CN116579841A - Credit wind control evaluation method and system and electronic equipment - Google Patents
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Abstract
The invention discloses a credit wind control evaluation method, a system and electronic equipment, wherein the credit wind control evaluation method comprises the following steps: the method comprises the steps that an applicant inputs identity information, performs applicant identity detection, proposes a credit application, matches personal data of the applicant in a credit database, and preprocesses the credit data to obtain preprocessed credit data of the applicant; analyzing potential relations and rules among data according to the pre-processed credit data of the applicant, and identifying and quantifying existing credit risks to obtain a data analysis result; establishing a risk assessment model according to the data analysis result, and analyzing credit risk of the applicant to obtain a credit assessment result of the applicant; and giving a credit decision to the applicant according to the credit evaluation result of the applicant. The applicant puts forward a credit application, automatically performs data analysis and risk assessment on personal data of the applicant, avoids manual approval, adapts to approval of a large number of credit applications, and improves the speed and accuracy of approval.
Description
Technical Field
The invention relates to the technical field of credit wind control systems, in particular to a credit wind control evaluation method, a credit wind control evaluation system and electronic equipment.
Background
In the economic field, including but not limited to financial field, business operations management, etc., effective risk monitoring is a necessary means to prevent and fight economic crimes. With the development of internet technology, traditional economic activities are gradually transferred to the internet, various network economic activities are rapidly developed, such as network shopping, network transaction and other business scenes, and the frequency of economic interactions is accelerated, which presents challenges for means of risk monitoring.
For the application of the applicant's credit request, the applicant needs to be subjected to a windy assessment, and the requirements are met to pass the credit request. However, in the conventional wind control evaluation, manual auditing and processing are required, and the requirement of fast approval of a large-scale credit application cannot be met.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is that in the traditional wind control evaluation, manual auditing and processing are needed, and the quick approval requirement for large-scale credit application can not be met.
In order to solve the problems, the invention discloses a credit wind control evaluation method, a credit wind control evaluation system and electronic equipment.
In a first aspect, the present invention provides a credit management method comprising the steps of:
the method comprises the steps that an applicant inputs identity information, performs applicant identity detection, proposes a credit application, matches personal data of the applicant in a credit database, and preprocesses the credit data to obtain preprocessed credit data of the applicant;
analyzing potential relations and rules among data according to the pre-processed credit data of the applicant, and identifying and quantifying existing credit risks to obtain a data analysis result;
establishing a risk assessment model according to the data analysis result, and analyzing credit risk of the applicant to obtain a credit assessment result of the applicant;
and giving a credit decision to the applicant according to the credit evaluation result of the applicant.
Preferably, the applicant further comprises the following steps before inputting the identity information:
the network server is connected to obtain personal data from a plurality of data sources and integrate the personal data into a credit database.
Preferably, the step of identifying the identity of the applicant, matching personal data of the applicant in a credit database, preprocessing the credit data, obtaining personal characteristics of the applicant and the credit data comprises the steps of:
the method comprises the steps that an applicant inputs identity information, living organism identification is carried out, and credit data of the applicant are matched in a credit database through the identity information of the applicant;
and (3) carrying out data cleaning and data conversion on credit data of the applicant, and carrying out feature extraction by combining with identity information of the applicant to obtain personal features of the applicant.
Preferably, said identifying and quantifying the credit risk present from the potential relationships and laws between applicant's pre-processed credit data analysis data comprises the steps of:
classifying the preprocessed credit data of the applicant according to personal characteristics, and sequentially comparing and analyzing the relation between the preprocessed credit data and the personal characteristics to obtain rules in the preprocessed credit data and obtain risks in various aspects of the applicant;
the pretreatment credit data and the correlation of the applicant are subjected to visual treatment and displayed to the applicant;
quantifying the credit risk of the applicant and obtaining analysis data of the credit risk of the applicant.
