Automatic pre-loan and post-loan evaluation method and system based on internal and external data sources

Abstract

The invention relates to the technical field of financial risk control, and provides an automatic pre-loan and post-loan evaluation method and system based on internal and external data sources, and the method comprises the following steps: obtaining internal and external data sources needed by credit evaluation; automatically reading the acquired internal and external data sources in a preset parameter input mode; testing the read internal and external data sources in two dimensions before loan and after loan and in different time spans of the overall sample and the time-phased sample to obtain an evaluation result; and automatically writing an evaluation result, and generating a credit risk evaluation report. The method comprises the following steps: automatically reading pre-acquired internal and external data sources in a preset input parameter form, evaluating the internal and external data sources in two dimensions before loan and after loan and different time spans of an overall sample and a time-phased sample, and outputting a credit risk evaluation report to realize an integrated automatic credit evaluation process. And the credit risk control evaluation efficiency is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of financial risk control technology, and specifically to an automated pre-loan and post-loan evaluation method and system based on internal and external data sources. Background Art

[0002] Existing financial risk control technologies often rely on EXCEL files and manual pivot table analysis for testing and evaluating internal and external data sources, or simply test and evaluate variables from the post-loan dimension. There is a lack of an integrated technology that can be automatically executed and output results, and can evaluate data from the pre-loan and post-loan perspectives, overall samples, and time-phased samples. As a result, it takes a lot of energy and time to analyze the effects of internal and external data sources. Summary of the invention

[0003] The present application provides an automated pre-loan and post-loan evaluation method based on internal and external data sources, which can solve the technical problems in the prior art that the testing and evaluation of internal and external data sources cannot be integrated, and the testing and evaluation efficiency is low.

[0004] In a first aspect, the present application provides an automated pre-loan and post-loan evaluation method based on internal and external data sources, comprising the following steps:

[0005] Access internal and external data sources required for credit assessment;

[0006] Automatically read the internal and external data sources through preset input parameters;

[0007] The internal and external data sources are tested in the pre-loan and post-loan dimensions, as well as in different time spans of the overall sample and the time-divided sample, to obtain the evaluation results;

[0008] Automatically write assessment results and generate credit risk assessment reports.

[0009] In combination with the first aspect, in one implementation, the internal and external data sources required for obtaining credit evaluation include internal and external variable data, application ID, application time, application decision results, application amount and post-loan indicators.

[0010] In combination with the first aspect, in one embodiment, in the method of preset input parameters, the preset input parameters include the file storage path of the internal and external data sources that need to be tested, the pre-loan indicators and post-loan indicators that need to be tested, the file storage path of the pre-loan indicators and post-loan indicators that need to be used, and the automatic output file storage path.

[0011] In combination with the first aspect, in one implementation, the read internal and external data sources are tested in two dimensions, pre-loan and post-loan, and in different time spans of the overall sample and the time-divided sample to obtain the evaluation results, specifically including the following steps:

[0012] Conduct pre-loan evaluation on the overall sample of internal and external data sources read to obtain pre-loan evaluation results;

[0013] Conduct post-loan evaluation on the overall sample of internal and external data sources read, and obtain the post-loan evaluation results of the overall sample;

[0014] Perform post-loan evaluation on the time-divided samples of the internal and external data sources read, and obtain the post-loan evaluation results of the time-divided samples.

[0015] In combination with the first aspect, in one implementation, performing a pre-loan evaluation on the overall sample of internal and external data sources read to obtain a pre-loan evaluation result specifically includes the following steps:

[0016] For the credit application samples, pre-loan assessment is conducted based on the application ID, application time, application decision result and application amount, as well as the credit application ID corresponding to each credit application, to test and obtain the pass rate, proportion and amount of the credit application samples;

[0017] For the sample of credit application, pre-loan assessment is conducted based on the application ID, application time, application decision result and application amount, and the pass rate, proportion and amount of the sample of credit application are tested and obtained;

[0018] For samples that include both credit application submissions and credit use application submissions, obtain the application ID, application submission time, application submission decision result and application amount for pre-loan evaluation, and test the pass rate, proportion and amount of the credit application submissions and credit use application submissions.

