CN115760379A - Method and device for determining repayment information of loan and terminal equipment - Google Patents
Method and device for determining repayment information of loan and terminal equipment Download PDFInfo
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Abstract
The disclosure provides a method, a device and terminal equipment for determining repayment information of a loan, and relates to the technical field of computers, wherein the method comprises the following steps: obtaining loan business data of a lender, wherein the loan business data comprise characteristic values of loan business in all dimensions; and determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension, and determining any reference repayment information as the repayment information corresponding to the loan service under the condition that the characteristic value of the loan service on the target dimension is matched with the reference characteristic value of any reference repayment information on the target dimension. Thereby improving the accuracy of determining repayment information for the loan.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for determining repayment information of a loan, and a terminal device.
Background
In order to control the risk of cash flow, it is necessary to accurately determine the repayment information for each loan in the loan pool on each future book age day. The repayment information can comprise a repayment proportion, a daily rate of return and a repayment proportion.
In the related art, repayment information of the loan on each account age day is generally determined according to the principal and interest rate of the loan due to each account age day agreed by the contract. However, the loan may be subject to early payment and default, resulting in inaccurate repayment information for the loan being determined.
Disclosure of Invention
The present disclosure presents a method and apparatus for determining repayment information for a loan to address at least the problem of inaccurate repayment information for a loan. The technical scheme of the disclosure is as follows:
obtaining loan service data of a lender, wherein the loan service data comprises characteristic values of loan services in all dimensions;
determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension;
and under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan service.
According to a second aspect of embodiments of the present disclosure, there is provided an apparatus for determining repayment information for a loan, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring loan service data of a lender, and the loan service data comprises characteristic values of loan services in all dimensions;
the determining module is used for determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension;
and the matching module is used for determining any reference repayment information as the repayment information corresponding to the loan service under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension.
According to a third aspect of the embodiments of the present disclosure, there is provided a terminal device, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute the instructions to carry out the method of determining repayment information for a loan as described in the embodiments of the first aspect above.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, wherein instructions, when executed by a processor of a terminal device, enable the terminal device to perform a method of determining repayment information for a loan as an embodiment of the above-described aspect.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising a computer program, which when executed by a processor, implements the method for determining repayment information for a loan of an embodiment of the above-described aspect.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects: after loan service data including characteristic values of loan services in all dimensions of a lending party are obtained, target dimensions corresponding to all preset reference repayment information and reference characteristic values of all the reference repayment information in the target dimensions are determined, and under the condition that the characteristic values of the loan services in the target dimensions are matched with the reference characteristic values of any reference repayment information in the target dimensions, any reference repayment information is determined to be repayment information corresponding to the loan services. Therefore, the accuracy of the reference repayment information is improved by presetting the reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension. And further, under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan service, so that the accuracy of determining the repayment information of the loan is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
Fig. 1 is a schematic flow chart illustrating a method for determining repayment information for a loan according to a first embodiment of the disclosure;
fig. 2 is a schematic flow chart of another method for determining repayment information for a loan, provided in accordance with a second embodiment of the disclosure;
fig. 3 is a schematic flow chart illustrating another method for determining repayment information for a loan, according to a third embodiment of the disclosure;
fig. 4 is a schematic structural diagram of an apparatus for determining repayment information of a loan, according to a fourth embodiment of the disclosure;
fig. 5 is a block diagram illustrating a terminal device for determining repayment information for a loan, according to an example embodiment.
Detailed Description
In order to make the technical solutions of the present disclosure better understood, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing 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 disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
In the technical scheme of the disclosure, the data acquisition, storage, use, processing and the like all conform to relevant regulations of national laws and regulations.
According to the method and the system, the reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension are preset in the system, and therefore the accuracy of the reference repayment information is improved. And further, under the condition that the characteristic value of the loan transaction in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan transaction, so that the accuracy of determining the repayment information is improved.
The method for determining the repayment information of the loan according to the embodiment of the disclosure is executed by the device for determining the repayment information of the loan (hereinafter referred to as the determining device) provided by the embodiment of the disclosure, and the device can be configured in equipment such as computer equipment and the like to determine the repayment information of the loan so as to solve the problem that the determined repayment information is inaccurate.
