Credit overdue risk monitoring system based on AI large model
Through the credit overdue risk monitoring system based on AI large-scale models, the problems of data processing complexity and insufficient risk prediction capabilities of credit risk monitoring in the existing technology are solved, efficient and accurate credit risk management is achieved, economic losses are reduced and personalized suggestions are provided.
Patent Information
- Application Number
- CN202411862423.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-05-27
AI Technical Summary
The existing technology has problems such as data processing complexity, insufficient risk prediction capabilities, and the inability to realize full-cycle credit risk monitoring and custom monitoring solutions in terms of credit risk monitoring, making it difficult to effectively improve the overall efficiency of credit risk management.
The credit overdue risk monitoring system based on AI large models is adopted, including data integration and management module, model training and optimization module, real-time detection and early warning module and feedback module, and real-time risk prediction and customized risk management suggestions are realized through multi-channel data collection, ETL processing, feature engineering and model training.
It significantly improves the accuracy and efficiency of credit overdue risk monitoring, improves the ability to respond to the risk profile in complex market environments, reduces economic losses caused by overdue loans, and provides personalized early warnings and action suggestions.
Smart Images
Figure CN120047231A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of fintech, and particularly relates to a credit overdue risk monitoring system based on an AI large model. Background Art
[0002] In the financial field, especially in credit services, overdue risk monitoring is an important part of the risk control of banks and financial institutions. Overdue loans not only bring financial risks to banks, but may also trigger a chain reaction, affecting financial stability. Traditional overdue risk assessment mainly relies on historical data within the bank and a single financial credit rating. This method has limitations in data processing and risk prediction capabilities. With the progress of artificial intelligence technology, especially the application of large models, overdue risks can be predicted more accurately, and credit requests can be monitored and intervened in a timely manner.
[0003] However, the existing technologies still have deficiencies in implementing full-cycle credit risk monitoring, improving the ability to analyze borrowers' behavior patterns, providing customizable monitoring solutions, realizing automated detection and early warning of data anomalies, and reducing the complexity of data processing by financial institutions. There is a need for a new technical solution to improve the overall efficiency of credit risk management. Summary of the Invention
[0004] (I) Objects of the Invention
[0005] In order to overcome the above deficiencies, the object of the present invention is to provide a credit overdue risk monitoring system based on an AI large model to solve the above technical problems.
[0006] (II) Technical Solutions
[0007] To achieve the above object, the technical solutions provided in the present application are as follows:
[0008] A credit overdue risk monitoring system based on an AI large model, comprising the following modules:
[0009] A data integration and management module, which collects multi-channel credit data, performs ETL processing, feature engineering, and data storage;
[0010] A model training and optimization module, which trains an AI model based on a large model to achieve credit overdue risk prediction;
[0011] A real-time detection and early warning module, which collects monitoring data in real time, performs risk prediction and assessment, and triggers an early warning.
[0012] A feedback module, which provides customized risk management suggestions.
[0013] Preferably, the data integration and management module specifically includes the following steps:
[0014] A1 Data access. The data comes from external data and internal data. External data includes third-party credit data and public data, and internal data includes customer information, transaction records, and loan details in the internal database. The data access methods include API calls, database access, and file upload;
[0015] A2 Data cleaning, including missing value handling, outlier detection, and data consistency;
[0016] A3 Feature engineering. Select the features most relevant to loan delinquency from a large number of features. Through normalization or standardization operations, make the feature distribution more suitable for model training. Calculate derived new features based on the original features to enhance the model's prediction ability;
[0017] A4 Data storage. Store the processed feature data in a data warehouse or a specific data table to provide input data for subsequent model training.
[0018] Preferably, the missing value handling includes filling and deleting missing data;
[0019] Outlier detection includes removing or correcting outlier data;
[0020] Data consistency includes unifying data formats and units.
