CN114780605A - Loan data processing method, loan data processing device, electronic device, and storage medium - Google Patents

Loan data processing method, loan data processing device, electronic device, and storage medium Download PDF

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CN114780605A
CN114780605A CN202210298978.5A CN202210298978A CN114780605A CN 114780605 A CN114780605 A CN 114780605A CN 202210298978 A CN202210298978 A CN 202210298978A CN 114780605 A CN114780605 A CN 114780605A
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repayment
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刘国华
程琬芸
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China Construction Bank Corp
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Abstract

The invention discloses a loan data processing method, a loan data processing device, electronic equipment and a storage medium, and relates to the technical field of computers. One embodiment of the method comprises: responding to the processing instruction of the loan data, acquiring a corresponding loan identifier, and inquiring basic information and repayment information of the loan; determining the service type of the loan, and matching to obtain a corresponding overdue calculation model and a corresponding default calculation model; calculating index distribution parameters of the overdue probability of the loan in each repayment period, and further determining the overdue probability of the loan in each repayment period; calculating default loss rate and default risk amount of the loan in each repayment period; and determining the loss rate of the loan according to the overdue probability, the default loss rate and the default risk amount of the loan in each repayment period so as to adjust the loan data corresponding to the loan. The method and the device can solve the problem that the overdue probability and the bad account rate cannot be accurately measured and calculated, and further the adjustment accuracy of the loan data is low.

Description

Loan data processing method, loan data processing apparatus, electronic device, and storage medium
Technical Field
The invention relates to the technical field of computers, in particular to a loan data processing method, a loan data processing device, electronic equipment and a loan data processing storage medium.
Background
In the financial field, before and after the loan products are released, data analysis and processing are generally needed to adjust loan data such as the fund preparation, price and the like of the loan in time, so as to ensure enough safety pads for the bad assets of the loan and ensure the quality of the property combination. In the prior art, a business worker can measure and calculate overdue probability and bad account rate of a loan in each repayment period through the existing experience based on repayment parameters of each period after the loan is released, and further can adjust each fund, price and the like of the loan.
Disclosure of Invention
In view of this, embodiments of the present invention provide a loan data processing method and apparatus, an electronic device, and a storage medium, which can solve the problem that adjustment accuracy of loan data is low due to the fact that overdue probability and bad account rate cannot be accurately measured.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a loan data processing method.
The loan data processing method of the embodiment of the invention comprises the following steps: responding to a processing instruction of the loan data, acquiring a corresponding loan identifier, and inquiring basic information and repayment information of the loan according to a corresponding data table in a location database; determining the service type of the loan based on the loan identifier to call a preset processing engine, and matching to obtain a corresponding overdue calculation model and a default calculation model; calling the overdue calculation model to calculate index distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, and further determining the overdue probability of the loan in each repayment period; calling the default calculation model to calculate the default loss rate and the default risk amount of the loan in each repayment period based on the basic information and the repayment information; and determining the loss rate of the loan according to the overdue probability, the default loss rate and the default risk amount of the loan in each repayment period so as to adjust the loan data corresponding to the loan.
In one embodiment, the repayment period comprises a paid period and an unpaid period;
based on the basic information and the repayment information, calculating an index distribution parameter of overdue probability in each repayment period corresponding to the loan, including:
calculating overdue probability in each repayment period based on the repayment information;
and calling a preset index calculation model to calculate index distribution parameters of the overdue probability of the loan in each repayment period based on the overdue probability in the repayment period.
In another embodiment, the corresponding data table in the location database, which is used for inquiring the basic information and repayment information of the loan, includes:
based on a corresponding data table in the loan identifier positioning database, inquiring basic information of the loan to match a target loan identifier similar to the basic information;
and inquiring corresponding repayment information based on the target loan identification.
In yet another embodiment, the matching of the target loan identity similar to the basic information comprises:
obtaining a pending loan identifier which is the same as the service type of the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;
and determining the target loan identifier similar to the basic information of the loan based on the similarity value.
In another embodiment, calculating the similarity between the basic information of the loan and the corresponding basic information of the pending identification comprises:
and counting the quantity of each parameter value in the basic information of the loan which is the same as each parameter value in the basic information corresponding to the identifier to be processed so as to determine the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed.
