CN116542759A - Transaction processing method, device, server and storage medium - Google Patents

Transaction processing method, device, server and storage medium Download PDF

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Publication number
CN116542759A
CN116542759A CN202310393338.7A CN202310393338A CN116542759A CN 116542759 A CN116542759 A CN 116542759A CN 202310393338 A CN202310393338 A CN 202310393338A CN 116542759 A CN116542759 A CN 116542759A
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loan
financial data
accuracy
preset
determining
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邱凤娟
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Bank of China Ltd
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Bank of China Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/12—Accounting
    • G06Q40/125—Finance or payroll
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

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Abstract

The application provides a transaction processing method, a transaction processing device, a server and a storage medium, which can be used in the financial field. The method comprises the following steps: acquiring a loan transaction request, wherein the request comprises an industry identifier of a loan applicant and financial data of the loan applicant; acquiring accuracy parameters of financial data corresponding to the industry identifier according to the loan request; determining the accuracy of the financial data according to the financial data and the accuracy parameters; if the accuracy meets the preset accuracy condition, determining a pre-loan feasibility value of the loan applicant according to the financial data and the accuracy parameters of the financial data; and determining the pre-loan review result of the loan applicant according to the pre-loan feasibility value. According to the method, the evaluation result is obtained without manual offline investigation and analysis, the financial data submitted by the loan applicant can be automatically subjected to pre-loan audit, the pre-loan audit efficiency is effectively improved, the time is saved, the audit process can be quickened, and the accurate pre-loan release audit result can be obtained.

Description

Transaction processing method, device, server and storage medium
Technical Field
The present disclosure relates to the field of finance, and in particular, to a transaction processing method, apparatus, server and storage medium.
Background
Loan is one of the main camping businesses of financial institutions, pre-loan investigation is an important link of credit management of the financial institutions, pre-loan investigation is an important premise for preventing risks and reducing bad account rate of the financial institutions, and the authenticity, accuracy and feasibility of investigation are significant for the safety of loans and directly related to the correctness of loan decisions.
At present, manual offline investigation and analysis are generally adopted, loan applicants provide various financial, running and invoice paper materials for financial institutions, and the financial institutions schedule the on-site investigation by the examiners according to the loan auditing flow and the related flow schedule, and the financial institutions conduct pre-loan auditing assessment according to the investigation results.
However, the manner of performing the pre-loan audit assessment based on the manual offline survey analysis is not only inefficient, and requires a lot of time and labor, but also the assessment results based on the manual manner are inaccurate. Therefore, there is a need for a pre-loan audit assessment that improves efficiency and accuracy of the assessment results, and reduces time and labor.
Disclosure of Invention
The application provides a transaction processing method, a transaction processing device, a server and a storage medium, which are used for solving the problem that a manual offline investigation and analysis mode for pre-loan audit evaluation is low in efficiency.
In a first aspect, the present application provides a method for processing a transaction, including:
acquiring a loan transaction request, wherein the loan transaction request comprises an industry identifier of a loan applicant and financial data of the loan applicant; wherein the financial data comprises: value added amount, net profit, invoice amount and business amount;
acquiring accuracy parameters of financial data corresponding to the industry identifier according to the loan request;
determining the accuracy of the financial data according to the financial data and accuracy parameters of the financial data;
if the accuracy of the financial data is determined to meet the preset accuracy condition, determining a pre-loan feasibility value of the loan applicant according to the financial data and the accuracy parameters of the financial data;
and determining a pre-loan audit result of the loan applicant according to the pre-loan feasibility value so as to perform corresponding transaction processing according to the pre-loan audit result.
In a second aspect, the present application provides a transaction processing apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a loan transaction request, and the loan transaction request comprises an industry identifier of a loan applicant and financial data of the loan applicant; wherein the financial data comprises: value added amount, net profit, invoice amount and business amount;
the determining module is used for acquiring financial data corresponding to the industry identifier and accuracy parameters of the financial data according to the loan request;
the determining module is also used for determining the accuracy of the financial data according to the financial data and the accuracy parameters of the financial data;
the determining module is further used for determining a pre-loan feasibility value of the loan applicant according to the financial data and the accuracy parameters of the financial data if the accuracy of the financial data is determined to meet the preset accuracy condition;
and the processing module is used for determining the pre-loan audit result of the loan applicant according to the pre-loan feasibility value so as to perform corresponding transaction processing according to the pre-loan audit result.
