Loan data processing method and device, electronic equipment and storage medium

Abstract

This invention discloses a loan data processing method, apparatus, electronic device, and storage medium, relating to the field of computer technology. One specific embodiment of the method includes: responding to a loan data processing instruction, obtaining the corresponding loan identifier, querying the loan's basic information and repayment information; determining the loan's business type, matching it with the corresponding overdue calculation model and default calculation model; calculating the exponential distribution parameters of the overdue probability for each repayment period, thereby determining the overdue probability of the loan in each repayment period; calculating the default loss rate and default risk amount for the loan in each repayment period; and determining the loan's loss rate based on the overdue probability, default loss rate, and default risk amount for each repayment period, to adjust the corresponding loan data. This embodiment can solve the problem of inaccurate calculation of overdue probability and bad debt rate, leading to low accuracy in loan data adjustment.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a loan data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] In the financial sector, loan products typically require data analysis and processing before and after issuance to adjust loan data such as reserves and pricing in a timely manner. This ensures a sufficient safety cushion for non-performing assets and guarantees the quality of the asset portfolio. Currently, business personnel can use repayment parameters from each repayment period after loan issuance, leveraging existing experience to calculate the probability of delinquency and the default rate for each repayment period. This allows for adjustments to loan reserves and pricing. However, when the loan issuance period is short, the available repayment parameters are limited, making it difficult to accurately calculate the probability of delinquency and the default rate using existing experience. Consequently, the accuracy of loan data adjustments is low. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a loan data processing method, apparatus, electronic device, and storage medium, which can solve the problem that it is impossible to accurately calculate the probability of delinquency and the bad debt rate, thus resulting in a low accuracy rate of loan data adjustment.

[0004] To achieve the above objectives, according to one aspect of the present invention, a loan data processing method is provided.

[0005] An embodiment of the present invention provides a loan data processing method comprising: responding to a loan data processing instruction, obtaining a corresponding loan identifier to locate the corresponding data table in a database, and querying the basic information and repayment information of the loan; determining the business type of the loan based on the loan identifier, and calling a preset processing engine to match and obtain a corresponding overdue calculation model and a default calculation model; calling the overdue calculation model to calculate the exponential distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, thereby determining the overdue probability of the loan in each repayment period; calling the default calculation model to calculate the default loss rate and 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 based on the overdue probability, default loss rate, and default risk amount of the loan in each repayment period, thereby adjusting the loan data corresponding to the loan.

[0006] In one embodiment, the repayment period includes a repaid period and an outstanding period;

[0007] Based on the basic information and the repayment information, calculate the exponential distribution parameters of the delinquency probability for each repayment period of the loan, including:

[0008] Calculate the probability of delinquency within each repayment period based on the aforementioned repayment information;

[0009] A preset index calculation model is invoked to calculate the exponential distribution parameters of the delinquency probability for each repayment period of the loan, based on the delinquency probability within the repayment period.

[0010] In another embodiment, the location database corresponds to a data table that queries basic loan information and repayment information, including:

[0011] Based on the corresponding data table in the loan identifier positioning database, the basic information of the loan is queried to match target loan identifiers that are similar to the basic information;

[0012] Based on the target loan identifier, query the corresponding repayment information.

[0013] In yet another embodiment, matching a target loan identifier similar to the basic information includes:

[0014] Obtain the pending loan identifier that has the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;

[0015] Based on the similarity value, a target loan identifier that is similar to the basic information of the loan is determined.

[0016] In yet another embodiment, calculating the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed includes:

[0017] The number of parameter values ​​in the basic information of the loan that are the same as the parameter values ​​in the basic information corresponding to the identifier to be processed is counted, and the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed is determined.

[0018] In yet another embodiment, querying the corresponding repayment information based on the target loan identifier includes:

[0019] Based on the target loan identifier, query the loss rate of the corresponding target loan to determine whether the loss rate of the target loan is empty;

[0020] If yes, then query the corresponding repayment information based on the target loan identifier; if no, then adjust the loan price based on the loss rate of the target loan.

[0021] In another embodiment, the loss rate of the loan is determined based on the delinquency probability, loss rate, and default risk amount of the loan in each repayment period, including:

[0022] For each repayment period, the delinquency probability, default loss rate, and default risk amount within the repayment period are multiplied to obtain the loss probability for the repayment period.

[0023] The loss rate of the loan is obtained by dividing the sum of the loss probabilities for each repayment period by the principal amount of the loan.

