Personal credit assessment classification method and system based on support vector machine
Through the support vector machine algorithm and Gaussian kernel function to process multi-dimensional credit data, the subjectivity and limitations of traditional evaluation methods are solved, and more accurate and efficient personal credit evaluation is achieved.
Patent Information
- Application Number
- CN202411416551.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-11
- Publication Date
- 2025-01-07
AI Technical Summary
The traditional personal credit assessment method has the subjectivity of manual review, the limitations of rule-based scoring, and the inability to capture nonlinear relationships, resulting in inconsistency and inaccuracy of evaluation results.
The support vector machine algorithm is adopted to collect and preprocess multi-dimensional data, extract key features, determine parameters using Gaussian kernel functions and cross-validation, train the model and apply it to actual credit evaluation tasks.
It improves the accuracy and computing efficiency of personal credit assessment, can handle complex credit data and nonlinear relationships more comprehensively, and adapt to the dynamic changes in credit risks.
Smart Images
Figure CN119272131A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the fields of financial technology and credit assessment technology, and in particular to a personal credit assessment classification method and system based on a support vector machine. Background Art
[0002] With the rapid development of financial technology, the importance of personal credit assessment in the financial field has become increasingly prominent. Personal credit status directly affects the credit decision-making, risk management and reasonable pricing of financial services of financial institutions. However, personal credit assessment faces many challenges, such as the diversity and complexity of data sources, the uncertainty and dynamic changes of credit risks, etc. Traditional personal credit assessment methods mainly rely on manual review and rule-based scoring models, but these methods have many limitations and may not be able to comprehensively and accurately assess personal credit status.
[0003] Disadvantages of existing technology:
[0004] 1. Subjectivity of manual review: Traditional personal credit assessment methods rely on manual review, which is highly subjective and easily affected by the experience and bias of the assessors, making it difficult to ensure the consistency and accuracy of the assessment results.
[0005] 2. Limitations of rule-based scoring: Rule-based scoring systems usually rely on limited indicators, which makes it difficult to fully and accurately reflect the true credit situation of individuals, and are not effective when dealing with large amounts of data and complex credit relationships.
[0006] 3. Unable to capture nonlinear relationships: Existing technologies are difficult to effectively capture potential patterns and nonlinear relationships in personal credit data and may miss important information. Summary of the invention
[0007] In view of the above problems, the present invention is proposed to provide a personal credit assessment classification method and system based on support vector machine that overcomes the above problems or at least partially solves the above problems.
[0008] According to one aspect of the present invention, a personal credit assessment classification method based on a support vector machine is provided, the assessment classification method comprising:
[0009] Collect multi-dimensional data about individuals;
[0010] Preprocessing the multi-dimensional data to obtain preprocessed data;
[0011] Extracting key features from the preprocessed data and encoding classification features;
[0012] Dividing the preprocessed data into data sets;
[0013] Train the model using the training set data to obtain a training model;
[0014] Use the validation set to evaluate the trained model;
[0015] The training model is applied to the actual personal credit assessment classification task.
[0016] Optionally, the multi-dimensional data includes: income level, debt situation, credit history and consumption behavior.
[0017] Optionally, the preprocessing of the multi-dimensional data specifically includes:
[0018] The collected data is cleaned, outliers and missing values are removed, and standardized or normalized.
[0019] Optionally, the extracting key features from the preprocessed data specifically includes: calculating a credit score and a debt-to-income ratio.
[0020] Optionally, dividing the preprocessed data into data sets specifically includes:
[0021] The processed data is reasonably divided into training set, validation set and test set to prepare for subsequent model training and evaluation.
[0022] Optionally, the step of training the model using the training set data specifically includes:
[0023] The support vector machine algorithm is used and the Gaussian kernel function is selected;
[0024] The optimal parameter Gamma value and penalty parameter C are determined through cross-validation technology, the model is trained using the training set data, and the parameters are continuously adjusted to optimize the model to obtain the training model.
[0025] Optionally, the using a validation set to evaluate the training model specifically includes: using the validation set to evaluate the trained model and screening out model parameters with the best performance.
