt item may be obtained according to a pre-established data table in the database; the determination may also be performed based on a target multilayer convolutional neural network created in advance, and the embodiment is not limited herein.
Optionally, table 1 shows a relationship table of the offset mortgage rate, the types of the mortgages, and the risk levels, as shown in table 1, the risk levels are sorted according to the priority from small to large, and each mortgage type corresponds to a corresponding risk level and the offset mortgage rate. The corresponding relationship between the deposit type, risk level and the offset rate can be obtained from table 1 to determine the offset rate of the deposit in the original debt item, and then the offset rate is substituted into the calculation formula of the assignable value of the deposit to determine the assignable value of the corresponding deposit in the original debt item. In table 1, the risk rating is 100, the minimum risk rating, and 800, the maximum risk rating.
Table 1: relationship table of the mortgage rate, the type of the mortgage and the risk level
Figure BDA0003873156510000111
Optionally, the bond attribute information of each bond in the original bond item can also be input into the target multilayer convolutional neural network through the bond attribute information of each bond in the original bond item, so as to obtain the corresponding risk level of the bond and the offset rate of the deposit corresponding to the bond.
S440, carrying out priority sequencing on the escorts in the original debt items according to a preset escort sequencing rule to obtain a sequenced escort list, splitting the original debt items based on the assignable value of each escort in the escort list to obtain at least two corresponding virtual debt items.
In this embodiment, the escortions in the original debt item may be prioritized and sorted according to the risk level corresponding to the escortion, the rating of the issuer registration place, the credit risk assessment result of the issuer, the amount of the escortion, and the number of the escortion, so as to obtain a sorted escortion list, and on this basis, the original debt item is split according to the assignable value of each escortion in the escortion list, and the debt balance is sequentially covered, so as to obtain at least two corresponding virtual debt items.
In an embodiment, the method for performing priority ranking on the deposit in the original debt item according to a preset deposit ranking rule to obtain a ranked deposit list includes:
and sequentially carrying out priority sequencing on the escorts in the original debt items according to the risk level of the bond, the rating of the issuer registration place and the credit risk evaluation result of the issuer to obtain a sequenced escort list.
In this embodiment, the security in the original debt item may be prioritized according to the risk level of the bond, the rating of the issuer registered location, and the credit risk assessment result of the issuer in sequence, so as to obtain the corresponding security list after the ranking. Specifically, sorting according to the priorities of the bond risk level from small to large, the issuer registration place rating from good to bad, and the issuer credit risk assessment result from good to bad, so as to obtain a list of the escorts after the sorting of the priorities. In addition, the security value and the security number can be sorted according to the priority from large to small to obtain the security list after the priority sorting.
Note that the bond risk level, issuer registry rating, and issuer credit risk assessment result may be determined by expert judgment. For example, the acceptance level of the current escort is higher, and the corresponding risk level of the bond is lower.
In one embodiment, the bond risk level and the mortgage rate are determined based on a pre-created target multi-layer convolutional neural network.
The target multilayer convolutional neural network refers to the multilayer convolutional neural network after sample training is carried out. The target multilayer convolutional neural network can comprise a pooling layer, a convolutional layer and a full-connection layer, the size of a specific pooling kernel and a corresponding step length, the size of a convolutional kernel and a corresponding step length can be adjusted according to the dimension corresponding to the bond attribute information of each bond.
In this embodiment, the bond attribute information of each bond in the original bond item may be input into the target multilayer convolutional neural network to obtain the corresponding risk level of the bond and the offset rate of the deposit corresponding to the bond.
In one embodiment, the bond risk level and the offset rate are determined based on a pre-created target multilayer convolutional neural network, and the determination comprises the following steps:
acquiring bond attribute information of each bond in the original bond items;
and inputting the attribute information of the bond into a target multilayer convolutional neural network to obtain the corresponding risk level of the bond and the rejection rate of the deposit corresponding to the bond.
