A Smart Monitoring and Early Warning System for Abnormal Housing Provident Fund Contribution and Loan Data

Abstract

This invention discloses an intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data, belonging to the field of big data technology for provident fund risk control. Based on existing technologies, this invention sets up five sequentially deployed functional modules: a multi-source data acquisition and access module, a data governance and unified data platform module, an intelligent anomaly monitoring engine module, a tiered early warning and closed-loop work order handling module, and a visualization dashboard and model iteration optimization module. The system establishes two data links: a forward serial data flow and a reverse closed-loop feedback flow. The intelligent anomaly monitoring engine adopts a dual-engine architecture of a rule engine and an AI intelligent algorithm in parallel. The forward link outputs tiered abnormal data and completes early warning dispatch and verification handling; the reverse link continuously updates rules and AI models. This invention can achieve automatic identification, tiered early warning, closed-loop handling, and autonomous iterative optimization of explicit and implicit anomalies across all scenarios of provident fund deposit, withdrawal, loan, and internal operations.

Description

Technical Field

[0001] This invention belongs to the field of big data technology for housing provident fund risk control, and more specifically, it relates to an intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data. Background Technology

[0002] Currently, housing provident fund management centers in various regions have generally deployed risk control and monitoring systems for deposit and loan business, which constitute the existing technical basis of this invention.

[0003] The existing system relies on traditional direct database connections and stand-alone ETL extraction methods, which can only access the core business database of the housing provident fund to obtain business data related to deposits, withdrawals, and loans; it lacks standardized connection channels and makes it difficult to routinely connect with external government data such as social security, taxation, real estate, civil affairs, and public security.

[0004] The existing system's risk control logic uses static business rules as the sole means of verification. It presets fixed verification items such as contribution base, contribution ratio, and loan access conditions according to the Housing Provident Fund Management Regulations, and relies on condition judgment to screen for explicit compliance anomalies. It is not equipped with an artificial intelligence algorithm recognition unit, and cannot rely on machine learning, graph computing, and time series analysis technologies to uncover hidden, organized, and gradual fraudulent behaviors.

[0005] The existing system lacks a unified data platform architecture for data processing. Various types of business data are scattered and stored in multiple heterogeneous databases. There is a lack of unified cleaning, field normalization, and cross-entity data association processes, resulting in prominent issues such as data duplication, missing fields, and inconsistent definitions. There is no automatic return and re-collection mechanism for unqualified data, which relies on manual inspection and correction by maintenance personnel.

[0006] The existing system's early warning output and investigation and handling are disconnected. Abnormal data is exported and relies on manual statistics and organization. Early warning information is transmitted offline via telephone and paper documents. There is no automated hierarchical early warning logic, so it is impossible to differentiate control measures according to the severity of risks. There is no supporting electronic automatic work order system, and the results of investigation and handling rely on manual Excel archiving.

[0007] The existing system uses a fixed template static report export mode, and staff manually summarize data to generate statistical reports on a regular basis. There is no reverse feedback link for business processing results. Cases of fraudulent withdrawals and loans verified by audits and false warning data cannot be automatically fed back to the risk control link. The system rules and verification parameters have been fixed for a long time and cannot be dynamically iterated and optimized to keep up with new fraud patterns.

[0008] To address the technical shortcomings of existing housing provident fund monitoring and early warning systems, such as limited data sources, a single risk control method, low data standardization, reliance on manual processing, and inability to iterate autonomously, this paper improves the overall system architecture based on the existing system hardware and basic business architecture. It establishes five interconnected modules: multi-source data collection, unified middleware, dual-engine intelligent monitoring, tiered work order processing, and visualized iteration. These are complemented by a dual-link system of forward business data flow and reverse data feedback, enabling intelligent identification of anomalies across all business operations, tiered early warning, closed-loop processing, and autonomous system optimization. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention provides an intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data.