The method and apparatus for determining repayment information for a loan according to embodiments of the disclosure is described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a method for determining repayment information for a loan, including the following steps, provided by an embodiment of the disclosure.
The dimensions may include lender information and loan information, such as a region where the lender is located, an industry to which the lender belongs, overdue repayment times of the lender, credit line granted by the lender, etc., a guarantee mode of the loan, a repayment mode of the loan, an annual interest rate of the loan contract, etc., which is not limited by the disclosure.
In the disclosure, a lender initiates a loan request to a loan provider through a lending client, and the loan provider delivers a loan to the lender after the loan approval is completed. At the same time, the loan transaction data in the loan request may be stored in the system. In addition, when the lender makes a payment every time, the payment information of each payment can be added to the corresponding loan transaction data. Optionally, the repayment information may include a repayment date, a repayment amount, a payment amount, and a payment amount. Or a repayment index value such as a repayment proportion, a daily profit margin, and a reimbursement proportion determined from the repayment amount, the payment amount, and the like, which is not limited by the present disclosure.
And 102, determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension.
In the disclosure, the preset reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension may be analyzed and predetermined on the data table of the historical loan through a repayment information prediction algorithm, and are preset in the system. The data table comprises loan service data of historical loans, and the target dimension can be a dimension influencing repayment information.
Or, the target dimension may be determined according to the feature value of each dimension in the data tables of the multiple historical loans and the repayment information in the multiple data tables, the multiple data tables are grouped based on the feature value of the target dimension in each data table, and then the reference feature value of each group in the target dimension and the corresponding reference repayment information are determined according to the feature value of the target dimension included in the data table of each group and the repayment information in the data table of each group.
Therefore, the target dimension, the plurality of reference repayment information and the reference characteristic value of each reference repayment information on the target dimension are determined by analyzing the data tables of the plurality of historical loans. Thereby improving the accuracy of the reference repayment information. And further improves the accuracy of determining repayment information of the loan.
And 103, under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan service.
In the disclosure, the difference between the characteristic value of the loan transaction in the target dimension and the reference characteristic value of each piece of reference repayment information in the target dimension may be calculated, and the smaller the difference, the closer the characteristic value of the loan transaction in the target dimension is to the reference characteristic value of the reference repayment information in the target dimension. Therefore, the reference repayment information corresponding to the minimum difference value can be determined as the repayment information of the loan service.
Optionally, when there are a plurality of loan transactions, the total repayment value may be determined according to the repayment information corresponding to each loan transaction. Specifically, the characteristic value of each loan transaction in the target dimension is matched with the reference characteristic value of the reference repayment information in the target dimension, and the repayment information corresponding to each loan transaction is determined. And then, determining the repayment amount of the loan on each account date according to the repayment proportion of the loan on each account date and the initial unrepaired principal. And determining the payment amount of the loan on each account age day according to the daily income rate, the initial outstanding principal and the account age difference of the loan on each account age day. And determining the payment amount of the loan on each account age day according to the payment proportion and the payment amount of the loan on each account age day. Then, the repayment amount, the payment amount and the payment amount of each loan on each account date are summarized, and the total repayment value of each account date can be determined.
In the disclosure, after loan service data including characteristic values of loan services in various dimensions of a lending party are obtained, a target dimension corresponding to preset reference repayment information and a reference characteristic value of each reference repayment information in the target dimension are determined, and under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, any reference repayment information is determined to be repayment information corresponding to the loan service. Therefore, the accuracy of the reference repayment information is improved by presetting the reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension. And further, under the condition that the characteristic value of the loan transaction in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan transaction, so that the accuracy of determining the repayment information of the loan is improved.
Fig. 2 is another method for determining repayment information for a loan, provided by an embodiment of the disclosure.
As shown in fig. 2, the method includes:
The repayment information of the historical loan can be calculated and determined through the related characteristic values in the corresponding data table and is stored in the data table. For example, the repayment proportion may be determined according to the repayment amount and the initial outstanding principal of each account date recorded in the data table. The first account age day outstanding principal of the loan is the loan contract amount in the data table. The loan at the beginning of each account age day is the initial unrepaired principal of the previous account age day, the repayment amount of the previous account age day is subtracted, and the payment amount of the previous account age day is added.
