Information processing system, method, device and medium based on bank loan platform

Abstract

The present invention relates to the field of information processing technology, and discloses an information processing system, method, device and medium based on a bank loan platform. The system includes: a data burial design module, which is used to determine a data burial plan based on the loan application process and user behavior characteristics; a data acquisition module, which is used to collect user data in real time according to the data burial plan, and send the user data to the back-end server; a data processing and analysis module, which is used to receive and process and analyze user data to determine data analysis results; a result application module includes a loan approval optimization unit, which is used to optimize the loan approval process based on the data analysis results, including: adjusting the loan amount and interest rate, making loan approval decisions, and speeding up the loan approval process. The present invention can collect user data of users in the loan application process in real time, analyze the user data to obtain analysis results, thereby adjusting the loan amount and interest rate, making loan approval decisions, and speeding up the loan approval process.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to an information processing system, method, device and medium based on a bank loan platform. Background Art

[0002] With the rapid development of digital financial technology, online lending has become a significant trend. All steps in a loan application can be completed efficiently online, without leaving home. This includes understanding the application requirements for various types of loans, preparing application materials, and submitting the loan application. Users first submit their application online, and consumer finance companies conduct preliminary screening and review. For applications with higher risks or requiring further verification, offline face-to-face interviews and home visits are arranged to more accurately assess the risk. The aforementioned online application and offline review method involves a lot of manual intervention in some parts of the approval process and is easily influenced by subjective factors, such as the experience, emotions, and biases of the reviewers, which can affect the objectivity and fairness of the approval results. Manual review is also less efficient. Therefore, there is an urgent need for an information processing solution that can improve the accuracy and efficiency of loan approvals. Summary of the Invention

[0003] Therefore, the technical problem to be solved by the present invention is to overcome the problems of low accuracy and low efficiency of manual review in related technologies.

[0004] In order to solve the above technical problems, the present invention provides an information processing system based on a bank loan platform, comprising:

[0005] A data tracking design module is used to determine a data tracking solution based on the business process and user behavior characteristics of the front-end H5 page of the bank loan platform; the business process includes the loan application process;

[0006] The data collection module is configured in the front-end H5 page and is used to collect user data in real time according to the data embedding scheme determined by the data embedding design module, and send the user data to the back-end server;

[0007] The user data includes user behavior data and application information; the behavior data includes: user browsing path, dwell time, and click events; the application information includes: application time, loan amount, loan term, repayment method, personal information (user name, age, and gender), business information (business size and business information), credit history (historical loan records and historical repayment records);

[0008] A data processing and analysis module, configured on the back-end server, for receiving, processing and analyzing the user data to determine a data analysis result;

[0009] The result application module includes a loan approval optimization unit, which is used to optimize the loan approval process according to the data analysis results, including: adjusting the loan amount and interest rate, making loan approval decisions, so as to speed up the loan approval process.

[0010] In an optional implementation, the data embedding design module includes:

[0011] A tracking point demand determination unit is used to determine tracking point demand based on the business process and user behavior characteristics of the front-end H5 page of the bank loan platform; the tracking point demand includes key nodes and data fields corresponding to the key nodes;

[0012] The tracking solution design unit is used to determine the tracking location according to the key nodes in the tracking requirements, determine the data type according to the data fields corresponding to the key nodes, and design the corresponding collection frequency.

[0013] In an optional embodiment, the data acquisition module includes:

[0014] A data collection unit is used to collect user data in real time according to the data burying scheme determined by the data burying design module; a data encryption unit is used to encrypt the user data to obtain encrypted user data; a data compression unit is used to compress the encrypted user data to obtain compressed user data; a data transmission unit is used to send the compressed user data to the back-end server.

[0015] In an optional embodiment, the data processing and analysis module includes:

[0016] A data processing unit is used to receive the user data and perform data processing on the user data to obtain the processed user data; the data processing includes: preprocessing and feature extraction; a data analysis unit is used to analyze the user data after data processing to obtain the data analysis results.

[0017] In an optional embodiment, the data analysis unit includes:

[0018] A model building unit, used to build a risk assessment model;

[0019] The model application unit is used to input the processed user data as input data into the risk assessment model to obtain the risk score corresponding to the user; the risk score is used to characterize the user's credit risk and loan risk.

[0020] In an optional embodiment, the loan approval optimization unit is specifically used to adjust the loan amount and interest rate according to the user's corresponding risk score, and automatically or manually make loan approval decisions based on the user's corresponding risk score and preset approval criteria.

[0021] In an optional embodiment, the data analysis unit further includes:

[0022] The user portrait construction unit is used to analyze the user data after data processing and construct a user portrait.

[0023] In a second aspect, the present invention provides an information processing method based on a bank loan platform, comprising:

[0024] Determine a data tracking solution based on the business process and user behavior characteristics of the bank loan platform's front-end H5 page; the business process includes the loan application process;

[0025] Collect user data in real time according to the data tracking solution and send the user data to the back-end server; the user data includes user behavior data and application information; the behavior data includes: user browsing path, dwell time and click events; the application information includes: application time, loan amount, loan term, repayment method, personal information (user name, age and gender), business information (business size and business information), credit history (historical loan records and historical repayment records);

[0026] Receive, process and analyze the user data to determine data analysis results;

[0027] Optimizing the loan approval process based on the data analysis results includes adjusting the loan amount and interest rate, and making loan approval decisions to speed up the loan approval process.

[0028] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the information processing method based on the bank loan platform of the second aspect mentioned above by executing the computer instructions.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein a single computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the information processing method based on a bank loan platform of any embodiment of the second aspect above.

[0030] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the information processing method of the second aspect.

[0031] The technical solution provided by the present invention has the following technical effects:

[0032] The technical solution of the embodiment of the present invention can determine the data embedding plan based on the business process and user behavior characteristics of the H5 page on the front end of the bank loan platform through the data embedding design module. This can accurately collect various data of users in the loan application process, including personal information (personal information, business operation information, credit history), behavioral data (browsing path, dwell time, click events) and device information (device model, operating system, network environment). Compared with manual review, which may rely on limited information and subjective judgment, this data collection method is more comprehensive and objective, providing a rich data foundation for accurate evaluation.

[0033] The data collection module collects user data in real time and sends it to the backend server. This ensures the data is up-to-date, avoiding the delays and untimely updates that can occur during manual data collection. This allows the backend data processing and analysis modules to analyze data based on the latest data, improving the accuracy of the results.

[0034] The data processing and analysis module receives, processes, and analyzes user data on the backend server, quickly generating data analysis results. This process avoids the tedious process of manually reviewing and analyzing user data one by one, greatly improving efficiency.