In yet another embodiment, querying the corresponding repayment information based on the target loan identification comprises:
inquiring the loss rate of the corresponding target loan based on the target loan identifier to judge whether the loss rate of the target loan is null;
if yes, inquiring corresponding repayment information based on the target loan identifier; if not, adjusting the price of the loan based on the loss rate of the target loan.
In yet another embodiment, determining the loss rate of the loan based on the probability of overdue, the loss to default rate, and the default risk amount for the loan over each repayment period comprises:
multiplying overdue probability, default loss rate and default risk amount in each repayment period to obtain loss probability of the repayment period;
and dividing the sum of the loss probability of each repayment period by the principal amount of the loan to obtain the loss rate of the loan.
In yet another embodiment, the loan data includes a loan price;
adjusting loan data corresponding to the loan, comprising:
and inquiring a preset price adjustment grade based on the loss rate of the loan to obtain a target price adjustment grade of the loan, and further adjusting the price of the loan.
To achieve the above object, according to another aspect of the embodiments of the present invention, there is provided a loan data processing apparatus.
The loan data processing apparatus of an embodiment of the present invention includes: the query unit is used for responding to the processing instruction of the loan data, acquiring the corresponding loan identifier, and querying the basic information and the repayment information of the loan by positioning the corresponding data table in the database;
the determining unit is used for determining the service type of the loan based on the loan identifier so as to call a preset processing engine, and a corresponding overdue calculation model and a default calculation model are obtained through matching;
the calculation unit is used for calling the overdue calculation model so as to calculate index distribution parameters of overdue probabilities in repayment periods corresponding to the loan based on the basic information and the repayment information, and further determine the overdue probabilities of the loan in the repayment periods;
the calculation unit is used for calling the default calculation model so as to calculate the default loss rate and the default risk amount of the loan in each repayment period based on the basic information and the repayment information;
and the adjusting unit is used for determining the loss probability of the loan according to the overdue probability, the default loss rate and the default risk amount of the loan in each repayment period so as to adjust the price of the loan.
In one embodiment, the repayment period comprises a paid period and an unpaid period;
the computing unit is specifically configured to:
calculating overdue probability in each repayment period based on the repayment information;
and calling a preset index calculation model to calculate index distribution parameters of the overdue probability of the loan corresponding to each repayment period based on the overdue probability of the repayment period.
In another embodiment, the query unit is specifically configured to:
based on a corresponding data table in the loan identifier positioning database, inquiring basic information of the loan to match a target loan identifier similar to the basic information;
and inquiring corresponding repayment information based on the target loan identification.
In another embodiment, the query unit is specifically configured to:
obtaining a to-be-processed loan identifier with the same service type as the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the corresponding basic information of the to-be-processed loan identifier;
and determining a target loan identifier similar to the basic information of the loan based on the similarity value.
In another embodiment, the query unit is specifically configured to:
obtaining a to-be-processed loan identifier with the same service type as the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the corresponding basic information of the to-be-processed loan identifier;
and determining a target loan identifier similar to the basic information of the loan based on the similarity value.
In another embodiment, the query unit is specifically configured to:
and counting the quantity of the parameter values in the basic information of the loan which are the same as the quantity of the parameter values in the basic information corresponding to the identifier to be processed so as to determine the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed.
In another embodiment, the query unit is specifically configured to:
inquiring the loss rate of the corresponding target loan based on the target loan identifier to judge whether the loss rate of the target loan is null;
if yes, inquiring corresponding repayment information based on the target loan identifier; if not, adjusting the price of the loan based on the loss rate of the target loan.
In another embodiment, the adjusting unit is specifically configured to:
multiplying overdue probability, default loss rate and default risk amount in each repayment period to obtain loss probability of the repayment period;
and dividing the sum of the loss probability of each repayment period by the principal amount of the loan to obtain the loss rate of the loan.
In yet another embodiment, the loan data includes a loan price;
the adjusting unit is specifically used for adjusting the position of the optical fiber;
and inquiring a preset price adjustment grade based on the loss rate of the loan to obtain a target price adjustment grade of the loan, and further adjusting the price of the loan.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus.
An electronic device according to an embodiment of the present invention includes: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the loan data processing method provided by the embodiment of the invention.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a computer-readable medium.
A computer-readable medium of an embodiment of the present invention stores thereon a computer program that, when executed by a processor, implements the loan data processing method provided by an embodiment of the present invention.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a computer program product.