[0019] In conjunction with the first aspect, in one implementation, performing post-loan evaluation on the overall sample of internal and external data sources read to obtain the overall sample post-loan evaluation result specifically includes the following steps:

[0020] For the credit application sample, obtain the post-loan performance of the credit application in the credit dimension, and map the post-loan performance of the credit application to the credit dimension. Test and obtain the credit application ID and the corresponding post-loan indicators under each credit application.

[0021] For the sample of credit application, the test obtains the post-loan performance of the credit dimension;

[0022] For samples that include both credit application submissions and credit use application submissions, test and obtain post-loan indicators.

[0023] In combination with the first aspect, in one implementation, performing post-loan evaluation on the time-divided samples of the internal and external data sources read to obtain the post-loan evaluation results of the time-divided samples specifically includes the following steps:

[0024] Combined with the post-loan evaluation of the overall sample, the long-term and short-term credit indicators are tested in the credit dimension according to the dimensions of the time period samples, and compared with the post-loan customer base of the overall sample.

[0025] In combination with the first aspect, in one embodiment, the assessment results are automatically written to generate a credit risk assessment report, which includes internal and external data sources, a variable effect summary page, a variable pre-loan and post-loan effect details page, a time period sample post-loan assessment result page, and a variable correlation display page.

[0026] In a second aspect, the present application provides an automated pre-loan and post-loan evaluation system based on internal and external data sources, including:

[0027] Data source acquisition module, used to obtain internal and external data sources required for credit assessment;

[0028] A data source reading module, which is in communication with the data source acquisition module and is used to automatically read the acquired internal and external data sources by means of preset input parameters;

[0029] A test evaluation module is connected to the data source reading module for testing the read internal and external data sources in two dimensions, pre-loan and post-loan, and in different time spans of the overall sample and the time-divided sample, to obtain evaluation results;

[0030] The credit risk assessment report generation module is in communication with the test assessment module and is used to automatically write the assessment results and generate a credit risk assessment report.

[0031] In conjunction with the second aspect, in one implementation, the test evaluation module includes:

[0032] A pre-loan evaluation unit, which is in communication with the data source reading module and is used to perform a pre-loan evaluation on the overall sample of the read internal and external data sources to obtain a pre-loan evaluation result;

[0033] The overall sample post-loan evaluation unit is connected to the data source reading module for performing post-loan evaluation on the overall sample of the internal and external data sources read to obtain the overall sample post-loan evaluation result;

[0034] The time-divided sample post-loan evaluation unit is communicatively connected with the data source reading module, and is used to perform post-loan evaluation on the read time-divided samples of the internal and external data sources, and obtain the post-loan evaluation results of the time-divided samples.

[0035] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0036] It automatically reads pre-acquired internal and external data sources in the form of preset input parameters, evaluates internal and external data sources in the two dimensions of pre-loan and post-loan, and in different time spans of overall samples and time-phased samples, and outputs a credit risk assessment report to realize an integrated and automated credit assessment process, effectively improving the efficiency of credit risk control assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flowchart of the automated pre-loan and post-loan evaluation method based on internal and external data sources for this application;

[0038] Figure 2 This is a functional module block diagram of the automated pre-loan and post-loan evaluation system based on internal and external data sources for this application. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0041] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0042] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0043] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0044] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0045] First, please refer to Figure 1 The present application provides an automated pre-loan and post-loan evaluation method based on internal and external data sources, comprising the following steps:

[0046] Step S1: Obtaining internal and external data sources required for credit assessment;

[0047] Step S2: automatically read the internal and external data sources obtained by preset input parameters;

[0048] Step S3: Test the read internal and external data sources in the two dimensions of pre-loan and post-loan and in different time spans of the overall sample and the time-divided sample to obtain the evaluation results;

[0049] Step S4: Automatically write the assessment results and generate a credit risk assessment report.

