Credit overdue intelligent early warning and intervention system based on real-time data analysis

Abstract

The invention discloses a credit overdue intelligent early warning and intervention system based on real-time data analysis. The system comprises six core modules including a data acquisition module, a data cleaning and preprocessing module, a real-time data analysis module, an intelligent early warning module, an intervention measure execution module and a model optimization module. The data acquisition module acquires multi-dimensional data of a borrower in real time through an API (Application Program Interface); the data preprocessing module processes data by adopting methods such as Z-Score standardization and the like; the real-time analysis module performs comprehensive analysis by using an ARIMA (Autoregressive Integrated Moving Average) model, an Apriori algorithm and a K-Means algorithm on the basis of an Apache Flink framework; the intelligent early warning module adopts a random forest algorithm to construct an early warning model; the intervention module automatically executes corresponding measures according to the risk level; the optimization module continuously improves various models. According to the invention, the risk early warning response time is shortened to a minute level, the early warning accuracy is improved by more than 30%, the labor cost is reduced by 40%, the credit default risk is effectively reduced by about 25%, and the intelligence and automation of credit risk management are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of credit risk management, and particularly relates to a credit overdue intelligent early warning and intervention system based on real-time data analysis. BACKGROUND

[0002] In traditional credit business, the assessment and early warning of credit overdue risk by financial institutions mainly rely on static historical data analysis and simple manual experience judgment. This traditional method has many obvious limitations in practical application. First, the traditional method cannot timely capture the real-time behavior changes of borrowers during the credit period, and it is also difficult to perceive the dynamic influence of external environmental factors, resulting in that the risk assessment results often lag behind the actual situation. Second, the manual judgment method is not only low in efficiency, but also subjective, different credit managers may make different risk assessments on the same borrower, and this inconsistency increases the uncertainty of risk management. In addition, in the face of massive credit business processing needs, the traditional manual processing method has been difficult to adapt to the development speed and scale of modern financial business.

[0003] With the rapid development of financial technology, although some financial institutions have begun to introduce data analysis technology to improve credit risk management, there is still much room for improvement in existing systems. Most systems on the market currently use a timed batch analysis method to process credit data, such as performing data analysis and risk assessment once a day at dawn or once a week on the weekend. This batch processing method results in poor timeliness of data processing, with a long time delay between data generation and risk identification, which cannot truly realize real-time monitoring of credit overdue risk. When the system identifies potential risks, it may have missed the best intervention opportunity. At the same time, the existing system also needs to be improved in terms of the accuracy of risk early warning, and lacks comprehensive analysis capability for multi-dimensional data, making it difficult to accurately identify complex risk patterns.

[0004] In the current complex economic environment, the influencing factors of credit risk are increasingly diversified, and the repayment ability and willingness of borrowers may change rapidly due to the influence of various internal and external factors. The traditional risk management method and the existing technical solution are difficult to meet the urgent needs of financial institutions for real-time monitoring and accurate early warning of credit risk. Financial institutions face the severe challenge of rising credit default risk and increasing non-performing asset ratio due to the inability to take timely and effective intervention measures. Therefore, the market urgently needs a credit overdue intelligent early warning and intervention system based on real-time data analysis, which integrates multi-source data, uses advanced analysis algorithms and machine learning technology to realize dynamic monitoring and intelligent management of credit risk, thereby improving the efficiency and accuracy of credit risk management and effectively reducing the operating risk of financial institutions. SUMMARY