Optionally, exception processing may be performed on the data tables corresponding to the multiple historical loans, so as to remove the data table containing the abnormal data, thereby improving the accuracy of the determined repayment information. For example, a data table with no records yet can be deleted, and a data table with a data loss rate of more than 30% can be deleted. Alternatively, for individual missing data, sample means, modes, interpolation or padding with appropriate historical data is used.
In the method, the degree of influence of the characteristic values of all dimensions on the repayment information of the loan is different, the dimension with small influence on the repayment information can be removed, the target dimension with large influence on the repayment information is screened out, interference factors are removed, and the accuracy of determining the repayment information is improved.
Optionally, the target dimension may be determined according to the number of co-occurrences between each feature value in each dimension in the plurality of data tables and the repayment information. For example, if the ratio of the number of times that a certain region corresponds to repayment information in a certain range under the region dimension to the total number of times that the region appears is greater than 90%, it is indicated that the region dimension is related to the repayment information, and the region dimension can be determined as the target dimension.
Optionally, a preset correlation analysis algorithm may be used to perform correlation analysis on the feature values of the same dimension in the multiple data tables and the payment information in the data table where the feature values of the same dimension are located, so as to determine the correlation between each dimension and the payment information. And in the case that the correlation degree between any dimension and the repayment information is greater than a preset threshold, the repayment information of the loan is influenced by the dimension, and the dimension can be determined to be the target dimension.
Optionally, when the repayment information includes a plurality of repayment index values, correlation analysis may be performed on feature values of the same dimension in the plurality of data tables and each index value in the data table where each feature value of the same dimension is located, and a correlation between each dimension and each index is determined, and then, the correlation between each dimension and the repayment information may be determined according to the correlation between each dimension and each index. For example, the correlation between each dimension and the repayment proportion, the daily profitability and the expenditure proportion can be determined, and the weighted sum of the correlation between each dimension and the repayment proportion, the daily profitability and the expenditure proportion can be determined as the correlation between each dimension and the repayment information. Alternatively, the maximum correlation between the payment proportion, the daily rate of return and the spending proportion in each dimension can be determined as the correlation between each dimension and the payment information.
And step 203, grouping the plurality of data tables based on the characteristic value on the target dimension in each data table.
In the present disclosure, a clustering algorithm may be utilized to group a plurality of data tables based on the distance between characteristic values on target dimensions in each data table. And when the information value of the grouping result is maximum, stopping clustering.
Alternatively, a regression decision tree method may be used to group the data tables based on the feature values in the target dimension in each data table. And when the mean square error of the grouping result is minimum, determining the grouping result as a final grouping result.
And step 204, determining a reference characteristic value of each group on the target dimension and corresponding reference repayment information according to the characteristic value on the target dimension contained in the data table of each group and the repayment information in the data table of each group.
In the present disclosure, the mean value of the feature values in the target dimension included in the data table of a certain group may be determined as the reference feature value in the target dimension of the group. The average value of the repayment information in the data table in each group can be determined as the reference repayment information corresponding to each group.
Alternatively, the sum of the repayment amount, the sum of the payment amount, and the sum of the payment amount of each loan transaction in a certain group per account date may be determined as the repayment amount, the payment amount, and the payment amount corresponding to each account date in the group. For example, the amount of money returned by the group 1 at the account age of 46 days = the sum of the amounts of money returned by the respective loan transactions at the account age of 46 days in the group 1. Wherein, the repayment amount, the payment amount and the payment amount of each loan transaction on each account age date are contained in the corresponding data tables.
Then, the principal that a certain group has not paid for at the beginning of the last account date of the current account date, the amount of money returned by the group on the last account date is subtracted, and the amount of money paid by the group on the last account date is added, so that the group is determined to have not paid for at the beginning of the current account date. For example, the initial outstanding principal for group k at account age n = initial outstanding principal for group k on the previous account age day-the remaining amount of group k on the previous account age day + the payment amount of group k on the previous account age day. The sum of the initial unpaid principal of each loan transaction in a certain group of each account-age day may be determined as the initial unpaid principal of the group on each account-age day.
