CN120181984A - A financial credit risk identification method and device - Google Patents
A financial credit risk identification method and device Download PDFInfo
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Abstract
本申请提出的一种金融信贷风险识别方法及装置，涉及大数据技术领域，首先通过第一用户的消费数据确定其用户类型，针对独立用户和家庭用户分别通过对出行数据的大数据分析以获得其职业信息和财务信息，进而结合确定出的财务信息和消费数据，确定出消费偏离信息，最后，综合消费偏离信息和历史信用信息，确定出第一风险评分。通过本申请的技术方案，通过多维度的大数据分析，可对第一用户的职业稳定度、消费稳定度、信用历史等多方面的数据进行综合，从而确定出较为准确的第一风险评分，以降低金融信贷风险。
The present application proposes a financial credit risk identification method and device, which relates to the field of big data technology. First, the user type of the first user is determined through the consumption data of the first user, and the occupational information and financial information of the independent user and the family user are obtained through big data analysis of the travel data, and then the consumption deviation information is determined by combining the determined financial information and consumption data. Finally, the first risk score is determined by combining the consumption deviation information and historical credit information. Through the technical solution of the present application, through multi-dimensional big data analysis, the data of the first user's occupational stability, consumption stability, credit history and other aspects can be integrated to determine a more accurate first risk score to reduce financial credit risks.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to a financial credit risk identification method and device.
Background
The identification accuracy of financial credit risk is information of great concern in the credit field, and the accuracy is directly related to financial security. In the prior art, financial credit risk identification can only evaluate from the dimensions of credit, historical overdue condition and the like of a user, the evaluation accuracy is not high, and the factors such as income stability and consumption habit of the user are not considered.
Disclosure of Invention
The invention aims to provide a financial credit risk identification method and a financial credit risk identification device, which are used for solving the technical problem that the financial credit risk identification degree is not high in the prior art.
The application provides a financial credit risk identification method, which is characterized by comprising the following steps:
S1, acquiring first consumption data, determining a user type of a first user according to the first consumption data, and acquiring a first financial relation group when the user type is a family user;
S2, acquiring first occupation information and first travel information corresponding to the first user or the first financial relation group, and inputting the first occupation information and the first travel information into a financial portrait determining model to determine a first financial portrait;
s3, determining first consumption deviation information of the first user or the first financial relation group according to the first financial portrait and the first consumption data;
And S4, determining a first risk score according to the first consumption deviation information of the first user or the first financial relation group.
Preferably, the step S1 includes the following sub-steps:
S11, acquiring first basic information of the first user, and determining a first basic portrait of the first user;
S12, acquiring first consumption data of the first user, and determining a first matching degree between the first consumption data and the first basic portrait;
And S13, when the first matching degree is lower than a first preset value, determining the user type of the first user as a family user, determining a first financial relation group, updating the first consumption data by using the consumption data of the first financial relation group, and otherwise, determining the user type of the first user as an independent user.
Preferably, the step S2 includes the following sub-steps:
S21, when the user type is an independent user, turning to S22, and when the user type is a family user, turning to S23;
S22, acquiring first occupation information of the first user and first travel information in a preset time interval, and turning to S24;
s23, acquiring first occupation information of the first financial relation group and first trip information in a preset time interval;
S24, inputting the user type, the first occupation information and the first travel information into the financial portrait determination model to determine a first financial portrait.
Preferably, the financial portrait determination model training process includes:
s241, acquiring a sample data set, wherein each piece of sample data comprises professional information data, trip information data, consumption data in a preset time interval, overdue condition data and purchase financial product type data corresponding to a historical user;
And S242, training a convolutional neural network model by using the sample data set to obtain the financial portrait determination model.
Preferably, the step S3 includes the following sub-steps:
S31, extracting second consumption data from the first financial portrait;
s32, determining first amount deviation information and first consumption type deviation information of the first consumption data and the second consumption data;
s33, determining the first consumption deviation information according to the first amount deviation information and the first consumption type deviation information.
Preferably, the step S4 includes the following substeps:
s41, acquiring first historical credit data of the first user or the first financial relation group;
S42, inputting the first historical credit data and the first consumption deviation information into a risk score determining model to determine the first risk score.
The application also provides a financial credit risk identification device which is used for realizing the financial credit risk identification method.