[0021] Preferably, the model training and optimization module includes the following steps:
[0022] B1 Training data preparation. Divide the dataset into a training set, a validation set, and a test set. Handle the class imbalance problem. Increase the minority class samples through oversampling or undersampling methods to generate more training samples to improve the model's generalization ability;
[0023] B2 Model training. Select a suitable machine learning model. The machine learning models include logistic regression, random forest, and neural network. Use the training set data to train the model. Among them, logistic regression finds the optimal parameters through the maximum likelihood estimation method, random forest constructs multiple decision trees and increases model diversity through random sampling, neural network designs the network structure and uses the backpropagation algorithm for training, and uses the validation set data for model selection and hyperparameter tuning to find the optimal model;
[0024] B3 Model evaluation. Use the test set data to evaluate the trained model. Pay attention to accuracy, recall, F1 score, AUC-ROC, and other relevant evaluation metrics to ensure that the model performance meets the requirements. Determine the threshold of the evaluation metrics according to business needs. If the requirements are not met, adjust the model structure or hyperparameters;
[0025] B4 Model Optimization: Further optimize the model performance based on the evaluation results and business requirements, including adjusting the model structure, optimizing hyperparameters, and introducing more features.
[0026] B5 Model Deployment: Deploy the trained model to the production environment to provide risk prediction services for the credit overdue risk monitoring system.
[0027] Preferably, when the real-time monitoring and warning module operates, it includes the following steps:
[0028] C1 Real-time data access: Collect the transaction data and repayment behavior information of borrowers in real time. These data will be input into the model for risk score calculation.
[0029] C2 Risk prediction: The model provided by the model training and optimization module calculates the risk score of the borrower based on the input real-time data. The risk score reflects the likelihood of the borrower's overdue repayment.
[0030] C3 Risk level assessment: Evaluate the risk level of the borrower according to the risk score output by the model, and evaluate it based on the comparison of business rules and the set threshold.
[0031] C4 Warning trigger: When the risk score of the borrower exceeds the set threshold, trigger a warning. The warning information includes borrower information, risk score, and possible reasons for overdue.
[0032] C5 Feedback and iteration: Collect the results after the warning is triggered. The results include whether the warning is accurate and the subsequent behavior of the borrower. Input these feedback information into the model training and optimization module to further optimize the model. At the same time, adjust the model parameters and improve the algorithm according to the feedback to improve the warning accuracy and reduce false positives.
[0033] Preferably, the personal feedback and suggestion module specifically includes the following steps:
[0034] D1 Customer portrait analysis: Analyze the basic information, behavior characteristics, and risk preferences of customers to form a customer portrait.
[0035] D2 Business analysis: Analyze the credit structure, asset quality, and liability cost to evaluate the overall risk situation of the bank.
[0036] D3 Risk countermeasure formulation: Based on the analysis results, formulate targeted risk countermeasures. The countermeasures include credit policy adjustment and asset allocation optimization.
[0037] D4 Risk management suggestions: Sort out the risk countermeasures into a suggestion report for the bank's reference and decision-making.
[0038] D5 Suggestion feedback and iteration: Collect the bank's feedback on the suggestions and continuously optimize the risk monitoring model and suggestion plan.
[0039] Beneficial effects:
[0040] 1. Significantly improve the accuracy and efficiency of financial institutions in credit overdue risk monitoring.
[0041] 2. Enhance the institution's ability to promptly respond to risk profiles in complex market environments.
[0042] 3. Reduce economic losses caused by overdue loans and strengthen risk management.
[0043] 4. Provide personalized early warnings and action suggestions, based on which institutions can take targeted measures. Description of the drawings
[0044] Figure 1 is a schematic diagram of the overall process of the present invention. Detailed implementation manners
[0045] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the specific implementation manners and with reference to the attached Figure 1 , and the present invention will be further described in detail. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following descriptions, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.
[0046] A credit overdue risk monitoring system based on an AI large model provided by the present invention includes the following modules:
[0047] A data integration and management module that collects credit data from multiple channels and performs ETL processing, feature engineering, and data storage;
[0048] A model training and optimization module that trains an AI model based on a large model to achieve credit overdue risk prediction;
[0049] A real-time detection and early warning module that collects monitoring data in real time, performs risk prediction and assessment, and triggers an early warning.
[0050] A feedback module that provides customized risk management suggestions.
[0051] The data flow in the process further includes a model feedback loop, that is, the feedback obtained from the early warning and feedback modules can be fed back to the model tuning iteration of the model training and optimization module, thereby continuously improving the accuracy and effectiveness of the model. In this way, a closed loop can be formed to continuously optimize the efficiency and effect of the entire monitoring system.