And determining the ratio of the total number of the counter books of a certain group on each account-age day to the initial outstanding fund of the group on each account-age day as the counter book ratio of the group on each account-age day. The ratio of the total of the charges of a certain group on each account date to the amount of the return money of the group on each account date is determined as the charge proportion of the group on each account date. And determining the ratio of the payment sum of a certain group on each account date to the product of the outstanding principal and the account difference of the group on each account date as the daily yield of the group on each account date.
Optionally, steps 201-205 may be operated off-line or on-line, which is not limited by the present disclosure.
And step 206, determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension.
And step 207, under the condition that the characteristic value of the loan transaction in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan transaction.
In the present disclosure, the specific implementation process of step 205 to step 207 may refer to the detailed description of any embodiment of the present disclosure, and is not described herein again.
According to the method and the device, the reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension are determined by grouping and analyzing the data tables of the historical loans, and the accuracy of the reference repayment information is improved. And further, under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan service, so that the accuracy of determining the repayment information of the loan is improved.
Fig. 3 is another method for determining repayment information for a loan, provided by an embodiment of the disclosure.
As shown in fig. 3, the method includes:
In the present disclosure, for a specific implementation process of step 301, reference may be made to detailed description of any embodiment of the present disclosure, and details are not repeated here.
In this disclosure, in order to facilitate analysis of whether a value of each dimension affects payment information, when a feature value in any dimension is a non-numerical type, each feature value in any dimension may be encoded to determine a reference value corresponding to each feature value in any dimension. For example, the feature value in the gender dimension may be a male or a female, and each feature value in the gender dimension may be encoded to determine that the reference value corresponding to the feature value male is 1 and the reference value corresponding to the feature value female is 0.
In this disclosure, in order to make the distribution of the reference values more reasonable, and thus whether the value of each dimension affects the analysis of the repayment information more accurately, each feature value in a dimension may be corrected by using a difference between the reference value corresponding to each feature value in the dimension and a mean value of the reference value corresponding to each feature value in the dimension.
And step 304, determining a target dimension according to the characteristic value of each dimension in the plurality of data tables and the repayment information in the plurality of data tables.
In the present disclosure, the specific implementation process of step 304 may refer to the detailed description of any embodiment of the present disclosure, and is not described herein again.
And 305, under the condition that the target dimensions are multiple, performing multiple collinearity analysis on the characteristic values of the multiple target dimensions, and determining a correlation coefficient between the target dimensions.
In the present disclosure, after analyzing whether each dimension has an influence on the repayment information based on a single dimension, a plurality of target dimensions having an influence on the repayment information may be determined. There may be strong correlation between multiple target dimensions, i.e. multiple target dimensions have the same effect on the payment information. Therefore, the multiple target dimensions with strong correlation can be subjected to duplicate removal, redundant variables are removed, and the accuracy of grouping the data table is improved.
In the present disclosure, a preset multi-red collinear analysis algorithm may be used to perform multiple collinear analysis on the feature values in multiple target dimensions, and determine the correlation coefficient between the target dimensions.
And step 306, removing the duplication of the multiple target dimensions according to the correlation coefficient among the target dimensions.
In the present disclosure, when the correlation coefficient between a certain target dimension and another target dimension is high, it indicates that the two target dimensions are strongly correlated, and the two target dimensions have the same influence on the payment information. At this time, a target dimension having a large correlation with the payment information may be retained. Or, one of the target dimensions may be selected to be reserved according to the service requirement. For example, if the historical successful borrowing times and the historical normal repayment times are strongly correlated, one of the times may be retained.
And 308, determining a reference characteristic value of each group on the target dimension and corresponding reference repayment information according to the characteristic value on the target dimension contained in the data table of each group and the repayment information in the data table in each group.
Step 309, obtaining loan transaction data of the lender, wherein the loan transaction data comprises characteristic values of the loan transaction in each dimension.
And 310, determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension.
And 311, under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan service.