[0024] In yet another embodiment, the loan data includes the loan price;

[0025] Adjusting the loan data corresponding to the aforementioned loan includes:

[0026] Based on the loan's loss rate, a preset price adjustment level is queried to determine the target price adjustment level for the loan, and then the loan price is adjusted accordingly.

[0027] To achieve the above objectives, according to another aspect of the present invention, a loan data processing apparatus is provided.

[0028] A loan data processing apparatus according to an embodiment of the present invention includes: a query unit, configured to respond to a loan data processing instruction, obtain a corresponding loan identifier to locate the corresponding data table in the database, and query the basic information and repayment information of the loan;

[0029] The determining unit is used to determine the business type of the loan based on the loan identifier, so as to call a preset processing engine to match and obtain the corresponding overdue calculation model and default calculation model;

[0030] The calculation unit is used to call the overdue calculation model to calculate the exponential distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, thereby determining the overdue probability of the loan in each repayment period.

[0031] The calculation unit is used to call the default calculation model to calculate the default loss rate and default risk amount of the loan in each repayment period based on the basic information and the repayment information.

[0032] The adjustment unit is used to determine the loss probability of the loan based on the delinquency probability, default loss rate, and default risk amount of the loan in each repayment period, so as to adjust the price of the loan.

[0033] In one embodiment, the repayment period includes a repaid period and an outstanding period;

[0034] The computing unit is specifically used for:

[0035] Calculate the probability of delinquency within each repayment period based on the aforementioned repayment information;

[0036] A preset index calculation model is invoked to calculate the exponential distribution parameters of the delinquency probability for each repayment period of the loan, based on the delinquency probability within the repayment period.

[0037] In yet another embodiment, the query unit is specifically used for:

[0038] Based on the corresponding data table in the loan identifier positioning database, the basic information of the loan is queried to match target loan identifiers that are similar to the basic information;

[0039] Based on the target loan identifier, query the corresponding repayment information.

[0040] In yet another embodiment, the query unit is specifically used for:

[0041] Obtain the pending loan identifier that has the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;

[0042] Based on the similarity value, a target loan identifier that is similar to the basic information of the loan is determined.

[0043] In yet another embodiment, the query unit is specifically used for:

[0044] Obtain the pending loan identifier that has the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;

[0045] Based on the similarity value, a target loan identifier that is similar to the basic information of the loan is determined.

[0046] In yet another embodiment, the query unit is specifically used for:

[0047] The number of parameter values ​​in the basic information of the loan that are the same as the parameter values ​​in the basic information corresponding to the identifier to be processed is counted, and the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed is determined.

[0048] In yet another embodiment, the query unit is specifically used for:

[0049] Based on the target loan identifier, query the loss rate of the corresponding target loan to determine whether the loss rate of the target loan is empty;

[0050] If yes, then query the corresponding repayment information based on the target loan identifier; if no, then adjust the loan price based on the loss rate of the target loan.

[0051] In yet another embodiment, the adjustment unit is specifically used for:

[0052] For each repayment period, the delinquency probability, default loss rate, and default risk amount within the repayment period are multiplied to obtain the loss probability for the repayment period.

[0053] The loss rate of the loan is obtained by dividing the sum of the loss probabilities for each repayment period by the principal amount of the loan.

[0054] In yet another embodiment, the loan data includes the loan price;

[0055] The adjustment unit is specifically used for;

[0056] Based on the loan's loss rate, a preset price adjustment level is queried to determine the target price adjustment level for the loan, and then the loan price is adjusted accordingly.

[0057] To achieve the above objectives, according to another aspect of the present invention, an electronic device is provided.

[0058] An electronic device according to an embodiment of the present invention includes: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the loan data processing method provided in the embodiment of the present invention.

[0059] To achieve the above objectives, according to another aspect of the present invention, a computer-readable medium is provided.

[0060] An embodiment of the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the loan data processing method provided in the embodiment of the present invention.

[0061] To achieve the above objectives, according to another aspect of the present invention, a computer program product is provided.

[0062] 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 provided in an embodiment of the present invention.

[0063] One embodiment of the above invention has the following advantages or beneficial effects: In this embodiment, after responding to the loan data processing instruction, the corresponding processing engine can be invoked according to the loan's business type, thereby matching and obtaining the corresponding overdue calculation model and default calculation model; the overdue calculation model is used to calculate the exponential distribution parameters of the overdue probability in each repayment period of the loan, thereby determining the overdue probability of the loan in each repayment period; and the default calculation model is used to calculate the default loss rate and default risk amount of the loan in each repayment period; thus, based on the overdue probability, default loss rate, and default risk amount of the loan in each repayment period, the loss probability of the loan can be determined, thereby adjusting the corresponding loan data. In this embodiment, the exponential distribution parameters of the overdue probability in each repayment period of the loan can be calculated using the loan's repayment information, thereby determining the overdue probability of the loan in each repayment period. Analyzing the exponential distribution parameters of the overdue probability of the loan data using repayment parameters allows for the determination of the distribution state of the overdue probability, accurately determining the overdue probability in each repayment period of the loan, improving the accuracy of loan loss probability calculation, and thus enabling accurate adjustment of loan data.