[0005] The present application aims to overcome the deficiencies of the prior art, and provides a credit overdue intelligent early warning and intervention system based on real-time data analysis, so as to solve the technical problems of poor timeliness, low accuracy and insufficient automation in traditional credit risk management.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions: The present application provides a credit overdue intelligent early warning and intervention system based on real-time data analysis, which comprises six core modules: a data acquisition module, a data cleaning and preprocessing module, a real-time data analysis module, an intelligent early warning module, an intervention measure execution module and a model optimization module. The data acquisition module is connected in real time with multiple third-party data suppliers through an API interface, and collects the basic information, credit history, consumption behavior, asset and liability and external environment data of borrowers in all directions, while supporting real-time extraction of related data from the internal business systems of financial institutions. The data cleaning and preprocessing module performs format unification, outlier processing and standardization processing on the collected raw data to ensure data quality and consistency. The real-time data analysis module is based on the ApacheFlink real-time computing framework and comprehensively uses multiple analysis methods such as time series analysis, association rule mining and clustering analysis to dynamically evaluate the repayment ability, repayment willingness and overdue risk of borrowers. The intelligent early warning module uses machine learning algorithms such as random forest to build an intelligent early warning model, and automatically triggers a hierarchical early warning mechanism according to the real-time analysis results and preset thresholds. The intervention measure execution module automatically matches and executes corresponding intervention measures according to different risk levels, realizing the automation of risk response. The model optimization module dynamically optimizes and adjusts various models in the system by continuously collecting actual operation data, ensuring the continuous improvement of system performance.

[0007] The present application builds a complete credit risk management closed-loop system through the technical path of real-time data acquisition, intelligent analysis and automated intervention. The system fully utilizes big data processing technology and artificial intelligence algorithms to realize the full-process automation and intelligentization from data acquisition to risk identification, from early warning triggering to intervention execution, significantly improving the timeliness and accuracy of credit risk management.

[0008] A credit overdue intelligent early warning and intervention system based on real-time data analysis, comprising: The data acquisition module is connected with the third-party data suppliers through an API interface, and collects the basic information, credit history data, consumption behavior data, asset and liability data and external data of borrowers in real time, and extracts data from the internal business systems of financial institutions in real time; The data cleaning and preprocessing module is connected with the data acquisition module, uses regular expression to identify and unify date format, processes text data through string conversion function, and uses Z-Score standardization method to standardize numerical data, wherein the standardization formula is x'=(x-μ) / σ, x is the original data, μ is the mean, and σ is the standard deviation; The real-time data analysis module is connected with the data cleaning and preprocessing module, is based on Apache Flink real-time computing framework, and includes a time series analysis unit, an association rule mining unit and a clustering analysis unit, wherein the time series analysis unit uses ARIMA model, the association rule mining unit uses Apriori algorithm, and the clustering analysis unit uses K-Means algorithm; The intelligent early warning module is connected with the real-time data analysis module, uses random forest algorithm to construct a warning model, and triggers the early warning mechanism when the risk assessment result exceeds the preset threshold; The intervention measure execution module is connected with the intelligent early warning module, matches and executes corresponding intervention measures according to the risk level; The model optimization module is connected with the intelligent early warning module and the intervention measure execution module, collects actual overdue data and intervention effect data, and adjusts parameters of the warning model and the data analysis model.

[0009] Further, the data cleaning and preprocessing module further includes Min-Max normalization processing, and the normalization formula is x''=(x-min) / (max-min), wherein x is the original data, min is the minimum value, and max is the maximum value, and the data is mapped to the [0, 1] interval.

[0010] Further, the time series analysis unit performs data stationarity test through ADF test method, performs seasonal adjustment through X-13-ARIMA-SEATS method, and uses AIC and BIC criteria to determine the optimal parameter combination (p, d, q) of the ARIMA model.

[0011] Further, the association rule mining unit sets a minimum support threshold, generates a candidate item set from a single item, iteratively calculates the support and filters the frequent item set, and generates strong association rules meeting the minimum confidence and minimum lift thresholds.

[0012] Further, the clustering analysis unit determines the number of clustering clusters K through the elbow rule, calculates the clustering error sum of squares SSE under different K values, selects the K value corresponding to the inflection point of the SSE curve, and constructs multiple decision trees through Bootstrap sampling.

[0013] Further, the intelligent early warning module adopts Pearson correlation coefficient for feature selection, and determines the optimal parameters of the random forest model through grid search combined with 5-fold cross-validation, including the number of decision trees n_estimators, the number of node splitting features max_features and the maximum depth of the tree max_depth.

[0014] Further, the intervention execution module divides the risk level into three levels of low risk, medium risk and high risk, sends a reminder SMS or email when the risk level is low, communicates by phone or visits when the risk level is medium, and starts legal litigation procedures or asset preservation measures when the risk level is high.