A computer program product according to an embodiment of the present invention includes a computer program that, when executed by a processor, implements the loan data processing method according to an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: in the embodiment of the invention, after responding to the processing instruction of the loan data, the corresponding processing engine can be called through the service type of the loan, and then the corresponding overdue calculation model and default calculation model are obtained through matching; calculating index distribution parameters of the loan corresponding to the overdue probability in each repayment period through a overdue calculation model, further determining the overdue probability of the loan in each repayment period, and calculating default loss rate and default risk amount of the loan in each repayment period through a default calculation model; therefore, the loss probability of the loan can be determined according to the overdue probability, the default loss rate and the default risk amount of the loan in each repayment period so as to adjust the loan data corresponding to the loan. In the embodiment of the invention, the index distribution parameters of the overdue probability in each repayment period of the loan can be calculated through the repayment information of the loan, so that the overdue probability of the loan in each repayment period can be determined, the index distribution parameters of the overdue probability of the loan data are analyzed through the repayment parameters, the distribution state of the overdue probability can be further determined, the overdue probability of each repayment period of the loan can be accurately calculated, the accuracy of calculation of the loss probability of the loan can be improved, and the loan data can be accurately adjusted.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a principal flow of a loan data processing method according to an embodiment of the invention;
fig. 2 is a schematic diagram of a main flow of a method for acquiring paid information according to an embodiment of the present invention;
fig. 3 is a schematic diagram of the main units of the loan data processing apparatus according to the embodiment of the invention;
FIG. 4 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;
FIG. 5 is a schematic block diagram of a computer system suitable for use in implementing embodiments of the present invention.
Detailed Description
Exemplary embodiments of the invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. According to the technical scheme, the data acquisition, storage, use, processing and the like meet the relevant regulations of national laws and regulations.
An embodiment of the present invention provides a loan data processing method, which may be executed by a loan data processing system, as shown in fig. 1, and includes:
s101: and responding to the processing instruction of the loan data, acquiring a corresponding loan identifier, and inquiring basic information and repayment information of the loan by positioning a corresponding data table in the database.
The loan data processing instruction can be automatically triggered by the loan data processing system or externally received. The processing of the loan data may be performed periodically after the loan is released, and may be performed on an as-needed basis. The processing instruction may include a loan identifier corresponding to the loan data, so that the loan data is processed for which loan this time. In the embodiment of the invention, the related data of each loan can be stored, particularly can be stored in a data table of a database, and the corresponding relation between the data table and the loan identifier is established, so that the corresponding data table can be positioned from the viewpoint of the data through the loan identifier in the step, and further the basic information and the repayment information of the loan can be inquired.
The basic information of the loan may include parameters such as loan price, loan interest rate, repayment period and the like, and the repayment information may include repayment amount, overdue period, overdue probability and the like of the repayment user in the repayment period.
In the embodiment of the present invention, the processing of the loan data may be processing of the entire loan data, or processing of loan data of one or more users who purchase the loan, so that in this step, the user identifier corresponding to the to-be-processed loan data may also be obtained from the processing instruction. If the user identification corresponding to the loan data to be processed is all users, which indicates that all the loan data need to be processed, the basic information of the loan and the repayment information corresponding to all the users can be obtained; if the user identifications corresponding to the to-be-processed loan data are not all users, the corresponding user identifications can be obtained from the processing instruction, so that the repayment information of the users representing the corresponding loans can be obtained from the database.
S102: and determining the service type of the loan based on the loan identifier so as to call a preset processing engine, and matching to obtain a corresponding overdue calculation model and a default calculation model.
In the embodiment of the invention, in order to accurately calculate the overdue related data and default related data, overdue calculation models and default calculation models corresponding to the service types of the loans can be preset, and the matching relation between the service types and the models is established.
It should be noted that both the overdue calculation model and the default calculation model are pre-trained, the overdue calculation model can be used for calculating overdue probability of the loan in each repayment period, and the default calculation model can be used for calculating default loss rate and default risk amount of the loan in each repayment period.
S103: and calling an overdue calculation model to calculate index distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, and further determining the overdue probability of the loan in each repayment period.