In a third aspect, the present application provides a server comprising: a processor, a memory, and a transceiver;
a processor, memory, and transceiver circuitry interconnect;
the memory stores computer-executable instructions;
a transceiver for transceiving data;
the processor executes computer-executable instructions stored in the memory to cause the processor to perform the method as described in the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for performing the method according to the first aspect when executed by a processor.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.
According to the transaction processing method, device, server and storage medium, the accuracy parameters of the corresponding financial data are preset for different industries, so that the accuracy parameters of the financial data corresponding to the industry identification are obtained according to the industry identification of the loan applicant, the accuracy of the financial data submitted by the loan applicant is determined according to the accuracy parameters of the financial data and the financial data submitted by the loan applicant, if the accuracy of the financial data is determined to meet the preset accuracy conditions, the pre-loan feasibility value of the loan applicant is determined according to the accuracy parameters of the financial data and the financial data, and accordingly the pre-loan approval result of the loan applicant is obtained according to the pre-loan feasibility value, corresponding transaction processing is performed according to the pre-loan approval result, the evaluation result is obtained without manual under-line investigation analysis, the pre-loan approval efficiency can be automatically carried out on the financial data submitted by the loan applicant, the time is saved, and meanwhile, the more accurate pre-loan approval result can be obtained.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a network architecture schematic diagram of a transaction processing method provided in the present application;
FIG. 2 is a flow chart of a transaction processing method provided in the present application;
FIG. 3 is a flow chart of another transaction processing method provided in the present application;
FIG. 4 is a scatter plot one of revenue provided by the present application;
FIG. 5 is a second scatter plot of revenue provided by the present application;
FIG. 6 is a schematic structural diagram of a transaction processing device provided in the present application;
fig. 7 is a block diagram of a server for implementing the transaction processing method of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.
The transaction processing method, device, server and storage medium of the present application may be used in the financial field, and may be used in any field other than the financial field.
For a clear understanding of the technical solutions of the present application, the prior art solutions will be described in detail first.
At present, manual offline investigation and analysis are generally adopted, loan applicants provide various financial, running and invoice paper materials for financial institutions, and the financial institutions schedule the on-site investigation by the examiners according to the loan auditing flow and the related flow schedule, and the financial institutions conduct pre-loan auditing assessment according to the investigation results.
However, the manner of performing the pre-loan audit assessment based on the manual offline survey analysis is not only inefficient, and requires a lot of time and labor, but also the assessment results based on the manual manner are inaccurate.
Therefore, aiming at the problem that the mode of carrying out pre-loan audit assessment based on manual offline investigation analysis in the prior art is low in efficiency, the inventor finds that accuracy parameters of corresponding financial data are set for different industries in advance, so that the accuracy parameters of the financial data corresponding to the industry identifier are obtained according to the industry identifier of the loan applicant, the accuracy of the financial data submitted by the loan applicant is determined according to the accuracy parameters of the financial data and the financial data submitted by the loan applicant, if the accuracy of the financial data is determined to meet preset accuracy conditions, the pre-loan feasibility value of the loan applicant is determined according to the accuracy parameters of the financial data and the financial data, so that the pre-loan audit result of the loan applicant is obtained according to the pre-loan feasibility value, corresponding transaction processing is carried out according to the pre-loan audit result, the manual offline investigation analysis is not needed, the pre-loan audit of the financial data submitted by the loan applicant can be automatically carried out, the pre-loan audit efficiency is effectively improved, the time is saved, and meanwhile, the more accurate pre-loan audit result can be obtained.
The inventor proposes the technical solution of the embodiments of the present application based on the inventive findings described above. The following describes a network architecture and an application scenario of a transaction processing method provided in the embodiments of the present application.