[0050] This application automatically reads pre-acquired internal and external data sources in the form of preset input parameters, evaluates the internal and external data sources in the two dimensions of pre-loan and post-loan, and in different time spans of overall samples and time-phased samples, and outputs a credit risk assessment report to realize an integrated and automated credit assessment process and effectively improve the efficiency of credit risk control assessment.

[0051] In one embodiment, the step S1: obtaining internal and external data sources required for credit evaluation, the internal and external data sources include internal and external variable data, application ID, application time, application decision results, application amount and post-loan indicators; further, the internal variable data includes a customer basic information database, a transaction record system, a credit application history record, and a customer relationship management (CRM) system; the external variable data includes credit reporting agency data, tax record data, industry data providers, social media data, etc.; wherein the internal and external variable data are used as evaluation input X, and various post-loan indicators are evaluation output Y.

[0052] In one embodiment, the step S2: by means of preset input parameters, the preset input parameters include the file storage path of the internal and external data sources that need to be tested, the pre-loan indicators and post-loan indicators that need to be tested, the file storage path of the pre-loan indicators and post-loan indicators that need to be used, and the automatic output file storage path; specifically, when testing the external data source, it is necessary to summarize all the test variables into a separate EXCEL / CSV file, summarize the data containing the pre-loan indicators and post-loan indicators into another separate file, and finally specify the path for report generation.

[0053] In one embodiment, in the step S2: by presetting the input parameters, the input types of the presetting input parameters are divided into three types:

[0054] The first type only includes the credit application submission;

[0055] The second type only includes the application for entry by letter;

[0056] The third type includes both the application for credit granting and the application for using credit.

[0057] For the first and second preset input parameter types, they include the file path of the internal and external data to be tested, the storage path of the pre-loan and post-loan indicators, the storage path of the automatically generated reports, and the business links of the data source in risk control; for the third preset input parameter type, they include the file path of the internal and external data to be tested, the storage path of the pre-loan and post-loan indicators, the storage path of the automatically generated reports, and the business links of the data source in risk control, among which the internal and external data to be tested for the credit application and the credit use application need to be stored separately.

[0058] In one embodiment, the step S3: testing the read internal and external data sources in two dimensions, pre-loan and post-loan, and different time spans of the overall sample and the time-divided sample to obtain the evaluation results, specifically includes the following steps:

[0059] Step S3A: Perform pre-loan evaluation on the overall sample of internal and external data sources read to obtain pre-loan evaluation results;

[0060] Step S3B: Perform post-loan evaluation on the overall sample of internal and external data sources read, and obtain the post-loan evaluation result of the overall sample;

[0061] Step S3C: Perform post-loan evaluation on the time-divided samples of the internal and external data sources read, and obtain the post-loan evaluation results of the time-divided samples;

[0062] In addition to step S3A, step S3B and step S3C, the method also includes correlation testing of internal and external data sources.

[0063] Conduct credit testing on internal and external data sources from multiple angles, including pre-loan and post-loan, as well as overall samples and time-divided samples, to obtain more comprehensive credit risk assessment conclusions and enhance credit risk control capabilities.

[0064] In one embodiment, the step S3A: performing a pre-loan evaluation on the overall sample of the read internal and external data sources to obtain the pre-loan evaluation results specifically includes the following steps:

[0065] For each data source variable sample, a coverage portrait for each time period, an overall pass rate portrait, and a pass rate portrait for each time period are produced; among them, customer portraits are divided into two stratification methods: equal frequency and enumeration.

[0066] In one embodiment, the step S3A: performing a pre-loan evaluation on the overall sample of the read internal and external data sources to obtain the pre-loan evaluation results specifically includes the following steps:

[0067] For the credit application samples, pre-loan assessment is conducted based on the application ID, application time, application decision result and application amount, as well as the credit application ID corresponding to each credit application, to test and obtain the pass rate, proportion and amount of the credit application samples;

[0068] For the sample of credit application, pre-loan assessment is conducted based on the application ID, application time, application decision result and application amount, and the pass rate, proportion and amount of the sample of credit application are tested and obtained;

[0069] For samples that include both credit application submissions and credit use application submissions, obtain the application ID, application submission time, application submission decision result and application amount for pre-loan evaluation, and test the pass rate, proportion and amount of the credit application submissions and credit use application submissions.