[0010] A smart monitoring and early warning system for abnormal housing provident fund deposit and loan data includes a multi-source data acquisition and access module, a data governance and unified data platform module, an intelligent anomaly monitoring engine module, a hierarchical early warning and closed-loop work order processing module, and a visualization dashboard and model iteration optimization module. The system is configured with two independent data interaction links; The first link is a forward serial data flow link; the multi-source data acquisition and access module, data governance and unified data platform module, intelligent anomaly monitoring engine module, hierarchical early warning and closed-loop work order handling module, and visualization cockpit and model iteration optimization module flow in one direction in sequence; The second link is the reverse closed-loop feedback link; the business processing data output by the hierarchical early warning and closed-loop work order processing module, the visualization cockpit and model iteration optimization module are fed back to the intelligent anomaly monitoring engine module through the data governance and unified data platform module. The multi-source data acquisition and access module is compatible with synchronous acquisition of multiple types of data sources; The data governance and unified data platform module integrates multiple units to achieve standardized processing of heterogeneous data. The intelligent anomaly detection engine adopts a dual-engine parallel architecture, consisting of a rule engine sub-module that uses the traditional rule system and an AI intelligent algorithm sub-module that adds multi-algorithm fusion. The tiered early warning and closed-loop work order handling module is configured with four-level risk classification and multi-mode early warning and control logic; The visual cockpit and model iteration optimization module combine three functions: data visualization, report generation, and automatic rule and model iteration. The multi-source data acquisition and access module aggregates all original business and external data. Raw data is sent to a data governance and unified data platform to complete cleaning, correlation, quality verification, and hierarchical storage. After standardization, the dataset is synchronously input into the rule engine submodule and the AI ​​intelligent algorithm submodule for parallel computation. The two types of submodules output explicit and implicit anomaly data, respectively. Abnormal data is pushed to the tiered early warning and closed-loop work order processing module to complete tiered early warning, automatic work order dispatch and business verification; The results of the investigation are partially pushed to the visualization module to complete data display and report generation; Another part of the processing results is fed back into the data governance platform through the reverse backflow link, and finally input into the intelligent anomaly monitoring engine to achieve iterative updates of rules and algorithms.

[0011] Preferably, the multi-source data acquisition and access module is divided into four independent acquisition sub-units; The four sub-units are: internal business data collection sub-unit, cross-departmental government affairs integration sub-unit, operation log collection sub-unit, and temporary caching sub-unit. The internal business data collection sub-unit captures detailed data on deposits, personal accounts, withdrawals, loan disbursements, and repayments through direct database connections and API interfaces. The cross-departmental government affairs integration sub-unit connects with external data from social security, individual income tax, real estate, civil affairs and marriage, public security identity, and credit reporting agencies, relying on the government affairs sharing encrypted channel. The operation log collection sub-unit captures all operation logs of teller logins, permission changes, business approvals, and process jumps in real time. The temporary caching sub-unit is equipped with a message queue component to temporarily cache various heterogeneous raw data. After each subunit asynchronously collects data, it is uniformly aggregated to a temporary cache subunit and then pushed down in batches to the data governance and unified data platform modules.

[0012] Preferably, the data governance and unified data platform module includes four types of functional units; The four types of units are: data cleaning unit, multi-source data association and fusion unit, data quality verification unit, and hierarchical storage unit. The data cleaning unit performs three types of processing actions: deleting duplicate data, filling in missing fields, and unifying data formats. The multi-source data association and fusion unit uses the resident ID number as the unique association key to bind employee housing provident fund information with external government information. The data quality verification unit performs integrity and consistency verification on the processed data. For data that fails the verification, the unit generates a re-collection instruction and sends it back to the multi-source data acquisition and access module. The hierarchical storage unit is equipped with two types of storage media: data warehouse and data lake. After cleaning and merging, qualified data is distinguished into real-time streaming data and batch historical data, and stored in data lake and data warehouse respectively to form a standardized dataset for downstream monitoring engines.

[0013] Preferably, the rule engine submodule of the intelligent anomaly monitoring engine is divided into four categories of rule knowledge bases; The four knowledge bases are, in order: deposit business rules base, withdrawal business rules base, loan business rules base, and internal operation risk control rules base; the deposit business rules base includes the upper and lower limits of the pre-deposited deposit base and the verification clauses for the statutory deposit ratio of 5% to 12%. The withdrawal business rule library includes compliance restrictions on withdrawals for home purchases and withdrawals upon resignation; the loan business rule library includes legal restrictions on continuous contribution period, number of family loans, and down payment ratio; and the internal operation risk control rule library includes restrictions on unauthorized operations and logins outside of working hours. The rules engine submodule retrieves standardized datasets one by one, matches them against preset clauses in the database, and automatically outputs a list of explicit compliance exceptions after a clause is matched.