[0064] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0065] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0066] Figure 1 This is a schematic diagram of a main process of a loan data processing method according to an embodiment of the present invention;

[0067] Figure 2 This is a schematic diagram of the main process of a method for obtaining repayment information according to an embodiment of the present invention;

[0068] Figure 3 This is a schematic diagram of the main units of a loan data processing apparatus according to an embodiment of the present invention;

[0069] Figure 4 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied;

[0070] Figure 5 This is a schematic diagram of the structure of a computer system suitable for implementing embodiments of the present invention. Detailed Implementation

[0071] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0072] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0073] This invention provides a loan data processing method, which can be executed by a loan data processing system, such as... Figure 1 As shown, the method includes:

[0074] S101: In response to the loan data processing instruction, obtain the corresponding loan identifier to locate the corresponding data table in the database and query the basic information and repayment information of the loan.

[0075] The loan data processing instructions can be automatically triggered by the loan data processing system or received externally. Loan data processing can be performed periodically after a loan is issued, or it can be executed based on demand. The processing instructions can include a loan identifier corresponding to the loan data, indicating which loan's data is being processed in this instance. In this embodiment of the invention, relevant data for each loan can be stored, specifically in a database table, establishing a correspondence between the data table and the loan identifier. Therefore, in this step, the corresponding data table can be located from the database using the loan identifier, allowing for the retrieval of basic loan information and repayment details.

[0076] Basic loan information can include parameters such as loan price, loan interest rate, and repayment period. Repayment information can include the amount repaid by the user within the repayment period, overdue amount, overdue period, and probability of overdue payment.

[0077] It should be noted that, in this embodiment of the invention, the processing of loan data can be performed on the overall loan data or on the loan data of one or more users who purchased the loan. Therefore, in this step, the user identifier corresponding to the loan data to be processed can also be obtained from the processing instruction. If the user identifier corresponding to the loan data to be processed is all users, it means that all loan data needs to be processed, and the basic information of the loan and the repayment information of all corresponding users can be obtained. If the user identifier corresponding to the loan data to be processed is not all users, the corresponding user identifier can be obtained from the processing instruction to obtain the repayment information of these users representing the corresponding loans from the database.

[0078] S102: Based on the loan identifier, determine the business type of the loan, and call the preset processing engine to match and obtain the corresponding overdue calculation model and default calculation model.

[0079] Since the basic information in loan data usually differs for different types of loans, in this embodiment of the invention, in order to accurately calculate the overdue related data and default related data, the overdue calculation model and default calculation model corresponding to each loan business type can be preset, and the matching relationship between each business type and the model can be established. Therefore, in this step, after the business type of the loan can be determined by the loan identifier, the processing engine is called to match the overdue calculation model and default calculation model required for loan data processing.

[0080] 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 to calculate the probability of overdue payment for a loan in each repayment period, while the default calculation model can be used to calculate the default loss rate and default risk amount for a loan in each repayment period.

[0081] S103: Call the overdue calculation model to calculate the exponential distribution parameters of the overdue probability in each repayment period of the loan based on basic information and repayment information, and then determine the overdue probability of the loan in each repayment period.

[0082] The repayment period can include repaid periods and outstanding periods. Repaid periods represent parameters within the repayment periods for which the loan user has already made payments, while outstanding periods represent parameters within the repayment periods for which the loan user has not yet made payments. In other words, this step can calculate the parameters for outstanding repayment periods based on the parameters within the repaid periods. The loan's repaid data can specifically include parameters indicating whether the loan user has defaulted in each repaid period. This allows for the calculation of the default probability for the loan user in each repaid period. Therefore, this step can then call a pre-defined index calculation model to calculate the exponential distribution parameters of the default probability for each repayment period based on the default probability within the repaid periods.

[0083] Because during the loan repayment process, users are usually required to repay the loan principal in one lump sum after a repayment delay, and are not allowed to make repayments in installments, a user can only be overdue once during the repayment process. Therefore, for a loan, a user can only be overdue within one repayment cycle. In other words, the time when a loan user is overdue in each repayment cycle is a mutually exclusive event.