[0070] The post-loan evaluation in step S3B and step S3C includes evaluation and analysis of test indicators such as sample post-loan indicators, LIFT (lift), KS (Kolmogorov-Smirnov), and IV (Information Value) portraits.

[0071] In one embodiment, the step S3B: performing post-loan evaluation on the overall sample of internal and external data sources read to obtain the post-loan evaluation result of the overall sample specifically includes the following steps:

[0072] For the credit application sample, obtain the post-loan performance of the credit application in the credit dimension, and map the post-loan performance of the credit application to the credit dimension. Test and obtain the credit application ID and the corresponding post-loan indicators under each credit application.

[0073] For the sample of credit application, the test obtains the post-loan performance of the credit dimension;

[0074] For samples that include both credit application submissions and credit use application submissions, test and obtain post-loan indicators.

[0075] In one embodiment, the step S3C: performing post-loan evaluation on the time-divided samples of the internal and external data sources read to obtain the post-loan evaluation results of the time-divided samples, specifically includes the following steps:

[0076] Combined with the post-loan evaluation of the overall sample, the long-term and short-term credit indicators are tested in the credit dimension according to the dimensions of the time period samples, and compared with the post-loan customer base of the overall sample.

[0077] The time period division method in step S3C includes a monthly division method.

[0078] In one embodiment, the step S4: automatically writes the assessment results to generate a credit risk assessment report, which includes internal and external data sources, a variable effect summary page, a variable pre-loan and post-loan effect details page, a time period sample post-loan assessment result page and a variable correlation display page; specifically, the pre-loan assessment results, the overall sample post-loan assessment results, and the time period sample post-loan assessment results are automatically written into the EXCEL file, and the analysis results are automatically written into the EXCEL file based on XLWT (XLWT is a third-party library in Python for operating Excel files, which is mainly used to create and write old versions of Excel files), and the XLWT technology is applied, and the module-based package calling method is used. Based on this understanding, when executing the above automated pre-loan and post-loan assessment method based on internal and external data sources, it is necessary to set the preset input parameters, and then execute the package called by the analysis section, and call the automated output package. The above three packages are all custom function libraries.

[0079] Second, please refer to Figure 2 The present application provides an automated pre-loan and post-loan evaluation system based on internal and external data sources, including a data source acquisition module 100, a data source reading module 200, a test evaluation module 300 and a credit risk evaluation report generation module 400; the data source acquisition module 100 is used to acquire the internal and external data sources required for credit evaluation; the data source reading module 200 is communicated with the data source acquisition module 100, and is used to automatically read the acquired internal and external data sources by means of preset input parameters; the test evaluation module 300 is communicated with the data source reading module 200, and is used to test the read internal and external data sources in the two dimensions of pre-loan and post-loan and in different time spans of the overall sample and the time period sample to obtain the evaluation results; the credit risk evaluation report generation module 400 is communicated with the test evaluation module 300, and is used to automatically write the evaluation results and generate a credit risk evaluation report.

[0080] In one embodiment, the test evaluation module includes:

[0081] A pre-loan evaluation unit, which is in communication with the data source reading module and is used to perform a pre-loan evaluation on the overall sample of the read internal and external data sources to obtain a pre-loan evaluation result;

[0082] The overall sample post-loan evaluation unit is connected to the data source reading module for performing post-loan evaluation on the overall sample of the internal and external data sources read to obtain the overall sample post-loan evaluation result;

[0083] The time-divided sample post-loan evaluation unit is communicatively connected with the data source reading module, and is used to perform post-loan evaluation on the read time-divided samples of the internal and external data sources, and obtain the post-loan evaluation results of the time-divided samples.