Preferably, said making a credit decision to the applicant based on the credit assessment result of the applicant comprises the steps of:
when the credit evaluation result of the applicant is good, determining that the applicant pays money;
when the credit evaluation result of the applicant is poor, refusing the decision of the applicant for paying money;
when the credit evaluation result of the applicant is abnormal, suggesting additional investigation and verification for the applicant, acquiring the credit evaluation result of the applicant again, and deciding whether to pay according to the result.
Preferably, said credit decision to the applicant is followed by the further step of:
updating the data in the database according to the accumulated data, optimizing the feature extraction mode, and detecting the credit status of the applicant in real time.
In a second aspect, the present invention provides a credit wind control system comprising:
the data preprocessing unit inputs identity information to perform application identity recognition, and puts forward a credit application, matches personal data of the applicant in a credit database, and preprocesses the credit data to obtain preprocessed credit data of the applicant;
the data analysis unit is used for analyzing potential relations and rules among data according to the pre-processed credit data of the applicant, identifying and quantifying existing credit risks and obtaining a data analysis result;
the credit evaluation unit is used for establishing a risk evaluation model according to the data analysis result, analyzing the credit risk of the applicant and obtaining the credit evaluation result of the applicant;
and the credit decision unit is used for making a credit decision for giving the applicant according to the credit evaluation result of the applicant.
Preferably, the data preprocessing unit includes:
matching a credit data unit, inputting identity information by the applicant, performing living organism identification, and matching credit data of the applicant in a credit database through the identity information of the applicant;
the personal characteristic acquisition unit is used for carrying out data cleaning and data conversion on credit data of the applicant, and carrying out characteristic extraction by combining with identity information of the applicant to acquire personal characteristics of the applicant.
Preferably, the method further comprises:
and the system optimization unit is used for updating the data in the database according to the accumulated data, optimizing the feature extraction mode and detecting the credit condition of the applicant in real time.
In a third aspect, the invention provides an electronic device comprising a memory and a processor, the memory having stored therein a computer program arranged to, when run, perform the credit management method of any one of the above;
the processor is configured to perform the credit management assessment method of any of the above by the computer program.
Compared with the prior art, the technical effects achieved by the embodiment of the invention include:
the invention discloses a credit management and evaluation method, a system and electronic equipment, wherein an applicant puts forward a credit application by inputting identity information, personal data of the applicant are matched in a credit database correspondingly, the personal data are preprocessed, data analysis and evaluation are carried out to quantify the credit risk of the applicant, and a credit decision of the applicant is given through the result of credit evaluation.
The applicant puts forward a credit application, automatically performs data analysis and risk assessment on personal data of the applicant according to the data of the applicant in the database, avoids manual approval, adapts to approval of a large number of credit applications, improves the speed of approval, and can also improve the accuracy of the approval.
Further, by means of automatic analysis and monitoring, some fraudulent behaviors, such as false identity information, false files and the like, are identified, so that the fraudulent behaviors are effectively treated, accurate risk control and credit decision support are provided for financial institutions, risks are reduced, and credit giving rate and profit rate of loans are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart showing the steps of a credit management and assessment method according to the present invention;
FIG. 2 is a flowchart II of a credit management and assessment method according to the present invention;
FIG. 3 is a flowchart showing the specific steps of step S1 of a credit management and control evaluation method according to the present invention;
FIG. 4 is a flowchart showing the specific steps of step S2 of a credit management and control evaluation method according to the present invention;
FIG. 5 is a flowchart showing the specific steps of step S4 of a credit management and control evaluation method according to the present invention;
FIG. 6 is a block diagram of a credit air control system provided by the present invention;
fig. 7 is a block diagram of a data preprocessing unit of a credit air control system provided by the invention.
