Loan litigation data automatic collection method based on RPA

Abstract

The invention relates to the technical field of science and technology finance, and provides an automatic loan litigation data collection method based on RPA, and the method comprises the steps: obtaining the identity information of a litigated user, and calling matched loan default information from a database; and then, matching the required litigation data in the knowledge base according to the information. And the server establishes communication with the credit and network loan system to obtain information such as contracts and borrowing data. The obtained information is preprocessed to form a litigation data summary. And finally, carrying out feature extraction and clustering analysis on the data, and sending a classification result to bank business personnel so as to make a collection strategy or legal action. According to the method, the collection efficiency is improved, manual errors are reduced, and systematic recording and classification of litigation documents are realized.

Description

Technical Field

[0001] The present invention relates to the field of science and technology finance technology, and in particular to an automatic collection method for loan litigation data based on RPA. Background Art

[0002] The collection of bank loan litigation materials is crucial to safeguarding the bank's rights and legal interests. These materials include loan contracts, repayment records, communication records, etc., which can be used to support the bank's claims in court. Reasonable and complete data collection helps ensure that the bank has sufficient evidence support in the legal process and provides a chance of winning. However, traditional manual litigation material collection is inefficient and prone to errors, resulting in long processing time, high costs and risks. Therefore, an automated litigation material collection method is needed to improve efficiency and reduce errors.

[0003] The current manual litigation data collection has the following disadvantages: Litigation data collection requires manual login to the online loan system and credit system to query the customer's contract, overdue materials, etc., which is time-consuming and prone to errors. For customers who need to collect litigation data, business personnel need to log in to the online loan and credit system to collect various litigation materials one by one, which requires a lot of manpower for inspection. Litigation data contains information such as contracts, overdue, and loans, which is difficult to record and classify systematically, affecting the efficiency and timeliness of subsequent processing. Summary of the invention

[0004] The purpose of the present invention is to provide an automatic collection method for loan litigation data based on RPA to solve the problem that litigation data collection requires manual login to the online loan system and credit system query, which is difficult to systematically record and classify, affecting the efficiency and timeliness of subsequent processing.

[0005] The present invention provides a method for automatically collecting loan litigation data based on RPA, comprising:

[0006] Step S101, the server obtains the identity information of the user being sued, and retrieves the user loan default information matching the identity information of the user being sued from the database;

[0007] Step S102, the server matches the user's loan default information in a pre-built knowledge base to obtain the required litigation information;

[0008] Step S103, the server establishes a communication connection with the credit system and the online loan system, the server obtains the loan contract and overdue record information through the online loan system, and the server obtains the business contract information, loan note information, loan and repayment record information and repayment plan information through the credit system;

[0009] Step S104, the server performs data preprocessing on the information obtained from the online loan system and the information obtained through the credit system to obtain summary information of litigation materials of the sued user;

[0010] In step S105, the server extracts data features from the litigation data summary information of the sued user and performs cluster analysis to obtain the litigation data classification results of the sued user. The server sends the litigation data classification results of the sued user to the mobile terminal of the bank business personnel.

[0011] Furthermore, the RPA-based automatic collection method for loan litigation data provided by the present invention includes step S101, where the server obtains the identity information of the user being sued, and retrieves the user loan default information matching the identity information of the user being sued from the database, including:

[0012] The server receives the identity information of the user being sued from the client or other system, and the identity information of the user being sued includes name, ID number and key identifier;

[0013] The server verifies the received identity information by comparing it with known reliable data sources to ensure its accuracy and completeness, either by comparing it with known reliable data sources or by using built-in logical verification rules.

[0014] When the identity information is verified correctly, the server will construct a database query request based on the verified identity information, especially those fields that can uniquely identify the user, such as the ID number.

[0015] The server sends the query request to the database storing the user's loan information. This database is a relational database such as MySQL, PostgreSQL, etc., or other types of database systems, depending on the system architecture and design.