[0084] It should be noted that, in this embodiment of the invention, a loan user's failure to repay within 90 days of the due date is defined as a "90-day overdue event." The probability of a loan user experiencing a 90-day overdue event in the first repayment period can be denoted as FPD90 (First Payment Default 90), the probability in the second repayment period as SPD90 (Second Payment Default 90), and the probability in the third repayment period as TPD90 (Third Payment Default 90), and so on, to determine the event identifier corresponding to each repayment period. For a single loan user, experiencing a 90-day overdue event in the first, second, and third repayment periods are mutually exclusive events. Therefore, the overdue probability within a repayment period represents the probability of a loan user experiencing a 90-day overdue event within that repayment period.

[0085] In this embodiment of the invention, the delinquency probability within each repayment period follows an exponential distribution. Therefore, the delinquency calculation model in this embodiment of the invention can be constructed based on the specific calculation method of calculating the delinquency probability through the exponential distribution parameter. Thus, in this step, the exponential distribution parameter λ of the delinquency probability within each repayment period corresponding to the loan can be calculated first.

[0086] Since overdue events occur mutually exclusively across repayment periods, an overdue event occurring in a particular repayment period indicates that no overdue events occurred before that repayment period. Formula 1 expresses the probability of an overdue event occurring in the x-th repayment period within t repayment periods, where t represents the number of repayment periods.

[0087] p(x≤t|x≠1)=1-e -λ(x-1) (1)

[0088] In Formula 1, when t = 1, P(x = 1) = FPD90; when t > 1, the formula can be transformed into Formula 2.

[0089] P(x>t|x≠1)=1-P(x≤t|x≠1)=1-[1-e -λ(t-1)] = e -λ(t-1) (2)

[0090] Since Formula 2 can also be expressed as Formula 3, Formula 4 can be derived by combining Formula 2 and Formula 3, and then the exponential distribution parameters of the overdue probability can be obtained as shown in Formula 5.

[0091]

[0092]

[0093]

[0094] Since the repayment information can include the probability of overdue payment in the first repayment period, the probability of overdue payment in the second repayment period, etc., the exponential distribution parameter of the overdue probability can be calculated based on the known overdue probability. In this embodiment of the invention, the repayment information includes FPD90, SPD90, and TPD90. Due to the principle of mutually exclusive event probability, we can obtain P(x≤3)=P(x=1Ux=2Ux=3). Furthermore, based on the characteristics of mutually exclusive events, we can obtain P(x≤3)FPD90+SPD90+TPD90. Therefore, the calculation formula for λ can be shown in Formula 6.

[0095]

[0096] After calculating the exponential distribution parameters of the delinquency probability for each repayment period in this step, the delinquency probability for each repayment period can be calculated using Formula 7.

[0097] P(x=t)=P(x=1, x=t)+P(x≠1, x=t)=P(x≠1, x=t)

[0098] =P(x=t|x≠1)*P(x≠1)

[0099] =P(x≠1)*[P(x≤t|x≠1)-P(x≤(t-1)|x≠1)]

[0100] = (1-FS)*(e -λ(t-2) -e -λ(t-1) (7)

[0101] In Formula 7, when t > 1, P(x = 1, x = t) = 0. However, when t = 1, P(x = t) is FPD90.

[0102] Formula 7 can be used to calculate P(x=2)=(1-FPD90)*(1-e -λ P(x=3)=(1-FPD90)*(e -λ -e -2λP(x=12)=(1-FPD90)*(e -10λ -e -11λ This allows us to determine the probability of delinquency for each repayment period.

[0103] S104: Call the default calculation model to calculate the default loss rate and default risk amount of the loan in each repayment period based on basic information and repayment information.

[0104] Specifically, the default calculation model can include the Loss Given Default (LGD) calculation model and the default risk amount calculation model.

[0105] If a loan user defaults on a payment in a repayment period, they are required to repay the loan principal in a lump sum. Failure to do so constitutes a default. The loss due to default is the ratio of the defaulted loan principal to the total outstanding loan principal. Both the defaulted loan principal and the total outstanding loan principal can be calculated using basic and repayment information. This allows for the pre-setting of a loss due to default rate calculation model, enabling the calculation of the loss due to default rate for each repayment period.

[0106] When a loan user defaults, the remaining principal on the loan may become unpayable, a situation known as default risk amount or exposure at default (EAD). Specifically, this step involves calculating the principal amount repaid in each repayment period at the end of that period, and then subtracting the sum of the principal repaid in each period from the total loan amount to arrive at the default risk amount.

[0107] Specifically, let the loan principal amount be G, the annual interest rate be a, and the repayment period be n (months). Then, according to the time value of money formula, the principal amount repaid in each period can be calculated as shown in Formula 8.