In the present disclosure, the specific implementation process of step 307 to step 311 may refer to the detailed description of any embodiment of the present disclosure, and is not described herein again.
According to the method and the device, the reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension are determined by grouping and analyzing the data tables of the historical loans, and the accuracy of the reference repayment information is improved. And further, under the condition that the characteristic value of the loan transaction in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan transaction, so that the accuracy of determining the repayment information of the loan is improved.
Fig. 4 is a block diagram illustrating an apparatus for determining repayment information for a loan, in accordance with an exemplary embodiment. Referring to fig. 4, the apparatus includes an obtaining module 410, a determining module 420, and a matching module 430.
The obtaining module 410 is configured to obtain loan transaction data of a lender, where the loan transaction data includes feature values of loan transactions in various dimensions;
the determining module 420 is configured to determine a target dimension corresponding to each preset reference payment information and a reference feature value of each reference payment information in the target dimension;
the matching module 430 is configured to determine any reference repayment information as repayment information corresponding to the loan service when the feature value of the loan service in the target dimension matches the reference feature value of any reference repayment information in the target dimension.
In a possible implementation manner of the embodiment of the present disclosure, the determining module 420 is further configured to:
obtaining data tables corresponding to a plurality of historical loans respectively, wherein the data tables comprise repayment information of the historical loans and characteristic values of the historical loans in all dimensions;
determining a target dimension according to the characteristic value of each dimension in the plurality of data tables and the repayment information in the plurality of data tables;
grouping the data tables based on the characteristic value on the target dimension in each data table;
and respectively determining the reference characteristic value of each group on the target dimension and the corresponding reference repayment information according to the characteristic value on the target dimension contained in the data table of each group and the repayment information in the data table in each group.
In a possible implementation manner of the embodiment of the present disclosure, the determining module 420 is configured to:
and determining the target dimension according to the co-occurrence times of each characteristic value under each dimension in the plurality of data tables and the repayment information.
In a possible implementation manner of the embodiment of the present disclosure, the determining module 420 is configured to:
carrying out correlation analysis on the feature values of the same dimension in the multiple data tables and the repayment information in the data tables where the feature values of the same dimension are located, and determining the correlation degree between each dimension and the repayment information;
and under the condition that the correlation between any dimension and the repayment information is greater than a preset threshold value, determining any dimension as a target dimension.
In a possible implementation manner of the embodiment of the present disclosure, the apparatus further includes a modification module, configured to:
under the condition that the characteristic value in any dimension is non-numerical, coding each characteristic value in any dimension, and determining a reference value corresponding to each characteristic value in any dimension;
and correcting each characteristic value in any dimension by using the difference between the reference value corresponding to each characteristic value in any dimension and the mean value of the reference value corresponding to each characteristic value in any dimension.
In a possible implementation manner of the embodiment of the present disclosure, the apparatus further includes a duplicate removal module, configured to:
under the condition that the target dimensions are multiple, performing multiple collinearity analysis on the characteristic values of the multiple target dimensions, and determining a correlation coefficient between the target dimensions;
and according to the correlation coefficient among the target dimensions, removing the duplication of the target dimensions.
In a possible implementation manner of the embodiment of the present disclosure, the determining module 420 is further configured to:
and under the condition that the loan service is multiple, determining a total repayment value according to repayment information corresponding to each loan service.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
In the disclosure, after loan service data including characteristic values of loan services in various dimensions of a lending party are obtained, a target dimension corresponding to preset reference repayment information and a reference characteristic value of each reference repayment information in the target dimension are determined, and under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, any reference repayment information is determined to be repayment information corresponding to the loan service. Therefore, the accuracy of the reference repayment information is improved by presetting the reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension. And further, under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan service, so that the accuracy of determining the repayment information of the loan is improved.
Fig. 5 is a block diagram illustrating a terminal device for determining repayment information for a loan, according to an example embodiment.
As shown in fig. 5, the terminal device 500 includes:
a memory 510 and a processor 520, a bus 530 connecting the various components (including the memory 510 and the processor 520), the memory 510 storing a computer program that, when executed by the processor 520, implements the method of determining repayment information for a loan according to embodiments of the disclosure.