[0016] After receiving the query request, the database performs the corresponding query operation and searches for matching records in the database based on the provided identity information.

[0017] The database returns the user loan information that matches the identity information of the user being sued, especially the default information, such as overdue loan records, default amount, default time, etc.

[0018] After receiving this data, the server performs necessary processing, such as format conversion, data encryption, etc., and then prepares to use this information for subsequent litigation material collection and analysis processes.

[0019] Furthermore, in the RPA-based automatic collection method for loan litigation materials provided by the present invention, in step S102, the server performs matching in a pre-built knowledge base according to the user's loan default information to obtain the required litigation materials, including:

[0020] The server analyzes the user's loan default information in detail and extracts key information points, including the default type, default amount and default time;

[0021] This information will be used for subsequent matching and retrieval in the knowledge base.

[0022] The server side is connected to a pre-built knowledge base that uses a specialized database system to store knowledge and rules related to loan defaults and litigation.

[0023] The pre-built knowledge base includes standard litigation material templates in loan default situations, relevant laws and regulations, and historical case information;

[0024] The server uses the parsed user loan default information as query conditions to match and search in the knowledge base;

[0025] According to the matching results, the server extracts the corresponding litigation document template or related information from the knowledge base.

[0026] According to the actual situation, the server will also make necessary customization and adjustments to the extracted litigation materials to ensure that they conform to the specific circumstances of the current case.

[0027] Furthermore, the RPA-based automatic collection method for loan litigation materials provided by the present invention, step S103, the server side establishes a communication connection with the credit system and the online loan system, the server side obtains the loan contract and overdue record information through the online loan system, and the server side obtains the business contract information, loan note information, loan and repayment record information and repayment plan information through the credit system, including:

[0028] The server uses a pre-configured API interface or a dedicated communication protocol to establish a secure communication connection with the server of the online loan system;

[0029] After the connection is established, the server will perform identity authentication and authorization to ensure that only legitimate requests can access the data of the online loan system.

[0030] The server constructs a specific data request based on the identity information of the loan defaulter. This request clearly specifies the loan contract and overdue record information that needs to be obtained. The request is sent to the online loan system and waits for a response.

[0031] After the online loan system processes the request, it returns the relevant loan contract and overdue record information to the server.

[0032] After receiving this information, the server performs necessary parsing, verification, and storage operations to ensure the accuracy and completeness of the data.

[0033] The server side uses a suitable interface or protocol to establish a communication connection with the server of the credit system.

[0034] Authentication and authorization steps are also necessary to ensure data security.

[0035] Construct and send a data request:

[0036] Based on the identity information of the loan defaulter, the server constructs a data request to the credit system. This request is intended to obtain the user's business contract information, loan information, loan and repayment record information, and repayment plan information.

[0037] The request is sent to the credit system for processing.

[0038] The credit system responds to the request and returns the required data.

[0039] The server receives this data, parses, verifies and stores it for subsequent use.

[0040] Finally, the server will integrate all the information obtained from the online loan system and the credit system to ensure the consistency and relevance of the data.

[0041] The integrated data is securely stored in the server-side database for use in subsequent steps (such as data preprocessing, feature extraction, etc.).

[0042] Furthermore, in the RPA-based automatic collection method for loan litigation data provided by the present invention, in step S104, the server side performs data preprocessing on the information obtained from the online loan system and the information obtained through the credit system to obtain summary information of the litigation data of the user being sued, including:

[0043] The server performs a preliminary check on the information obtained from the online loan system and credit system to identify and delete duplicate, invalid or erroneous data.

[0044] For missing key data, the server will try to supplement it through other reliable channels, or mark it as missing for subsequent processing.

[0045] Since the online loan system and credit system use different data formats and standards, the server side needs to convert this data into a unified format.

[0046] For example, key information such as dates and amounts will be converted into a unified format to ensure data consistency and comparability.