[0014] Preferably, the AI ​​intelligent algorithm submodule of the intelligent anomaly monitoring engine integrates four types of independent computing components; The four types of components are: Isolation Forest Anomaly Detection Component, DBSCAN Clustering Component, LSTM Time Series Analysis Component, and Graph Neural Network Association Mining Component. The Isolation Forest Component is used to identify discrete anomalies where a single account's single transaction amount deviates from the norm. The DBSCAN Clustering Component is used to screen for group anomalies such as concentrated batch account openings, concentrated suspension of payments, and concentrated fraudulent withdrawals by groups. The LSTM Time Series Analysis Component is used to track long-term fluctuations in personal account deposits and withdrawals to identify gradual anomalies. The Graph Neural Network Component is used to mine the relationships between agents, depositing employees, and property owners to identify concealed collusion between insiders and outsiders to commit loan fraud. The four types of components read the same batch of standardized data in parallel, perform independent calculations, summarize the results of hidden anomalies, and merge them with the output of the rule engine.

[0015] Preferably, the graded early warning and closed-loop work order handling module includes a four-level risk grading unit and a multi-channel early warning push unit; the four-level risk grading unit is divided into four risk levels: general, attention, high risk, and emergency; the system automatically matches the corresponding risk level based on the abnormal comprehensive risk score; the multi-channel early warning push unit is equipped with four handling trigger modes: general risk corresponds to the automatic ledger archiving mode; attention risk corresponds to the handler's SMS reminder mode; high risk corresponds to the business system pop-up warning and business acceptance permission locking mode; emergency risk corresponds to the temporary freezing mode of the involved account and loan approval. After the abnormal data output by the monitoring engine is entered into the classification unit for classification, the system automatically activates the corresponding early warning push mode according to the classification results.

[0016] Preferably, the graded early warning and closed-loop work order processing module also includes an electronic work order circulation unit; the electronic work order circulation unit binds three basic pieces of information: anomaly number, anomaly category, and risk judgment basis; the unit receives graded anomaly information and automatically generates an online inspection work order; the system automatically assigns the work order to the corresponding inspector's account based on the business department and job authority; after the inspector completes the offline verification, they enter the verification conclusion in the work order system; the verification conclusion is divided into two types: anomaly confirmed and data misreporting; After a work order is completed, the system automatically saves the work order file and extracts the handling conclusion as the source data for reverse feedback.

[0017] Preferably, the visualization cockpit and model iteration optimization module are configured with a BI visualization display unit; the BI visualization display unit has three sub-display modules: a risk overview screen, a risk stratification graph, and a fund flow view; the risk overview screen displays the total number of warnings across the entire domain, the number of processed work orders, and the proportion of work orders pending verification in real time; the risk stratification graph supports drill-down by city, business type, and risk level to view details of single-point anomalies; the fund flow view connects the fund data of deposits, personal account withdrawals, and loan disbursements across the entire chain; operation mode: the BI visualization display unit synchronizes the full ledger data of the warning and handling module in real time and dynamically refreshes the content of various visualization charts.

[0018] Preferably, the visualization cockpit and model iteration optimization module are equipped with an automatic report generation unit; the automatic report generation unit has three types of fixed templates built in: monthly audit report, quarterly risk analysis report, and special statistical report on loan fraud; the unit automatically captures the full-cycle early warning, verification, and handling summary data according to a preset cycle; the unit automatically fills in the field content according to the corresponding template and generates a downloadable report file; the generated report file is automatically archived and stored in the system archive database; Once the report is generated, the report metadata is synchronously stored in the data governance platform to participate in subsequent data statistics and model training.

[0019] Preferably, the visualization cockpit and model iteration optimization module are configured with a rule and model update training unit; the rule and model update training unit receives work order processing data transmitted from the reverse closed-loop feedback link; the unit splits the sample data into two categories; the first category is verified real fraud business samples; the second category is misjudged samples that are given false warnings; real fraud samples are automatically imported into the rule engine submodule to supplement and update the knowledge base of each business rule; misjudged samples and newly added unknown abnormal samples are uniformly sent to the AI ​​intelligent algorithm submodule; each AI algorithm component completes internal parameter retraining based on the newly added samples; After the parameters and rules are updated, the system will automatically replace the original configuration and enable the updated rules and algorithms in the next round of data monitoring.