[0052] Preferably, the data integration and management module specifically includes the following steps:
[0053] A1 Data access, where the data comes from external data and internal data. External data includes third-party credit data and public data, and internal data includes customer information, transaction records, and loan details in the internal database. The data access methods include API calls, database access, and file upload;
[0054] A2 Data cleaning, including missing value handling, outlier detection, and data consistency;
[0055] A3 Feature engineering, selecting the features most relevant to loan delinquency from a large number of features, and through normalization or standardization operations, making the feature distribution more suitable for model training. Derive new features based on the original features to enhance the model's prediction ability;
[0056] A4 Data storage, storing the processed feature data in a data warehouse or a specific data table to provide input data for subsequent model training.
[0057] The missing value handling includes filling and deleting missing data;
[0058] Outlier detection includes removing or correcting outlier data;
[0059] Data consistency includes unifying data formats and units.
[0060] Preferably, the model training and optimization module includes the following steps:
[0061] B1 Training data preparation, dividing the dataset into a training set, a validation set, and a test set, handling the class imbalance problem, and increasing the minority class samples through oversampling or undersampling methods to generate more training samples to improve the model's generalization ability;
[0062] B2 Model training, the model training specifically includes:
[0063] B21 Select a suitable machine learning model. The machine learning models include logistic regression, random forest, and neural network. Risk prediction may involve the comprehensive influence of multiple factors. Try three models: logistic regression, random forest, and deep neural network. Logistic regression is a simple and effective classification model, performing well on linearly separable data, and can quickly obtain results with strong interpretability. Random forest is an ensemble learning method that improves prediction accuracy by constructing multiple decision trees and voting, having certain advantages in handling high-dimensional data and non-linear relationships. Neural network can automatically learn complex patterns and features in the data and has strong fitting ability for large-scale data and complex problems.
[0064] Use the training set data to train the model. In logistic regression, the optimal parameters are found through the maximum likelihood estimation method. In random forest, multiple decision trees are constructed and the model diversity is increased through random sampling. In neural network, the network structure is designed and trained using the backpropagation algorithm.
[0065] Divide the collected customer data into a training set, a validation set, and a test set. Suppose we have 10,000 customer data samples, and 6,000 of them are used as the training set. For the logistic regression model, use the training set data for parameter estimation and find the optimal parameter values through the maximum likelihood estimation method to minimize the loss function of the model on the training set. For the random forest model, determine hyperparameters such as the number of decision trees and the maximum depth, use the training set data to construct multiple decision trees, and increase the model diversity and generalization ability through random sampling and feature selection. For the neural network model, design an appropriate network structure, including the number of layers, the number of neurons, activation functions, etc., and use the training set data for backpropagation algorithm training, continuously adjusting the network weights and biases to enable the model to accurately fit the training data.
[0066] Use the validation set data for model selection and hyperparameter tuning to find the optimal model. Use 2,000 data samples as the validation set. In the logistic regression model, try different regularization parameters, such as L1 regularization and L2 regularization, and observe the performance changes of the model on the validation set. By adjusting the regularization parameters, overfitting can be prevented and the generalization ability of the model can be improved. In the random forest model, change the number of decision trees and the maximum depth, and observe the changes in metrics such as accuracy and recall of the model on the validation set. Select the optimal combination of hyperparameters to make the model perform best on the validation set. In the neural network model, adjust hyperparameters such as the network structure and learning rate. Try different numbers of layers, numbers of neurons, and learning rates, and observe the convergence speed and performance of the model on the validation set. Through multiple experiments and comparisons, find the optimal network structure and learning rate.
[0067] Model evaluation: Use the test set data to evaluate the trained model, focusing on metrics such as accuracy, recall, F1-score, and AUC-ROC and other relevant evaluation metrics to ensure that the model performance meets the requirements. Determine the threshold of the evaluation metrics according to business needs. If the requirements are not met, adjust the model structure or hyperparameters.