[0047] The server side integrates the information obtained from the two systems to form a complete data set.

[0048] This includes matching and merging the loan contracts and overdue record information of the online lending system with the business contract information, loan note information, loan and repayment record information, and repayment plan information of the credit system.

[0049] The server ensures that data retrieved from both systems can be correctly associated with the same user through user identity information or other unique identifiers.

[0050] This helps build a comprehensive user credit profile and provide detailed evidence for subsequent legal proceedings.

[0051] Based on the integrated data set, the server extracts key information related to the litigation, such as the user's total loan amount, number of overdue payments, overdue amount, etc. This information will be used to construct the core content of the litigation materials.

[0052] The extracted key information is formatted into a document or report suitable for use in legal proceedings.

[0053] This includes generating detailed lists of borrowing and overdue records, the implementation of repayment plans, etc.

[0054] The generated litigation material summary information will be securely stored on the server and backup will be created to prevent data loss.

[0055] At the same time, this information will be exported into a specific file format (such as PDF, Excel, etc.) for easy transmission and printing.

[0056] Furthermore, in the RPA-based automatic collection method for loan litigation data provided by the present invention, in step S105, the server extracts data features from the litigation data summary information of the sued user, and performs cluster analysis to obtain the litigation data classification result of the sued user, and the server sends the litigation data classification result of the sued user to the mobile terminal of the bank business personnel, including:

[0057] The server analyzes the litigation data summary information and identifies key data features related to the litigation, including the loan amount, number of overdue payments, overdue duration, and repayment ratio;

[0058] Based on the identified key features, the server constructs a feature vector for each user being sued;

[0059] A feature vector is a mathematical representation of user data that reflects the user's performance on different features.

[0060] To ensure the accuracy of clustering analysis, the server standardizes the extracted features.

[0061] Standardization can eliminate the impact of dimensions and numerical ranges between different features, so that all features have the same weight in cluster analysis.

[0062] Select a suitable clustering algorithm. The server selects a suitable clustering algorithm, such as K-means, hierarchical clustering, etc., based on the characteristics and requirements of the data.

[0063] The goal of clustering algorithms is to group similar data points together into distinct clusters.

[0064] Perform cluster analysis: Use the selected clustering algorithm to perform cluster analysis on the standardized feature vectors; through iterative calculations, divide the data points into different clusters, each cluster represents a group of users with similar characteristics;

[0065] Evaluate clustering results: The server will evaluate the clustering results to ensure the effectiveness and accuracy of the clustering. Evaluation indicators may include silhouette coefficient, Davies-Bouldin index, etc., which are used to quantify the quality of clustering.

[0066] Classification result labeling: Based on the results of cluster analysis, the server assigns a label to each cluster, which represents the common characteristics of the users in the cluster. For example, one cluster may be labeled as "high-risk overdue users" and another cluster may be labeled as "low-risk occasional overdue users."

[0067] The server will sort out the classification results and may generate visual reports or charts to help bank staff better understand the user classification. Finally, the server will send the classification results and related visual reports to the mobile terminal of the bank staff through a secure channel. Bank staff can formulate more accurate collection strategies or take other corresponding legal actions based on these classification results.

[0068] Beneficial effects of the present invention:

[0069] Improve collection efficiency: By leveraging RPA technology and automated process control, automatic login, query and collection are achieved, reducing the time and effort of manual operations and improving collection efficiency.

[0070] Reduce manual errors: Through automated process control, the accuracy of litigation data collection is improved and errors in manual operations are reduced.