In this embodiment, the attribute information of the bond may include a category corresponding to the bond, a rating of a registered issuer, a credit risk assessment result of the issuer, a positive correlation of the bond, and attribute information of other dimensions of the bond.
In this embodiment, by acquiring the category corresponding to each bond in the original bond, the issuer registration place rating, the issuer credit risk assessment result, the positive correlation relationship of the bonds, and the bond attribute information of other dimensions of the bonds, the bond attribute information is input into the target multilayer convolutional neural network, so as to obtain the corresponding bond risk level and the mortgage rate of the mortgage corresponding to the bond.
Illustratively, the method comprises the steps of selecting multiple bond-type offset products as samples based on pre-acquired inline data and/or extravehicular data, labeling the risk level and the offset rate of bonds in a manner of expert study and judgment, and constructing a training sample library. With a single sample B i For example, the bond attribute information of each bond is respectively expressed as: p is a radical of 1 Indicates the bond type, p 2 Represents the rating of the issuer registry, p 3 Indicating issuer Standard Credit Risk assessment results, p 4 In addition to representing the positive correlation of bonds, attribute information of other dimensions of bonds can be used as a training index p as required n Single sample B i Can be expressed as follows: b is i ＝[p 1 ,p 2 ,p 3 ,p 4 ,...,p n ]A 1 to B i ＝[p 1 ,p 2 ,p 3 ,p 4 ,...,p n ]Inputting into target multilayer convolution neural network to obtain corresponding bond risk grade and the offset rate of deposit corresponding to bond, and using S i Wherein r represents the bond risk level, m represents the offset deposit rate S i ＝[r,m]And is a row vector of (1 x 2).
In one embodiment, the creation process of the target multilayer convolutional neural network includes:
selecting a plurality of pledge types of bond based on pre-acquired inline data and/or offline data;
taking an offset pledge, a bond risk grade with a label and an offset pledge rate as training samples;
and inputting the training samples into the original multilayer convolutional neural network to obtain a corresponding target multilayer convolutional neural network.
The original multilayer convolutional neural network refers to a multilayer convolutional neural network for sample training. The original multi-layer convolutional neural network may contain pooling layers, convolutional layers, fully-connected layers.
In this embodiment, inline data refers to data internal to a bank and may include monetary pledges, money orders, book tickets, deposit slips, bonds, financial bonds, and the like. The out-of-bank data refers to data of a third party organization outside the bank, and can include life insurance policies, bills, shares, stocks and the like with specific cash value which can be pledged by law.
In this embodiment, based on the in-line data and/or the out-of-line data acquired in advance, the offset products of a plurality of bond categories are selected, the offset products, the bond risk levels with labels and the offset rate are used as training samples, and the training samples are input to the original multilayer convolutional neural network to obtain the corresponding target multilayer convolutional neural network. Note that the bond risk level may be determined by means of expert judgment, experience, and the like, for example, subjectively, if the acceptability degree of the current collateral is considered to be relatively high, it may be considered that the bond risk level corresponding to the current collateral is relatively low, and therefore, when marking the bond risk level, the marking means may be the marking "1" with the high bond risk level, the marking "0" with the low bond risk level, the marking "H" with the high bond risk level, and the marking "L" with the low bond risk level, and the marking means in the bond risk level in this embodiment is not limited. Similarly, in this embodiment, the mortgage rate may also be set by the service department according to expert experience, analysis, and the like, and the way of marking the mortgage rate is similar to the above way, which is not limited in this embodiment.
S450, forming a one-to-one mapping relation between the virtual debt items and the security products based on the virtual debt items and the corresponding security products.
In this embodiment, the original debt items are split based on the assignable value of each escort in the escort list, after at least two corresponding virtual debt items are obtained, the balance of the debt items is sequentially covered, and a one-to-one mapping relationship between the virtual debt items and the escorts is formed.