As shown in fig. 1, the network architecture corresponding to the transaction processing method provided by the embodiment of the present invention includes: a user terminal 1, a server 2, and a financial institution terminal 3. The server 2 is communicatively connected to the user terminal 1 and the financial institution terminal 3, respectively. The loan applicant submits financial data and industry identification in an operation interface of a client of the user terminal 1, and clicks a confirmation button, so that a loan transaction request is triggered, the server 2 analyzes the loan transaction request to obtain the industry identification of the loan applicant and the financial data of the loan applicant, and according to the loan request, accuracy parameters of the financial data corresponding to the industry identification are obtained, wherein the financial data comprises: value added amount, net profit, invoice amount and business amount; the server 2 determines the accuracy of the financial data according to the financial data and the accuracy parameters of the financial data; if the accuracy of the financial data is determined not to be in accordance with the preset accuracy condition, the server 2 does not execute loan transaction processing, and generates and transmits prompt information carrying that the accuracy of the financial data of the loan applicant is not in accordance with the preset accuracy condition to the user terminal 1, and the user supplements the financial data; if the accuracy of the financial data accords with the preset accuracy condition, determining a pre-loan feasibility value of the loan applicant according to the financial data and the accuracy parameter of the financial data; the server 2 determines the pre-loan audit result of the loan applicant according to the pre-loan feasibility value to perform corresponding transaction processing according to the pre-loan audit result, for example, the server 2 sends the pre-loan audit result of the loan applicant to the financial institution terminal 3 to perform loan processing by the financial institution. The evaluation result can be obtained without manual offline investigation and analysis, the financial data submitted by the loan applicant can be automatically subjected to pre-loan audit, the pre-loan audit efficiency is effectively improved, the time is saved, the audit process can be quickened, and the more accurate pre-loan audit result can be obtained.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flow chart of a transaction processing method provided in the present application, where the method is applied to a server. Wherein the server may be a digital computer representing various forms. Such as laptop computers, desktop computers, workstations, blade servers, mainframe computers, and other suitable computers. As shown in fig. 2, the method includes:
step 201, obtaining a loan transaction request, wherein the loan transaction request comprises an industry identifier of a loan applicant and financial data of the loan applicant; wherein the financial data comprises: value added amount, net profit, invoice amount, and business amount.
In this embodiment, the loan transaction request is obtained, the loan transaction request is analyzed, and the industry identifier of the loan applicant and the financial data of the loan applicant are obtained, and industries include service industry, sales industry, manufacturing industry and the like, and each industry has its unique industry identifier. Wherein the financial data includes value added amount, net profit, invoice amount, and business amount.
Step 202, according to the loan request, acquiring accuracy parameters of financial data corresponding to the industry identifier.
Specifically, accuracy parameters of financial data are preset for each industry, an industry parameter statistical table is preset, a mapping relation between each industry identifier and the accuracy parameters of the financial data is recorded in the industry parameter statistical table, and the accuracy parameters of the financial data corresponding to the industry identifier of a loan applicant in the mapping relation are obtained according to a loan request, wherein the accuracy parameters of the financial data comprise: value added value weight k 1 Weight k of invoice amount 2 Entry invoice amount weight k 3 See table 1.
Table 1 is an industry parameter statistics table:
k 1 k 2 k 3
sales industry (001) 0.5 0.25 0.25
…
Wherein 001 is the corresponding industry identifier of the sales industry, and the value added value weight k of the sales industry 1 Sales invoice amount weight k of 0.5 2 An entry invoice amount weight k of 0.25 for the sales industry 3 0.25.
Specifically, the value of the value-added value weight k 1 To determine the proportion of financial status using value added amounts, the invoice amount weight k is sold 2 To determine the proportion of financial status using the invoice amount, the invoice amount is entered by weight k 3 The proportion of the financial condition is determined for the use of the entry invoice amount.
It should be noted that the accuracy parameters of the financial data may also include other parameters, not limited to the above parameters. Value added value weight k 1 Weight k of invoice amount 2 Entry invoice amount weight k 3 The corresponding values may be set according to actual industry conditions.
In step 203, the accuracy of the financial data is determined based on the financial data and the accuracy parameters of the financial data.
In this embodiment, an accuracy model of the financial data is configured in advance, and the financial data and accuracy parameters of the financial data are analyzed and processed by adopting the accuracy model of the financial data, so as to obtain accuracy of the financial data.
If it is determined that the accuracy of the financial data meets the preset accuracy condition, step 204, the lending feasibility value of the loan applicant is determined according to the financial data and the accuracy parameters of the financial data.
In this embodiment, whether the accuracy of the financial data accords with the preset accuracy condition is determined, if the accuracy of the financial data accords with the preset accuracy condition, which indicates that the accuracy of the financial data submitted by the loan applicant is high, the pre-loan feasibility value of the loan applicant is determined according to the accuracy parameters of the financial data and the financial data, so as to perform feasibility analysis.
Optionally, if the accuracy of the financial data is determined to meet the preset accuracy condition, it is indicated that the accuracy of the financial data submitted by the loan applicant is insufficient, and the loan applicant is required to supplement the financial data.
And 205, determining a pre-loan audit result of the loan applicant according to the pre-loan feasibility value so as to perform corresponding transaction processing according to the pre-loan audit result.
In this embodiment, a preset pre-loan feasibility threshold is obtained, and a pre-loan audit result of the loan applicant is determined according to the pre-loan feasibility threshold and the preset pre-loan feasibility threshold, wherein the pre-loan audit result is that the audit is passed or failed, the audit is passed, the loan can be paid, and the audit is failed.