[0035] The Loan Approval Optimization Unit in the Results Application Module optimizes the loan approval process based on data analysis results, such as adjusting loan amounts and interest rates and making loan approval decisions. This data-driven decision-making approach expedites the loan approval process, reduces the back-and-forth communication and document supplementation that can occur during manual review, and improves overall approval efficiency.

[0036] Through comprehensive and real-time data collection and automated data analysis, more accurate information can be provided for loan approvals, reducing inaccuracies caused by factors such as limited information and subjective judgment during manual review, thereby effectively addressing the low accuracy of manual loan reviews. Automated data processing and data-based approval process optimization reduce manual intervention, accelerate the approval process, and address the low efficiency of manual review. Therefore, the technical solution of the present invention can, to a certain extent, address the technical issues of low accuracy and low efficiency of manual loan reviews. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1This is a schematic diagram of the structure of an information processing system based on a bank loan platform according to an embodiment of the present invention;

[0039] Figure 2 Schematic diagram of a data embedding solution according to an embodiment of the present invention;

[0040] Figure 3 is a schematic diagram of a visual analysis result according to an embodiment of the present invention;

[0041] Figure 4 1 is a flow chart of an information processing method based on a bank loan platform according to an embodiment of the present invention;

[0042] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0044] To this end, embodiments of the present invention provide an information processing system, method, device, and medium based on a bank loan platform to solve the problems of low accuracy and low efficiency of manual review.

[0045] According to an embodiment of the present invention, an embodiment of an information processing system based on a bank loan platform is provided. It should be noted that a single system is used to implement the following embodiments and optional implementations. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0046] Figure 1 FIG is a structural diagram of another information processing system based on a bank loan platform according to an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides an information processing system based on a bank loan platform, and the information processing system based on a bank loan platform includes:

[0047] Data embedding design module 11, data acquisition module 12, data processing and analysis module 13 and result application module 14.

[0048] The data embedding design module 11 includes: a embedding requirement determination unit 111 and a embedding solution design unit 112 .

[0049] The data acquisition module 12 includes: a data acquisition unit 121 , a data encryption unit 122 , a data compression unit 123 , and a data transmission unit 124 .

[0050] The data processing and analysis module 13 includes a data processing unit 131 and a data analysis unit 132. The data analysis unit 132 includes a model building unit 1321 and a model application unit 1322. The data analysis unit 132 also includes a user portrait building unit 1323.

[0051] The result application module 14 includes a loan approval optimization unit 141 .

[0052] The data embedding design module 11 is used to determine the data embedding scheme based on the business process and user behavior characteristics of the front-end H5 page of the bank loan platform. The business process includes the loan application process. The business process can also include: product browsing and comparison process, user authentication and authorization process, consultation and customer service process, repayment management process, etc. As an example, the data embedding scheme is as follows Figure 2 shown.

[0053] Product browsing and comparison process:

[0054] Product Page Views: When a user enters a loan product display page, we record the loan product type (e.g., personal consumer loans, housing loans, business loans, etc.) and the duration of each product's browsing (dwell time). This helps us understand the user's initial interest in different loan products. For example, a longer dwell time on a housing loan product page may indicate potential demand for a home loan.

[0055] Product Details Viewing: Track users' clicks to view loan product details, recording the details they view, including interest rates, loan terms, repayment methods, and loan limits. This data can be used to analyze the loan product features that users prioritize, providing a basis for targeted marketing and product optimization. For example, if many users frequently check whether a product's early repayments include penalty clauses, this may require a clearer explanation in the product description.

[0056] Product comparison behavior: When users use the product comparison function provided by the platform (if any), record the product combinations compared and the comparison dimensions (such as interest rate, loan limit flexibility, etc.). This helps understand the trade-offs users make when choosing loan products and adjust product strategies.

[0057] 2. User authentication and authorization process:

[0058] Authentication steps: During user authentication, such as uploading a photo of their ID card, entering their phone number to obtain a verification code, and performing facial recognition (if applicable), record the completion status of each step, including any errors or repeated attempts. This data is crucial for optimizing the authentication process, improving the user experience, and mitigating identity verification risks. For example, if many users repeatedly fail the facial recognition step, it may be necessary to investigate whether there are technical issues with the feature or provide clearer operational instructions.

[0059] Authorization operations: Track user authorizations for the platform to access their credit information, bank statements, and other information, including whether the authorization was successful and when. Understanding user acceptance of authorization operations and potential issues can help improve authorization reminders and instructions, while also enabling better tracking of the user credit data acquisition process.

[0060] 3. Consultation and customer service process:

[0061] Online consultation initiation: Record the entry point from which users initiate online consultations (e.g., on the product details page, during the application process, or on a dedicated customer service page) and the types of questions asked (e.g., product details, application requirements, interest rate calculation, etc.). This can help understand the points of confusion users encounter during the loan process, allowing for optimization of product introductions and customer service knowledge bases. Customer service communication details: If the platform has a chat-based customer service function, record the content of the chats between customer service and users, the duration of the chats, and the resolution of the issues. This data helps evaluate the quality of customer service, and can also uncover common user questions for optimization of the platform's FAQ section.

[0062] Customer Service Feedback: This tool tracks user feedback on customer service, including ratings (e.g., satisfied, average, dissatisfied) and specific content. This is crucial for improving customer service quality and increasing user satisfaction.

[0063] 4. Repayment management process (for users who have already taken out loans):

[0064] Repayment plan review: When a user with a loan logs into the platform to review their repayment plan, we record the frequency of reviews, the specific time of review (e.g., close to the due date, far in advance), and whether they print or download the repayment plan. This helps us understand the user's attention to and management habits regarding repayment plans.

[0065] Repayment Reminder Interaction: For repayment reminder messages sent by the platform (SMS, push notifications, etc.), record whether the user views the reminder and the action taken after viewing it (such as ignoring it, clicking to proceed to the repayment page, etc.). This data helps optimize repayment reminder strategies and improve repayment rates.

[0066] Repayment operation records: When a user makes a repayment (e.g., online payment, automatic repayment settings, etc.), details such as the repayment method, whether the repayment was successful, and whether the repayment was made early are recorded. This is important for assessing the user's repayment ability and credit status.

[0067] In this embodiment, the data embedding design module 11 may include:

[0068] The tracking requirement determination unit 111 is used to determine the tracking requirement based on the business process and user behavior characteristics of the front-end H5 page of the bank loan platform. The tracking requirement includes key nodes and data fields corresponding to the key nodes.

[0069] The tracking solution design unit 112 is used to determine the tracking location according to the key nodes in the tracking requirements, determine the data type according to the data fields corresponding to the key nodes, and design the corresponding collection frequency.