[0071] Systematic recording and classification: By preserving the collected litigation information and classifying the information, the systematic recording and classification of litigation information is achieved, which improves the efficiency of subsequent processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0073] Figure 1 A flow chart of a method for automatically collecting loan litigation data based on RPA provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0075] See also Figure 1 The present invention provides a method for automatically collecting loan litigation data based on RPA, comprising:

[0076] Step S101, the server obtains the identity information of the user being sued, and retrieves the user loan default information matching the identity information of the user being sued from the database;

[0077] Step S102, the server matches the user's loan default information in a pre-built knowledge base to obtain the required litigation information;

[0078] Step S103, the server establishes a communication connection with the credit system and the online loan system, the server obtains the loan contract and overdue record information through the online loan system, and the server obtains the business contract information, loan note information, loan and repayment record information and repayment plan information through the credit system;

[0079] Step S104, the server performs data preprocessing on the information obtained from the online loan system and the information obtained through the credit system to obtain summary information of litigation materials of the sued user;

[0080] In step S105, the server extracts data features from the litigation data summary information of the sued user and performs cluster analysis to obtain the litigation data classification results of the sued user. The server sends the litigation data classification results of the sued user to the mobile terminal of the bank business personnel.

[0081] Furthermore, the RPA-based automatic collection method for loan litigation data provided by the present invention includes step S101, where the server obtains the identity information of the user being sued, and retrieves the user loan default information matching the identity information of the user being sued from the database, including:

[0082] The server receives the identity information of the user being sued from the client or other system, and the identity information of the user being sued includes name, ID number and key identifier;

[0083] The server verifies the received identity information by comparing it with known reliable data sources to ensure its accuracy and completeness, either by comparing it with known reliable data sources or by using built-in logical verification rules.

[0084] When the identity information is verified correctly, the server will construct a database query request based on the verified identity information, especially those fields that can uniquely identify the user, such as the ID number.

[0085] The server sends the query request to the database storing the user's loan information. This database is a relational database such as MySQL, PostgreSQL, etc., or other types of database systems, depending on the system architecture and design.

[0086] After receiving the query request, the database performs the corresponding query operation and searches for matching records in the database based on the provided identity information.

[0087] The database returns the user loan information that matches the identity information of the user being sued, especially the default information, such as overdue loan records, default amount, default time, etc.

[0088] After receiving this data, the server performs necessary processing, such as format conversion, data encryption, etc., and then prepares to use this information for subsequent litigation material collection and analysis processes.

[0089] Furthermore, in the RPA-based automatic collection method for loan litigation materials provided by the present invention, in step S102, the server performs matching in a pre-built knowledge base according to the user's loan default information to obtain the required litigation materials, including:

[0090] The server analyzes the user's loan default information in detail and extracts key information points, including the default type, default amount and default time;

[0091] This information will be used for subsequent matching and retrieval in the knowledge base.

[0092] The server side is connected to a pre-built knowledge base that uses a specialized database system to store knowledge and rules related to loan defaults and litigation.

[0093] The pre-built knowledge base includes standard litigation material templates in loan default situations, relevant laws and regulations, and historical case information;

[0094] The server uses the parsed user loan default information as query conditions to match and search in the knowledge base;

[0095] According to the matching results, the server extracts the corresponding litigation document template or related information from the knowledge base.

[0096] According to the actual situation, the server will also make necessary customization and adjustments to the extracted litigation materials to ensure that they conform to the specific circumstances of the current case.

[0097] Furthermore, the RPA-based automatic collection method for loan litigation materials provided by the present invention, step S103, the server side establishes a communication connection with the credit system and the online loan system, the server side obtains the loan contract and overdue record information through the online loan system, and the server side obtains the business contract information, loan note information, loan and repayment record information and repayment plan information through the credit system, including:

[0098] The server uses a pre-configured API interface or a dedicated communication protocol to establish a secure communication connection with the server of the online loan system;

[0099] After the connection is established, the server will perform identity authentication and authorization to ensure that only legitimate requests can access the data of the online loan system.

[0100] The server constructs a specific data request based on the identity information of the loan defaulter. This request clearly specifies the loan contract and overdue record information that needs to be obtained. The request is sent to the online loan system and waits for a response.

[0101] After the online loan system processes the request, it returns the relevant loan contract and overdue record information to the server.