[0084] Among them, the functional implementation of each module in the above-mentioned automated pre-loan and post-loan evaluation system based on internal and external data sources corresponds to the steps in the above-mentioned automated pre-loan and post-loan evaluation method embodiment based on internal and external data sources, and their functions and implementation processes will not be repeated here one by one.

[0085] On the third aspect, the embodiments of the present application provide an automated pre-loan and post-loan evaluation device based on internal and external data sources. The automated pre-loan and post-loan evaluation device based on internal and external data sources can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0086] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces, which are used to interconnect the components of the automated pre-loan and post-loan evaluation device based on internal and external data sources, and interfaces used to interconnect the automated pre-loan and post-loan evaluation device based on internal and external data sources with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0087] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0088] The processor may be a general-purpose processor, which may call the automated pre-loan and post-loan evaluation program based on internal and external data sources stored in the memory, and execute the automated pre-loan and post-loan evaluation method based on internal and external data sources provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the automated pre-loan and post-loan evaluation program based on internal and external data sources is called may refer to the various embodiments of the automated pre-loan and post-loan evaluation method based on internal and external data sources of the present application, which will not be repeated here.

[0089] The automated pre-loan and post-loan evaluation equipment based on internal and external data sources can be an industrial control computer (IPC), a programmable controller (PLC), or other equipment with data processing capabilities.

[0090] The communication interface includes input / output (I / O) interface, analog interface and logic interface, etc., which are used to realize the interconnection of devices inside the automatic pre-loan and post-loan evaluation equipment based on internal and external data sources, and the interface used to realize the interconnection between the automatic pre-loan and post-loan evaluation equipment based on internal and external data sources and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an RS485 interface, etc.; the user device can be a display, a keyboard, etc.

[0091] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0092] The processor may be a general-purpose processor, which may call the automated pre-loan and post-loan evaluation program based on internal and external data sources stored in the memory, and execute the automated pre-loan and post-loan evaluation method based on internal and external data sources provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the automated pre-loan and post-loan evaluation program based on internal and external data sources is called may refer to the various embodiments of the automated pre-loan and post-loan evaluation method based on internal and external data sources of the present application, which will not be repeated here.

[0093] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0094] The readable storage medium of the present application stores an automated pre-loan and post-loan evaluation program based on internal and external data sources, wherein when the automated pre-loan and post-loan evaluation program based on internal and external data sources is executed by a processor, the steps of the automated pre-loan and post-loan evaluation method based on internal and external data sources as described above are implemented.

[0095] Among them, the method implemented when the automated pre-loan and post-loan evaluation program based on internal and external data sources is executed can refer to the various embodiments of the automated pre-loan and post-loan evaluation method based on internal and external data sources of the present application, and will not be repeated here.

[0096] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0097] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0098] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An automated pre-loan and post-loan evaluation method based on internal and external data sources, characterized in that: The following steps are involved: Access internal and external data sources required for credit assessment; Automatically read the internal and external data sources through preset input parameters; The internal and external data sources are tested in the pre-loan and post-loan dimensions, as well as in different time spans of the overall sample and the time-divided sample, to obtain the evaluation results; Automatically write assessment results and generate credit risk assessment reports.

2. The automated pre-loan and post-loan evaluation method based on internal and external data sources as claimed in claim 1, characterized in that: The internal and external data sources required for obtaining credit evaluation include internal and external variable data, application ID, application time, application decision result, application amount and post-loan indicators.

3. The automated pre-loan and post-loan evaluation method based on internal and external data sources as claimed in claim 1, characterized in that: In the method of preset input parameters, the preset input parameters include the file storage path of the internal and external data sources that need to be tested, the pre-loan indicators and post-loan indicators that need to be tested, the file storage path of the pre-loan indicators and post-loan indicators that need to be used, and the automatic output file storage path.