Illustratively, the number of the debt item corresponding to the original debt item is 00001, the balance y of the debt item, the number of the original debt item is 10000, there are 3 deposits, which are deposit 1, corresponding deposit number 10000, assignable value a, and risk level 100; deposit 2, corresponding deposit number 20000, assignable value b, and risk level 200; deposit 3, corresponding deposit number 30000, assignable value c, risk level 300; in this embodiment, the method includes performing priority sorting on an escort in original debt items according to a preset escort sorting rule to obtain a sorted escort list, splitting original debt items 10000 based on an assignable value of each escort in the escort list to obtain corresponding 3 virtual debt items, where each virtual debt item corresponds to a corresponding virtual debt item number (the corresponding virtual debt item numbers are: 000011, 000012, and 000013 in sequence), and forming a one-to-one mapping relationship between a virtual debt item and an escort based on the virtual debt item and the corresponding escort, as shown in table 2 below. Table 2 is a table of one-to-one mapping relationship between the virtual debt items and the corresponding escort formed by the virtual debt items and the corresponding escorts, and the ordering rule of the escorts in this embodiment is as follows: the risk grade is from small to large, the grade of the issuer registration place is from good to bad, the evaluation result of the issuer standard credit risk is from good to bad, the amount of the deposit is from large to small, and the number of the deposit is from small to large.
Table 2: the virtual debt items and the corresponding escortions form a one-to-one mapping relation table between the virtual debt items and the escortions
Figure BDA0003873156510000151
As shown in table 2, the original debt is split based on the assignable value of each escort, and when the balance of the debt is covered, virtual debt 000011 is covered by escort 10000, virtual debt 000012 is covered by escort 20000, and virtual debt 000013 is covered by escort 30000, that is, when virtual debt covering is performed, the assignable value of the escort of escort 10000 is a, the balance a of the debt can be covered, and the corresponding risk exposure is 0, that is, there is no risk exposure; the value of the deposit 20000 can be assigned to b to cover the balance b of the debt, the corresponding risk exposure is 0, the value of the deposit 30000 can be assigned to c, the balance y of the debt can cover the minimum value y-a-b corresponding to c, and the risk exposure is: y-a-b-Min (c, y-a-b).
According to the technical scheme in the embodiment, the escorts in the original debt items are subjected to priority ranking according to a preset escort ranking rule to obtain a ranked escort list, the original debt items are split based on the assignable value of each escort in the escort list to obtain at least two corresponding virtual debt items, and therefore the debt security item with the highest priority can be enabled to cover the debt items preferentially, and the exposure risk is reduced.
In an embodiment, fig. 5 is a block diagram of a data processing apparatus according to an embodiment of the present invention, which is suitable for a situation when a financial institution performs a splitting process on collateral data, and the apparatus may be implemented by hardware/software. The method can be configured in the electronic device to implement a data processing method in the embodiment of the invention. As shown in fig. 5, the apparatus includes: an information acquisition module 510, a value determination module 520, and a splitting module 530.
The information obtaining module 510 is configured to obtain bond information and escort information in an original debt item; the original debt items comprise at least two escorts and at least two bonds, and the escorts correspond to the bonds one by one;
a value determining module 520, configured to determine, according to the bond information and the deposit information, an assignable value of a corresponding deposit in the original debt item;
the splitting module 530 is configured to split the original debt items based on the assignable value and a preconfigured deposit sorting rule to obtain at least two virtual debt items, and form a one-to-one mapping relationship between the virtual debt items and the deposit.
According to the embodiment of the invention, the information acquisition module splits the original debt items in the original debt items into one-to-many complex relationships with the escort by the aid of the information acquisition module and the splitting module, and splits the original debt items according to the distributable value of the escort and the preset escort sequencing rule to form one-to-one mapping relationship between the virtual debt items and the escort, so that supervision reporting and report statistical analysis of the escort can be conveniently carried out, and meanwhile, the guarantee capability of the escort on the debt items can be accurately identified, and risk early warning can be timely carried out when the escort is insufficient.