According to the method and the device, the accuracy parameters of the corresponding financial data are preset for different industries, so that the accuracy parameters of the financial data corresponding to the industry identification are obtained according to the industry identification of the loan applicant, the accuracy of the financial data submitted by the loan applicant is determined according to the accuracy parameters of the financial data and the financial data submitted by the loan applicant, if the accuracy of the financial data is determined to meet the preset accuracy conditions, the pre-loan feasibility value of the loan applicant is determined according to the accuracy parameters of the financial data and the financial data, and accordingly the pre-loan audit result of the loan applicant is obtained according to the pre-loan feasibility value, corresponding transaction processing is carried out according to the pre-loan audit result, the evaluation result is obtained without manual under-line investigation and analysis, the pre-loan audit on the financial data submitted by the loan applicant can be automatically carried out, the pre-loan audit efficiency is effectively improved, the time is saved, and meanwhile, the pre-loan audit process can be accelerated, and the more accurate pre-loan audit result can be obtained.
Fig. 3 is a flow chart of another transaction processing method provided in the present application, where the method is applied to a server, as shown in fig. 3, and the method includes:
step 301, obtaining a loan transaction request, wherein the loan transaction request comprises an industry identifier of a loan applicant and financial data of the loan applicant; wherein the financial data comprises: value added amount, net profit, invoice amount, and business amount.
In this embodiment, the loan transaction request is obtained, the loan transaction request is analyzed, and the industry identifier of the loan applicant and the financial data of the loan applicant are obtained, and industries include service industry, sales industry, manufacturing industry and the like, and each industry has its unique industry identifier. Wherein the financial data includes a value added amount, a net profit, an invoice amount, and a business amount.
Step 302, according to the loan request, acquiring accuracy parameters of financial data corresponding to the industry identifier.
Specifically, accuracy parameters of financial data are preset for each industry, an industry parameter statistical table is preset, a mapping relation between each industry identifier and the accuracy parameters of the financial data is recorded in the industry parameter statistical table, and the accuracy parameters of the financial data corresponding to the industry identifier of a loan applicant in the mapping relation are obtained according to a loan request, wherein the accuracy parameters of the financial data comprise: increment amount weight, sell invoice amount weight, and enter invoice amount weight.
Step 303, determining the accuracy of the financial data based on the financial data and the accuracy parameters of the financial data.
In one possible implementation, determining the accuracy of the financial data from the financial data and accuracy parameters of the financial data includes:
and adopting an accuracy model of the financial data to analyze and process the value added amount, the net profit, the sales invoice amount, the entry invoice amount, the business cost, the business income, the increment weight, the sales invoice amount weight and the entry invoice amount weight, and obtaining the accuracy of the financial data.
In this embodiment, the invoice amount includes a sales invoice amount and an entry invoice amount, the sales includes a sales cost and a sales income, and the accuracy parameters of the financial data include an increment weight, a sales invoice amount weight, and an entry invoice amount weight. Substituting the value added amount, the net profit, the sales invoice amount, the entry invoice amount, the business cost, the business income, the increment weight, the sales invoice amount weight and the entry invoice amount weight into an accuracy model of the financial data, and calculating to obtain the accuracy of the financial data, wherein the accuracy model of the financial data is expressed as:
wherein A is accuracy of financial data, k 1 To increase the value of the weight, k 2 To sell invoice amount weight, k 3 To weight the amount of the entry invoice, a 1 To increase the value of the amount, a 2 To make a net profit, a 3 To sell invoice amount, a 4 For business income, a 5 To enter invoice amount, a 6 Is business cost.
If it is determined that the accuracy of the financial data does not meet the preset accuracy condition, step 304, the loan transaction processing is not executed, and a prompt message carrying that the accuracy of the financial data of the loan applicant does not meet the preset accuracy condition is generated and sent.
In the embodiment, comparing an absolute value of accuracy of the financial data with a preset accuracy threshold value, and determining whether the accuracy of the financial data accords with a preset accuracy condition according to a comparison result; if the accuracy of the financial data is determined to be not in accordance with the preset accuracy condition, which means that the accuracy of the financial data submitted by the loan applicant is insufficient, and the loan applicant is required to submit more financial data, the loan transaction processing is not executed, and a prompt message carrying that the accuracy of the financial data of the loan applicant is not in accordance with the preset accuracy condition is generated and sent.
Optionally, the transaction processing method further includes:
if the absolute value of the accuracy of the financial data is smaller than the preset accuracy threshold, determining that the accuracy of the financial data meets the preset accuracy condition; and if the absolute value of the accuracy of the financial data is larger than or equal to the preset accuracy threshold, determining that the accuracy of the financial data does not meet the preset accuracy condition.