[0108]

[0109] We can derive this from Formula 8. Where i represents an integer between 0 and x.

[0110] S105: Determine the loss rate of a loan based on the delinquency probability, loss rate, and default risk amount in each repayment period, in order to adjust the corresponding loan data.

[0111] Specifically, in this step, for each repayment period, the delinquency probability, default loss rate, and default risk amount within that repayment period can be multiplied to obtain the loss probability for that repayment period. In this way, the loss probability for each repayment period can be obtained, and then the sum of the loss probabilities for each repayment period is divided by the principal amount of the loan to obtain the loss rate of the loan.

[0112] In this embodiment of the invention, after obtaining the loan loss rate, loan data can be adjusted based on the loan loss rate. Loan data may include loan reserves, loan prices, etc. Taking loan prices as an example, this embodiment of the invention can configure corresponding price adjustment levels based on different loan loss rates. For example, price adjustment levels may include price increase levels, price decrease levels, etc., and a mapping relationship can be established between each price adjustment level and the corresponding loss rate. Thus, in this step, based on the loan loss rate, a preset price adjustment level can be queried to obtain the target price adjustment level for the loan, and then the loan price can be adjusted based on the target price adjustment level.

[0113] In this embodiment of the invention, the exponential distribution parameter of the delinquency probability in each repayment period of a loan can be calculated using the loan repayment information. This allows for the determination of the delinquency probability in each repayment period. By analyzing the exponential distribution parameter of the delinquency probability of loan data using repayment parameters, the distribution state of the delinquency probability can be determined, thereby improving the accuracy of loan loss probability calculation and enabling accurate adjustment of loan data.

[0114] It should be noted that loan data can be analyzed and processed before a loan is issued. However, at this time, there is no corresponding loan data for the loan. Therefore, similar loans can be identified from the issued loans, and the loan data of the similar loans can be memorized for analysis and processing. The result is then used as the processing result for this loan.

[0115] Therefore, the following combination Figure 1 The illustrated embodiments provide a detailed explanation of the method for obtaining repayment information in this invention. Figure 2 As shown, the method includes:

[0116] S201: In response to the loan data processing instruction, obtain the corresponding loan identifier.

[0117] S202: Based on the data table corresponding to the loan identifier positioning database, query the basic information of the loan to match a target loan identifier that is similar to the basic information.

[0118] In this embodiment of the invention, relevant data for each loan can be stored, specifically in a database table, and a correspondence between the data table and the loan identifier can be established. Therefore, in this step, the corresponding data table can be located from the database using the loan identifier, and the basic information of the loan can be retrieved from it. Since there is no corresponding repayment information for the loan, a similar target loan identifier can be matched based on the basic information of the loan.

[0119] Specifically, in this step, matching the target loan identifier can be performed as follows: obtain the pending loan identifiers that have the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier; based on the similarity value, determine the target loan identifiers that are similar to the basic information of the loan.

[0120] By identifying the loan's business type, pending loan identifiers with the same business type can be obtained. Then, the basic information corresponding to each pending loan identifier can be queried, allowing for the calculation of the similarity between the basic information of the loan and the basic information corresponding to the pending identifier. In this embodiment of the invention, the pending loan identifier corresponding to the highest similarity value can be determined as the target loan identifier similar to the basic information of the loan.

[0121] In this step, the basic information may include the loan price, loan interest rate, and number of repayment periods. Based on this, the number of times that each parameter value in the basic information of the loan is the same as the number of times that each parameter value in the basic information of the identifier to be processed is the same can be counted. This number is then determined as the similarity between the basic information of the loan and the basic information of the identifier to be processed.

[0122] S203: Query the corresponding repayment information based on the target loan identifier.

[0123] It should be noted that for the target loan identifier, which is similar to the loan in the loan data processing, its corresponding loss rate may have already been calculated previously. In this case, it is not necessary to go through the process again. Figure 1 The calculation is performed in the embodiment shown. Therefore, before executing this step, the loss probability of the corresponding target loan can be queried based on the target loan identifier to determine whether the loss probability of the target loan is empty. If so, the corresponding repayment information is queried based on the target loan identifier, that is, step S203 is executed, and then processed through step S102. If not, the loan price is adjusted based on the loss probability of the target loan.

[0124] In this embodiment of the invention, the exponential distribution parameter of the delinquency probability in each repayment period of a loan can be calculated using the loan repayment information. This allows for the determination of the delinquency probability in each repayment period. By analyzing the exponential distribution parameter of the delinquency probability of loan data using repayment parameters, the distribution state of the delinquency probability can be determined, thereby improving the accuracy of loan loss probability calculation and enabling accurate adjustment of loan data.