The repayment period may include a repayment period and a non-repayment period, the repayment period represents a parameter in the repayment period of the loan user being already repayed, and the non-repayment period represents a parameter in the repayment period of the loan user being not yet repayed, that is, in this step, the parameter in the repayment period of the non-repayment may be calculated based on the parameter in the repayment period of the repayment. The repayment data of the loan may specifically include a parameter indicating whether the loan user is overdue within each repayment period, and then the overdue probability of the loan user within each repayment period may be calculated.
In the loan repayment process, the user usually requires the user to pay the loan fund once after the repayment is overdue, the repayment is not allowed, and the repayment user only has the overdue condition once in the repayment process, so that for the loan, one user only can overdue in one repayment period, namely, the overdue time of the repayment of the loan user in each repayment period is a mutual exclusion event.
It should be noted that, in the embodiment of the present invention, the condition that the loan user has an overdue repayment period and cannot repay in an overdue period of 90 days is determined as an overdue 90 event, the probability that the loan user has the overdue 90 event in the First repayment period may be denoted as FPD90(First Payment Default 90), the probability that the loan user has the overdue 90 event in the Second repayment period may be denoted as SPD90(Second Payment Default 90), the probability that the loan user has the overdue 90 event in the Third repayment period may be denoted as TPD90(Third Payment Default 90), and the analogy may determine the event identifier corresponding to the overdue 90 event in each repayment period of the loan user. For a loan user, the loan user generates an overdue 90 event in the first repayment period, generates an overdue 90 event in the second repayment period and generates an overdue 90 event in the third repayment period, which are mutual exclusion events. Therefore, the overdue probability in the repayment period represents the probability that the loan user will have an overdue 90 event in the repayment period.
In the embodiment of the invention, the overdue probability in each repayment period meets the index distribution, so that the overdue calculation model in the embodiment of the invention can be constructed based on a specific calculation mode of calculating the overdue probability through the index distribution parameters, and the index distribution parameter lambda of the overdue probability in each repayment period corresponding to the loan can be calculated in the step.
Since the user is mutually exclusive when overdue events occur in each payment period, the overdue events occur in a certain payment period, namely that the overdue events do not occur before the payment period. The repayment period number of the loan is expressed by formula 1, and the probability of the overdue event occurring in the xth repayment period within the t repayment periods can be expressed as shown in formula 1.
p(x≤t|x≠1)＝1-e-λ(x-1) (1)
In equation 1, when t is 1, P (x is 1) FPD 90; at t > 1, the formula can be converted to that shown in formula 2.
P(x＞t|x≠1)＝1-P(x≤t|x≠1)＝1-[1-e-λ(t-1)]＝e-λ(t-1) (2)
Since the formula 2 can also be expressed as the formula 3, the formula 4 can be obtained by combining the formula 2 and the formula 3, and the index distribution parameter of the overdue probability is obtained as shown in the formula 5.
Figure RE-GDA0003652101050000091
Figure RE-GDA0003652101050000092
Figure RE-GDA0003652101050000093
Since the repayment information may include the probability of overdue time occurring in the first repayment period, the probability of overdue time occurring in the second repayment period, and the like, the exponential distribution parameter of the overdue probability may be calculated based on the known overdue probability. In the embodiment of the present invention, the repayment information includes FPD90, SPD90, and TPD90, and due to the probability occurrence principle of the mutual exclusion event, P (x ≦ 3) ═ P (x ═ 1Ux ═ 2Ux ═ 3) can be obtained, and then P (x ≦ 3) FPD90+ SPD90+ TPD90 can be obtained from the characteristic of the mutual exclusion event, and further, the calculation formula of λ can be obtained as shown in formula 6.
Figure RE-GDA0003652101050000094
After the index distribution parameters of the overdue probability of the loan corresponding to each repayment period are calculated in the step, the overdue probability of each repayment period can be calculated through a formula 7.
P(x＝t)＝P(x＝1，x＝t)+P(x≠1，x＝t)＝P(x≠1，x＝t)
＝P(x＝t|x≠1)*P(x≠1)
＝P(x≠1)*[P(x≤t|x≠1)-P(x≤(t-1)|x≠1)]
＝(1-FS)*(e-λ(t-2)-e-λ(t-1)) (7)
In equation 7, P (x-1, x-t) is 0 when t > 1. If t is equal to 1, P (x is equal to t) is FPD 90.