A program/utility 580 having a set (at least one) of program modules 570 may be stored, for instance, in memory 510, such program modules 570 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. The program modules 570 generally perform the functions and/or methods of the embodiments described in this disclosure.
The terminal device 500 may also communicate with one or more external devices 590 (e.g., keyboard, pointing device, display 591, etc.), one or more devices that enable a user to interact with the terminal device 500, and/or any devices (e.g., network card, modem, etc.) that enable the terminal device 500 to communicate with one or more other computing devices. Such communication may occur over input/output (I/O) interfaces 592. Also, the terminal device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 593. As shown, the network adapter 593 communicates with the other modules of the terminal device 500 over a bus 530. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the terminal device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processor 520 executes various functional applications and data processing by executing programs stored in the memory 510.
It should be noted that, for the implementation process and the technical principle of the terminal device of this embodiment, reference is made to the foregoing explanation of the method for determining repayment information of a loan according to the embodiment of the present disclosure, and details are not described herein again.
According to the method, after loan business data of a lender, which comprise characteristic values of loan businesses in all dimensions, are obtained, target dimensions corresponding to preset reference repayment information and reference characteristic values of all the reference repayment information in the target dimensions are determined, and under the condition that the characteristic values of the loan businesses in the target dimensions are matched with the reference characteristic values of any one of the reference repayment information in the target dimensions, any one of the reference repayment information is determined to be repayment information corresponding to the loan businesses. Therefore, the accuracy of the reference repayment information is improved by presetting the reference repayment information, the target dimension and the reference characteristic value of each reference repayment information on the target dimension. And further, under the condition that the characteristic value of the loan transaction in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension, determining any reference repayment information as repayment information corresponding to the loan transaction, so that the accuracy of determining the repayment information of the loan is improved.
In an exemplary embodiment, the present disclosure also provides a computer readable storage medium comprising instructions, such as a memory comprising instructions, executable by a processor of a terminal device to perform the above method. Alternatively, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
To implement the above embodiments, the present disclosure also provides a computer program product, which, when executed by a processor of a terminal device, enables the terminal device to perform the method of determining repayment information for a loan as described above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (17)
1. A method of determining repayment information for a loan, comprising:
obtaining loan business data of a lender, wherein the loan business data comprise characteristic values of the loan business in all dimensions;
determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension;
and under the condition that the characteristic value of the loan service on the target dimension is matched with the reference characteristic value of any reference repayment information on the target dimension, determining that the any reference repayment information is repayment information corresponding to the loan service.
2. The method of claim 1, wherein before the determining the target dimension corresponding to each preset reference payment information and the reference characteristic value of each reference payment information in the target dimension, the method further comprises:
obtaining data tables corresponding to a plurality of historical loans respectively, wherein the data tables comprise repayment information of the historical loans and characteristic values of the historical loans in all dimensions;
determining the target dimension according to the characteristic value of each dimension in the data tables and the repayment information in the data tables;
grouping a plurality of the data tables based on the characteristic value on the target dimension in each data table;
and respectively determining the reference characteristic value of each group on the target dimension and the corresponding reference repayment information according to the characteristic value on the target dimension contained in the data table of each group and the repayment information in the data table in each group.
3. The method of claim 2, wherein determining the target dimension according to the eigenvalue of each of the plurality of data tables and the repayment information in the plurality of data tables comprises:
and determining the target dimension according to the co-occurrence times of each characteristic value and repayment information under each dimension in the data tables.
4. The method of claim 2, wherein determining the target dimension according to the eigenvalue of each of the plurality of data tables and the repayment information in the plurality of data tables comprises:
carrying out correlation analysis on the feature values of the same dimension in the data tables and the repayment information of the feature values of the same dimension in the data tables, and determining the correlation degree between each dimension and the repayment information;
and under the condition that the correlation degree between any dimension and the repayment information is larger than a preset threshold value, determining that any dimension is the target dimension.
5. The method of claim 2, wherein prior to determining the target dimension based on the eigenvalues of each of the plurality of data tables and repayment information in the plurality of data tables, further comprising:
under the condition that the characteristic value in any dimension is non-numerical, coding each characteristic value in any dimension, and determining a reference value corresponding to each characteristic value in any dimension;
and correcting each characteristic value in any dimension by using the difference between the reference value corresponding to each characteristic value in any dimension and the mean value of the reference value corresponding to each characteristic value in any dimension.