Reference numerals
1. A credit wind control system; 10. a data preprocessing unit; 11. matching the credit data units; 12. a personal characteristic acquisition unit; 20. a data analysis unit; 30. a credit evaluation unit; 40. a credit decision unit; 50. and a system optimization unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, in which like reference numerals represent like components. It will be apparent that the embodiments described below are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the embodiments of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of embodiments of the invention. As used in the specification of the embodiments of the invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
In a first aspect, referring to FIG. 1, the present invention provides a credit wind control assessment method comprising the steps of:
s1: the method comprises the steps that an applicant inputs identity information to conduct application identity recognition, a credit application is proposed, personal data of the applicant are matched in a credit database, and the credit data are preprocessed to obtain preprocessed credit data of the applicant;
s2: analyzing potential relations and rules among data according to the pre-processed credit data of the applicant, and identifying and quantifying existing credit risks to obtain a data analysis result;
s3: establishing a risk assessment model according to the data analysis result, and analyzing credit risk of the applicant to obtain a credit assessment result of the applicant;
s4: and giving a credit decision to the applicant according to the credit evaluation result of the applicant.
Specifically, in step S1, identity information of the applicant is input, the applicant is identified by performing living organism identification on the applicant, the applicant is judged to be a true person, photo substitution detection is avoided, personal data of the corresponding applicant is matched in a credit database by using the identity information of the applicant, the personal data of the applicant is preprocessed, including data cleaning and data conversion, normal use of the data is ensured, and feature extraction is performed on the data, the personal features of the applicant are captured, subsequent data analysis can be facilitated, and the personal information and the credit data are classified into identity features, transaction features, relationship features and portrait features.
Specifically, in step S2, the potential relationship and rule between the data are found by analyzing the pre-processed credit data of the applicant, and the methods of comparative analysis, statistical analysis, etc. are adopted to determine the risk, and at the same time, the data are subjected to data visualization processing, the data are displayed to the applicant in a graphic manner for viewing, the credit risk of the applicant is quantified, and evaluation is performed according to aspects of credit middle behavior score, application credit score, anti-fraud score, post-credit assessment score, etc., so as to obtain the analysis data of the credit risk of the applicant. The pre-processing credit data of the applicant is mined and analyzed, potential relations and rules among the data can be found, and risk factors in the data can be identified.
Specifically, in step S3, the risk assessment of the credit of the applicant is performed by establishing a risk assessment model, a loss-benefit model is established through the credit data of the applicant, and the financial institution loss and benefit after the application of credit by the applicant are analyzed, so as to determine the risk of the credit of the applicant. And scoring in the aspects of credit status, repayment capability, financial status, guarantee capability and the like of the applicant, comprehensively giving credit scores of the applicant, judging according to comprehensive score data of the applicant and the scores of all aspects, and making subsequent decisions.
Specifically, in step S4, a credit decision is made according to the risk assessment result, and the credit decision is supported and suggested by the above data analysis processing, and meanwhile, when an uncertain factor is found, risk assessment and data manual confirmation can be performed again.
It will be appreciated that personal data of the applicant is obtained through a plurality of data sources, integrated into a database for the data, and when the applicant requests to apply for credit, the data is extracted from the database, pre-processing and data analysis are performed on the credit data of the applicant, the credit risk of the applicant is assessed according to the data analysis result, and the credit request of the applicant is decided according to the credit assessment result.
Referring to fig. 2, the method further comprises the following steps before the applicant inputs identity information:
s0: the network server is connected to obtain personal data of the applicant from a plurality of data sources and integrate the personal data into a credit database.
Specifically, in step S0, the personal data corresponding to the applicant is directly searched through the web server, where the personal data includes personal information, credit history, status of liability and social relationship, and the data source for obtaining the personal data of the applicant includes credit report, social media data, consumption data of the individual, and the like, and meanwhile, the data source may also obtain specific consumption status of the bank account of the applicant through the server. It will be appreciated that acquiring personal data of an applicant from multiple data sources allows a more comprehensive assessment of the applicant's credit status and risk level to be made more comprehensive, avoiding the inability to accurately determine high risk applicants due to a single data source.
Referring to FIG. 2, the credit decision to the applicant further comprises the following steps:
s5: updating the data in the database according to the accumulated data, optimizing the feature extraction mode, and detecting the credit status of the applicant in real time.