[0026] Optionally, the applying the training model to an actual personal credit assessment classification task specifically includes:
[0027] Use the test set to test the final model to verify its generalization ability;
[0028] And apply the trained model to the actual personal credit assessment classification task.
[0029] The present invention also provides a personal credit evaluation classification system based on support vector machine, which applies the personal credit evaluation classification method based on support vector machine described above, and the evaluation classification system includes:
[0030] Source data collection module, used to collect multi-dimensional data of individuals;
[0031] A data preprocessing module, used to preprocess the multi-dimensional data to obtain preprocessed data;
[0032] A feature engineering module, used to extract key features from the preprocessed data and encode classification features;
[0033] A data set division module, used for dividing the preprocessed data into data sets;
[0034] A model training module is used to train the model using the training set data to obtain a training model;
[0035] Model evaluation module, used to evaluate the training model using the validation set;
[0036] The model application module is used to apply the training model to the actual personal credit assessment classification task.
[0037] The present invention provides a personal credit assessment classification method and system based on support vector machine, the assessment classification method includes: collecting multi-dimensional data of individuals; preprocessing the multi-dimensional data to obtain preprocessed data; extracting key features from the preprocessed data and encoding classification features; dividing the preprocessed data into data sets; training the model using training set data to obtain a training model; using a validation set to evaluate the training model; applying the training model to actual personal credit assessment classification tasks. The calculation efficiency and accuracy of personal credit assessment are improved.
[0038] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.
[0040] Figure 1 A flowchart of a personal credit assessment classification method based on a support vector machine provided in an embodiment of the present invention;
[0041] Figure 2A block diagram of a personal credit assessment classification system based on a support vector machine provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0042] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0043] The terms "comprises" and "having" and any variations thereof in the description embodiments, claims and drawings of the present invention are intended to cover non-exclusive inclusions, for example, including a series of steps or units.
[0044] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.
[0045] like Figure 1 As shown, a personal credit assessment classification method based on support vector machine, the assessment classification method includes:
[0046] Collect multi-dimensional data of individuals; pre-process the multi-dimensional data to obtain pre-processed data; extract key features from the pre-processed data and encode classification features; divide the pre-processed data into data sets; train the model using the training set data to obtain a training model; evaluate the training model using the validation set; and apply the training model to an actual personal credit assessment classification task.
[0047] A personal credit assessment classification system based on support vector machine, comprising: a data collection module, a data preprocessing module, a feature engineering module, a data set partitioning module, a model training module, a model assessment module and a model application module;
[0048] Source data collection module: extensively collects multi-dimensional data of individuals, including income level, debt status, credit history, consumption behavior, etc.
[0049] Data preprocessing module: cleans the collected data, removes outliers and missing values, and performs standardization or normalization to ensure data quality and consistency.
[0050] Feature Engineering Module: Extract key features from preprocessed data, such as calculating credit scores, debt-to-income ratios, etc., and encode categorical features.
[0051] Dataset partitioning module: The processed data is reasonably divided into training set, validation set and test set to prepare for subsequent model training and evaluation.
[0052] Model training module: Use support vector machine algorithm and Gaussian kernel function (RBF kernel). Determine the optimal parameter Gamma value and penalty parameter C through cross-validation and other techniques, train the model using training set data, and continuously adjust parameters to optimize the model.
[0053] Model evaluation module: Use the validation set to evaluate the trained model and select the model parameters with the best performance.
[0054] Model application module: Use the test set to test the final model, verify its generalization ability, and apply the model to the actual personal credit assessment classification task.
[0055] Model structure:
[0056] The structure of the SVM model mainly covers the following key parts:
[0057] 1. Decision function: This is a classification decision function calculated based on the characteristics of the sample and the crucial support vector. This function is like the brain of the model, which can accurately output the classification result based on the input sample characteristics.
[0058] 2. Kernel function selection: Based on the characteristics of the data, carefully select the most appropriate kernel function to cleverly handle nonlinear problems and ensure that the model can accurately capture the complex patterns and relationships in the data.
[0059] 3. Parameter adjustment: By prudently adjusting the penalty parameter C, the performance of the model can be optimized and improved so that it can better adapt to different application scenarios and data characteristics.