[0015] Further, the model optimization module calculates the prediction accuracy, recall rate, F1 value and AUC evaluation index of the model, and adjusts the model parameters or introduces new feature variables by comparing the prediction results with the actual situation.

[0016] Further, the intelligent early warning module generates early warning information including the basic information of the borrower, the risk assessment result, the risk level and the cause analysis of the overdue, and sends it to the credit management personnel through SMS, email and system pop-up window.

[0017] The beneficial effects of the present application are: First, through real-time data acquisition and streaming computing technology, the response time of risk early warning is shortened from several days to minutes or even seconds, significantly improving the timeliness of risk early warning. Second, by fusing multi-dimensional data and various analysis models, the accuracy of the system's credit overdue risk assessment is improved by more than 30% compared with traditional methods, greatly improving the accuracy of risk identification. Third, through intelligent algorithms and automated processes, the human intervention link is reduced, the labor cost is reduced by about 40%, and the work efficiency is improved by more than 50%, realizing the intelligentization and automation of credit risk management. Fourth, through timely and accurate risk identification and effective intervention measures, financial institutions can reduce credit default risk by about 25%, and non-performing asset ratio by 15%-20%, effectively protecting the asset safety of financial institutions. Fifth, through the continuous learning mechanism of the model optimization module, the system can continuously adapt to changes in market environment and customer behavior, maintaining long-term efficient operation. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0019] Figure 1 It is a schematic diagram of the overall architecture of the system of the present application. Figure 2 The data processing flowchart of the application is shown in the figure. Figure 3 The intelligent early warning model construction flowchart of the application is shown in the figure. Figure 4 The risk level and intervention measure correspondence diagram of the application is shown in the figure. DETAILED DESCRIPTION

[0020] The application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be implemented by those skilled in the art for some known technologies; and the drawings are only used to describe the embodiments in more detail, and are not intended to specifically limit the application.

[0021] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiments can include specific features, structures or characteristics, but not necessarily every embodiment includes the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in combination with an embodiment, it should be within the knowledge of those skilled in the related art to realize this feature, structure or characteristic in combination with other embodiments (whether or not explicitly described).

[0022] Generally, the terms can be understood at least in part from the use in the context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure or characteristic in the singular or can be used to describe combinations of features, structures or characteristics in the plural. In addition, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but can instead, depending at least in part on the context, allow the presence of other factors not necessarily explicitly described.

[0023] The application provides a credit overdue intelligent early warning and intervention system based on real-time data analysis. The system collects and analyzes credit-related data in real time, uses intelligent algorithm model to accurately assess and warn credit overdue risk, and automatically triggers corresponding intervention measures. As shown in the system architecture diagram, the system includes six core modules: data acquisition module, data cleaning and preprocessing module, real-time data analysis module, intelligent early warning module, intervention measure execution module and model optimization module. Figure 1

[0024] ​The data collection module is responsible for real-time collection of multi-dimensional credit-related data. This module connects with banks, credit agencies, e-commerce platforms, social media platforms, and other third-party data suppliers through API interfaces to achieve real-time data acquisition. The collected data includes the borrower's basic information such as age, gender, occupation, income, credit history data such as credit card repayment records, other loan repayment records, consumption behavior data such as consumption amount, consumption frequency, consumption place, asset and liability data such as real estate, vehicles, savings, liabilities, and external data such as macroeconomic data, industry data, social media data, etc. At the same time, this module also supports real-time data extraction from internal business systems of financial institutions such as credit management systems, customer relationship management systems, etc., to ensure the comprehensiveness and real-time nature of data sources.