In this embodiment, whether the accuracy of the financial data meets the preset accuracy condition is determined according to the comparison result, specifically, if the absolute value of the accuracy of the financial data is smaller than the preset accuracy threshold, it is indicated that the accuracy of the financial data submitted by the loan applicant is high, the accuracy of the financial data is determined to meet the preset accuracy condition, and further, the pre-loan feasibility value of the loan applicant is determined according to the financial data, so as to perform the loan feasibility analysis. If the absolute value of the accuracy of the financial data is greater than or equal to a preset accuracy threshold, the fact that the accuracy of the financial data submitted by the loan applicant is insufficient is indicated, and the accuracy of the financial data is determined to be not in accordance with a preset accuracy condition.
If it is determined that the accuracy of the financial data meets the preset accuracy condition, the lending feasibility value of the loan applicant is determined according to the financial data and the accuracy parameters of the financial data, step 305.
In one possible implementation, determining the pre-loan feasibility value of the loan applicant based on the financial data and the accuracy parameter of the financial data, comprises:
fitting the financial data in the preset time to obtain a corresponding fitting function; and determining the feasibility numerical value before credit according to the accuracy parameters of the financial data and the corresponding fitting function.
In this embodiment, the preset time may be set according to actual situations, for example, if it is determined that the accuracy of the financial data meets the preset accuracy condition, fitting the financial data within ten years to obtain a corresponding fitting function, deriving the corresponding fitting function to obtain a corresponding derivative, and obtaining the lending feasibility value according to the accuracy parameter of the financial data and the corresponding derivative.
Optionally, fitting the financial data in a preset time to obtain a corresponding fitting function, including:
fitting the business income in the preset time to obtain a first function, fitting the production cost in the preset time to obtain a second function, and fitting the net profit in the preset time to obtain a third function; fitting the pin invoice data in the preset time to obtain a fourth function, fitting the entry invoice data in the preset time to obtain a fifth function, and fitting the increment data in the preset time to obtain a sixth function.
In this embodiment, the financial data includes business income, production cost, net profit, entry invoice data, sales invoice data and value added data, and the business income, production cost, net profit, entry invoice data, sales invoice data and value added data are fitted respectively to obtain corresponding fitting functions, specifically, the business income in a preset time is fitted to obtain a first function, the production cost in the preset time is fitted to obtain a second function, and the net profit in the preset time is fitted to obtain a third function; fitting the pin invoice data in the preset time to obtain a fourth function, fitting the entry invoice data in the preset time to obtain a fifth function, and fitting the increment data in the preset time to obtain a sixth function.
Optionally, determining the pre-credit feasibility value based on the accuracy parameters of the financial data and the corresponding fitting function includes:
acquiring preset coefficients corresponding to business income, production cost, net profit, entry invoice data, sales invoice data and value added line data respectively; deriving the first function, the second function, the third function, the fourth function, the fifth function and the sixth function to obtain derivatives corresponding to the functions; and adopting a preconfigured lending analysis function to analyze and process the increment weight, the sales invoice amount weight, the inlet invoice amount weight, the derivative corresponding to each function and the corresponding preset coefficient, and obtaining the feasibility value before lending.
In this embodiment, the accuracy parameters of the financial data include an increment weight, a sell invoice amount weight, and an enter invoice amount weight. And acquiring preset coefficients corresponding to the business income, the production cost, the net profit, the entry invoice data, the sales invoice data and the increment line data respectively, wherein the preset coefficients of the business income are expressed as the proportion of the business income in the loan analysis function. For the first function f 1 (x) Deriving to obtain derivative f 1 ' s (x); for the second function f 2 (x) Deriving to obtain derivative f 2 ' s (x); for the third function f 3 (x) Deriving to obtain derivative f 3 ' s (x); for the fourth function g 1 (x) Deriving to obtain derivative g' 1 (x) The method comprises the steps of carrying out a first treatment on the surface of the For the fifth function g 2 (x) Deriving to obtain derivative g 2 (x) The method comprises the steps of carrying out a first treatment on the surface of the For the sixth function g 3 (x) Deriving to obtain derivative g' 3 (x)。
Further, a preconfigured lending analysis function is adopted to analyze and process the increment value weight, the sales invoice amount weight, the inlet invoice amount weight, the derivative corresponding to each function and the corresponding preset coefficient, and a feasibility value before lending is obtained, wherein the preconfigured lending analysis function is expressed as:
m(x)＝k 1 (b 1 f 1 '(x)+b 2 g 1 '(x))+k 2 (b 3 f 2 '(x)+b 4 g 2 '(x))+k 3 (b 5 f 3 '(x)+b 6 g 3 '(x))
formula (2)
Wherein m (x) is a pre-lending feasibility number, k 1 To increase the value of the weight, k 2 To sell invoice amount weight, k 3 B, weighting the amount of the entry invoice 1 Preset coefficient for business income, b 2 B is a preset coefficient of production cost 3 Preset coefficient for net profit, b 4 Preset coefficient for entry invoice data, b 5 Preset coefficient for invoice data of sale item, b 6 For the preset coefficient of the increment data, optionally b 1 、b 2 、b 3 、b 4 、b 5 、b 6 May be set to 0.5 or may be set according to the actual duty cycle.