Specifically, the credit data in the credit database needs to be updated in real time, information related to the applicant can be obtained through a network, meanwhile, the credit data of the applicant is collected in the database, and the conditions of the evaluation process and the collection risk are summarized by combining the conditions of the data analysis and the processing process and the credit application results of each applicant, so that the feature extraction mode is optimized, and the extracted features are more relevant to the applicant. And (3) detecting the credit condition of the applicant in real time for the applicant who has applied for credit, analyzing the credit condition of the applicant according to the bank account entering and account exiting condition, daily consumption condition and repayment condition of the applicant, evaluating the credit of the applicant in real time, giving out an alarm of poor credit condition of the applicant when the credit condition of the applicant is poor, limiting the subsequent credit application condition of the applicant, providing accurate risk control for a financial institution, reducing the risk and ensuring the credit giving rate and profit rate of the loan.
Referring to fig. 3, the steps of identifying the identity of the applicant, matching personal data of the applicant in a credit database, preprocessing the credit data, obtaining personal characteristics of the applicant and the credit data include the following steps:
s11: the method comprises the steps that an applicant inputs identity information, living organism identification is carried out, and credit data of the applicant are matched in a credit database through the identity information of the applicant;
s12: and (3) carrying out data cleaning and data conversion on credit data of the applicant, and carrying out feature extraction by combining with identity information of the applicant to obtain personal features of the applicant.
Specifically, the living organism identification mode comprises facial behavior detection, eye movement detection, hand behavior detection or heart rate pulse detection, and the application for the applicant can be ensured by the living organism identification mode, so that the application of the other people through the detection by adopting photos is avoided. The credit data of the applicant is subjected to feature extraction, and is distinguished according to identity features, transaction features, relationship features and portrait features, wherein the identity features comprise data such as gender, age and borrowing frequency of the applicant, the transaction features comprise data such as income condition of the applicant and contact person condition, the relationship features comprise data such as borrowing overdue condition and residence information of the applicant, the portrait features comprise consumption capability and family condition of the applicant, and the personal information data can be classified by dividing personal features of the applicant, so that subsequent data analysis and modeling are facilitated. In addition, for the applicant who is making the credit application again, the applicant's data may be updated to ensure that the applicant's data in the credit database is up-to-date.
Referring to FIG. 4, the identification and quantification of the credit risk present based on the potential relationships and laws between applicant's pre-processed credit data analysis data includes the steps of:
s21: classifying the preprocessed credit data of the applicant according to personal characteristics, and sequentially comparing and analyzing the relation between the preprocessed credit data and the personal characteristics to obtain rules in the preprocessed credit data and obtain risks in various aspects of the applicant;
s22: the pretreatment credit data and the correlation of the applicant are subjected to visual treatment and displayed to the applicant;
s23: quantifying the credit risk of the applicant and obtaining analysis data of the credit risk of the applicant.
Specifically, according to personal feature classification, identity features, transaction features, relationship features and portrait features, the relationship among the features is compared and analyzed in sequence, the comprehensive features search the credit rule of the applicant to obtain the credit risk of the applicant, the credit data of the applicant are visually processed, the credit data and the related relationship are displayed in a chart mode, the explanation of on-site personnel of a financial institution can be facilitated, the credit risk is scored through an artificial intelligence algorithm, and the credit risk of the applicant is quantized, so that the analysis data of the credit risk of the applicant is obtained.
It will be appreciated that by quantifying the applicant's existing credit risk and modeling the quantified data with respect to the applicant's credit data, subsequent analytical evaluation is facilitated and applicant credit assessment results are obtained more quickly.
Referring to FIG. 5, the making of a credit decision to an applicant based on the result of the applicant's credit assessment includes the steps of:
s41: when the credit evaluation result of the applicant is good, determining that the applicant pays money;
s42: when the credit evaluation result of the applicant is poor, refusing the decision of the applicant for paying money;
s43: when the credit evaluation result of the applicant is abnormal, suggesting additional investigation and verification for the applicant, acquiring the credit evaluation result of the applicant again, and deciding whether to pay according to the result.