The application provides a financial credit risk identification method and a financial credit risk identification device, which relate to the technical field of big data, wherein the user type of a first user is determined according to consumption data of the first user, professional information and financial information of the independent user and a family user are obtained through big data analysis of travel data respectively, consumption deviation information is determined by combining the determined financial information and consumption data, and finally, consumption deviation information and historical credit information are combined to determine a first risk score. According to the technical scheme, through multi-dimensional big data analysis, the data of the first user in various aspects such as the job stability, the consumption stability, the credit history and the like can be integrated, so that a more accurate first risk score is determined, and financial credit risks are reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
FIG. 1 is a flow chart of an implementation of a financial credit risk identification method of the present invention.
Fig. 2 is a method execution flow chart of step S2 of a financial credit risk identification method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
The present invention will now be described in detail with reference to the drawings and the specific embodiments thereof, wherein the exemplary embodiments and the description are for the purpose of illustrating the invention only and are not to be construed as limiting the invention.
The invention provides a financial credit risk identification method and a financial credit risk identification device.
The embodiment provides a financial credit risk identification method, and the flow of the method is shown in fig. 1, and specifically comprises the following steps:
s1, acquiring first consumption data of a first user, determining the user type of the first user according to the first consumption data, and acquiring a first financial relation group when the user type is a family user.
When the income conditions of different users are similar, different consumption habits directly influence the financial conditions of the users, and in the step, the user type of the first user is determined according to the consumption conditions of the first user, and the associated users are determined.
While the information filled out by the user may determine its family members, the financial property sharing relationship of different families may be different, so that the first financial relationship group corresponding to the first user is determined by the first consumption data of the first user in this step.
The step S1 comprises the following substeps:
s11, acquiring first basic information of the first user, and determining a first basic portrait of the first user.
In this step, the first base portrayal characterizing the first user group type is determined from the first base information of the first user.
The first basic information includes information of gender, age, occupation type, and the like of the first user.
And S12, acquiring first consumption data of the first user, and determining a first matching degree of the first consumption data and the first basic portrait.
The first consumption data refers to consumption data of the first user in a preset historical time interval, and specifically may include purchased goods or services, and the goods may include physical goods, virtual goods, and the services may include travel services, and the like.
The first degree of matching is used to characterize the degree of matching of the first user with the first consumption data. For example, if the first user is a woman who is 20 to 30 years old and is engaged in financial work, the matching degree with the consumer goods such as the drinking water, the shaver and the like is low.
And S13, when the first matching degree is lower than a first preset value, determining the user type of the first user as a family user, determining a first financial relation group, updating the first consumption data by using the consumption data of the first financial relation group, and otherwise, determining the user type of the first user as an independent user.
In general, when the consumer data of a user has a low matching degree with the own basic information, it is in a case where other users constitute an organization such as a home, and thus it is necessary to consider that the first financial relationship group is established for the first user. For example, when the consumption data of the first user indicates that the first user is buying goods for other adults or infants, the first user is indicated to belong to a family user, and the first user and other users in the same family can be formed into the first financial relation group at the moment.
The first preset value may be determined empirically or may be obtained through machine learning model evaluation. The second consumption data refers to consumption data of each member in the first financial relationship group.
And when the first user is determined to be a family user, determining the first financial relation group by combining family member conditions in basic information filled by the first user. For example, the first user may have filled in family member information in advance, and when it is determined that the user type is a family user, the family member information may be determined as a member in the first financial relationship group.
S2, acquiring first occupation information and first travel information corresponding to the first user or the first financial relation group, and inputting the first occupation information and the first travel information into a financial portrait determining model to determine a first financial portrait.
As shown in fig. 2, in this step, consumption data, credit data, and the like of the user having a higher similarity with the first user or the first financial relationship group may be obtained by combining professional information filled in advance by the first user and travel information in a preset history time interval, so as to determine a first financial portrait of a similar crowd.
S21, when the user type is an independent user, the method proceeds to S22, and when the user type is a family user, the method proceeds to S23.
The first financial portrait is characterized by 1 person and multiple persons for two different user types, namely independent users and family users, so that portrait forecast is needed.
S22, acquiring the first occupation information of the first user and the first travel information in a preset time interval, and turning to S24.