[0068] Use the test set data to evaluate the trained model:
[0069] Use the remaining 2,000 data samples as the test set. For the logistic regression model, calculate the accuracy, recall, F1-score, and AUC-ROC value on the test set. Assume the accuracy is 80%, the recall is 75%, the F1-score is 77.5%, and the AUC-ROC value is 0.85. These metrics can reflect the model's predictive ability and accuracy for customer churn. For the random forest model, calculate the above evaluation metrics as well. Assume the accuracy is 85%, the recall is 80%, the F1-score is 82.5%, and the AUC-ROC value is 0.90. It shows that the random forest model performs better in customer churn prediction. For the neural network model, conduct the evaluation. Assume the accuracy is 88%, the recall is 82%, the F1-score is 85%, and the AUC-ROC value is 0.92. It indicates that the neural network model has high predictive performance on this issue.
[0070] B32 Ensure that the model's performance meets the requirements:
[0071] According to specific business requirements and actual situations, determine the thresholds for evaluation metrics. For example, for the customer churn prediction problem, it may be required that the accuracy is not less than 80%, the recall is not less than 75%, and the AUC-ROC value is not less than 0.85. If the performance of a certain model does not meet the requirements, consider further adjusting the model structure, hyperparameters, or collecting more data for retraining.
[0072] B4 Model optimization, according to the evaluation results and business requirements, further optimize the model performance, including adjusting the model structure, optimizing hyperparameters, and introducing more features.
[0073] B5 Model deployment: Deploy the trained model to the production environment to provide risk prediction services for the credit overdue risk monitoring system.
[0074] The real-time monitoring and warning module includes the following steps during operation:
[0075] C1 Real-time data access, collect the transaction data and repayment behavior information of borrowers in real time, and these data will be input into the model for risk score calculation;
[0076] C2 Risk prediction, the model provided by the model training and optimization module calculates the risk score of the borrower based on the input real-time data, and the risk score reflects the possibility of the borrower's overdue repayment;
[0077] C3 Risk level assessment, evaluate the risk level of the borrower according to the risk score output by the model, and evaluate according to the comparison of business rules and the set thresholds;
[0078] C4 Warning trigger, when the risk score of the borrower exceeds the set threshold, trigger a warning, and the warning information includes borrower information, risk score, and possible reasons for overdue.
[0079] C5 Feedback and iteration, collect the results after the warning is triggered. The results include whether the warning is accurate and the subsequent behavior of the borrower. Input these feedback information into the model training and optimization module for further optimizing the model. At the same time, adjust the model parameters and improve the algorithm according to the feedback to improve the warning accuracy and reduce false positives.
[0080] Preferably, the personal feedback and suggestion module specifically includes the following steps:
[0081] D1 Customer portrait analysis, analyze the basic information, behavior characteristics and risk preferences of customers to form a customer portrait;
[0082] D2 Business analysis, analyze the credit structure, asset quality and liability cost to evaluate the overall risk situation of the bank;
[0083] D3 Risk countermeasure formulation, based on the analysis results, formulate targeted risk countermeasures. The countermeasures include credit policy adjustment and asset allocation optimization;
[0084] D4 Risk management suggestion, sort out the risk countermeasures into a suggestion report for the bank to refer to for decision-making;
[0085] D5 Suggestion feedback and iteration, collect the bank's feedback on the suggestions, and continuously optimize the risk monitoring model and suggestion plan.
[0086] The present invention improves the accuracy of risk assessment by using an AI large model for rapid learning and iteration. The real-time monitoring system dynamically tracks credit risks and provides timely warnings when risks change. Customized feedback and suggestions provide a way to optimize risk monitoring according to the characteristics of each financial institution. Thus, it significantly improves the accuracy and efficiency of financial institutions in credit overdue risk monitoring, enhances the institution's ability to respond promptly to the risk profile in a complex market environment, reduces economic losses caused by overdue loans, and strengthens risk management. At the same time, personalized warnings and action suggestions are provided, and the institution can take targeted measures according to these suggestions.
[0087] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.
[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A credit overdue risk monitoring system based on AI big model, characterized by: Includes the following modules: Data integration and management module, which collects credit data from multiple channels and performs ETL processing, feature engineering and data storage; Model training and optimization module, which trains AI models based on large models to predict credit overdue risks; Real-time detection and early warning module collects monitoring data in real time, conducts risk prediction and assessment, and triggers early warning. Feedback module provides customized risk management suggestions.