Specifically, after analyzing the data of the applicant, acquiring a credit risk quantification value of the applicant, performing risk assessment on the quantified value to obtain a risk assessment result, performing credit decision on the applicant according to the risk assessment result to determine whether to pay money to the applicant, when the assessment result of the applicant is abnormal, such as the situations that personal identity information is in doubt, the authenticity of a file is to be verified, and the like, information data and confirmation of the file can be provided for the applicant, meanwhile, data corresponding to the applicant in a server are updated in real time, a request for manual audit can be provided, and after the information of the applicant is updated and completed, credit assessment is performed again, credit decision is performed according to the new assessment result to realize approval, the credit risk of the applicant is automatically identified, fraudulent credit is prevented from being fraudulently taken by a fraudster using means such as false identity information and counterfeit file, and the credit risk of the applicant is judged according to the credit assessment result, so that fraudulent behaviors are effectively applied.
Referring to fig. 6, the present invention provides a credit wind control system 1, comprising:
the data preprocessing unit 10, the applicant inputs the identity information, carries on the applicant identity detection, and puts forward the credit application, match the personal data of the applicant in the credit database, preprocess the credit data, get the preprocessed credit data of the applicant;
a data analysis unit 20 for analyzing potential relations and rules among data according to the pre-processed credit data of the applicant, identifying and quantifying the existing credit risk, and obtaining a data analysis result;
a credit evaluation unit 30 for establishing a risk evaluation model according to the data analysis result, analyzing the credit risk of the applicant, and obtaining the credit evaluation result of the applicant;
the credit decision unit 40 makes a credit decision to be given to the applicant based on the credit evaluation result of the applicant.
Specifically, the data preprocessing unit 10 performs identity authentication of the applicant, extracts from a database when the applicant requests to apply for credit, preprocesses credit data of the applicant, classifies the credit data into a plurality of categories, and acquires personal features such as identity features, transaction features, relationship features, portrait features and the like; the data analysis unit 20 adopts methods such as comparative analysis, statistical analysis and the like to judge risks existing in the preprocessed credit data, and at the same time, performs data visualization processing on the data, displays the data to the applicant in a chart mode for viewing, quantifies the credit risks of the applicant, and evaluates the credit risks according to aspects such as credit middle-line performance scores, application credit scores, anti-fraud scores, post-credit assessment scores and the like to obtain analysis data of the credit risks of the applicant; the credit evaluation unit 30 establishes a risk evaluation model on the data analysis result, performs visual analysis on the data analysis result, establishes a loss-benefit model, analyzes loss and benefit conditions of a financial institution, thereby judging credit risk of the applicant, scoring in aspects of credit condition, repayment capability, financial condition, guarantee capability and the like of the applicant, and comprehensively giving credit scores of the applicant; the credit decision unit 40 makes a credit decision based on the result of the credit evaluation unit 30, determining whether to apply for credit for the applicant.
It can be understood that the applicant puts forward a credit application, automatically performs data analysis and risk assessment on personal data of the applicant according to the data of the applicant in the database, avoids manual approval, improves the approval speed, and can also improve the auditing accuracy. Further, by means of automatic analysis and monitoring, some fraudulent behaviors, such as false identity information, false files and the like, are identified, so that the fraudulent behaviors are effectively treated, accurate risk control and credit decision support are provided for financial institutions, risks are reduced, and credit giving rate and profit rate of loans are improved.
The data sources for acquiring the applicant can be credit reports, social media data, consumption data and the like, and credit conditions and risk levels of the applicant can be comprehensively evaluated.
Referring to fig. 7, the data preprocessing unit 10 includes:
the matched credit data unit 11, the applicant inputs identity information, carries out living organism identification, and matches the credit data of the applicant in a credit database through the identity information of the applicant;
the personal characteristic acquisition unit 12 performs data cleaning and data conversion on the credit data of the applicant, and performs characteristic extraction in combination with the identity information of the applicant to acquire personal characteristics of the applicant.