6. The method of claim 2, wherein after determining the target dimension according to the eigenvalue of each of the plurality of data tables and the payment information in the plurality of data tables, further comprising:
under the condition that the target dimensions are multiple, performing multiple collinearity analysis on the characteristic values on the multiple target dimensions, and determining a correlation coefficient between the target dimensions;
and according to the correlation coefficient between the target dimensions, carrying out deduplication on the target dimensions.
7. The method of claim 1, further comprising:
and under the condition that the loan services are multiple, determining a total repayment value according to repayment information corresponding to each loan service.
8. An apparatus for determining repayment information for a loan, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring loan service data of a lender, and the loan service data comprises characteristic values of the loan service in all dimensions;
the determining module is used for determining a target dimension corresponding to each preset reference repayment information and a reference characteristic value of each reference repayment information on the target dimension;
and the matching module is used for determining that any reference repayment information is repayment information corresponding to the loan service under the condition that the characteristic value of the loan service in the target dimension is matched with the reference characteristic value of any reference repayment information in the target dimension.
9. The apparatus of claim 8, wherein the determination module is further configured to:
obtaining data tables corresponding to a plurality of historical loans respectively, wherein the data tables comprise repayment information of the historical loans and characteristic values of the historical loans in all dimensions;
determining the target dimension according to the characteristic value of each dimension in the data tables and the repayment information in the data tables;
grouping a plurality of the data tables based on the characteristic value on the target dimension in each data table;
and respectively determining a reference characteristic value of each group on the target dimension and corresponding reference repayment information according to the characteristic value on the target dimension contained in the data table of each group and the repayment information in the data table in each group.
10. The apparatus of claim 9, wherein the determination module is to:
and determining the target dimension according to the co-occurrence times of each characteristic value and repayment information under each dimension in the data tables.
11. The apparatus of claim 9, wherein the determination module is to:
performing correlation analysis on the repayment information in the data tables of the feature values of the same dimension in the multiple data tables and the feature values of the same dimension, and determining the correlation degree between each dimension and the repayment information;
and under the condition that the correlation degree between any dimension and the repayment information is larger than a preset threshold value, determining that any dimension is the target dimension.
12. The apparatus of claim 9, further comprising a correction module to:
under the condition that the characteristic value in any dimension is non-numerical, coding each characteristic value in any dimension, and determining a reference value corresponding to each characteristic value in any dimension;
and correcting each characteristic value in any dimension by respectively using the difference between the reference value corresponding to each characteristic value in any dimension and the mean value of the reference value corresponding to each characteristic value in any dimension.
13. The apparatus of claim 9, further comprising a deduplication module to:
under the condition that the target dimensions are multiple, performing multiple collinearity analysis on the characteristic values on the multiple target dimensions, and determining a correlation coefficient between the target dimensions;
and according to the correlation coefficient between the target dimensions, carrying out deduplication on the target dimensions.
14. The apparatus of claim 8, wherein the determination module is further configured to:
and under the condition that the loan businesses are multiple, determining a total repayment value according to repayment information corresponding to each loan business.
15. A terminal device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of determining repayment information for a loan of any of claims 1-7.
16. A computer readable storage medium having instructions that, when executed by a processor of a terminal device, enable the terminal device to perform the method of determining repayment information for a loan of any one of claims 1-7.
17. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method of determining repayment information for a loan of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211580971.9A CN115760379A (en) | 2022-12-09 | 2022-12-09 | Method and device for determining repayment information of loan and terminal equipment |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211580971.9A CN115760379A (en) | 2022-12-09 | 2022-12-09 | Method and device for determining repayment information of loan and terminal equipment |
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| CN115760379A true CN115760379A (en) | 2023-03-07 |
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| CN202211580971.9A Pending CN115760379A (en) | 2022-12-09 | 2022-12-09 | Method and device for determining repayment information of loan and terminal equipment |
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| Country | Link |
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| CN (1) | CN115760379A (en) |
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