[0020] Compared with the prior art, the present invention has the following beneficial effects: Multi-source, full-domain data collection breaks down the limitations of existing data silos. Compared to existing technologies that can only collect internal business data of the housing provident fund, this system achieves full-dimensional data synchronization of internal business, cross-departmental government affairs, and operation logs through four types of collection sub-units. It completes data interoperability among multiple departments by relying on encrypted government affairs channels and realizes cross-verification of employee information by using the ID card as the primary key. It avoids the defect of being unable to identify false business due to missing information from the data source level.

[0021] Standardized data governance continuously improves the quality of all data. It relies on data cleaning, correlation and fusion, and quality verification units to complete the normalization of heterogeneous data, automatically re-collect and correct unqualified data, and hierarchical storage to achieve classified management of real-time data and historical data. This solves the problems of messy data sources, inconsistent field definitions, and dirty data interfering with risk control judgments in the original system, and provides a high-quality basic dataset for intelligent monitoring.

[0022] The dual-engine collaborative detection fully covers both explicit and implicit anomaly scenarios. The rule engine inherits existing mature legal provisions and quickly screens for compliance-related explicit anomalies such as base amount, ratio, and loan term. The four types of AI algorithms supplement the identification of unknown anomalies that cannot be covered by the rules, such as group fraud, implicit collusion to commit loan fraud, and long-term gradual fund fluctuations. Compared with single rule screening, the coverage of anomaly identification is greatly improved.

[0023] Tiered early warning and electronic work orders enable a closed-loop risk management system. By matching differentiated early warning and control measures with four levels of risk classification, and combining automated work order dispatch and online result archiving, the system replaces the inefficient handling mode of manual registration, telephone notification, and paper archiving. It standardizes the entire process of audit and handling, and shortens the risk management cycle.

[0024] Reverse data feedback enables autonomous iteration, continuously reducing the system's false alarm rate. Relying on the terminal module, it optimizes the processing sample feedback, expands the rule base with real-world cases, and iterates AI model parameters using false alarm samples. The system can autonomously update its risk control logic as new fraud methods are introduced, addressing the shortcomings of existing, rigid technical rules that cannot dynamically optimize to keep up with new loan and withdrawal fraud methods. Over long-term operation, the system's early warning accuracy continues to improve. Attached Figure Description

[0025] Figure 1 This is the overall system architecture diagram of the present invention; Detailed Implementation

[0026] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0027] Please see Figure 1This invention provides an intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data, including a multi-source data acquisition and access module, a data governance and unified data platform module, an intelligent anomaly monitoring engine module, a hierarchical early warning and closed-loop work order processing module, and a visualization dashboard and model iteration optimization module; The system is configured with two independent data interaction links; The first link is a forward serial data flow link; the multi-source data acquisition and access module, data governance and unified data platform module, intelligent anomaly monitoring engine module, hierarchical early warning and closed-loop work order handling module, and visualization cockpit and model iteration optimization module flow in one direction in sequence; The second link is the reverse closed-loop feedback link; the business processing data output by the hierarchical early warning and closed-loop work order processing module, the visualization cockpit and model iteration optimization module are fed back to the intelligent anomaly monitoring engine module through the data governance and unified data platform module. The multi-source data acquisition and access module is compatible with synchronous acquisition of multiple types of data sources; The data governance and unified data platform module integrates multiple units to achieve standardized processing of heterogeneous data. The intelligent anomaly detection engine adopts a dual-engine parallel architecture, consisting of a rule engine sub-module that uses the traditional rule system and an AI intelligent algorithm sub-module that adds multi-algorithm fusion. The tiered early warning and closed-loop work order handling module is configured with four-level risk classification and multi-mode early warning and control logic; The visual cockpit and model iteration optimization module combine three functions: data visualization, report generation, and automatic rule and model iteration. The multi-source data acquisition and access module aggregates all original business and external data. Raw data is sent to a data governance and unified data platform to complete cleaning, correlation, quality verification, and hierarchical storage. After standardization, the dataset is synchronously input into the rule engine submodule and the AI ​​intelligent algorithm submodule for parallel computation. The two types of submodules output explicit and implicit anomaly data, respectively. Abnormal data is pushed to the tiered early warning and closed-loop work order processing module to complete tiered early warning, automatic work order dispatch and business verification; The results of the investigation are partially pushed to the visualization module to complete data display and report generation; Another part of the processing results is fed back into the data governance platform through the reverse backflow link, and finally input into the intelligent anomaly monitoring engine to achieve iterative updates of rules and algorithms.