[0070] Difficulties in constructing traffic data using traditional technologies:

[0071] Poor data tracking quality: incorrect or missed tracking, uneven quality, and risky data usage.

[0072] Irregular tracking point design leads to scattered and chaotic tracking points, which is not conducive to unified use. Various types of data lack consent management, making it difficult to realize the value of data.

[0073] Low business utilization and no use are consuming costs.

[0074] The pain points that various roles in bank loan platforms previously faced in traffic data practices:

[0075] Data users (business departments): Data tracking is not managed uniformly. Logic is meaningless, and attribute content and responsible persons are missing.

[0076] Tracking point developers: There is no record of the addition, modification, abandonment, and decommissioning of tracking points. We dare not touch the old tracking point codes, and the code is becoming more and more chaotic.

[0077] The party that demands tracking (operators of bank loan platforms, including pre-loan, mid-loan, and post-loan teams): Tracking demand has nowhere to be maintained, and flexible and controlled version management is impossible.

[0078] Data managers (operators of bank loan platforms): What is the current status of traffic data assets? There is a lack of governance and management tools for the number of tracking points, data volume, cost consumption, and number of effective tracking points.

[0079] The tracking demand determination unit is specifically used to break down the loan application process: the loan application process is subdivided into multiple sub-processes, such as user registration and login, filling in personal information (personal information, corporate information, etc.), uploading information (ID card photo, business license, etc.), selecting loan product type and amount, submitting application for review, etc.

[0080] Analyze user interactions within each sub-process, such as user input behavior and actions on required and optional fields when filling in personal information. Also analyze file selection and feedback processing on upload success or failure when uploading documents.

[0081] Identify key behaviors: Identify user behaviors that have a significant impact on loan approval, such as the length of time spent browsing the loan product details page (reflecting the user's attention to and understanding of the product), the number of modifications made when filling in credit history information (which may indicate the authenticity and stability of the information), and the number of times the entire application page is repeatedly viewed before submitting the application (reflecting the user's level of caution).

[0082] Determine tracking needs: Based on the above business process decomposition and key behavior analysis, determine the key nodes for tracking. For example: Registration login page: Login button click, registration success event, etc. Personal information filling page: Name, age, gender and other fields start input event, input completion event, save information event. Business information page: Business size field input, business information text box content change event, etc. Credit history page: Historical loan record query button click, historical repayment record display event, etc. Loan product selection page: Product click event, credit limit selection event, etc. Application submission page: Submit button click event, application submission success event, etc.

[0083] Determination of corresponding data fields: For each key node, determine the data fields that need to be collected. For example: registration and login page: user name, password, registration time, login time, login IP address, etc. Personal information filling page: name, age, gender, ID number, contact information, etc. Business information page: company size (number of employees, registered capital and other specific data), business scope (text description), company establishment time, etc. Credit history page: historical loan amount, loan term, repayment method, repayment time, whether there is an overdue record, etc. Loan product selection page: product name, product number, selected amount, interest rate type, etc. Application submission page: application time, application number, application status, etc.

[0084] The tracking plan design unit is specifically used to determine the tracking location, data type and collection frequency.

[0085] Determining the tracking location: For front-end H5 pages, tracking is typically done within JavaScript functions triggered by relevant events. For example, to track a click event on a login button, insert the data collection code within the click response function. For data collection after page load (such as obtaining device information when the page first loads), track data within the page's `onload` event function.

[0086] Data type determination: The data type is determined based on the nature of the data field. For example, text information such as name and company name is a string type. Age, number of employees, and loan amount are numeric types. Registration time, login time, and repayment time are date and time types. Device model and operating system are string types (enumeration values ​​for device type and operating system can be predefined to facilitate subsequent analysis). Information such as whether there is an overdue payment record is a Boolean type (true if there is an overdue payment, false if there is no overdue payment).

[0087] Determine the collection frequency: For some one-time events, such as successful registration, successful application submission, etc., the collection frequency is 1 time, that is, the data is collected when the event occurs. For the user's continuous behavior data on the page, such as browsing paths, a higher collection frequency can be set, such as collecting the URL information of the user's current page every 1 second to fully record the user's browsing trajectory. For changes in the content of the user information input field, it can be collected every time the content changes to ensure the real-time and integrity of the data. As for device information, since it is basically determined when the user enters the page and does not change frequently, it can be collected once when the page loads.

[0088] Point embedding location: Home page: Button clicks: such as the "Apply Now" button, "Learn More" button, etc., to record the user's entry intention. Page dwell time: record the time the user stays on the home page to evaluate the attractiveness of the home page. Application process page: Form filling: input events for each input box, including input content, input time, etc., to understand the fluency of user filling and possible problems encountered. Drop-down menu selection: record the user's selection when selecting drop-down menus such as loan term and repayment method. Submit button: record the time and number of times the user submits the application. Result page: Approval result display: record the approval results (passed, rejected, etc.) seen by the user and the time to view the results. Buttons guiding the next step: such as clicks on buttons such as "Reapply" and "View Details".

[0089] Data types: 1) Behavioral data: A. Click events: Records the buttons, links, and other elements clicked by users. B. Page browsing events: Records the time when users enter and leave each page. C. Form filling data: Includes input content, selected options, etc. 2) Status data: A. Network status: Records the user's network status during the operation, such as good, poor, etc. 3) Device information: Includes device type, operating system version, etc. 4) Business data: Information such as loan application amount, term, and purpose, and 5) Approval result status (approved, rejected).

[0090] Collection Frequency: 1) Real-time Collection: Important actions such as button clicks and page transitions should be collected in real time to provide timely understanding of user actions. 2) Scheduled Collection: Page dwell time can be collected at regular intervals (e.g., 10 seconds) and the total dwell time calculated. During form filling, input status can be collected at regular intervals (e.g., 30 seconds) to prevent data loss.

[0091] The data collection module 12 is configured in the front-end H5 page and is used to collect user data in real time according to the data embedding plan determined by the data embedding design module, and send the user data to the back-end server.

[0092] In this embodiment, after the user data is sent to the back-end server, the user data collected in real time may be screened, and user data meeting the filtering conditions may be screened by inputting the filtering conditions.

[0093] In this embodiment, user data includes, but is not limited to, user behavior data and application information. Behavioral data includes: user browsing path, dwell time, and click events. Application information includes: application time, loan amount, loan term, repayment method; personal information (user name, age, and gender); business information (business size and operating information, etc.); and credit history (historical loan records and repayment records).