[0025] The data cleaning and preprocessing module standardizes the collected raw data. In terms of date format unification, when the collected date data exists in multiple formats such as "YYYY-MM-DD", "DD / MM / YYYY", "MM-DD-YYYY", etc., the system first identifies the date pattern in the data using regular expressions, for example, for the "YYYY-MM-DD" format, the regular expression is "^[0-9]{4}-[0-9]{2}-[0-9]{2}$". After identification, all date data is converted to the standard "YYYY-MM-DD" format using the date conversion function in the pandas library of Python. For string format specification, the system converts all string data to lowercase format using the lower() function in Python and uses the strip() function to remove extra spaces at both ends of the string. In terms of data range standardization, for numerical data such as the borrower's income, debt amount, etc., the Z-Score standardization method is used, setting the original data as x, its mean as μ, and standard deviation as σ. The calculation formula of the standardized data x' is x'=(x-μ) / σ, which converts the data to a standard normal distribution with mean 0 and standard deviation 1. For scenarios requiring normalization, the Min-Max normalization method is used, setting the minimum value of the original data as min and the maximum value as max. The calculation formula of the normalized data x'' is x''=(x-min) / (max-min), which maps the data to the interval [0,1].

[0026] The real-time data analysis module uses big data processing technology and Apache Flink real-time computing framework to perform real-time analysis on the preprocessed data. For example, Figure 2The data processing flowchart shown illustrates the various data analysis models established in this module. For time series analysis models, specifically for borrowers' income and expenditure time series data, a stationarity test is first performed using the ADF test. The test statistic is calculated and compared with the critical value to determine data stationarity. If the data is not stationary, the difference method is used to process the data until it meets the stationarity requirement. Simultaneously, the X-13-ARIMA-SEATS seasonal adjustment method is used to remove the influence of seasonal factors from the data. In model selection, AIC and BIC are used as standards. By iterating through different parameter combinations (p, d, q, where p is the autoregressive order, d is the difference order, and q is the moving average order), the AIC and BIC values ​​of the model under each combination are calculated, and the parameter combination with the smallest AIC and BIC values ​​is selected as the optimal model parameters. For the association rule mining model, the Apriori algorithm is used for frequent itemset mining. A minimum support threshold is set, and candidate 1-itemsets are generated starting from a single item. The support of each candidate 1-itemset is calculated, and frequent 1-itemsets that meet the minimum support threshold are selected. Then, candidate 2-itemsets are generated based on the frequent 1-itemsets, and this process is repeated until no new frequent itemsets can be generated. For the clustering analysis model, the K-Means clustering algorithm is selected. The elbow rule is used to determine the appropriate number of clusters K. The sum of squared clustering errors (SSE) for different K values ​​is calculated. A curve is plotted with K as the x-axis and SSE as the y-axis. The K value corresponding to the position where the curve shows a clear inflection point is the optimal number of clusters.

[0027] The intelligent early warning module, based on real-time data analysis results and combined with preset overdue risk assessment indicators and thresholds, uses a random forest algorithm to construct an intelligent early warning model. For example... Figure 3The intelligent early warning model construction flowchart is shown. The module first obtains the processed analysis result data from the real-time data analysis module, integrates these data with historical credit overdue data, and divides the data set into a training set and a test set according to a 7:3 or 8:2 ratio. In terms of feature engineering, key features related to credit overdue risk are extracted from the real-time data analysis results, the Pearson correlation coefficient is used to calculate the correlation between each feature and the overdue label (1 for overdue and 0 for non-overdue), and features with low correlation are removed. Numerical features are standardized by Z-Score, and categorical features are converted to numerical data using label encoding. In model training, key parameters such as the number of decision trees (n_estimators), the number of features considered when splitting each node (max_features), and the maximum depth of the tree (max_depth) are set, and the optimal parameter combination is found through grid search or random search combined with 5-fold cross-validation. The model constructs multiple decision trees through random sampling with replacement (Bootstrap sampling), each decision tree splits nodes based on Gini coefficient or information gain criteria, and finally determines the overall prediction result through a voting mechanism. When the risk assessment result of the borrower exceeds the preset threshold, the system automatically triggers the early warning mechanism, and sends warning information to credit managers and relevant departments in various ways such as SMS, email, and system pop-up windows.