[0125] To address the problems existing in the prior art, embodiments of the present invention provide a loan data processing device 300, such as... Figure 3 As shown, the device 300 includes:

[0126] The query unit 301 is used to respond to the processing instructions of loan data, obtain the corresponding loan identifier, locate the corresponding data table in the database, and query the basic information and repayment information of the loan.

[0127] The determining unit 302 is used to determine the business type of the loan based on the loan identifier, so as to call a preset processing engine to match and obtain the corresponding overdue calculation model and default calculation model;

[0128] The calculation unit 303 is used to call the overdue calculation model to calculate the exponential distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, thereby determining the overdue probability of the loan in each repayment period;

[0129] The calculation unit 303 is used to call the default calculation model to calculate the default loss rate and default risk amount of the loan in each repayment period based on the basic information and the repayment information.

[0130] The adjustment unit 304 is used to determine the loss probability of the loan based on the delinquency probability, default loss rate and default risk amount of the loan in each repayment period, so as to adjust the price of the loan.

[0131] It should be understood that the manner in which embodiments of the present invention are implemented is different from the implementation method. Figure 1 The embodiments shown are the same and will not be described again here.

[0132] In one embodiment, the repayment period includes a repaid period and an outstanding period;

[0133] The computing unit 303 is specifically used for:

[0134] Calculate the probability of delinquency within each repayment period based on the aforementioned repayment information;

[0135] A preset index calculation model is invoked to calculate the exponential distribution parameters of the delinquency probability for each repayment period of the loan, based on the delinquency probability within the repayment period.

[0136] In yet another embodiment, the query unit is specifically used for:

[0137] Based on the corresponding data table in the loan identifier positioning database, the basic information of the loan is queried to match target loan identifiers that are similar to the basic information;

[0138] Based on the target loan identifier, query the corresponding repayment information.

[0139] In yet another embodiment, the query unit 301 is specifically used for:

[0140] Obtain the pending loan identifier that has the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;

[0141] Based on the similarity value, a target loan identifier that is similar to the basic information of the loan is determined.

[0142] In yet another embodiment, the query unit 301 is specifically used for:

[0143] Obtain the pending loan identifier that has the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier;

[0144] Based on the similarity value, a target loan identifier that is similar to the basic information of the loan is determined.

[0145] In yet another embodiment, the query unit 301 is specifically used for:

[0146] The number of parameter values ​​in the basic information of the loan that are the same as the parameter values ​​in the basic information corresponding to the identifier to be processed is counted, and the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed is determined.

[0147] In yet another embodiment, the query unit 301 is specifically used for:

[0148] Based on the target loan identifier, query the loss rate of the corresponding target loan to determine whether the loss rate of the target loan is empty;

[0149] If yes, then query the corresponding repayment information based on the target loan identifier; if no, then adjust the loan price based on the loss rate of the target loan.

[0150] In yet another embodiment, the adjustment unit 304 is specifically used for:

[0151] For each repayment period, the delinquency probability, default loss rate, and default risk amount within the repayment period are multiplied to obtain the loss probability for the repayment period.

[0152] The loss rate of the loan is obtained by dividing the sum of the loss probabilities for each repayment period by the principal amount of the loan.

[0153] In yet another embodiment, the loan data includes the loan price;

[0154] The adjustment unit 304 is specifically used for;

[0155] Based on the loan's loss rate, a preset price adjustment level is queried to determine the target price adjustment level for the loan, and then the loan price is adjusted accordingly.

[0156] It should be understood that the manner in which embodiments of the present invention are implemented is different from the implementation method. Figure 1 or Figure 2 The embodiments shown are the same and will not be described again here.

[0157] In this embodiment of the invention, the exponential distribution parameter of the delinquency probability in each repayment period of a loan can be calculated using the loan repayment information. This allows for the determination of the delinquency probability in each repayment period. By analyzing the exponential distribution parameter of the delinquency probability of loan data using repayment parameters, the distribution state of the delinquency probability can be determined, thereby improving the accuracy of loan loss probability calculation and enabling accurate adjustment of loan data.

[0158] According to embodiments of the present invention, an electronic device and a readable storage medium are also provided.

[0159] An electronic device according to an embodiment of the present invention includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the loan data processing method provided in the embodiment of the present invention.

[0160] Figure 4 An exemplary system architecture 400 is shown for which the loan data processing method or loan data processing apparatus of embodiments of the present invention can be applied.