Specifically, taking the incomes of a loan applicant as an example, a scatter diagram of incomes of 12 years total from 2011 to 2022 years is obtained, referring to fig. 4, a polynomial function f corresponding to the incomes is fitted by using a fitting tool based on the scatter diagram of the incomes 1 (x) X represents time data, e.g., see FIG. 5, a cubic polynomial is fitted to f 1 (x)＝0.0306x 3 -1.3427x 2 +34.443x+951.19 for f 1 (x) Deriving to obtain derivative f 1 ′(x)＝0.0918x 2 -2.68554x+34.443. The corresponding functions can be obtained by adopting a fitting tool based on the scatter diagram of the production cost, the scatter diagram of the net profit, the scatter diagram of the entry invoice data, the scatter diagram of the sales invoice data and the scatter diagram of the increment, and the corresponding derivatives can be obtained by deriving the functions.
And 306, determining a pre-loan audit result of the loan applicant according to the pre-loan feasibility value so as to perform corresponding transaction processing according to the pre-loan audit result.
In one possible implementation, determining a pre-loan audit result for a loan applicant based on the pre-loan feasibility value, comprises:
acquiring a preset pre-credit feasibility threshold value; and comparing the preset feasibility threshold before loan with the feasibility value before loan, and determining the result of the loan applicant's loan review before loan according to the comparison result.
In this embodiment, a preset pre-loan feasibility threshold is obtained, where the pre-loan feasibility threshold is set according to practical situations, for example, set to 0.5, the preset pre-loan feasibility threshold is compared with a pre-loan feasibility value, and a pre-loan review result of the loan applicant is determined according to the comparison result, and the pre-loan review result is classified into a review failed one and a review pass one, where the pre-loan review result is a review pass one or a review failed one, the review pass can be performed to release a loan, and the review failed one is not performed to release a loan.
Optionally, determining a pre-loan audit result of the loan applicant based on the comparison result, comprising:
if the pre-loan feasibility value is larger than a preset pre-loan feasibility threshold value, determining that the pre-loan assessment result of the loan applicant is approved; if the pre-loan feasibility value is less than or equal to a preset pre-loan feasibility threshold, determining that the pre-loan review result of the loan applicant is not passed.
In this embodiment, the pre-loan feasibility threshold is set according to the actual situation, for example, set to 0.5, and if the pre-loan feasibility value is greater than the pre-loan feasibility threshold, that is, m (x) > 0.5, which indicates that the loan applicant is in forward development as a whole, it is determined that the pre-loan audit result of the loan applicant is audit passing, and the audit passing can be performed. If the pre-loan feasibility value is smaller than or equal to a preset pre-loan feasibility threshold, namely m (x) is less than or equal to 0.5, indicating that the loan applicant is in the overall stagnated development, determining that the pre-loan assessment result of the loan applicant is that the assessment is not passed, and not suggesting to carry out loan assessment.
According to the method and the device, the evaluation result is obtained without manual offline investigation and analysis, the financial data submitted by the loan applicant can be automatically subjected to pre-loan auditing, the pre-loan auditing efficiency is effectively improved, and the auditing flow can be quickened while the time is saved. The verification can be passed, the loan can be paid out, and the advice that the verification is not passed is paid out, so that a more accurate result of loan payment before loan verification can be obtained.
Fig. 6 is a schematic structural diagram of a transaction processing device provided in the present application, and as shown in fig. 6, a transaction processing device 600 provided in this embodiment includes an obtaining module 601, a determining unit 602, and a processing unit 603.