Wherein the first occupation information is occupation information filled in by the first user in advance, and the filled in occupation information should be subdivided as much as possible so as to more accurately determine the financial situation of the first user later.
The first travel information comprises navigation information, taxi taking information, express information, takeaway information or the like of the first user taking a working place as a departure place or a destination in the preset time interval, and the working and rest time conditions of the first user can be obtained through analysis of the first travel information, so that the financial condition of the first user is obtained through prediction according to the working and rest time conditions obtained through analysis and the first professional information.
The preset time interval may be set according to specific needs, preferably may be 3 months or longer, and the process goes to S24.
S23, acquiring first occupation information of the first financial relation group and first trip information in a preset time interval.
Wherein the first professional information is professional information filled in by a user in the first financial relation group in advance, and the filled professional information should be subdivided as much as possible so as to more accurately determine the financial situation of the first financial relation group later. In a representation, the first professional information may be a vector of two or more dimensions.
Similarly, the first travel information is also a multidimensional vector, and includes a sub-dimension corresponding to each user in the first financial relationship group, and each sub-dimension further includes travel information corresponding to each user in the first financial relationship group. In general, a type of financial relationship group combination also presents different rules of corresponding work and rest information, for example, if a husband is responsible for making money on duty and a wife is full-time taitaitai, the corresponding first trip information is often that the husband goes early and late, and the wife does not have much trip information, and further, in combination with the professional condition of the husband, the financial condition corresponding to the financial relationship group can be accurately acquired through big data analysis.
The preset time interval can be set according to specific needs, and preferably can be 3 months or longer.
S24, inputting the user type, the first occupation information and the first travel information into the financial portrait determination model to determine a first financial portrait.
In this step, the user type, the first occupation information and the first travel information are input into the financial portrait determination model, so that the information such as credit data, consumption data and the like of the user group with high similarity to the first user or the first financial relation group is determined by combining big data, and the first financial portrait is obtained.
The financial portrait determination model training process is as follows:
S241, acquiring a sample data set.
In this step, a sample dataset is obtained for training the financial representation determination model.
Each piece of sample data comprises professional information data, trip information data, consumption data in a preset time interval, overdue condition data, purchase financial product type data and the like corresponding to one historical user. The consumption data comprise the consumption amount and the type of the purchased goods of the historical user in a preset time interval.
And S242, training a convolutional neural network model by using the sample data set to obtain the financial portrait determination model.
In the training process, professional information data and trip information data in the sample data are used as input data, and consumption data, overdue condition data and purchase financial product type data in a preset time interval are used as output data.
And after training is finished, verifying the financial portrait determining model by using a test set, and when the verification accuracy is higher than a preset value, obtaining the financial portrait determining model after training is finished.
In the first financial portrait, the included data types at least include user type, consumption data in a preset time interval, overdue condition data, purchase financial product type data and the like.
And S3, determining first consumption deviation information of the first user or the first financial relation group according to the first financial portrait and the first consumption data.
Because the first financial portrait acquired in S2 may be used to represent a user or a group having a higher similarity to the first user or the first financial relationship group, the first consumption deviation information for representing abnormal consumption habits of the first user or the first financial relationship group may be obtained by comparing the first financial portrait acquired in S2 with the first consumption data.
Specifically, the step S3 may include the following steps:
and S31, extracting second consumption data from the first financial portrait.
The second consumption data is used for representing individuals or groups with higher similarity with the first user or the first financial relation group, and the consumption data in a preset time interval can comprise consumption amount, specific information of purchasing goods and the like.
And S32, determining first amount deviation information and first consumption type deviation information of the first consumption data and the second consumption data.
The first amount deviation information may be determined by comparing the amount values in the first consumption data and the second consumption data. For example, if the first consumption data is 1 ten thousand yuan and the second consumption data is 2 ten thousand yuan, it may be determined that the first amount deviation information is minus 1 ten thousand yuan.
The first consumption type deviation information may be determined by comparing merchandise specific information in the first consumption data and the second consumption data. Specifically, the commodity specific information may be input into a commodity type determining model to determine commodity type information corresponding to the first consumption data or the second consumption data. Preferably, the commodity type information is classified according to the grade of the commodity specific information, for example, the commodity type information can be classified into a luxury, a light luxury, a popular brand and the like, and the grade classification can be performed based on the price, the public praise and the like of the commodity.