[0161] like Figure 4As shown, system architecture 400 may include terminal devices 401, 402, and 403, a network 404, and a server 405. Network 404 serves as the medium for providing communication links between terminal devices 401, 402, and 403 and server 405. Network 404 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0162] Users can use terminal devices 401, 402, and 403 to interact with server 405 via network 404 to receive or send messages, etc. Various client applications can be installed on terminal devices 401, 402, and 403.

[0163] Terminal devices 401, 402, and 403 can be, but are not limited to, smartphones, tablets, laptops, and desktop computers, etc.

[0164] Server 405 can be a server that provides various services. The server can analyze and process data such as received product information query requests, and feed back the processing results (such as product information - just an example) to the terminal device.

[0165] It should be noted that the loan data processing method provided in this embodiment of the invention is generally executed by server 405, and correspondingly, the loan data processing device is generally located in server 405.

[0166] It should be understood that Figure 4 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0167] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing embodiments of the present invention. Figure 5 The computer system shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0168] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0169] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0170] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs the functions defined above in the system of this invention.

[0171] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0172] The flowcharts and block diagrams in the accompanying drawings 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 a flowchart or block diagram may represent a unit, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0173] The units described in the embodiments of the present invention can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including a query unit, a determination unit, a calculation unit, and an adjustment unit. The names of these units do not necessarily limit the specific unit; for example, a query unit can also be described as a "unit for information query function."

[0174] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to perform the loan data processing method provided by the present invention.

[0175] In 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 in the embodiments of the present invention.

[0176] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A loan data processing method, characterized in that, include: In response to loan data processing instructions, obtain the corresponding loan identifier to locate the corresponding data table in the database and query the basic loan information and repayment information; Based on the loan identifier, the business type of the loan is determined, and a preset processing engine is invoked to match and obtain the corresponding overdue calculation model and default calculation model; The overdue calculation model is invoked to calculate the exponential distribution parameters of the overdue probability of the loan in each repayment period based on the basic information and the repayment information, thereby determining the overdue probability of the loan in each repayment period; The default calculation model is invoked to calculate the default loss rate and default risk amount of the loan in each repayment period based on the basic information and repayment information. Based on the delinquency probability, default loss rate, and default risk amount of the loan in each repayment period, the loss rate of the loan is determined to adjust the loan data corresponding to the loan. The repayment period includes the paid period and the outstanding period; Based on the basic information and the repayment information, calculate the exponential distribution parameters of the delinquency probability for each repayment period of the loan, including: The probability of delinquency in each repayment period is calculated based on the parameter of whether delinquency occurred in each repayment period. A preset index calculation model is invoked to calculate the exponential distribution parameters of the delinquency probability in each repayment period of the loan based on the delinquency probability in the already repaid period. The delinquency probability in the repayment period represents the probability that a user will have a delinquency event in that repayment period. The delinquency event is mutually exclusive for a user in each repayment period. A user having a delinquency event in one repayment period means that no delinquency event has occurred before that repayment period. The overdue calculation model can be constructed based on the specific calculation method of overdue probability using exponential distribution parameters. Here, t represents the number of loan repayment periods, and P represents the probability of an overdue event occurring in the x-th repayment period within t repayment periods. The repaid information includes FPD90, representing the probability of an overdue event occurring in the first repayment period. When t>1, the exponential distribution parameter λ for the overdue probability in each repayment period is calculated as follows: The formula for calculating the probability of delinquency for the loan in each repayment period is as follows: P(x=t)=P(x≠1)*[P(x≤t|x≠1)-P(x≤(t-1)|x≠1)]; Based on the above formula, P(x=2)=(1-FPD90)*(1-e -λ P(x=3)=(1-FPD90)*(e -λ -e -2λ P(x=12)=(1-FPD90)*(e -10λ -e -11λ By analogy, the probability of overdue events occurring within each repayment period can be obtained. The loan data includes the loan price; Adjusting the loan data corresponding to the loan includes: querying a preset price adjustment level based on the loss rate of the loan to obtain the target price adjustment level of the loan, and then adjusting the price of the loan. The price adjustment level is configured based on the loss rate of different loans, and a mapping relationship is established between each price adjustment level and the corresponding loss rate. The price adjustment levels are price increase level and price decrease level.

2. The method according to claim 1, characterized in that, The corresponding data table in the location database allows querying basic loan information and repayment information, including: Based on the corresponding data table in the loan identifier positioning database, the basic information of the loan is queried to match target loan identifiers that are similar to the basic information; Based on the target loan identifier, query the corresponding repayment information.