From equation 7, P (x ═ 2) ═ (1-FPD90) (1-e) can be calculated-λ)、 P(x＝3)＝(1-FPD90)*(e-λ-e-2λ)、P(x＝12)＝(1-FPD90)* (e-10λ-e-11λ) And so on to derive the overdue probability of each payment cycle.
S104: and calling the default calculation model to calculate the default loss rate and the default risk amount of the loan in each repayment period based on the basic information and the repayment information.
The Default calculation model may specifically include a Loss of Default (Loss Given Default, LGD) calculation model and a Default risk amount calculation model.
The loan user needs to pay the principal of the loan once after a certain repayment period has an overdue event, and if the loan user fails to pay the principal of the loan, the loan user indicates that a default event occurs. The default loss represents the ratio of the loan principal default of the loan user to the total loan principal of the loan user, wherein the loan principal default of the loan user and the total loan principal of the loan user can be calculated by the basic information and repayment information. Through the method, the default loss rate calculation model can be preset, and further the default loss rate corresponding to each repayment period can be calculated.
When a loan user breaks the agreement, the principal whose loan remains as a repayment may be subject to irrecoverable updates, i.e., the amount of the risk of the breach, which may also be denoted as Exposure to the risk of breach (EAD). Specifically, in this step, the principal amount paid in each repayment period may be calculated at the end of each repayment period, and the default risk amount may be obtained by subtracting the sum of the principal amounts of the repayment loan in each repayment period from the total loan amount.
Specifically, if the principal amount of the loan is G, the annual rate is a, and the repayment period is n (month), the principal amount returned in each period can be calculated according to the time value formula of the capital and is shown in the formula 8,
Figure RE-GDA0003652101050000101
as can be derived from equation 8, it is,
Figure RE-GDA0003652101050000111
wherein i represents an integer between 0 and x.
S105: and determining the loss rate of the loan according to the overdue probability, the default loss rate and the default risk amount of the loan in each repayment period so as to adjust the loan data corresponding to the loan.
Specifically, in this step, for each repayment period, the overdue probability, the default loss rate, and the default risk amount in the repayment period may be multiplied to obtain the loss probability of the repayment period, so that the loss probability of each repayment period may be obtained, and the loss rate of the loan may be obtained by dividing the sum of the loss probabilities of each repayment period by the principal amount of the loan.
In embodiments of the invention, after deriving the loss rate of the loan, the loan data, which may include the loan fund, the loan price, etc., may be adjusted based on the loss of the loan. Taking the example that the loan data includes the loan price, in the embodiment of the present invention, the corresponding price adjustment level may be configured based on the loss rate of different loans, for example, the price adjustment level may include a price increase level, a price decrease level, and the like, and a mapping relationship may be established for each price adjustment level and the corresponding loss. Therefore, in the step, the preset price adjustment level can be inquired based on the loss rate of the loan so as to obtain the target price adjustment level of the loan, and further the price of the loan is adjusted based on the target price adjustment level.
In the embodiment of the invention, the index distribution parameters of the overdue probability in each repayment period of the loan can be calculated through the repayment information of the loan, so that the overdue probability of the loan in each repayment period can be determined, the index distribution parameters of the overdue probability of the loan data are analyzed through the repayment parameters, the distribution state of the overdue probability can be further determined, the overdue probability of each repayment period of the loan can be accurately calculated, the accuracy of calculation of the loss probability of the loan can be improved, and the loan data can be accurately adjusted.
It should be noted that, before the loan is released, the loan data may also be analyzed, but at this time, the loan does not have corresponding loan data yet, so that a similar loan may be determined from the released loan, so as to memorize the loan data of the similar loan for analysis, and determine the result as the processing result of the present loan.
Therefore, the following describes, in conjunction with the embodiment shown in fig. 1, a method for acquiring payment information in the embodiment of the present invention, as shown in fig. 2, the method includes:
s201: and responding to the processing instruction of the loan data, and acquiring the corresponding loan identifier.
S202: and inquiring the basic information of the loan based on a corresponding data table in the loan identifier positioning database so as to match the target loan identifier similar to the basic information.
In the embodiment of the invention, the related data of each loan can be stored, particularly can be stored in a data table of a database, and the corresponding relation between the data table and the loan identifier is established, so that the corresponding data table can be positioned from the database through the loan identifier in the step, and further the basic information of the loan is inquired. Since the loan does not have corresponding repayment information, a target loan identifier similar to the loan identifier can be matched based on the basic information of the loan at the moment.