In one embodiment, the collateral information at least comprises: the retention rate of the material; a value determination module 520 comprising:
the first price value determining unit is used for determining the actual value of the corresponding security product according to the bond information;
and the second value determining unit is used for determining the distributable value of the corresponding deposit in the original debt item according to the actual value and the mortgage rate.
In one embodiment, the first value determining unit includes:
the minimum value determining subunit is used for determining the minimum value of the bond according to the bond information;
and the actual value determining subunit is used for determining the actual value of the corresponding escort according to the minimum value of the bonds and the number of the bonds.
In one embodiment, the bond information includes at least one of: bond face value; bond issuance value; bond net value; the number of bonds.
In an embodiment, the splitting module 530 includes:
the debt item determining unit is used for carrying out priority sequencing on the escorts in the original debt items according to a preset escort sequencing rule to obtain a sequenced escort list; splitting the original debt items based on the assignable value of each escort in the escort list to obtain at least two corresponding virtual debt items;
and the mapping relation determining unit is used for forming one-to-one mapping relation between the virtual debt item and the escort on the basis of the virtual debt item and the corresponding escort.
In one embodiment, the debt determination unit comprises:
and the sequencing subunit is used for sequencing the priority of the escorts in the original debt items according to the risk level of the bond, the rating of the issuer registration place and the credit risk evaluation result of the issuer in sequence to obtain a sequenced escort list.
In an embodiment, the data processing module further includes:
the exposure determining unit is used for determining the current risk exposure of the original debt after the original debt is split based on the distributable value and a preset escort sequencing rule to obtain at least two virtual debts;
and the strategy execution unit is used for executing a corresponding processing strategy according to the current risk exposure and a pre-configured risk exposure threshold.
In an embodiment, the exposure determination unit includes:
a balance obtaining subunit, configured to obtain a debt balance of the original debt;
and the exposure determining subunit is used for determining the current risk exposure of the original debt according to the debt balance and the total value of the collateral contained in the original debt.
In one embodiment, a policy enforcement unit includes:
a result determining subunit, configured to determine an exposure comparison result between the current risk exposure and a pre-configured risk exposure threshold;
and the strategy execution subunit is used for executing a corresponding processing strategy according to the exposure comparison result.
In one embodiment, the bond risk level and the offset rate are both determined based on a pre-created target multi-layer convolutional neural network.
In one embodiment, the bond risk level and the offset rate are determined based on a pre-created target multi-layer convolutional neural network, including:
an information acquisition unit, configured to acquire bond attribute information of each bond in the original bond item;
and the information determining unit is used for inputting the bond attribute information into a target multilayer convolutional neural network to obtain the corresponding bond risk level and the rejection rate of the deposit corresponding to the bond.
In one embodiment, the creation process of the target multilayer convolutional neural network comprises:
selecting a plurality of pledge types of bond based on pre-acquired inline data and/or offline data;
taking the offset and pledge, and the bond risk level and the offset and pledge rate with the label as training samples;
and inputting the training sample into an original multilayer convolution neural network to obtain a corresponding target multilayer convolution neural network.
The data processing device provided by the embodiment of the invention can execute the data processing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
In an embodiment, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The electronic device 10 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 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number 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, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of 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, or the like. The processor 11 performs the various methods and processes described above, such as a data processing method.
In some embodiments, the data processing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may 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 into the RAM 13 and executed by the processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a 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 may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may 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. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
In an embodiment, the embodiment of the present invention further includes a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the data processing method according to any embodiment of the present invention.
Computer program product in implementing the computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (16)

1. A data processing method, comprising:
acquiring bond information and security information in the original debt item; the original debt items comprise at least two escorts and at least two bonds, and the escorts and the bonds are in one-to-one correspondence;
determining the assignable value of a corresponding escort in the original debt item according to the bond information and the escort information;
splitting the original debt items based on the distributable value and a preset escort sequencing rule to obtain at least two virtual debt items, and forming a one-to-one mapping relation between the virtual debt items and the escort.