[0094] Design a metrics system for data tracking and collection, including user personal information, behavioral data, and device information. Personal information can include the user's name, age, and gender. Behavioral data can include browsing paths, dwell time, and click events. Device information can include the user's device model, operating system, and network environment. By comprehensively collecting these metrics, you can ensure the comprehensiveness and accuracy of the data.

[0095] In this embodiment, the user data also includes device information, and the device information includes: the user's device model, operating system, and network environment.

[0096] In this embodiment, the data acquisition module 12 may include:

[0097] The data collection unit 121 is used to collect user data in real time according to the data embedding plan determined by the data embedding design module.

[0098] In this embodiment, the data collection unit 121 is specifically used to collect data at the buried point location according to the corresponding collection frequency and data type.

[0099] The data encryption unit 122 is used to encrypt the user data to obtain encrypted user data.

[0100] In this embodiment, user data can be encrypted using conventional techniques in the art to obtain encrypted user data. For example, symmetric encryption algorithms, asymmetric encryption algorithms, hash encryption, etc. As an example, the AES algorithm is used for encryption: the key length is determined. AES supports 128-bit, 192-bit, and 256-bit keys. The longer the key length, the higher the security, but the encryption and decryption speed may be slightly slower. For example, if a 128-bit key is selected, a key with a length of 16 bytes (128 bits converted to bytes) can be generated using a secure random number generator. Encryption process: After the front-end H5 page or back-end server (depending on the specific encryption implementation location) obtains the user data, the data is encrypted using the selected AES algorithm and key. For example, in a JavaScript environment (front-end), some mature encryption libraries (such as CryptoJS) can be used to implement it. After the back-end server receives the encrypted user data (assuming it is transmitted in Base64 encoded form), it needs to first perform Base64 decoding and then use the same key to decrypt it.

[0101] The data compression unit 123 is configured to compress the encrypted user data to obtain compressed user data.

[0102] In this embodiment, the encrypted user data may be compressed using conventional technical means in the art to obtain compressed user data, for example, a lossless compression algorithm, such as the GZIP algorithm.

[0103] The data transmission unit 124 is configured to send the compressed user data to a backend server.

[0104] In this embodiment, the compressed user data can be sent to the backend server using conventional technical means in the art, for example, using HTTP protocol, message queue, etc.

[0105] In this example, a data tracking SDK is introduced into the front-end H5 page of the bank loan platform to achieve real-time collection of user behavior. The data tracking SDK can collect user behavior data such as clicks, inputs, and swipes on the page and send it to the back-end server for processing in real time.

[0106] The data processing and analysis module 13 is configured in the back-end server and is used to receive, process and analyze user data to determine data analysis results.

[0107] In this embodiment, the data processing and analysis module 13 may include:

[0108] The data processing unit 131 is used to receive user data and process it to obtain processed user data. Data processing includes preprocessing and feature extraction. Preprocessing includes data cleaning: removing invalid, erroneous, or duplicate data, formatting, and normalization. The collected user data is cleaned to remove invalid, erroneous, or duplicate data. For example, if the user's age field contains an obviously unreasonable value (such as over 150 years old), the record is corrected or deleted. The user data is formatted and normalized to make it suitable for subsequent analysis. Feature extraction: Features are extracted from user data, such as the user's browsing path, operation frequency, and the standardization of input content. These features are combined with the user's application information, such as basic information and financial data, to construct a more comprehensive feature set. Features that have a significant impact on risk assessment are selected from a large amount of user data, such as age and income level in personal information, number of overdue payments and number of previous loans in credit history, and frequency of recent loan product browsing in behavioral data. Furthermore, some features can be combined or new features can be derived, such as combining age and income level to generate a comprehensive indicator reflecting repayment ability. Categorical features (such as gender and occupation) need to be encoded and converted into numerical form for model processing. Common encoding methods include one-hot encoding. For numerical features, normalization can be performed to ensure that different features are in the same dimension to facilitate model learning. For example, the values ​​can be mapped to the range of 0-1 or standardized (with a mean of 0 and a variance of 1).

[0109] The data analysis unit 132 is used to analyze the processed user data to obtain data analysis results.

[0110] In this embodiment, the data analysis unit 132 may include: a model building unit 1321 and a model application unit 1322 .

[0111] The model building unit 1321 is used to build a data analysis model. The data analysis model includes a risk assessment model, a fraud detection model, an approval efficiency prediction model, etc.

[0112] In this embodiment, data analysis models can be constructed using machine learning, data mining, and other technologies. Data analysis models include, but are not limited to, risk assessment models, fraud detection models, and approval efficiency prediction models.

[0113] Real-time data analysis through data analysis models: Rapidly analyze incoming data to calculate key indicators such as user risk scores and fraud risk. Analyze bottlenecks and potential issues in the approval process, such as excessively long processing times at certain stages and high rates of user error in information entered.

[0114] Dynamic policy adjustments can be made through the Results Application module: Approval processes and policies can be adjusted dynamically based on data analysis results. For example, for users with high risk scores, the approval process can be streamlined and timelines shortened. Applications with a high risk of fraud can be reviewed more rigorously or even rejected.

[0115] For the risk assessment model, in this embodiment, the processed user data is divided into a training set, a validation set, and a test set according to a certain ratio. Typically, the training set is used to train the model, allowing it to learn the relationship between features and risks. The validation set is used to adjust the model's hyperparameters during the model training process and select the optimal model configuration. The test set is used to ultimately evaluate the model's performance on unseen data. A common partition ratio is 7:2:1 (training set: validation set: test set). Training with the training set: Taking the logistic regression model as an example, training is performed using the divided training set data (assuming that the feature data is stored in the X_train matrix and the corresponding risk label data is stored in the y_train vector. The risk label can be 0 for low risk and 1 for high risk). Hyperparameter adjustment (optional): For some models (such as the maximum depth of the decision tree and the number of trees in the random forest), hyperparameter adjustment is required using the validation set to find the optimal parameter combination for optimal model performance. Common hyperparameter adjustment methods include grid search and random search. Model Evaluation: The trained model is evaluated using the test set data (feature data is X_test, and the corresponding risk label is y_test). Common evaluation metrics include accuracy, precision, recall, F1 value, and area under the ROC curve (AUC). Risk Score Acquisition and Application: User data (after undergoing the same feature engineering process as above) that needs to be assessed in actual applications is fed into the trained risk assessment model. The model will output the corresponding prediction result (for classification models, 0 or 1 represents the risk category. For models that can output probabilities, such as logistic regression, the probability value between 0 and 1 can be used as a risk score, with higher probabilities indicating greater risk). Based on this risk score, the user's credit risk and loan risk are characterized and decisions are made. For example, a threshold (such as 0.5) is set. Users with risk scores greater than this threshold are considered high-risk users, and more cautious measures may be taken in loan approval (such as reducing the loan limit, increasing the interest rate, or directly rejecting the loan application).