[0028] The multi-source data acquisition and access module is divided into four independent acquisition sub-units; The four sub-units are: internal business data collection sub-unit, cross-departmental government affairs integration sub-unit, operation log collection sub-unit, and temporary caching sub-unit. The internal business data collection sub-unit captures detailed data on deposits, personal accounts, withdrawals, loan disbursements, and repayments through direct database connections and API interfaces. The cross-departmental government affairs integration sub-unit connects with external data from social security, individual income tax, real estate, civil affairs and marriage, public security identity, and credit reporting agencies, relying on the government affairs sharing encrypted channel. The operation log collection sub-unit captures all operation logs of teller logins, permission changes, business approvals, and process jumps in real time. The temporary caching sub-unit is equipped with a message queue component to temporarily cache various heterogeneous raw data. After each subunit asynchronously collects data, it is uniformly aggregated to a temporary cache subunit and then pushed down in batches to the data governance and unified data platform modules.

[0029] The data governance and unified data platform module comprises four functional units; The four types of units are: data cleaning unit, multi-source data association and fusion unit, data quality verification unit, and hierarchical storage unit. The data cleaning unit performs three types of processing actions: deleting duplicate data, filling in missing fields, and unifying data formats. The multi-source data association and fusion unit uses the resident ID number as the unique association key to bind employee housing provident fund information with external government information. The data quality verification unit performs integrity and consistency verification on the processed data. For data that fails the verification, the unit generates a re-collection instruction and sends it back to the multi-source data acquisition and access module. The hierarchical storage unit is equipped with two types of storage media: data warehouse and data lake. After cleaning and merging, qualified data is distinguished into real-time streaming data and batch historical data, and stored in data lake and data warehouse respectively to form a standardized dataset for downstream monitoring engines.

[0030] The rule engine submodule of the intelligent anomaly monitoring engine is divided into four categories of rule knowledge bases; The four knowledge bases are, in order: deposit business rules base, withdrawal business rules base, loan business rules base, and internal operation risk control rules base; the deposit business rules base includes the upper and lower limits of the pre-deposited deposit base and the verification clauses for the statutory deposit ratio of 5% to 12%. The withdrawal business rule library includes compliance restrictions on withdrawals for home purchases and withdrawals upon resignation; the loan business rule library includes legal restrictions on continuous contribution period, number of family loans, and down payment ratio; and the internal operation risk control rule library includes restrictions on unauthorized operations and logins outside of working hours. The rules engine submodule retrieves standardized datasets one by one, matches them against preset clauses in the database, and automatically outputs a list of explicit compliance exceptions after a clause is matched.

[0031] The AI ​​intelligent algorithm submodule of the intelligent anomaly monitoring engine integrates four types of independent computing components; The four types of components are: Isolation Forest Anomaly Detection Component, DBSCAN Clustering Component, LSTM Time Series Analysis Component, and Graph Neural Network Association Mining Component. The Isolation Forest Component is used to identify discrete anomalies where a single account's single transaction amount deviates from the norm. The DBSCAN Clustering Component is used to screen for group anomalies such as concentrated batch account openings, concentrated suspension of payments, and concentrated fraudulent withdrawals by groups. The LSTM Time Series Analysis Component is used to track long-term fluctuations in personal account deposits and withdrawals to identify gradual anomalies. The Graph Neural Network Component is used to mine the relationships between agents, depositing employees, and property owners to identify concealed collusion between insiders and outsiders to commit loan fraud. The four types of components read the same batch of standardized data in parallel, perform independent calculations, summarize the results of hidden anomalies, and merge them with the output of the rule engine.