3. The method according to claim 2, characterized in that, The matching of target loan identifiers similar to the basic information includes: Obtain the pending loan identifier that has the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier; Based on the similarity value, a target loan identifier that is similar to the basic information of the loan is determined.

4. The method according to claim 3, characterized in that, Calculating the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed includes: The number of parameter values ​​in the basic information of the loan that are the same as the parameter values ​​in the basic information corresponding to the identifier to be processed is counted, and the similarity between the basic information of the loan and the basic information corresponding to the identifier to be processed is determined.

5. The method according to claim 2, characterized in that, Based on the target loan identifier, query the corresponding repayment information, including: Based on the target loan identifier, query the loss rate of the corresponding target loan to determine whether the loss rate of the target loan is empty; If yes, then query the corresponding repayment information based on the target loan identifier; if no, then adjust the loan price based on the loss rate of the target loan.

6. The method according to claim 1, characterized in that, The loss rate of the loan is determined based on the delinquency probability, loss rate, and default risk amount in each repayment period, including: For each repayment period, the delinquency probability, default loss rate, and default risk amount within the repayment period are multiplied to obtain the loss probability for the repayment period. The loss rate of the loan is obtained by dividing the sum of the loss probabilities for each repayment period by the principal amount of the loan.

7. A loan data processing device, characterized in that, include: The query unit is used to respond to loan data processing instructions, obtain the corresponding loan identifier, locate the corresponding data table in the database, and query the basic information and repayment information of the loan. The determining unit is used to determine the business type of the loan based on the loan identifier, so as to call a preset processing engine to match and obtain the corresponding overdue calculation model and default calculation model; The calculation unit is used to call the overdue calculation model to calculate the exponential distribution parameters of the overdue probability of the loan in each repayment period based on the parameter of whether overdue has occurred in each repayment period, thereby determining the overdue probability of the loan in each repayment period. The calculation unit is used to call the default calculation model to calculate the default loss rate and default risk amount of the loan in each repayment period based on the basic information and the repayment information. The adjustment unit is used to determine the loss probability of the loan based on the delinquency probability, default loss rate and default risk amount of the loan in each repayment period, so as to adjust the price of the loan. The repayment period includes the paid period and the outstanding period; The computing unit is specifically used for: Calculate the probability of delinquency within each repayment period based on the aforementioned repayment information; A preset index calculation model is invoked to calculate the exponential distribution parameters of the delinquency probability in each repayment period of the loan based on the delinquency probability in the already repaid period. The delinquency probability in the repayment period represents the probability that a user will have a delinquency event in that repayment period. The delinquency event is mutually exclusive for a user in each repayment period. A user having a delinquency event in one repayment period means that no delinquency event has occurred before that repayment period. The overdue calculation model can be constructed based on the specific calculation method of overdue probability using exponential distribution parameters. Here, t represents the number of loan repayment periods, and P represents the probability of an overdue event occurring in the x-th repayment period within t repayment periods. The repaid information includes FPD90, representing the probability of an overdue event occurring in the first repayment period. When t>1, the exponential distribution parameter λ for the overdue probability in each repayment period is calculated as follows: The formula for calculating the probability of delinquency for the loan in each repayment period is as follows: P(x=t)=P(x≠1)*[P(x≤t|x≠1)-P(x≤(t-1)|x≠1)]; Based on the above formula, P(x=2)=(1-FPD90)*(1-e -λ P(x=3)=(1-FPD90)*(e -λ -e -2λ P(x=12)=(1-FPD90)*(e -10λ -e -11λ By analogy, the probability of overdue events occurring within each repayment period can be obtained. The loan data includes the loan price; The adjustment unit is specifically used to: query a preset price adjustment level based on the loss rate of the loan to obtain the target price adjustment level of the loan, and then adjust the price of the loan. The price adjustment level is configured based on the loss rate of different loans, and a mapping relationship is established between each price adjustment level and the corresponding loss rate. The price adjustment level is a price increase level and a price decrease level.

8. The apparatus according to claim 7, characterized in that, The query unit is specifically used for: Based on the corresponding data table in the loan identifier positioning database, the basic information of the loan is queried to match target loan identifiers that are similar to the basic information; Based on the target loan identifier, query the corresponding repayment information.

9. The apparatus according to claim 8, characterized in that, The query unit is specifically used for: Obtain the pending loan identifier that has the same business type as the loan, query the corresponding basic information, and then calculate the similarity between the basic information of the loan and the basic information corresponding to the pending loan identifier; Based on the similarity value, a target loan identifier that is similar to the basic information of the loan is determined.

10. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

11. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

12. A computer program product, comprising a computer program, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

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