[0116] The model application unit 1322 is used to input the processed user data as input data into the risk assessment model to obtain the user's corresponding risk score. The risk score is used to represent the user's credit risk and loan risk.

[0117] In this embodiment, the risk score calculation includes:

[0118] User data collection: Collect multi-dimensional data of users, including but not limited to personal information (such as age, occupation, income, etc.), business information (such as business size, revenue, profitability, etc.), credit history (such as past loan records, repayment status, etc.), behavioral data (such as operating behavior and stay time on the H5 page).

[0119] Indicator setting: Determine the weights for each data point. For example, credit history may be weighted more heavily because it directly reflects a user's repayment ability and credit standing. Business information will also be weighted to reflect a user's financial strength and stability.

[0120] Scoring model: A mathematical model (risk assessment model) is used to comprehensively calculate various indicators. Common models that can be used as risk assessment models include linear regression, logistic regression, and decision trees. These models convert the values ​​of different indicators into a specific risk score.

[0121] In this embodiment, risk assessment model construction: A machine learning algorithm can be used to train a risk assessment model based on user data. The risk assessment model learns how to predict the user's loan risk based on the characteristics.

[0122] Real-time analysis: When users operate on the H5 page, the server processes the user data and behavioral data in real time and uses the trained model to perform risk assessment. The model outputs a risk score that reflects the user's credit risk and loan risk.

[0123] In this embodiment, the data analysis unit 132 may further include: a user portrait construction unit 1323, which is used to analyze the processed user data and construct a user portrait.

[0124] The following are the detailed steps to build a user profile:

[0125] 1. Data Collection and Integration

[0126] 1. Personal Information Collection: Age: Obtained from user registration information or real-name authentication channels. People of different ages often have different consumption and loan needs. For example, young people may prefer consumer loans for education or electronic products, while middle-aged people may be more interested in housing loans or business investment loans.

[0127] Occupation: This information is supplemented through user-provided workplace and position information, or through third-party data platforms. Occupation is closely related to income stability, loan purpose, and repayment ability. For example, civil servants and employees of large corporations typically have relatively stable incomes, which may give them an advantage in loan approval. However, the income of freelancers or early-stage entrepreneurs fluctuates significantly, so risk assessment requires a comprehensive approach.

[0128] Income: This can be reported proactively by the user, but must be verified in conjunction with other data. Some bank loan platforms may require users to provide information such as payroll statements and tax returns to more accurately determine their income level and assess their repayment capacity and credit risk. For example, high-income users may require higher loan amounts and have stronger repayment capabilities, but they may also face more investment temptations and potential risks.

[0129] 2. Business operation information collection (for business owners or related users) Business size: Determined based on information such as the number of employees, asset size, and office space. Large enterprises may have advantages in financing channels and costs, and their loan needs are often related to strategic decisions such as expansion and mergers and acquisitions. Small and micro enterprises may be more concerned with working capital loans to maintain daily operations. Due to their smaller size, they are relatively less resilient to risks. Therefore, when conducting risk assessments, it is necessary to focus on their operational stability and cash flow. Revenue and profitability: Data is obtained through corporate financial statements, tax return records, or integration with corporate financial software. Enterprises with stable revenue growth and good profitability are generally considered low-risk customers when applying for loans and may receive more favorable loan terms. Conversely, enterprises with declining revenue or long-term losses may face stricter scrutiny of their loan applications, and banks may require additional guarantees or collateral.

[0130] 3. Credit history collection of past loan records: Query all historical loan information of users from the credit reporting system, including lending institutions, loan amounts, loan terms, repayment status, etc. Users who have repaid their loans on time and in full many times have higher risk scores and lower credit risks. Users with a history of overdue payments, especially those with a large number of overdue payments or a long overdue period, have significantly increased credit risks. Banks may increase interest rates, reduce loan amounts, or directly reject applications when approving loans. Repayment status: Detailed analysis of the repayment details of each loan, such as whether there are early repayments, partial repayments, the frequency and duration of overdue repayments, etc. Early repayments may reflect that the user has ample funds or is sensitive to interest rates. Frequent overdue repayments indicate that there are problems with the user's repayment ability or willingness, and are important negative indicators for credit risk assessment.

[0131] 4. Behavioral data collection (based on H5 pages) Operational behavior: Record user clicks, swipes, submissions, and other operations on the H5 page. For example, users frequently click on high-value loan product pages, which may imply that they have a large demand for funds. In-depth browsing of the loan product details page, including checking information such as interest rates, repayment methods, and terms, indicates that users have a high level of attention and willingness to understand the loan products. In subsequent marketing or risk assessments, relevant information can be provided or audits can be strengthened. Dwell time: Statistics on the time users spend on different pages or specific functional modules. Staying on the credit assessment prompt page for a long time may indicate that the user is more concerned about their credit status or is carefully checking the information. Staying on the loan application submission page for too long without submitting may indicate hesitation in the loan decision. Further analysis of the reasons is needed, such as whether there are difficulties in filling in the information, doubts about the loan terms, etc., so as to provide corresponding assistance or optimize the page design.

[0132] 2. Data cleaning and preprocessing:

[0133] 1. Missing value processing For missing data in personal information, business information or credit history, first determine the reason for the missingness and the importance of the data. If key data is missing (such as income information is missing and cannot be verified through other means), it may be necessary to further communicate with the user to obtain supplementary information, or make reasonable inferences or fill in based on other relevant information of the user. For example, for users who are teachers with a long working history, the average income level of teachers with the same title in the same region can be used for filling. For non-critical data missing (such as the detailed house number in the company's registered address is missing), the mode can be used for filling (if there are many identical values ​​in the field) or the missing value can be ignored directly. The specific processing method depends on the usage scenario of the data and its impact on the accuracy of the model.

[0134] 2. Outlier Handling: Personal information is checked for outliers, such as age outside the reasonable range (over 120 years old or under 0 years old), income that is too high or too low (significantly inconsistent with the occupational or regional average), etc. Outliers require further verification of their authenticity. If the information is incorrectly entered by the user or due to data entry errors, they should be corrected promptly. In special cases (such as high-income celebrities or corporate executives), relevant supporting documentation should be collected for confirmation. If unusually large fluctuations in revenue or profits occur in business operating information, in-depth analysis of the underlying causes is required. These could include major strategic adjustments, sudden market changes, or financial data falsification. Comparison and verification with multiple data sources, such as corporate financial statements and industry reports, ensures data reliability. Any unusual overdue records in credit history (e.g., a loan that has been overdue for several years and has not been processed, while the user has a good record of other loans) requires investigation into any special disputes or system errors, and appropriate corrections or annotations should be made.