[0102] After receiving this information, the server performs necessary parsing, verification, and storage operations to ensure the accuracy and completeness of the data.

[0103] The server side uses a suitable interface or protocol to establish a communication connection with the server of the credit system.

[0104] Authentication and authorization steps are also necessary to ensure data security.

[0105] Construct and send a data request:

[0106] Based on the identity information of the loan defaulter, the server constructs a data request to the credit system. This request is intended to obtain the user's business contract information, loan information, loan and repayment record information, and repayment plan information.

[0107] The request is sent to the credit system for processing.

[0108] The credit system responds to the request and returns the required data.

[0109] The server receives this data, parses, verifies and stores it for subsequent use.

[0110] Finally, the server will integrate all the information obtained from the online loan system and the credit system to ensure the consistency and relevance of the data.

[0111] The integrated data is securely stored in the server-side database for use in subsequent steps (such as data preprocessing, feature extraction, etc.).

[0112] Furthermore, in the RPA-based automatic collection method for loan litigation data provided by the present invention, in step S104, the server side performs data preprocessing on the information obtained from the online loan system and the information obtained through the credit system to obtain summary information of the litigation data of the user being sued, including:

[0113] The server performs a preliminary check on the information obtained from the online loan system and credit system to identify and delete duplicate, invalid or erroneous data.

[0114] For missing key data, the server will try to supplement it through other reliable channels, or mark it as missing for subsequent processing.

[0115] Since the online loan system and credit system use different data formats and standards, the server side needs to convert this data into a unified format.

[0116] For example, key information such as dates and amounts will be converted into a unified format to ensure data consistency and comparability.

[0117] The server side integrates the information obtained from the two systems to form a complete data set.

[0118] This includes matching and merging the loan contracts and overdue record information of the online lending system with the business contract information, loan note information, loan and repayment record information, and repayment plan information of the credit system.

[0119] The server ensures that data retrieved from both systems can be correctly associated with the same user through user identity information or other unique identifiers.

[0120] This helps build a comprehensive user credit profile and provide detailed evidence for subsequent legal proceedings.

[0121] Based on the integrated data set, the server extracts key information related to the litigation, such as the user's total loan amount, number of overdue payments, overdue amount, etc. This information will be used to construct the core content of the litigation materials.

[0122] The extracted key information is formatted into a document or report suitable for use in legal proceedings.

[0123] This includes generating detailed lists of borrowing and overdue records, the implementation of repayment plans, etc.

[0124] The generated litigation material summary information will be securely stored on the server and backup will be created to prevent data loss.

[0125] At the same time, this information will be exported into a specific file format (such as PDF, Excel, etc.) for easy transmission and printing.

[0126] Furthermore, in the RPA-based automatic collection method for loan litigation data provided by the present invention, in step S105, the server extracts data features from the litigation data summary information of the sued user, and performs cluster analysis to obtain the litigation data classification result of the sued user, and the server sends the litigation data classification result of the sued user to the mobile terminal of the bank business personnel, including:

[0127] The server analyzes the litigation data summary information and identifies key data features related to the litigation, including the loan amount, number of overdue payments, overdue duration, and repayment ratio;

[0128] Based on the identified key features, the server constructs a feature vector for each user being sued;

[0129] A feature vector is a mathematical representation of user data that reflects the user's performance on different features.

[0130] To ensure the accuracy of clustering analysis, the server standardizes the extracted features.

[0131] Standardization can eliminate the impact of dimensions and numerical ranges between different features, so that all features have the same weight in cluster analysis.

[0132] Select a suitable clustering algorithm. The server selects a suitable clustering algorithm, such as K-means, hierarchical clustering, etc., based on the characteristics and requirements of the data.

[0133] The goal of clustering algorithms is to group similar data points together into distinct clusters.