The obtaining module 601 is configured to obtain a loan transaction request, where the loan transaction request includes an industry identifier of a loan applicant and financial data of the loan applicant; wherein the financial data comprises: value added amount, net profit, invoice amount and business amount; the determining module 602 is configured to obtain financial data corresponding to the industry identifier and accuracy parameters of the financial data according to the loan request. The determination module 602 is further configured to determine the accuracy of the financial data based on the financial data and the accuracy parameters of the financial data. The determining module 602 is further configured to determine a pre-loan feasibility value of the loan applicant according to the accuracy parameters of the financial data and the accuracy parameters of the financial data if the accuracy of the financial data is determined to meet the preset accuracy condition. The processing module 603 is configured to determine a pre-loan audit result of the loan applicant according to the pre-loan feasibility value, so as to perform corresponding transaction processing according to the pre-loan audit result.
Optionally, the determining module 602 is further configured to analyze and process the added value, the net profit, the sales invoice amount, the entry invoice amount, the business cost, the business income, the added value weight, the sales invoice amount weight, and the entry invoice amount weight by using an accuracy model of the financial data, so as to obtain accuracy of the financial data.
Optionally, the determining module 602 is further configured to fit the financial data within a preset time to obtain a corresponding fitting function; and determining the feasibility numerical value before credit according to the accuracy parameters of the financial data and the corresponding fitting function.
Optionally, the determining module 602 is further configured to fit the business income within the preset time to obtain a first function, fit the production cost within the preset time to obtain a second function, and fit the net profit within the preset time to obtain a third function; fitting the pin invoice data in the preset time to obtain a fourth function, fitting the entry invoice data in the preset time to obtain a fifth function, and fitting the increment data in the preset time to obtain a sixth function.
Optionally, the determining module 602 is further configured to obtain preset coefficients corresponding to the business income, the production cost, the net profit, the entry invoice data, the sales invoice data and the added value data; deriving the first function, the second function, the third function, the fourth function, the fifth function and the sixth function to obtain derivatives corresponding to the functions; and adopting a preconfigured lending analysis function to analyze and process the increment weight, the sales invoice amount weight, the inlet invoice amount weight, the derivative corresponding to each function and the corresponding preset coefficient, and obtaining the feasibility value before lending.
Optionally, the processing module 603 is further configured to obtain a preset pre-credit feasibility threshold; and comparing the preset feasibility threshold before loan with the feasibility value before loan, and determining the result of the loan applicant's loan review before loan according to the comparison result.
Optionally, the processing module 603 is further configured to determine that the result of the lending applicant's lending review is passing if the lending feasibility value is greater than or equal to a preset lending feasibility threshold; if the pre-loan feasibility value is smaller than a preset pre-loan feasibility threshold value, determining that the pre-loan review result of the loan applicant is not passed.
Optionally, the determining module 602 is further configured to determine that the accuracy of the financial data meets a preset accuracy condition if an absolute value of the accuracy of the financial data is less than a preset accuracy threshold; if the absolute value of the accuracy of the financial data is greater than or equal to a preset accuracy threshold, determining that the accuracy of the financial data does not meet a preset accuracy condition.
Optionally, the processing module 603 is further configured to, if it is determined that the accuracy of the financial data does not meet the preset accuracy condition, not perform the loan transaction processing, and generate and send a prompt message carrying that the accuracy of the financial data of the loan applicant does not meet the preset accuracy condition.
Fig. 7 is a block diagram of a server for implementing the transaction processing method of the present application, and as shown in fig. 7, the server 700 includes: memory 701, processor 702, and transceiver 703.
A processor 702, a memory 701 and a transceiver 703;
a transceiver 703 for transceiving data;
memory 701 stores computer-executable instructions;
processor 702 executes computer-executable instructions stored in memory 701, causing processor 502 to perform the methods provided by any of the embodiments described above.
In an exemplary embodiment, there is also provided a computer-readable storage medium having stored therein computer-executable instructions for performing the method of any one of the above embodiments by a processor.
In an exemplary embodiment, a computer program product is also provided, comprising a computer program for executing the method of any of the above embodiments by a processor.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (12)

1. A method of processing a transaction, the method comprising:
acquiring a loan transaction request, wherein the loan transaction request comprises an industry identifier of a loan applicant and financial data of the loan applicant; wherein the financial data comprises: value added amount, net profit, invoice amount and business amount;
acquiring accuracy parameters of financial data corresponding to the industry identifier according to the loan request;
determining the accuracy of the financial data according to the financial data and accuracy parameters of the financial data;
if the accuracy of the financial data is determined to meet the preset accuracy condition, determining a pre-loan feasibility value of the loan applicant according to the financial data and the accuracy parameters of the financial data;
and determining a pre-loan audit result of the loan applicant according to the pre-loan feasibility value so as to perform corresponding transaction processing according to the pre-loan audit result.