Specifically, the matching of the target loan identifier in this step may be specifically implemented as: obtaining a pending loan identifier which is the same as the service type of the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the corresponding basic information of the pending loan identifier; based on the value of the similarity, a target loan identifier similar to the basic information of the loan is determined.
The method comprises the steps that a to-be-processed loan identification with the same service type as the loan can be obtained through the service type of the loan, and then basic information corresponding to each to-be-processed loan identification can be inquired, so that the similarity between the basic information of the loan and the basic information corresponding to the to-be-processed identification can be calculated. In the embodiment of the invention, the to-be-processed loan identifier corresponding to the highest value of the similarity can be determined as the target loan identifier similar to the basic information of the loan.
In this step, the basic information may include loan price, loan interest rate, and repayment period number, and based on this, the number of the basic information about the loan with the same parameter value as the basic information about the identifier to be processed is counted, and the number is determined as the similarity between the basic information about the loan and the basic information about the identifier to be processed.
S203: and inquiring corresponding repayment information based on the target loan identification.
It should be noted that, for the target loan identifier, which is a loan identifier similar to the loan processed by the loan data, the corresponding loss rate may have been calculated before again, and at this time, the calculation need not be performed through the embodiment shown in fig. 1, so before this step is performed, the loss probability of the corresponding target loan may also be queried based on the target loan identifier to determine whether the loss probability of the target loan is null; if yes, inquiring corresponding repayment information based on the target loan identifier, namely executing step S203, and further processing through step S102; if not, adjusting the price of the loan based on the loss probability of the target loan.
In the embodiment of the invention, the index distribution parameters of the overdue probability in each repayment period of the loan can be calculated through the repayment information of the loan, so that the overdue probability of the loan in each repayment period can be determined, the index distribution parameters of the overdue probability of the loan data are analyzed through the repayment parameters, the distribution state of the overdue probability can be further determined, the overdue probability of each repayment period of the loan can be accurately calculated, the accuracy of calculation of the loss probability of the loan can be improved, and the loan data can be accurately adjusted.
In order to solve the problems in the prior art, an embodiment of the invention provides a loan data processing apparatus 300, as shown in fig. 3, the apparatus 300 comprising:
the query unit 301 is configured to, in response to the loan data processing instruction, obtain a corresponding loan identifier, and query basic information and repayment information of a loan by locating a corresponding data table in the database;
a determining unit 302, configured to determine a service type of the loan based on the loan identifier, so as to invoke a preset processing engine, and obtain a corresponding overdue calculation model and default calculation model through matching;
the calculating unit 303 is configured to invoke the overdue calculation model, so as to calculate an index distribution parameter of overdue probability of the loan in each repayment period based on the basic information and the repayment information, and further determine the overdue probability of the loan in each repayment period;
the calculating unit 303 is configured to invoke the default calculating model, so as to calculate a default loss rate and a default risk amount of the loan in each repayment period based on the basic information and the repayment information;
an adjusting unit 304, configured to determine the loss probability of the loan according to the overdue probability, the default loss rate, and the default risk amount of the loan in each repayment period, so as to adjust the price of the loan.
It should be understood that the manner of implementing the embodiment of the present invention is the same as that of implementing the embodiment shown in fig. 1, and is not described herein again.
In one embodiment, the repayment period comprises a paid period and an unpaid period;
the calculating unit 303 is specifically configured to:
calculating overdue probability in each repayment period based on the repayment information;
and calling a preset index calculation model to calculate index distribution parameters of the overdue probability of the loan corresponding to each repayment period based on the overdue probability of the repayment period.
In another embodiment, the query unit is specifically configured to:
based on a corresponding data table in the loan identifier positioning database, inquiring basic information of the loan to match a target loan identifier similar to the basic information;
and inquiring corresponding repayment information based on the target loan identification.
In another embodiment, the querying unit 301 is specifically configured to:
obtaining a pending loan identifier which is the same as the service type of the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;
and determining a target loan identifier similar to the basic information of the loan based on the similarity value.