[0060] Gaussian kernel function
[0061] The Gaussian kernel function is a commonly used kernel function and is widely used in machine learning and statistics.
[0062] The mathematical expression of the Gaussian kernel function is:
[0063]
[0064] Among them, x and x ′ is the input vector and σ is the bandwidth parameter of the kernel function.
[0065] The Gaussian kernel function has the following important characteristics:
[0066] Ability to map data to a high-dimensional space, so that linearly inseparable problems become separable in high-dimensional space. For example, in support vector machines (SVMs), by using Gaussian kernel functions, nonlinearly separable data can be processed.
[0067] It has locality, that is, higher weights are given to points that are closer, and lower weights are given to points that are farther away. For example, in image processing, for adjacent pixels, the Gaussian kernel function can better capture the similarities between them.
[0068] It has a certain degree of robustness to noise.
[0069] Beneficial effects:
[0070] 1. Improve assessment accuracy: Utilize the advantages of support vector machine algorithm in processing small samples, high dimensions and nonlinear data to more accurately classify and assess personal credit.
[0071] 2. Comprehensive analysis of credit data: Through multi-dimensional data collection and feature engineering, personal credit-related information can be processed and analyzed more comprehensively and deeply.
[0072] 3. Adapt to complex credit situations: It can effectively deal with the uncertainty and dynamic changes of personal credit risk and provide financial institutions with a more reliable basis for credit assessment decisions.
[0073] The above specific implementation methods further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific implementation methods of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A personal credit assessment classification method based on support vector machine, characterized in that: The evaluation classification method includes: Collect multi-dimensional data about individuals; Preprocessing the multi-dimensional data to obtain preprocessed data; Extracting key features from the preprocessed data and encoding classification features; Dividing the preprocessed data into data sets; Train the model using the training set data to obtain a training model; Use the validation set to evaluate the trained model; The training model is applied to the actual personal credit assessment classification task.
2. According to claim 1, a personal credit assessment classification method based on support vector machine is characterized in that: The multi-dimensional data includes: income level, debt situation, credit history and consumption behavior.
3. According to claim 1, a personal credit assessment classification method based on support vector machine is characterized in that: The preprocessing of the multi-dimensional data specifically includes: The collected data is cleaned, outliers and missing values are removed, and standardized or normalized.
4. According to claim 1, a personal credit assessment classification method based on support vector machine is characterized in that: The extracting key features from the preprocessed data specifically includes: calculating a credit score and a debt-to-income ratio.
5. The personal credit assessment classification method based on support vector machine according to claim 1 is characterized in that: The dividing the preprocessed data into data sets specifically includes: The processed data is reasonably divided into training set, validation set and test set to prepare for subsequent model training and evaluation.
6. A personal credit assessment classification method based on support vector machine according to claim 1, characterized in that: The use of training set data to train the model specifically includes: The support vector machine algorithm is used and the Gaussian kernel function is selected; The optimal parameter Gamma value and penalty parameter C are determined through cross-validation technology, the model is trained using the training set data, and the parameters are continuously adjusted to optimize the model to obtain the training model.
7. The method for personal credit assessment and classification based on support vector machine according to claim 1, characterized in that: The use of the validation set to evaluate the training model specifically includes: using the validation set to evaluate the trained model and screening out the model parameters with the best performance.
8. The method for personal credit assessment and classification based on support vector machine according to claim 1, characterized in that: The application of the training model to the actual personal credit assessment classification task specifically includes: Use the test set to test the final model to verify its generalization ability; And apply the trained model to the actual personal credit assessment classification task.
9. A personal credit assessment classification system based on support vector machine, using a personal credit assessment classification method based on support vector machine as described in any one of claims 1 to 8, characterized in that: The evaluation classification system includes: Source data collection module, used to collect multi-dimensional data of individuals; A data preprocessing module, used to preprocess the multi-dimensional data to obtain preprocessed data; A feature engineering module, used to extract key features from the preprocessed data and encode classification features; A data set division module, used for dividing the preprocessed data into data sets; A model training module is used to train the model using the training set data to obtain a training model; Model evaluation module, used to evaluate the training model using the validation set; The model application module is used to apply the training model to the actual personal credit assessment classification task.