4. The automated pre-loan and post-loan evaluation method based on internal and external data sources as claimed in claim 1, characterized in that: The internal and external data sources are tested in the two dimensions of pre-loan and post-loan and in different time spans of the overall sample and the time-divided sample to obtain the evaluation results, which specifically includes the following steps: Conduct pre-loan evaluation on the overall sample of internal and external data sources read to obtain pre-loan evaluation results; Conduct post-loan evaluation on the overall sample of internal and external data sources read, and obtain the post-loan evaluation results of the overall sample; Perform post-loan evaluation on the time-divided samples of the internal and external data sources read, and obtain the post-loan evaluation results of the time-divided samples.

5. The automated pre-loan and post-loan evaluation method based on internal and external data sources as claimed in claim 4, characterized in that: The pre-loan evaluation is performed on the overall sample of the internal and external data sources read to obtain the pre-loan evaluation result, which specifically includes the following steps: For the credit application samples, pre-loan assessment is conducted based on the application ID, application time, application decision result and application amount, as well as the credit application ID corresponding to each credit application, to test and obtain the pass rate, proportion and amount of the credit application samples; For the sample of credit application, pre-loan assessment is conducted based on the application ID, application time, application decision result and application amount, and the pass rate, proportion and amount of the sample of credit application are tested and obtained; For samples that include both credit application submissions and credit use application submissions, obtain the application ID, application submission time, application submission decision result and application amount for pre-loan evaluation, and test the pass rate, proportion and amount of the credit application submissions and credit use application submissions.

6. The automated pre-loan and post-loan evaluation method based on internal and external data sources as claimed in claim 4, characterized in that: The post-loan evaluation of the overall sample of internal and external data sources read to obtain the post-loan evaluation result of the overall sample specifically includes the following steps: For the credit application sample, obtain the post-loan performance of the credit application in the credit dimension, and map the post-loan performance of the credit application to the credit dimension. Test and obtain the credit application ID and the corresponding post-loan indicators under each credit application. For the sample of credit application, the test obtains the post-loan performance of the credit dimension; For samples that include both credit application submissions and credit use application submissions, test and obtain post-loan indicators.

7. The automated pre-loan and post-loan evaluation method based on internal and external data sources as claimed in claim 4, characterized in that: The step of performing post-loan evaluation on the time-divided samples of the internal and external data sources read to obtain the post-loan evaluation results of the time-divided samples specifically includes the following steps: Combined with the post-loan evaluation of the overall sample, the long-term and short-term credit indicators are tested in the credit dimension according to the dimensions of the time period samples, and compared with the post-loan customer base of the overall sample.

8. The automated pre-loan and post-loan evaluation method based on internal and external data sources as claimed in claim 1, characterized in that: The automatic writing of assessment results generates a credit risk assessment report, which includes internal and external data sources, a variable effect summary page, a variable pre-loan and post-loan effect details page, a time-divided sample post-loan assessment result page, and a variable correlation display page.

9. An automated pre-loan and post-loan evaluation system based on internal and external data sources, characterized in that: The following steps are involved: Data source acquisition module, used to obtain internal and external data sources required for credit assessment; A data source reading module, which is in communication with the data source acquisition module and is used to automatically read the acquired internal and external data sources by means of preset input parameters; A test evaluation module is connected to the data source reading module for testing the read internal and external data sources in two dimensions, pre-loan and post-loan, and in different time spans of the overall sample and the time-divided sample, to obtain evaluation results; The credit risk assessment report generation module is in communication with the test assessment module and is used to automatically write the assessment results and generate a credit risk assessment report.

10. The automated pre-loan and post-loan evaluation system based on internal and external data sources as claimed in claim 9, characterized in that: The test evaluation module includes: A pre-loan evaluation unit, which is in communication with the data source reading module and is used to perform a pre-loan evaluation on the overall sample of the read internal and external data sources to obtain a pre-loan evaluation result; The overall sample post-loan evaluation unit is connected to the data source reading module for performing post-loan evaluation on the overall sample of the internal and external data sources read to obtain the overall sample post-loan evaluation result; The time-divided sample post-loan evaluation unit is communicatively connected with the data source reading module, and is used to perform post-loan evaluation on the read time-divided samples of the internal and external data sources, and obtain the post-loan evaluation results of the time-divided samples.

Images