In another embodiment, the querying unit 301 is specifically configured to:
obtaining a to-be-processed loan identifier with the same service type as the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the corresponding basic information of the to-be-processed loan identifier;
and determining the target loan identifier similar to the basic information of the loan based on the similarity value.
In another embodiment, the querying unit 301 is specifically configured to:
and counting the quantity of the parameter values in the basic information of the loan which are the same as the quantity of the parameter values in the basic information corresponding to the identifier to be processed so as to determine the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed.
In another embodiment, the querying unit 301 is specifically configured to:
inquiring the loss rate of the corresponding target loan based on the target loan identifier to judge whether the loss rate of the target loan is null;
if yes, inquiring corresponding repayment information based on the target loan identifier; if not, adjusting the price of the loan based on the loss rate of the target loan.
In another embodiment, the adjusting unit 304 is specifically configured to:
multiplying overdue probability, default loss rate and default risk amount in each repayment period to obtain loss probability of the repayment period;
and dividing the sum of the loss probability of each repayment period by the principal amount of the loan to obtain the loss rate of the loan.
In yet another embodiment, the loan data includes a loan price;
the adjusting unit 304 is specifically configured to;
and inquiring a preset price adjustment grade based on the loss rate of the loan to obtain a target price adjustment grade of the loan, and further adjusting the price of the loan.
It should be understood that the embodiment of the present invention is implemented in the same manner as the embodiment shown in fig. 1 or fig. 2, and is not repeated herein.
In the embodiment of the invention, the index distribution parameters of the overdue probability in each repayment period of the loan can be calculated through the repayment information of the loan, so that the overdue probability of the loan in each repayment period can be determined, the index distribution parameters of the overdue probability of the loan data are analyzed through the repayment parameters, the distribution state of the overdue probability can be further determined, the overdue probability of each repayment period of the loan can be accurately calculated, the accuracy of calculation of the loss probability of the loan can be improved, and the loan data can be accurately adjusted.
According to an embodiment of the present invention, an electronic device and a readable storage medium are also provided.
The electronic device of the embodiment of the invention comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a loan data processing method as provided by embodiments of the invention.
Fig. 4 illustrates an exemplary system architecture 400 of a loan data processing method or loan data processing apparatus to which embodiments of the invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 via a network 404 to receive or send messages or the like. Various client applications may be installed on the terminal devices 401, 402, 403.
The terminal devices 401, 402, 403 may be, but are not limited to, smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server that provides various services, and the server may analyze and process data such as a received product information query request, and feed back a processing result (for example, product information — just an example) to the terminal device.
It should be noted that the loan data processing method provided in the embodiment of the present invention is generally executed by the server 405, and accordingly, a loan data processing apparatus is generally provided in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for an implementation.
Referring now to FIG. 5, a block diagram of a computer system 500 suitable for use in implementing embodiments of the present invention is shown. The computer system illustrated in FIG. 5 is only an example and should not impose any limitations on the scope of use or functionality of embodiments of the invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted on the storage section 508 as necessary.
In particular, according to embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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 portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present invention, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a unit, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a query unit, a determination unit, a calculation unit, and an adjustment unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, a query unit may also be described as a "unit of an information query function".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not assembled into the device. The computer-readable medium carries one or more programs which, when executed by a device, cause the device to perform the loan data processing method provided by the present invention.
As another aspect, the present invention also provides a computer program product including a computer program that, when executed by a processor, implements the loan data processing method provided by the embodiments of the present invention.
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 occur 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 (15)

1. A loan data processing method, comprising:
responding to a processing instruction of the loan data, acquiring a corresponding loan identifier, and inquiring basic information and repayment information of the loan according to a corresponding data table in a location database;
determining the service type of the loan based on the loan identifier to call a preset processing engine, and matching to obtain a corresponding overdue calculation model and a default calculation model;
calling the overdue calculation model to calculate index distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, and further determining the overdue probability of the loan in each repayment period;
calling the default calculation model to calculate the default loss rate and the default risk amount of the loan in each repayment period based on the basic information and the repayment information;
and determining the loss rate of the loan according to the overdue probability, the default loss rate and the default risk amount of the loan in each repayment period so as to adjust the loan data corresponding to the loan.