[0135] 3. Data standardization and normalization For numerical data, such as age, income, corporate revenue, etc., standardization is performed to make them have uniform dimensions and distribution characteristics. Common standardization methods include Z-score standardization, which converts the data into a standard normal distribution with a mean of 0 and a standard deviation of 1 by calculating the difference between each data point and the mean and dividing it by the standard deviation. This can avoid the adverse effects on model training caused by large differences in numerical values ​​between different features. For some data with a clear range of values ​​or proportional relationships, such as risk scores (usually between 0-100) or the frequency of page operations (values ​​between 0-1 represent relative proportions), normalization can be used to map the data to a specific interval to facilitate the model to understand and compare the importance of different features.

[0136] 3. User portrait label construction:

[0137] 1. Basic Attribute Tags: Age Tags: Categorize users by age range, such as 20-30 years old for youth, 31-50 years old for middle-aged, and 51 years old and above for seniors. Different age tags can be further subdivided, such as youth can be divided into 20-25 years old (just entering the workforce) and 26-30 years old (starting a career), allowing for more accurate analysis of loan needs and risk profiles across age groups. Occupation Tags: Categorize users by industry (e.g., finance, internet, manufacturing) and occupational type (e.g., manager, technician, general employee). For example, those working in the financial industry may have a deeper understanding of financial products and may prioritize favorable interest rates and flexible repayment options when choosing a loan. Meanwhile, general manufacturing employees may prioritize loan amounts and approval speed to meet their daily needs or purchase a house or car. Income Level Tags: Categorize users into low-, middle-, and high-income groups based on local income levels and industry averages. For example, in first-tier cities, an annual income below 100,000 yuan may be considered low-income, 100,000-500,000 yuan as middle-income, and 500,000 yuan and above as high-income. The income level label is directly related to the loan amount and repayment ability, and is an important basis for credit risk assessment and loan product recommendations.

[0138] 2. Credit Risk Label: A risk score is calculated based on a user's credit history using a risk scoring model. This score is then assigned a credit rating, such as Excellent (80-100 points), Good (60-79 points), Fair (40-59 points), and Poor (0-39 points). The risk score provides a visual reflection of a user's overall credit profile. A higher credit rating indicates a higher loan approval rate and lower interest rates. Conversely, users with lower credit ratings may face loan restrictions or higher loan costs. Overdue Risk Label: This assesses a user's overdue risk based on factors such as the number, duration, and amount of past loan delinquencies. The labels are categorized as Low Risk (no overdue payments or occasional short-term overdue payments), Medium Risk (a certain number of overdue payments that have been repaid or are improving), and High Risk (multiple overdue payments with significant amounts and duration). Overdue risk labels help banks implement proactive risk mitigation measures, such as requiring high-risk users to provide additional collateral or increase down payment requirements.

[0139] 3. Behavioral Characteristic Tags: Activity Tags: Users are categorized as highly active, moderately active, and lowly active based on metrics such as login frequency, number of actions, and dwell time on the H5 page. Highly active users are likely to have a high level of interest in and demand for loan products and can be targeted for regular personalized loan product information and promotions. Lowly active users require further analysis of the reasons for their inactivity, such as a poor platform experience or product mismatch, so that appropriate improvement measures can be implemented. Product Preference Tags: Users' product preferences are determined by analyzing their click and browsing behavior on the page across different loan product types (e.g., consumer loans, home loans, commercial loans, etc.) and product features (e.g., interest rates, repayment terms, and loan amounts). For example, a user who frequently browses home loan products and focuses on low interest rates and long loan terms can be labeled as a home loan preference user, allowing for targeted recommendations of suitable home loan products and related services in subsequent marketing.

[0140] 4. Business operating status label (for business owners) Business size label: Based on indicators such as the number of employees and asset size, enterprises are divided into small and micro enterprises, medium-sized enterprises, and large enterprises. Enterprises of different sizes have obvious differences in loan demand, risk tolerance, and financing channels. Small and micro enterprises are generally more dependent on external financing such as bank loans, and the loan amount is relatively small. Large enterprises may have more diverse financing options, and loan demand is more related to strategic expansion and capital operations. Business stability label: Based on factors such as the company's revenue growth rate, profit stability, and market share changes, the business stability of the company is assessed and divided into stable growth, fluctuation, and decline types. Companies with stable growth have more advantages in loan approval and can obtain more favorable loan terms. Companies in decline may face higher risk assessments and stricter loan approval processes. Banks may require companies to provide detailed business improvement plans and guarantee measures.

[0141] 4. User portrait update and maintenance:

[0142] 1. Regular update mechanism: Update user profiles at fixed intervals (e.g., monthly or quarterly). Over time, users' personal information, business operations, credit history, and behavioral data may change. For example, a user's income may increase or decrease due to a promotion, salary increase, or industry change. A company's revenue and profitability will change with adjustments to the market environment and business strategies. Credit history will be continuously updated with new loan applications and repayment records. User behavior on the H5 page will also vary due to platform function optimization, product updates, or changes in personal needs. By regularly updating user profiles, the user's latest status can be reflected in a timely manner, ensuring that the bank uses accurate and valid data in business processes such as loan approval, risk assessment, and product recommendations.

[0143] 2. In addition to regular updates, a real-time update mechanism should be established to immediately update the user profile when certain key events occur. For example, after a user successfully applies for and obtains a new loan, their credit history and debt status change, and the relevant profile tags should be updated promptly. When a business undergoes major equity changes, asset restructuring, or legal proceedings, this information may have a significant impact on the business's operating conditions and credit risk, and the business owner's user profile also needs to be updated immediately. In addition, some important user actions on the H5 page, such as submitting a loan application, modifying personal information, or conducting large-scale financial transactions, can also serve as real-time update triggers, allowing banks to keep abreast of user dynamics and make appropriate business decisions.

[0144] 3. Data Quality Monitoring and Maintenance: During the user profile update process, strengthen data quality monitoring and maintenance. Regularly check data integrity, accuracy, and consistency, and promptly identify and address issues such as missing data, outliers, and erroneous data. Establish data backup and recovery mechanisms to prevent data loss or corruption from adversely impacting user profile construction and application. Furthermore, as business evolves and data sources change, continuously optimize data collection and processing processes to improve data quality and availability, providing a solid foundation for the accurate construction and effective application of user profiles.