Specifically, the matched credit data unit 11 detects whether the applicant applies for the true principal or not through a living organism identification mode, so that false fraud is avoided; the matched credit data unit 11 obtains credit data corresponding to the applicant according to the identity of the applicant in a credit database, the personal characteristic obtaining unit 12 performs data cleaning and data conversion on the credit data obtained by the matched credit data unit 11 to ensure the true and reliable data and normal use, then performs characteristic extraction on the credit data, and the characteristic extraction is distinguished according to identity characteristics, transaction characteristics, relationship characteristics and portrait characteristics, and can classify personal information data by dividing personal characteristics of the applicant, so that the follow-up data analysis and modeling are facilitated.
It will be appreciated that the data preprocessing unit 10 performs a pre-processing on the credit data acquired in the credit database, so that the personal credit data is divided according to the characteristics, and the applicant is classified, so that the subsequent analysis can be quickened, and meanwhile, the data in the database can be updated.
With continued reference to fig. 6, the system further includes a system optimization unit 50 for updating the data in the database according to the accumulated data, optimizing the feature extraction mode, and detecting the credit status of the applicant in real time.
Specifically, personal data of all the applicant applying for credit is accumulated in the database, the system can autonomously learn and optimize own characteristic-mentioned modes and model-building algorithms through analysis and credit evaluation of the personal data and built-in machine learning algorithms, and the data in the database is continuously updated under continuous data accumulation, so that the real-time performance of the data is ensured.
It can be appreciated that the system optimization unit 50 can combine the accumulated data and experience, optimize and adjust the system, and better judge the credit risk of the credit application of the applicant more accurately, so as to ensure that the system operation can be adapted to the current market environment and ensure that the fraudulent behavior is accurately identified; and continuously updating the data in the database, ensuring the real-time property of the data source, and evaluating the credit condition and risk level of the applicant more comprehensively.
The system optimizes the system by using algorithms such as machine learning, deep learning and the like, can follow up the current market environment in time, so that the system can respond and adjust a risk control strategy in time, the risk management capability is improved, and meanwhile, the system can update personal data in a database and credit data processing modes in time through daily use, and the processing speed is improved.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, the memory having stored therein a computer program arranged to perform the credit wind control assessment method described above at run-time; the processor is configured to perform the credit management method described above by the computer program.
Specifically, the electronic device may analyze and evaluate the credit application of the credit applicant according to a credit management and control evaluation method, acquire credit data of the applicant from a plurality of data sources, collect the credit data of all the applicant into a credit database, perform data analysis on the credit data of the applicant, evaluate the credit risk of the applicant, quantify the credit risk, make a credit decision through the quantified value, and determine whether to pass the loan application of the applicant.
It can be understood that by executing the credit management and wind control evaluation method through the computer program, the credit data of the applicant can be automatically acquired and analyzed, the credit risk of the applicant can be identified, and the problems of low manual auditing speed and low efficiency can be avoided. Accurate risk control and credit decision support can be provided for financial institutions, risks are reduced, and the credit giving rate and profit margin of loans are improved.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be attached, detached, or integrated, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms should not be understood as necessarily being directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, one skilled in the art can combine and combine the different embodiments or examples described in this specification.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A credit management method, comprising the steps of:
the method comprises the steps that an applicant inputs identity information, performs applicant identity detection, proposes a credit application, matches personal data of the applicant in a credit database, and preprocesses the credit data to obtain preprocessed credit data of the applicant;
analyzing potential relations and rules among data according to the pre-processed credit data of the applicant, and identifying and quantifying existing credit risks to obtain a data analysis result;
establishing a risk assessment model according to the data analysis result, and analyzing credit risk of the applicant to obtain a credit assessment result of the applicant;
and giving a credit decision to the applicant according to the credit evaluation result of the applicant.