2. The method of claim 1, wherein the payment periods include a paid period and a not paid period;
based on the basic information and the repayment information, calculating an index distribution parameter of overdue probability in each repayment period corresponding to the loan, including:
calculating overdue probability in each repayment period based on the repayment information;
and calling a preset index calculation model to calculate index distribution parameters of the overdue probability of the loan in each repayment period based on the overdue probability in the repayment period.
3. The method of claim 1, wherein the step of querying the corresponding data table in the location database for basic information and repayment information of the loan comprises:
based on a corresponding data table in the loan identifier positioning database, inquiring basic information of the loan to match a target loan identifier similar to the basic information;
and inquiring corresponding repayment information based on the target loan identification.
4. The method of claim 3, wherein matching the target loan identity similar to the base information comprises:
obtaining a pending loan identifier which is the same as the service type of the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;
and determining a target loan identifier similar to the basic information of the loan based on the similarity value.
5. The method of claim 4, wherein calculating the similarity between the basic information of the loan and the corresponding basic information of the identifier to be processed comprises:
and counting the quantity of each parameter value in the basic information of the loan which is the same as each parameter value in the basic information corresponding to the identifier to be processed so as to determine the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed.
6. The method of claim 3, wherein querying corresponding repayment information based on the target loan identification comprises:
inquiring the loss rate of the corresponding target loan based on the target loan identifier to judge whether the loss rate of the target loan is null;
if yes, inquiring corresponding repayment information based on the target loan identifier; if not, adjusting the price of the loan based on the loss rate of the target loan.
7. The method of claim 1, wherein determining the loss rate of the loan based on the probability of overdue, the loss due default, and the amount of risk due default for the loan over each repayment period comprises:
multiplying overdue probability, default loss rate and default risk amount in each repayment period to obtain loss probability of the repayment period;
and dividing the sum of the loss probability of each repayment period by the principal amount of the loan to obtain the loss rate of the loan.
8. The method of claim 1, wherein the loan data comprises a loan price;
adjusting loan data corresponding to the loan, comprising:
and inquiring a preset price adjustment grade based on the loss rate of the loan to obtain a target price adjustment grade of the loan, and further adjusting the price of the loan.
9. A loan data processing apparatus, comprising:
the query unit is used for responding to the processing instruction of the loan data, acquiring the corresponding loan identifier, and querying the basic information and the repayment information of the loan by positioning the corresponding data table in the database;
the determining unit is used for determining the service type of the loan based on the loan identifier so as to call a preset processing engine, and a corresponding overdue calculation model and a default calculation model are obtained through matching;
the calculation unit is used for calling the overdue calculation model to calculate index distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, and further determine the overdue probability of the loan in each repayment period;
the calculation unit is used for calling the default calculation model so as to calculate the default loss rate and the default risk amount of the loan in each repayment period based on the basic information and the repayment information;
and the adjusting unit is used for determining the loss probability of the loan according to the overdue probability, the default loss rate and the default risk amount of the loan in each repayment period so as to adjust the price of the loan.
10. The apparatus of claim 9, wherein the payment period comprises a paid period and an unpaid period;
the computing unit is specifically configured to:
calculating overdue probability in each repayment period based on the repayment information;
and calling a preset index calculation model to calculate index distribution parameters of the overdue probability of the loan corresponding to each repayment period based on the overdue probability of the repayment period.
11. The apparatus according to claim 9, wherein the query unit is specifically configured to:
based on a corresponding data table in the loan identifier positioning database, inquiring basic information of the loan to match a target loan identifier similar to the basic information;
and inquiring corresponding repayment information based on the target loan identification.
12. The apparatus according to claim 11, wherein the query unit is specifically configured to:
obtaining a pending loan identifier which is the same as the service type of the loan to inquire corresponding basic information, and further calculating the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;
and determining a target loan identifier similar to the basic information of the loan based on the similarity value.
13. An electronic device, comprising:
one or more processors;
a storage device to store one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-8.
14. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-8.
15. A computer program product comprising a computer program, characterized in that the program realizes the method according to any of claims 1-8 when executed by a processor.
CN202210298978.5A 2022-03-25 2022-03-25 Loan data processing method, loan data processing device, electronic device, and storage medium Pending CN114780605A (en)

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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210298978.5A CN114780605A (en) 2022-03-25 2022-03-25 Loan data processing method, loan data processing device, electronic device, and storage medium

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Publication Number Publication Date
CN114780605A true CN114780605A (en) 2022-07-22

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