[0145] In this embodiment, the data analysis unit 132 may further include:

[0146] User behavior analysis unit, used to analyze page browsing behavior: tracking users' browsing paths on H5 pages to understand user preferences and behavior patterns. Analyzing operation frequency and dwell time: analyzing the frequency and dwell time of users in different operations (such as filling out forms and clicking buttons).

[0147] Device analysis unit for analyzing device type and operating system: Identifying the user's device type and operating system to facilitate personalized services. Geographic location analysis: Understanding the user's location information through GPS data for risk assessment and market positioning.

[0148] Personal information analysis unit, used to analyze user basic information: Analyze personal information submitted by users, such as age and occupation, to assess loan needs. Analyze financial status: Analyze financial information submitted by users, such as income and liabilities, to assess repayment ability.

[0149] The default prediction unit is used to predict the user's future default probability to help banks formulate loan strategies.

[0150] The server processes and analyzes the collected data in real time to build user profiles and risk assessment models. Based on user behavior data, the server can analyze information such as loan needs and credit status, providing a basis for loan approval. For example, users' browsing paths and dwell time can be used to analyze their interests and needs. Click events can also be used to analyze user behavior habits and credit intentions.

[0151] The result application module 14 includes a loan approval optimization unit 141, which is used to optimize the loan approval process according to the data analysis results, including: adjusting the loan amount and interest rate, making loan approval decisions, so as to speed up the loan approval process.

[0152] In this embodiment, the loan approval optimization unit 141 is specifically configured to adjust the loan amount and interest rate according to the risk score corresponding to the user, and to automatically or manually make a loan approval decision based on the risk score corresponding to the user and preset approval criteria.

[0153] In this example, loan approval decisions are made automatically or manually based on risk scores and pre-set approval criteria. High-risk loan applications may be rejected or require additional review and collateral. Automated approval tools (such as RPA Robotic Process Automation) can be used to reduce manual intervention. Strategy optimization: Loan limits, interest rates, terms, and other policies are adjusted based on market changes and user needs.

[0154] The loan approval optimization unit 141 is also used to identify and eliminate redundant links and invalid operations in the approval process, optimize the process sequence and node settings, and improve approval efficiency.

[0155] In this embodiment, the result application module also includes a monitoring and feedback unit, which is used to monitor approved loans in real time and detect potential risks in a timely manner. The monitoring results and loan performance are constructed and fed back to the model for continuous optimization of the risk assessment model. The monitoring and feedback unit is also used to establish a risk monitoring mechanism to monitor abnormal situations in the loan approval process in real time. Set an early warning threshold, and once the early warning condition is triggered, immediately activate the emergency response mechanism. Feedback and iteration: User feedback collection: Collect user feedback through user surveys, satisfaction surveys, etc. Analyze user feedback data to understand user needs and pain points. Effect evaluation: Evaluate the effect of the optimized approval process and strategy. Evaluation indicators include but are not limited to approval pass rate, approval time, user satisfaction, etc. Continuous optimization: Continuously optimize the approval process and strategy based on the evaluation results and user feedback.

[0156] In this way, the technical solution of the present invention can achieve real-time assessment of loan risks, improve approval efficiency and accuracy, and reduce default risks.

[0157] In an optional embodiment, the result application module 14 further includes: a product optimization unit for optimizing the front-end H5 page of the bank loan platform in terms of product functions, design, marketing strategies, etc. based on the data analysis results.

[0158] The user experience improvement unit is used to optimize the user experience and improve user satisfaction and conversion rate based on user behavior data.

[0159] The risk control unit is used to identify and warn of potential risks, reduce bad debt rates, and ensure the stable operation of banking business.

[0160] In an optional embodiment, the data embedding design module 11 further includes:

[0161] The custom event tracking unit is used to customize the tracking of specific events according to business needs to capture user behavior data in specific scenarios.

[0162] The tracking verification unit is used to verify the correctness and validity of the tracking code to ensure the accuracy and completeness of data collection.

[0163] In an optional embodiment, the data processing and analysis module 13 further includes:

[0164] The data visualization unit is used to visualize the analyzed data in the form of charts, reports, etc., to facilitate business personnel's understanding and application.

[0165] The trend prediction unit predicts future user behavior and market trends based on historical data and behavioral patterns, providing data support for business decisions.

[0166] In an optional embodiment, the information processing system based on the bank loan platform further includes:

[0167] The user privacy protection module is used to desensitize user sensitive information during data collection, processing and storage to ensure the security and compliance of user privacy.

[0168] The permission management module is used to set different users' access and operation permissions to system functions to ensure data security and system stability.

[0169] In an optional embodiment, the data embedding design module 11 and the data acquisition module 12 are implemented through a front-end framework or SDK (software development kit) to facilitate system integration and maintenance.

[0170] The data processing and analysis module 13 uses big data processing technology and machine learning algorithms to improve the efficiency and accuracy of data analysis.

[0171] In an optional implementation, the system can monitor the operation of data points in real time, including various links such as data collection, transmission, processing and storage, and promptly detect and handle abnormal situations to ensure the stability of the system and the reliability of the data.

[0172] In an optional embodiment, the information processing system based on the bank loan platform also includes an alarm notification module for sending alarm notifications to relevant personnel via email, text message, etc. when abnormal situations are discovered, so that timely processing can be carried out. A logging module is used to record system operation logs and key information during data processing to facilitate problem tracing and troubleshooting.

[0173] The present invention also provides a full-process data tracking service for a front-end H5 page based on a bank loan platform. This service provides a full-process service from data tracking design, data collection, processing and analysis to result application, including but not limited to:

[0174] We provide consulting and solution development services for data tracking design. We also provide front-end SDK or framework integration and maintenance services. We also provide back-end data processing and analysis services, including data cleansing, integration, visualization, and trend forecasting. We also offer advice and strategic support for business optimization and risk control.

[0175] The technical solution of the present invention embeds data points in the front-end H5 page to realize the real-time collection, transmission and analysis of users' behavioral data in the application process of the bank loan platform, providing banks with accurate user portraits and risk assessments, and improving the accuracy and efficiency of loan approval.

[0176] The result application module can be used for business optimization:

[0177] Approval process optimization: Analyze data during the approval process, optimize the approval process, and improve approval efficiency.

[0178] Product and service optimization: Adjust loan products and services based on user needs and behaviors to improve user satisfaction.

[0179] The result application module can be used for monitoring and early warning:

[0180] Abnormal behavior monitoring: Real-time monitoring of data streams, detection of abnormal behavior, and timely triggering of early warning mechanisms.

[0181] Performance indicator monitoring: Monitor key business indicators, such as approval speed and pass rate, to ensure stable business operation.