2. The credit wind control assessment method according to claim 1, further comprising the steps of, prior to the applicant entering identity information:
the network server is connected to obtain personal data from a plurality of data sources and integrate the personal data into a credit database.
3. The credit wind control assessment method according to claim 1, wherein said identifying the identity of the applicant, matching personal data of the applicant in a credit database, preprocessing the credit data, obtaining personal characteristics of the applicant and the credit data comprises the steps of:
the method comprises the steps that an applicant inputs identity information, living organism identification is carried out, and credit data of the applicant are matched in a credit database through the identity information of the applicant;
and (3) carrying out data cleaning and data conversion on credit data of the applicant, and carrying out feature extraction by combining with identity information of the applicant to obtain personal features of the applicant.
4. The credit wind control assessment method according to claim 1, wherein said analyzing potential relationships and laws between data from applicant's pre-processed credit data, identifying and quantifying existing credit risk, comprises the steps of:
classifying the preprocessed credit data of the applicant according to personal characteristics, and sequentially comparing and analyzing the relation between the preprocessed credit data and the personal characteristics to obtain rules in the preprocessed credit data and obtain risks in various aspects of the applicant;
the pretreatment credit data and the correlation of the applicant are subjected to visual treatment and displayed to the applicant;
quantifying the credit risk of the applicant and obtaining analysis data of the credit risk of the applicant.
5. The credit wind control assessment method according to claim 1, wherein said making a credit decision to the applicant based on the result of the credit assessment of the applicant comprises the steps of:
when the credit evaluation result of the applicant is good, determining that the applicant pays money;
when the credit evaluation result of the applicant is poor, refusing the decision of the applicant for paying money;
when the credit evaluation result of the applicant is abnormal, suggesting additional investigation and verification for the applicant, acquiring the credit evaluation result of the applicant again, and deciding whether to pay according to the result.
6. The credit wind control assessment method according to claim 1, further comprising the following steps after the credit decision to the applicant:
updating the data in the database according to the accumulated data, optimizing the feature extraction mode, and detecting the credit status of the applicant in real time.
7. A credit wind control system, comprising:
the data preprocessing unit inputs identity information to perform application identity recognition, and puts forward a credit application, matches personal data of the applicant in a credit database, and preprocesses the credit data to obtain preprocessed credit data of the applicant;
the data analysis unit is used for analyzing potential relations and rules among data according to the pre-processed credit data of the applicant, identifying and quantifying existing credit risks and obtaining a data analysis result;
the credit evaluation unit is used for establishing a risk evaluation model according to the data analysis result, analyzing the credit risk of the applicant and obtaining the credit evaluation result of the applicant;
and the credit decision unit is used for making a credit decision for giving the applicant according to the credit evaluation result of the applicant.
8. The credit wind control system according to claim 7, wherein the data preprocessing unit includes:
matching a credit data unit, inputting identity information by the applicant, performing living organism identification, and matching credit data of the applicant in a credit database through the identity information of the applicant;
the personal characteristic acquisition unit is used for carrying out data cleaning and data conversion on credit data of the applicant, and carrying out characteristic extraction by combining with identity information of the applicant to acquire personal characteristics of the applicant.
9. The credit air control system according to claim 7, further comprising:
and the system optimization unit is used for updating the data in the database according to the accumulated data, optimizing the feature extraction mode and detecting the credit condition of the applicant in real time.
10. An electronic device comprising a memory and a processor, characterized in that: the memory having stored therein a computer program arranged to perform the credit management assessment method of any one of claims 1 to 6 at run-time;
the processor is arranged to perform the credit management method of any one of claims 1 to 6 by means of the computer program.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117172908A (en) * | 2023-09-05 | 2023-12-05 | 中铁商业保理有限公司 | Business loan stage decision-making auxiliary system based on consumption bill analysis |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117172908A (en) * | 2023-09-05 | 2023-12-05 | 中铁商业保理有限公司 | Business loan stage decision-making auxiliary system based on consumption bill analysis |
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