[0134] Perform cluster analysis: Use the selected clustering algorithm to perform cluster analysis on the standardized feature vectors; through iterative calculations, divide the data points into different clusters, each cluster represents a group of users with similar characteristics;

[0135] Evaluate clustering results: The server will evaluate the clustering results to ensure the effectiveness and accuracy of the clustering. Evaluation indicators may include silhouette coefficient, Davies-Bouldin index, etc., which are used to quantify the quality of clustering.

[0136] Classification result labeling: Based on the results of cluster analysis, the server assigns a label to each cluster, which represents the common characteristics of the users in the cluster. For example, one cluster may be labeled as "high-risk overdue users" and another cluster may be labeled as "low-risk occasional overdue users."

[0137] The server will sort out the classification results and may generate visual reports or charts to help bank staff better understand the user classification. Finally, the server will send the classification results and related visual reports to the mobile terminal of the bank staff through a secure channel. Bank staff can formulate more accurate collection strategies or take other corresponding legal actions based on these classification results.

[0138] The technical solution of the present invention solves the problem that litigation data collection requires manual login to the online loan system and credit system query, which is difficult to systematically record and classify, affecting the efficiency and timeliness of subsequent processing in the following ways:

[0139] Automated login and query: The present invention utilizes RPA (robotic process automation) technology to achieve automatic login to the online loan system and credit system without manual operation, thereby significantly improving the efficiency of data collection.

[0140] Automatic data collection and integration: The server automatically obtains the required loan litigation information from the online loan system and credit system through a pre-configured API interface or a dedicated communication protocol, thus realizing the automatic data collection. The collected data will be pre-processed, formed into a unified format, and integrated to ensure that it is easy to analyze and use later.

[0141] Systematic recording and classification: The collected litigation materials are systematically recorded and classified by constructing a database and knowledge base, as well as technical means such as data feature extraction and cluster analysis.

[0142] Reduce human errors: Automated process control reduces errors in manual operations and improves accuracy.

[0143] Real-time update and feedback: The server can send the classification results to the mobile terminals of banking personnel in real time to ensure the timeliness and accuracy of the information.

[0144] The specific implementation of the present invention in actual use includes the following steps:

[0145] Obtaining the identity information of the user being sued: The server receives and verifies the identity information of the user being sued from the client. After verification, the matching user loan default information is obtained from the database based on the information.

[0146] Matching in the knowledge base to obtain the required litigation materials: The server side parses the user's loan default information and extracts key information points. Use this information to match and search in the knowledge base to extract the corresponding litigation material template or information.

[0147] Establish communication with credit and online loan systems and obtain information: Establish connections with online loan systems and credit systems through pre-configured API interfaces or communication protocols. Obtain relevant information such as loan contracts, overdue records, business contracts, etc. from these two systems based on user identity information.

[0148] Data preprocessing to obtain litigation data summary: Conduct a preliminary check on the acquired information and delete duplicate, invalid or erroneous data. Convert the data into a unified format and integrate it to extract key information related to the litigation.

[0149] Data feature extraction and cluster analysis: Analyze litigation materials, identify key data features, construct feature vectors for each user and perform standardization. Select appropriate clustering algorithms for cluster analysis, assign labels based on the results, and generate visual reports. Send classification results and reports to mobile terminals of banking personnel through secure channels.

[0150] The present invention can improve the collection efficiency and accuracy of loan litigation materials, reduce manual errors, and realize the systematic recording and classification of litigation materials.

[0151] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention or some parts of the embodiments.

[0152] The above-described embodiments of the present invention do not limit the protection scope of the present invention.

Claims

1. A method for automatically collecting loan litigation data based on RPA, characterized in that: include; Step S101, the server obtains the identity information of the user being sued, and retrieves the user loan default information matching the identity information of the user being sued from the database; Step S102, the server matches the user's loan default information in a pre-built knowledge base to obtain the required litigation information; Step S103, the server establishes a communication connection with the credit system and the online loan system, the server obtains the loan contract and overdue record information through the online loan system, and the server obtains the business contract information, loan note information, loan and repayment record information and repayment plan information through the credit system; Step S104, the server performs data preprocessing on the information obtained from the online loan system and the information obtained through the credit system to obtain summary information of litigation materials of the sued user; In step S105, the server extracts data features from the litigation data summary information of the sued user and performs cluster analysis to obtain the litigation data classification results of the sued user. The server sends the litigation data classification results of the sued user to the mobile terminal of the bank business personnel.