[0028] The intervention measure execution module automatically matches the corresponding intervention measures according to the risk level of the early warning information. For low-risk early warning, the system automatically sends reminder SMS or email to the borrower to remind him to repay on time; for medium-risk early warning, credit managers can communicate with the borrower through telephone, home visit, etc., understand the actual situation, provide repayment advice and assistance; for high-risk early warning, the system automatically starts legal proceedings or asset preservation measures to minimize the loss of financial institutions. At the same time, this module will record the execution of the intervention measures and the feedback information of the borrower, which will be used as a reference for subsequent risk assessment and early warning.

[0029] The model optimization module regularly collects actual credit overdue data and execution effect data of intervention measures, and optimizes and adjusts the intelligent early warning model and data analysis model. By comparing the model prediction results with the actual situation, the prediction accuracy, recall rate, F1 value, AUC and other evaluation indicators of the model are calculated, and the parameters of the model are adjusted or new feature variables are introduced to continuously improve the accuracy and reliability of the model. Figure 4 As shown in the figure, this module ensures that the system can adapt to the changing market environment and customer behavior patterns through continuous feedback and optimization mechanisms.

[0030] Embodiment One To better illustrate the technical solutions of the present application, a specific example is described in detail below. A commercial bank deploys the credit overdue intelligent early warning and intervention system based on real-time data analysis described in the present application to manage its consumer credit business.

[0031] During system implementation, the data acquisition module connects with the People's Bank of China credit center, Alipay, WeChat payment and other third-party platforms through API interface, and collects the credit report, consumption transaction record and other data of the borrower in real time. At the same time, the borrower's account balance changes, salary records and other information are extracted from the bank's internal core business system in real time. For a 35-year-old borrower Zhang, the system collects his monthly income of 15000 yuan, good credit card repayment record, consumption amount of 8000 yuan, 12000 yuan and 9500 yuan in the past 3 months, and housing loan balance of 800000 yuan and other multi-dimensional data.

[0032] The data cleaning and preprocessing module processes the collected data, converts different formats of date data into "YYYY-MM-DD" format, and converts the borrower's occupation information "engineer", "ENGINEER", "engineer" into "engineer". For numerical data such as income data 15000 yuan and debt amount 800000 yuan, Z-Score standardization method is used for processing. Assuming that the mean of income data μ is 12000 and the standard deviation σ is 3000, the standardized income data x'=(15000-12000) / 3000=1, indicating that the income level of the borrower is higher than the average level by one standard deviation.

[0033] The real-time data analysis module uses time series analysis model to analyze Zhang's income and expenditure data, and predicts his cash flow in the next 3 months through ARIMA model. After ADF test confirms the data stationarity, the optimal parameters are determined through grid search as ARIMA(2,1,1). The model prediction shows that Zhang's disposable income in the next 3 months will be 7000 yuan, 6500 yuan and 7200 yuan respectively. Association rule mining finds that when the monthly consumption amount exceeds 80% of the income and continues for 2 months, the overdue probability increases to 35%. Cluster analysis classifies Zhang into the "medium risk" group, and the overdue rate of this group is 15%.

[0034] The intelligent early warning module synthesizes various analysis results, and calculates the overdue risk probability of Zhang as 28% through a random forest model. The system preset medium risk threshold is 25%, and the high risk threshold is 40%, so Zhang is judged as a medium risk customer. The system immediately sends early warning information to the credit manager Li through SMS and email: "Customer Zhang (ID number: XXXXX) current overdue risk score is 28%, belongs to medium risk level. Main risk factors: recent consumption expenditure growth is obvious, monthly average consumption accounts for 76% of income; predicted future cash flow is tight. It is suggested to take telephone communication to understand the situation and provide repayment plan adjustment suggestions." The intervention measure execution module automatically generates a work task for the credit manager according to the medium risk level. The manager Li contacts Zhang through telephone the day after receiving the early warning, and learns that the recent expenditure increase is caused by house decoration. After communication, the bank provides a repayment plan adjustment scheme for Zhang, which adjusts the original monthly repayment of 8000 yuan to 6000 yuan for the first three months, and then restores the normal repayment amount. Zhang accepts the scheme and repays on time in the subsequent period, successfully avoiding the occurrence of overdue.