2. The method of claim 1, wherein the invoice amount comprises: the amount of the sales invoice and the amount of the entry invoice; the turnover includes: business cost and revenue; the accuracy parameters of the financial data include: increment amount weight, sales invoice amount weight and entry invoice amount weight;
said determining the accuracy of the financial data from said financial data and accuracy parameters of said financial data comprises:
and adopting an accuracy model of financial data to analyze and process the value added amount, the net profit, the sales invoice amount, the entry invoice amount, the business cost, the business income, the value added amount weight, the sales invoice amount weight and the entry invoice amount weight, and obtaining the accuracy of the financial data.
3. The method of claim 1, wherein said determining a pre-loan feasibility value for the loan applicant based on the financial data and an accuracy parameter of the financial data, comprises:
fitting the financial data in the preset time to obtain a corresponding fitting function;
and determining the pre-credit feasibility value according to the accuracy parameters of the financial data and the corresponding fitting function.
4. A method according to claim 3, wherein the financial data comprises: business income, production cost, net profit, entry invoice data, sales invoice data and value added amount data;
fitting the financial data in the preset time to obtain a corresponding fitting function, wherein the fitting function comprises the following steps:
fitting the business income in preset time to obtain a first function, fitting the production cost in preset time to obtain a second function, and fitting the net profit in preset time to obtain a third function;
fitting the entry invoice data in the preset time to obtain a fourth function, fitting the entry invoice data in the preset time to obtain a fifth function, and fitting the increment invoice data in the preset time to obtain a sixth function.
5. The method of claim 4, wherein the accuracy parameters of the financial data comprise: increment amount weight, sales invoice amount weight and entry invoice amount weight;
said determining said pre-credit feasibility value based on said accuracy parameters of said financial data and said corresponding fitting function, comprising:
acquiring preset coefficients corresponding to business income, production cost, net profit, entry invoice data, sales invoice data and value added line data respectively;
deriving the first function, the second function, the third function, the fourth function, the fifth function and the sixth function to obtain derivatives corresponding to the functions;
and adopting a preconfigured lending analysis function to analyze and process the value added weight, the sales invoice amount weight, the entry invoice amount weight, the derivative corresponding to each function and the corresponding preset coefficient, and obtaining the feasibility value before lending.
6. The method of any one of claims 1 to 5, wherein said determining a pre-loan audit result for the loan applicant based on the pre-loan feasibility value, comprises:
acquiring a preset pre-credit feasibility threshold value;
and comparing the preset pre-loan feasibility threshold value with the pre-loan feasibility value, and determining a pre-loan verification result of the loan applicant according to the comparison result.
7. The method of claim 6, wherein the determining the pre-loan audit result for the loan applicant based on the comparison result comprises:
if the pre-loan feasibility value is larger than or equal to the preset pre-loan feasibility threshold value, determining that the pre-loan assessment result of the loan applicant is approved;
and if the pre-loan feasibility value is smaller than the preset pre-loan feasibility threshold, determining that the pre-loan assessment result of the loan applicant is not approved.
8. The method as recited in claim 1, further comprising:
if the absolute value of the accuracy of the financial data is smaller than a preset accuracy threshold, determining that the accuracy of the financial data meets a preset accuracy condition;
and if the absolute value of the accuracy of the financial data is larger than or equal to a preset accuracy threshold, determining that the accuracy of the financial data does not meet a preset accuracy condition.
9. The method as recited in claim 1, further comprising:
if the accuracy of the financial data is determined to be not in accordance with the preset accuracy condition, not executing loan transaction processing, and generating and sending prompt information carrying that the accuracy of the financial data of the loan applicant is not in accordance with the preset accuracy condition.
10. A transaction processing device, the device comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a loan transaction request, and the loan transaction request comprises an industry identifier of a loan applicant and financial data of the loan applicant; wherein the financial data comprises: value added amount, net profit, invoice amount and business amount;
the determining module is used for acquiring financial data corresponding to the industry identifier and accuracy parameters of the financial data according to the loan request;
the determining module is also used for determining the accuracy of the financial data according to the financial data and the accuracy parameters of the financial data;
the determining module is further used for determining a pre-loan feasibility value of the loan applicant according to the financial data and the accuracy parameters of the financial data if the accuracy of the financial data is determined to meet the preset accuracy condition;
and the processing module is used for determining the pre-loan audit result of the loan applicant according to the pre-loan feasibility value so as to perform corresponding transaction processing according to the pre-loan audit result.
11. A server, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 9.
12. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 9.
CN202310393338.7A 2023-04-13 2023-04-13 Transaction processing method, device, server and storage medium Pending CN116542759A (en)

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