[0032] The tiered early warning and closed-loop work order handling module includes a four-level risk grading unit and a multi-channel early warning push unit. The four-level risk grading unit is divided into four risk levels: general, attention, high risk, and emergency. The system automatically matches the corresponding risk level based on the abnormal comprehensive risk score. The multi-channel early warning push unit is equipped with four handling trigger modes: general risk corresponds to the automatic ledger archiving mode; attention risk corresponds to the handler's SMS reminder mode; high risk corresponds to the business system pop-up warning and business acceptance permission locking mode; and emergency risk corresponds to the temporary freezing of the involved account and loan approval mode. After the abnormal data output by the monitoring engine is entered into the classification unit for classification, the system automatically activates the corresponding early warning push mode according to the classification results.

[0033] The tiered early warning and closed-loop work order processing module also includes an electronic work order circulation unit. This unit binds three basic pieces of information: anomaly number, anomaly category, and risk assessment criteria. Upon receiving the tiered anomaly information, the unit automatically generates an online audit work order. The system automatically assigns the work order to the corresponding auditor's account based on the business department and job authority. After completing offline verification, the auditor enters the verification conclusion into the work order system. Verification conclusions are categorized into two types: confirmed anomaly and false alarm. After a work order is completed, the system automatically saves the work order file and extracts the handling conclusion as the source data for reverse feedback.

[0034] The visualization cockpit and model iteration optimization module are configured with a BI visualization display unit. This unit includes three sub-display modules: a risk overview screen, a risk stratification graph, and a fund flow view. The risk overview screen displays real-time key indicators such as the total number of warnings across the entire domain, the number of processed work orders, and the percentage of work orders awaiting verification. The risk stratification graph supports drill-down by city, business type, and risk level to view detailed anomalies at individual points. The fund flow view connects the entire chain of fund data, including deposits, withdrawals from personal accounts, and loan disbursements. Operation: The BI visualization display unit synchronizes all ledger data from the warning and handling module in real-time, dynamically refreshing various visualization charts.

[0035] The visualization cockpit and model iteration optimization module are equipped with an automatic report generation unit; the automatic report generation unit has three types of fixed templates: monthly audit report, quarterly risk analysis report, and special statistical report on loan fraud; the unit automatically captures the full-cycle early warning, verification, and handling summary data according to a preset cycle; the unit automatically fills in the field content according to the corresponding template and generates a downloadable report file; the generated report file is automatically archived and stored in the system archive database; Once the report is generated, the report metadata is synchronously stored in the data governance platform to participate in subsequent data statistics and model training.

[0036] The visualization cockpit and model iteration optimization module configure rules and model update training units; the rules and model update training units receive work order processing data transmitted from the reverse closed-loop feedback link; the unit splits the sample data into two categories: the first category is verified real fraud business samples; the second category is misjudged samples that are given false warnings; real fraud samples are automatically imported into the rule engine submodule to supplement and update the knowledge base of each business rule; misjudged samples and newly added unknown abnormal samples are uniformly sent to the AI ​​intelligent algorithm submodule; each AI algorithm component completes internal parameter retraining based on the new samples; after the parameters and rules are updated, the system automatically replaces the original configuration and enables the updated rules and algorithms in the next round of data monitoring.