[0035] The model optimization module reviews the case after a quarter, finds that the system's early warning is accurate and effective, and Zhang does have short-term financial pressure but avoids overdue through timely intervention. The system adds "decoration expenditure" as a new feature variable to the analysis model, and adjusts the risk weight of consumption proportion, so that the model's identification ability for similar cases is further improved. After continuous optimization, the credit overdue rate of the bank using the system decreases from the original 3.2% to 2.1%, and the non-performing asset rate decreases by 34%, fully verifying the effectiveness and practicality of the present application.

[0036] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order not to cause unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits are not described in detail.

[0037] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principle of the present application, a number of improvements and refinements can be made, which should also be regarded as the protection scope of the present application.

Claims

1. A credit delinquency intelligent early warning and intervention system based on real-time data analysis, characterized in that, include: The data acquisition module connects with third-party data providers through API interfaces to collect borrowers' basic information, credit history data, consumption behavior data, asset and liability data, and external data in real time, and also extracts data from the financial institution's internal business system in real time. The data cleaning and preprocessing module is connected to the data acquisition module. It uses regular expressions to identify and unify date formats, processes text data through string conversion functions, and uses the Z-Score standardization method to standardize numerical data. The standardization formula is x'=(x-μ) / σ, where x is the original data, μ is the mean, and σ is the standard deviation. The real-time data analysis module is connected to the data cleaning and preprocessing module. Based on the Apache Flink real-time computing framework, it includes a time series analysis unit, an association rule mining unit, and a clustering analysis unit. The time series analysis unit uses the ARIMA model, the association rule mining unit uses the Apriori algorithm, and the clustering analysis unit uses the K-Means algorithm. The intelligent early warning module is connected to the real-time data analysis module and uses the random forest algorithm to build an early warning model. When the risk assessment result exceeds a preset threshold, the early warning mechanism is triggered. An intervention execution module is connected to the intelligent early warning module and matches and executes corresponding intervention measures according to the risk level. The model optimization module is connected to both the intelligent early warning module and the intervention execution module, and collects actual overdue data and intervention effect data to adjust the parameters of the early warning model and the data analysis model.

2. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The data cleaning and preprocessing module also includes Min-Max normalization processing, with the normalization formula being x''=(x-min) / (max-min), where x is the original data, min is the minimum value, and max is the maximum value, mapping the data to the [0,1] interval.

3. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The time series analysis unit uses the ADF test to test the stationarity of the data, employs the X-13-ARIMA-SEATS method for seasonal adjustment, and uses the AIC and BIC criteria to determine the optimal parameter combination (p,d,q) of the ARIMA model.

4. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The association rule mining unit sets a minimum support threshold, generates candidate itemsets starting from a single item, iteratively calculates the support and filters frequent itemsets, and generates strong association rules that satisfy the minimum confidence and minimum lift thresholds.

5. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The clustering analysis unit determines the number of clusters K using the elbow rule, calculates the sum of squared clustering errors SSE for different K values, selects the K value corresponding to the inflection point of the SSE curve, and constructs multiple decision trees through Bootstrap sampling.

6. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The intelligent early warning module uses Pearson correlation coefficient for feature selection and determines the optimal parameters of the random forest model through grid search combined with 5-fold cross-validation, including the number of decision trees n_estimators, the number of node split features max_features, and the maximum tree depth max_depth.

7. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The intervention implementation module divides the risk level into three levels: low risk, medium risk, and high risk. When the risk is low, a reminder SMS or email is sent; when the risk is medium, telephone communication or in-person visit is conducted; and when the risk is high, legal proceedings or asset preservation measures are initiated.

8. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The model optimization module calculates the model's prediction accuracy, recall, F1 score, and AUC evaluation metrics, and adjusts the model parameters or introduces new feature variables by comparing the prediction results with the actual situation.

9. The intelligent early warning and intervention system for credit delinquency based on real-time data analysis according to claim 1, characterized in that, The early warning information generated by the intelligent early warning module includes the borrower's basic information, risk assessment results, risk level, and analysis of the reasons for delinquency, and is sent to credit management personnel via SMS, email, and system pop-ups.

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