2. The automatic collection method of loan litigation data based on RPA as claimed in claim 1, characterized in that: S101, the server obtains the identity information of the user being sued, and retrieves the user loan default information matching the identity information of the user being sued from the database, including: The server receives the identity information of the user being sued from the client or other system, and the identity information of the user being sued includes name, ID number and key identifier; The server verifies the received identity information by comparing it with known reliable data sources. When the identity information is verified to be correct, the server constructs a database query request based on the verified identity information. The server sends the query request to the database storing the user's loan information. After receiving the query request, the database performs the corresponding query operation and searches for matching records in the database based on the provided identity information.

3. The method according to claim 1, characterized in that S102, the server matches the user's loan default information in a pre-built knowledge base to obtain the required litigation materials, including; The server analyzes the user's loan default information in detail and extracts key information points, including the default type, default amount and default time; The server side is connected to a pre-built knowledge base, which includes standard litigation material templates in loan default situations, relevant laws and regulations, and historical case information; The server uses the parsed user loan default information as query conditions to match and search in the knowledge base; According to the matching results, the server extracts the corresponding litigation document template or related information from the knowledge base.

4. The method according to claim 1, characterized in that S103, the server establishes a communication connection with the credit system and the online loan system, the server obtains the loan contract and overdue record information through the online loan system, and the server obtains the business contract information, loan note information, loan and repayment record information and repayment plan information through the credit system, including; The server uses a pre-configured API interface or a dedicated communication protocol to establish a secure communication connection with the server of the online loan system; The server constructs a specific data request based on the identity information of the loan defaulter, and the request is sent to the online loan system, waiting for a response; After processing the request, the online loan system returns the relevant loan contract and overdue record information to the server; The server side will integrate all the information obtained from the online loan system and the credit system; The integrated data is securely stored in the server-side database.

5. The method according to claim 1, characterized in that S104, the server performs data preprocessing on the information obtained from the online loan system and the information obtained through the credit system to obtain summary information of the litigation materials of the sued user, including: The server performs a preliminary check on the information obtained from the online loan system and credit system to identify and delete duplicate, invalid or erroneous data; The server converts the information obtained from the online loan system and the credit system into a unified format; The server integrates the information obtained from the online loan system and the credit system to form a data set, which includes matching and merging the loan contract and overdue record information of the online loan system with the business contract information, loan note information, loan and repayment record information and repayment plan information of the credit system; The data obtained from the two systems is associated with the user by the server through the user's identity information or other unique identifier; The server extracts key information related to the litigation based on the integrated data set. The key information related to the litigation includes the user's total loan amount, number of overdue payments, and overdue amount.

6. The method according to claim 1, characterized in that S105, the server extracts data features from the litigation data summary information of the sued user, performs cluster analysis, obtains the classification result of the litigation data of the sued user, and sends the classification result of the litigation data of the sued user to the mobile terminal of the bank business personnel, including: The server analyzes the litigation data summary information and identifies key data features related to the litigation, including the loan amount, number of overdue payments, overdue duration, and repayment ratio; Based on the identified key features, the server constructs a feature vector for each user being sued; The server normalizes the extracted features, selects a suitable clustering algorithm, and uses the selected clustering algorithm to perform cluster analysis on the normalized feature vectors; Through iterative calculation, the data points are divided into different clusters, each cluster represents a group of users with similar characteristics; Based on the results of cluster analysis, the server assigns a label to each cluster, representing the common characteristics of the users in that cluster.

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