Claims

1. A smart monitoring and early warning system for abnormal housing provident fund deposit and loan data, characterized in that: It includes a multi-source data acquisition and access module, a data governance and unified data platform module, an intelligent anomaly monitoring engine module, a hierarchical early warning and closed-loop work order handling module, and a visualization dashboard and model iteration optimization module; The system is configured with two independent data interaction links; The first link is a forward serial data flow link; the multi-source data acquisition and access module, data governance and unified data platform module, intelligent anomaly monitoring engine module, hierarchical early warning and closed-loop work order handling module, and visualization cockpit and model iteration optimization module flow in one direction in sequence; The second link is the reverse closed-loop feedback link; the business processing data output by the hierarchical early warning and closed-loop work order processing module, the visualization cockpit and model iteration optimization module are fed back to the intelligent anomaly monitoring engine module through the data governance and unified data platform module. The multi-source data acquisition and access module is compatible with synchronous acquisition of multiple types of data sources; The data governance and unified data platform module integrates multiple units to achieve standardized processing of heterogeneous data. The intelligent anomaly detection engine adopts a dual-engine parallel architecture, consisting of a rule engine sub-module that uses the traditional rule system and an AI intelligent algorithm sub-module that adds multi-algorithm fusion. The tiered early warning and closed-loop work order handling module is configured with four-level risk classification and multi-mode early warning and control logic; The visual cockpit and model iteration optimization module combine three functions: data visualization, report generation, and automatic rule and model iteration. The multi-source data acquisition and access module aggregates all original business and external data. Raw data is sent to a data governance and unified data platform to complete cleaning, correlation, quality verification, and hierarchical storage. After standardization, the dataset is synchronously input into the rule engine submodule and the AI ​​intelligent algorithm submodule for parallel computation. The two types of submodules output explicit and implicit anomaly data, respectively. Abnormal data is pushed to the tiered early warning and closed-loop work order processing module to complete tiered early warning, automatic work order dispatch and business verification; The results of the investigation are partially pushed to the visualization module to complete data display and report generation; Another part of the processing results is fed back into the data governance platform through the reverse backflow link, and finally input into the intelligent anomaly monitoring engine to achieve iterative updates of rules and algorithms.

2. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 1, characterized in that: The multi-source data acquisition and access module is divided into four independent acquisition sub-units; The four sub-units are: internal business data collection sub-unit, cross-departmental government affairs integration sub-unit, operation log collection sub-unit, and temporary caching sub-unit. The internal business data collection sub-unit captures detailed data on deposits, personal accounts, withdrawals, loan disbursements, and repayments through direct database connections and API interfaces. The cross-departmental government affairs integration sub-unit connects with external data from social security, individual income tax, real estate, civil affairs and marriage, public security identity, and credit reporting agencies, relying on the government affairs sharing encrypted channel. The operation log collection sub-unit captures all operation logs of teller logins, permission changes, business approvals, and process jumps in real time. The temporary caching sub-unit is equipped with a message queue component to temporarily cache various heterogeneous raw data. After each subunit asynchronously collects data, it is uniformly aggregated to a temporary cache subunit and then pushed down in batches to the data governance and unified data platform modules.

3. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 1, characterized in that: The data governance and unified data platform module comprises four functional units; The four types of units are: data cleaning unit, multi-source data association and fusion unit, data quality verification unit, and hierarchical storage unit. The data cleaning unit performs three types of processing actions: deleting duplicate data, filling in missing fields, and unifying data formats. The multi-source data association and fusion unit uses the resident ID number as the unique association key to bind employee housing provident fund information with external government information. The data quality verification unit performs integrity and consistency verification on the processed data. For data that fails the verification, the unit generates a re-collection instruction and sends it back to the multi-source data acquisition and access module. The hierarchical storage unit is equipped with two types of storage media: data warehouse and data lake. After cleaning and merging, qualified data is distinguished into real-time streaming data and batch historical data, and stored in data lake and data warehouse respectively to form a standardized dataset for downstream monitoring engines.

4. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 1, characterized in that: The rule engine submodule of the intelligent anomaly monitoring engine is divided into four categories of rule knowledge bases; The four knowledge bases are, in order: deposit business rules base, withdrawal business rules base, loan business rules base, and internal operation risk control rules base; the deposit business rules base includes the upper and lower limits of the pre-deposited deposit base and the verification clauses for the statutory deposit ratio of 5% to 12%. The withdrawal business rule library includes compliance restrictions on withdrawals for home purchases and withdrawals upon resignation; the loan business rule library includes legal restrictions on continuous contribution period, number of family loans, and down payment ratio; and the internal operation risk control rule library includes restrictions on unauthorized operations and logins outside of working hours. The rules engine submodule retrieves standardized datasets one by one, matches them against preset clauses in the database, and automatically outputs a list of explicit compliance exceptions after a clause is matched.

5. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 4, characterized in that: The AI ​​intelligent algorithm submodule of the intelligent anomaly monitoring engine integrates four types of independent computing components; The four types of components are: Isolation Forest Anomaly Detection Component, DBSCAN Clustering Component, LSTM Time Series Analysis Component, and Graph Neural Network Association Mining Component. The Isolation Forest Component is used to identify discrete anomalies where a single account's single transaction amount deviates from the norm. The DBSCAN Clustering Component is used to screen for group anomalies such as concentrated batch account openings, concentrated suspension of payments, and concentrated fraudulent withdrawals by groups. The LSTM Time Series Analysis Component is used to track long-term fluctuations in personal account deposits and withdrawals to identify gradual anomalies. The Graph Neural Network Component is used to mine the relationships between agents, depositing employees, and property owners to identify concealed collusion between insiders and outsiders to commit loan fraud. The four types of components read the same batch of standardized data in parallel, perform independent calculations, summarize the results of hidden anomalies, and merge them with the output of the rule engine.

6. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 1, characterized in that: The graded early warning and closed-loop work order handling module includes a four-level risk grading unit and a multi-channel early warning push unit; the four-level risk grading unit is divided into four risk levels: general, attention, high risk, and emergency. The system automatically matches the corresponding risk level based on the comprehensive risk score of anomalies; The multi-channel early warning push unit is equipped with four handling trigger modes: general risk corresponds to the automatic ledger archiving mode; risk of concern corresponds to the SMS reminder mode for the person in charge; high-risk corresponds to the pop-up warning and locking of business acceptance permissions in the business system mode; and emergency risk corresponds to the temporary freezing of the involved account and loan approval mode. After the abnormal data output by the monitoring engine is entered into the classification unit for classification, the system automatically activates the corresponding early warning push mode according to the classification results.

7. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 6, characterized in that: The tiered early warning and closed-loop work order processing module also includes an electronic work order circulation unit. This unit binds three basic pieces of information: anomaly number, anomaly category, and risk assessment criteria. Upon receiving the tiered anomaly information, the unit automatically generates an online audit work order. The system automatically assigns the work order to the corresponding auditor's account based on the business department and job authority. After completing offline verification, the auditor enters the verification conclusion into the work order system. Verification conclusions are categorized into two types: confirmed anomaly and false alarm. After a work order is completed, the system automatically saves the work order file and extracts the handling conclusion as the source data for reverse feedback.

8. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 1, characterized in that: The visualization cockpit and model iteration optimization module are configured with a BI visualization display unit; The BI visualization unit consists of three sub-display modules: Risk Overview Dashboard, Risk Layering Graph, and Fund Flow View. The risk overview dashboard displays the total number of warnings across the entire domain, the number of processed work orders, and the percentage of work orders pending verification in real time. The risk stratification map allows for drill-down by city, business type, and risk level to view detailed anomalies at individual points. The fund flow view connects the entire chain of fund data, including deposits, withdrawals from personal accounts, and loan disbursements. Operation mode: The BI visualization unit synchronizes the full ledger data of the warning and handling module in real time and dynamically refreshes the content of various visualization charts.

9. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 8, characterized in that: The visualization cockpit and model iteration optimization module are equipped with an automatic report generation unit; the automatic report generation unit has three types of fixed templates: monthly audit report, quarterly risk analysis report, and special statistical report on loan fraud; the unit automatically captures the full-cycle early warning, verification, and handling summary data according to a preset cycle; the unit automatically fills in the field content according to the corresponding template and generates a downloadable report file; The generated report files are automatically archived and stored in the system's archive database; Once the report is generated, the report metadata is synchronously stored in the data governance platform to participate in subsequent data statistics and model training.

10. The intelligent monitoring and early warning system for abnormal housing provident fund deposit and loan data according to claim 9, characterized in that: The configuration rules for the visualization cockpit and model iteration optimization module, and the model update training unit; The rule and model update training unit receives work order processing data transmitted through the reverse closed-loop feedback link; the unit splits the sample data into two categories: the first category is verified genuine fraud business samples; the second category is misjudged samples that trigger false alarms; genuine fraud samples are automatically imported into the rule engine submodule to supplement and update the knowledge base of various business rules; misjudged samples and newly added unknown abnormal samples are uniformly sent to the AI ​​intelligent algorithm submodule; each AI algorithm component completes internal parameter retraining based on the new samples; after the parameters and rules are updated, the system automatically replaces the original configuration and enables the updated rules and algorithms in the next round of data monitoring.

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