2. According to claim 1, a credit overdue risk monitoring system based on AI big model is characterized in that: The data integration and management module specifically includes the following steps: A1 data access: data comes from external data and internal data. External data includes third-party credit data and public data. Internal data includes customer information, transaction records and loan details in the internal database. Data access methods include API calls, database access and file uploads. A2 data cleaning, including missing value processing, outlier detection and data consistency; A3 feature engineering: select the features most relevant to loan delinquency from a large number of features, make the feature distribution more suitable for model training through normalization or standardization operations, and derive new features based on the original features to enhance the model's prediction ability; A4 data storage stores the processed feature data in a data warehouse or a specific data table to provide input data for subsequent model training.
3. According to claim 2, a credit overdue risk monitoring system based on AI big model is characterized in that: The missing value processing includes filling and deleting missing data; Outlier detection involves removing or correcting anomalous data; Data consistency includes unified data formats and units.
4. According to claim 1, a credit overdue risk monitoring system based on AI big model is characterized in that: The model training and optimization module includes the following steps: B1 Training data preparation: divide the data set into training set, validation set and test set, deal with the class imbalance problem, increase the minority class samples by oversampling or undersampling, and generate more training samples to improve the generalization ability of the model; B2 Model training: Select a suitable machine learning model, including logistic regression, random forest and neural network. Use training set data to train the model. Logistic regression finds the optimal parameters through maximum likelihood estimation method. Random forest builds multiple decision trees and increases model diversity through random sampling. Neural network designs network structure and uses back propagation algorithm for training. Use validation set data for model selection and hyperparameter tuning to find the optimal model. B3 Model evaluation: Use the test set data to evaluate the trained model, pay attention to accuracy, recall, F1 score, AUC-ROC and other related evaluation indicators to ensure that the model performance meets the requirements. Determine the threshold of the evaluation indicator according to business needs. If it does not meet the requirements, adjust the model structure or hyperparameters; B4 model optimization: further optimize model performance based on evaluation results and business needs, including adjusting model structure, optimizing hyperparameters, and introducing more features. B5 Model deployment: Deploy the trained model to the production environment to provide risk prediction services for the credit overdue risk monitoring system.
5. According to claim 1, a credit overdue risk monitoring system based on AI big model is characterized in that: The real-time monitoring and early warning module comprises the following steps during operation: C1 real-time data access collects borrowers’ transaction data and repayment behavior information in real time, which will be input into the model for risk scoring calculation; C2 Risk Prediction: The model provided by the model training and optimization module calculates the borrower's risk score based on the input real-time data. The risk score reflects the possibility of the borrower defaulting on repayment; C3 risk level assessment, assessing the borrower’s risk level based on the risk score output by the model, and assessing based on the comparison of business rules and set thresholds; C4 warning trigger: when the borrower's risk score exceeds the set threshold, the warning is triggered. The warning information includes the borrower's information, risk score, and possible reasons for overdue payment; C5 feedback and iteration, collect the results after the warning is triggered, including whether the warning is accurate and the borrower's subsequent behavior, and input this feedback information into the model training and optimization module to further optimize the model. At the same time, adjust the model parameters and improve the algorithm based on the feedback to improve the accuracy of the warning and reduce false positives.
6. The credit overdue risk monitoring system based on AI big model according to claim 1 is characterized in that: The personal feedback and suggestion module specifically includes the following steps: D1 Customer portrait analysis: analyzing the customer’s basic information, behavioral characteristics and risk preferences to form a customer portrait; D2 business analysis, analyzing credit structure, asset quality and liability costs, and assessing the overall risk profile of the bank; D3 Risk countermeasure formulation: based on the analysis results, formulate targeted risk countermeasures, including credit policy adjustment and asset allocation optimization; D4 Risk management recommendations: organize risk countermeasures into recommendation reports for banks to refer to for decision-making; D5 provides feedback and iteration on recommendations, collecting feedback from banks on recommendations and continuously optimizing risk monitoring models and recommendations.