2. The method of claim 1, wherein the collateral information includes at least: the retention rate of the material; the determining the assignable value of the corresponding escortion in the original debt item according to the bond information and the escortion information comprises the following steps:
determining the actual value of a corresponding escort according to the bond information;
and determining the distributable value of the corresponding deposit in the original debt item according to the actual value and the mortgage rate.
3. The method of claim 2, wherein determining an actual value of a corresponding collateral based on the bond information comprises:
determining the minimum value of the bond according to the bond information;
and determining the actual value of the corresponding security according to the minimum value of the bonds and the number of the bonds.
4. The method according to any one of claims 1-3, wherein the bond information includes at least one of: bond face value; bond issuance value; bond net value; the number of bonds.
5. The method of claim 1, wherein the splitting the original debt based on the assignable value and a pre-configured escort sorting rule to obtain at least two virtual debt and forming a one-to-one mapping relationship between the virtual debt and the escort comprises:
performing priority ranking on the security in the original debt items according to a preset security ranking rule to obtain a ranked security list; splitting the original debt items based on the assignable value of each escort in the escort list to obtain at least two corresponding virtual debt items;
and forming a one-to-one mapping relation between the virtual debt item and the escort based on the virtual debt item and the corresponding escort.
6. The method of claim 5, wherein the prioritizing the collateral in the original debt item according to a preconfigured collateral sorting rule to obtain a sorted list of collateral, comprising:
and sequentially carrying out priority sequencing on the escorts in the original debt items according to the risk level of the bond, the rating of the issuer registration place and the credit risk evaluation result of the issuer to obtain a sequenced escort list.
7. The method of claim 1, wherein after said splitting said original debt based on said allocatable value and pre-configured collateral sorting rules resulting in at least two virtual debt, further comprising:
determining a current risk exposure of the original debt;
and executing a corresponding processing strategy according to the current risk exposure and a preset risk exposure threshold.
8. The method of claim 7, wherein said determining a current risk exposure of said original debt comprises:
acquiring debt balance of the original debt;
determining a current risk exposure of the original debt item according to the debt balance and a total value of an collateral contained in the original debt item.
9. The method according to claim 7, wherein said performing a corresponding processing strategy according to said current risk exposure and a preconfigured risk exposure threshold comprises:
determining an exposure comparison result between the current risk exposure and a pre-configured risk exposure threshold;
and executing a corresponding processing strategy according to the exposure comparison result.
10. The method of claim 6, wherein the bond risk level and the mortgage rate are each determined based on a pre-created target multi-layer convolutional neural network.
11. The method of claim 10, wherein the bond risk level and the mortgage rate are each determined based on a pre-created target multi-layer convolutional neural network, comprising:
acquiring bond attribute information of each bond in the original bond items;
and inputting the bond attribute information into a target multilayer convolutional neural network to obtain the corresponding bond risk level and the rejection rate of the deposit corresponding to the bond.
12. The method according to claim 10 or 11, wherein the creation process of the target multilayer convolutional neural network comprises:
selecting a plurality of bond types of the collateral deposit based on the pre-acquired inline data and/or the extra-inline data;
taking the offset and pledge, and the bond risk level and the offset and pledge rate with the label as training samples;
and inputting the training sample into an original multilayer convolution neural network to obtain a corresponding target multilayer convolution neural network.
13. A data processing apparatus, comprising:
the information acquisition module is used for acquiring bond information and escort information in the original debt items; the original debt items comprise at least two escorts and at least two bonds, and the escorts and the bonds are in one-to-one correspondence;
the value determining module is used for determining the distributable value of a corresponding security in the original debt item according to the bond information and the security information;
and the splitting module is used for splitting the original debt items based on the distributable value and a preset escort sequencing rule to obtain at least two virtual debt items and form a one-to-one mapping relation between the virtual debt items and the escort.
14. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of claims 1-12.
15. A computer-readable storage medium, having stored thereon computer instructions for causing a processor, when executing the computer instructions, to implement the data processing method of any one of claims 1-12.
16. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, carries out the data processing method according to any one of claims 1-12.
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