Preferably, the first consumption type deviation information may be determined by a consumption type deviation determination model, which may be obtained by training a convolutional neural network model. In the training sample data, commodity specific information can be used as input information, and manually marked grade information can be used as output information.
S33, determining the first consumption deviation information according to the first amount deviation information and the first consumption type deviation information.
The first consumption deviation information may be a two-dimensional vector formed by combining the first amount deviation information and the first consumption type deviation information. The first consumption deviation information may also determine weight values of the first consumption deviation information and the first consumption type deviation information according to actual situations of the first consumption deviation information and the first consumption type deviation information, so as to obtain a set of two-dimensional weight vectors.
And S4, determining a first risk score according to the first consumption deviation information of the first user or the first financial relation group.
In this step, the first consumption deviation information is determined according to the historical credit data and the historical overdue condition of the first user or the first financial relation group, and the first risk score corresponding to the first user or the first financial relation group is determined.
The step S4 comprises the following substeps:
S41, acquiring first historical credit data of the first user or the first financial relation group.
The first historical credit data includes historical lending information, historical repayment information, historical overdue default conditions and the like of the first user or the first financial relationship group.
S42, inputting the first historical credit data and the first consumption deviation information into a risk score determining model to determine the first risk score.
In the first consumption deviation information, influence factors in aspects of professional conditions, consumption habits and the like are integrated, and in the first historical credit data, influence factors in aspects of credit habits and the like of users are reflected. Thus, by the first historical credit data and the first consumption deviation information may represent factors of various aspects such as job stability, consumption stability, credit history, etc. of the first user or the first financial relationship group, the first risk score for characterizing financial risk may be well determined.
Preferably, the first risk score may be determined by a risk score model, that is, by training a machine learning model through historical sample data, and the machine learning model in the prior art may be specifically used, which is not described herein.
The application also provides a financial credit risk identification device for executing the financial credit risk identification method.
According to the financial credit risk identification method and device, firstly, the user type is determined through consumption data of a first user, professional information and financial information of the independent user and a family user are obtained through big data analysis of trip data respectively, consumption deviation information is determined by combining the determined financial information and consumption data, and finally, consumption deviation information and historical credit information are combined to determine a first risk score. According to the technical scheme, through multi-dimensional big data analysis, the data of the first user in various aspects such as the job stability, the consumption stability, the credit history and the like can be integrated, so that a more accurate first risk score is determined, and financial credit risks are reduced.
The foregoing description is only of the preferred embodiments of the invention, and all changes and modifications that come within the meaning and range of equivalency of the structures, features and principles of the invention are therefore intended to be embraced therein.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110078073A1 (en) * | 2009-09-30 | 2011-03-31 | Suresh Kumar Annappindi | System and method for predicting consumer credit risk using income risk based credit score |
| CN114444682A (en) * | 2021-10-18 | 2022-05-06 | 中国联合网络通信集团有限公司 | Training method and device of neural network model |
| CN115392916A (en) * | 2022-08-30 | 2022-11-25 | 中国银行股份有限公司 | Abnormal consumption control method and device, electronic equipment and storage medium |
| CN118569981A (en) * | 2024-07-29 | 2024-08-30 | 四川世纪银通科技有限公司 | Customer repayment risk prediction method and system based on consumption portraits |
| CN119180713A (en) * | 2024-09-18 | 2024-12-24 | 深圳百流科技有限公司 | Financial risk prediction method, prediction device and computer equipment |
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- 2025-03-12 CN CN202510287885.6A patent/CN120181984A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110078073A1 (en) * | 2009-09-30 | 2011-03-31 | Suresh Kumar Annappindi | System and method for predicting consumer credit risk using income risk based credit score |
| CN114444682A (en) * | 2021-10-18 | 2022-05-06 | 中国联合网络通信集团有限公司 | Training method and device of neural network model |
| CN115392916A (en) * | 2022-08-30 | 2022-11-25 | 中国银行股份有限公司 | Abnormal consumption control method and device, electronic equipment and storage medium |
| CN118569981A (en) * | 2024-07-29 | 2024-08-30 | 四川世纪银通科技有限公司 | Customer repayment risk prediction method and system based on consumption portraits |
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