[0182] The result application module can be used for data visualization:

[0183] Real-time reports: Generate real-time reports and present analysis results in charts to facilitate management to make decisions quickly.

[0184] Dashboard: Create management dashboards to centrally display key business indicators and monitoring results.

[0185] Decision support: Real-time data: The server processes and analyzes user behavior data on the H5 page in real time, such as click behavior and operation frequency. Decision basis: These analysis results serve as an important basis for approval decisions, helping approval personnel or automated approval systems quickly make decisions on loan approval or rejection.

[0186] Risk assessment: Risk scoring: Generate a risk score through real-time analysis of user data to reflect the user's credit status and loan risk.

[0187] Approval criteria: Risk scores directly affect approval results. Users with high risk scores may receive faster approval and more favorable loan terms.

[0188] Approval process optimization: Efficiency improvement: Real-time data analysis can help banks identify bottlenecks in the approval process, thereby optimizing the process and improving approval efficiency. Automated approval: For low-risk loan applications, automated approval can be implemented to reduce manual intervention and speed up approval.

[0189] Personalized services: Customized approval: Based on the user's risk score and behavioral data, personalized approval suggestions and loan conditions are provided. Customized marketing: Analyze user behavior to provide users with customized loan products and services to improve marketing conversion rates.

[0190] Risk Control: Risk Identification: By analyzing data in real time, the server can promptly identify potential high-risk loan applications and take appropriate risk control measures. Early Warning System: Establish an early warning system to monitor the approval status of loan applications in real time and promptly detect and address risks.

[0191] User experience: Improved transparency: Real-time feedback on approval status and decision-making basis improves user experience and satisfaction.

[0192] Feedback loop: Collect user feedback and loan performance, continuously optimize approval processes and models, and improve approval quality and user satisfaction.

[0193] Real-time data stream: The server receives data from the H5 page in real time, such as user behavior data, personal information, etc.

[0194] Data analysis engine: Use the big data analysis engine to analyze real-time data (user data collected in real time) to generate risk scores, risk assessments and other results.

[0195] Approval decision support system: Feedback data analysis results to the approval decision support system in real time to assist approvers or automatic approval systems in making decisions.

[0196] Approval process optimization: Based on real-time analysis results, optimize the approval process to improve approval efficiency and quality.

[0197] User feedback loop: Collect user feedback and loan performance, continuously optimize approval processes and models, and improve approval quality and user satisfaction.

[0198] The technical solution of the present invention can significantly speed up the approval process. The core advantages of the technical solution of the present invention lie in automation and efficiency improvement, which are specifically reflected in the following aspects:

[0199] Automated Approval: Leveraging real-time data analysis, the server automatically generates risk scores and risk assessments, enabling quick loan approval or rejection decisions. This automated approval process reduces manual intervention, making it faster and more efficient.

[0200] Reduce human errors: Manual approval may be affected by factors such as the approver's experience and fatigue, while the automated approval system is based on data and algorithms, reducing human errors and improving the accuracy of approval.

[0201] Real-time decision support: Data processed and analyzed in real time can provide real-time support for approval decisions, allowing approvers to make decisions based on the latest data rather than outdated information.

[0202] Process Optimization: By real-time monitoring and analyzing data, bottlenecks in the approval process can be identified, leading to process optimization and improved approval efficiency. For low-risk loan applications, automated approval can be implemented to reduce approval time.

[0203] Personalized approval: Based on user data analysis, personalized approval suggestions and loan conditions can be provided, thereby speeding up the approval process and improving the quality of approval.

[0204] The server processes and analyzes the collected data in real time, including not only loan demand and credit status, but also the following aspects:

[0205] User behavior analysis: Browsing paths and dwell time: Analyze users' browsing paths and dwell time on H5 pages to understand their interests and needs. Click events: Analyze users' operating habits and preferences by clicking on different buttons or links. Operation frequency: Count the frequency of users performing different operations (such as filling out forms and submitting applications) to assess user engagement and activity.

[0206] Device Information: Device type and operating system: Identify the user's device type and operating system to facilitate personalized services. Geographic location: Use GPS data to understand the user's location information for risk assessment and market positioning.

[0207] Personal Information: Basic Information: Analyze basic information submitted by users, such as age and occupation, to assess loan needs. Financial Status: Analyze financial information submitted by users, such as income and liabilities, to assess repayment ability.

[0208] Risk Assessment: Risk Scoring: Use machine learning models to assess user risk scores to assess loan risks.

[0209] Default prediction: Predicting users’ future default probability to help banks formulate loan strategies.

[0210] Business Optimization: Approval process optimization: Analyze data during the approval process, optimize the approval process, and improve approval efficiency. Product and service optimization: Adjust loan products and services based on user needs and behaviors to improve user satisfaction.

[0211] Monitoring and early warning: Abnormal behavior monitoring: Real-time monitoring of data flow, detection of abnormal behavior, and timely triggering of early warning mechanisms. Performance indicator monitoring: Monitoring key business indicators, such as approval speed and approval rate, to ensure stable business operations.

[0212] Data visualization: Real-time reports: Generate real-time reports and present analysis results in charts to facilitate quick decision-making by management. Dashboard: Create management dashboards to centrally display key business indicators and monitoring results.

[0213] Through real-time processing and analysis of these contents, the technical solution of the present invention can provide banks with in-depth business insights, optimize user experience, improve approval efficiency, reduce operating costs, and effectively control risks.

[0214] Calculation method: 1. Risk score calculation:

[0215] Data collection: Collect multi-dimensional data of users, including but not limited to personal basic information (such as age, occupation, income, etc.), business information (such as company size, revenue, profitability, etc.), credit history (such as past loan records, repayment status, etc.), behavioral data (such as operating behavior and stay time on the H5 page).

[0216] Indicator setting: Determine the weights for each data point. For example, credit history may be weighted more heavily because it directly reflects a user's repayment ability and credit standing. Business information will also be weighted to reflect a user's financial strength and stability.

[0217] Scoring model: This method uses mathematical models to comprehensively calculate various indicators. Common models include linear regression, logistic regression, and decision trees. These models convert the values ​​of different indicators into a specific risk score.

[0218] II. Loan Limit and Interest Rate Adjustment: Generally speaking, higher risk scores are associated with higher loan limits. Different risk score ranges can be set, corresponding to different loan limit ranges. For example, users with high risk scores may receive a higher loan limit, while users with low risk scores may receive a lower loan limit. At the same time, the user's actual needs and risk tolerance are taken into consideration. If the user's business is performing well and has a clear purpose for the funds and repayment plan, the loan limit may be appropriately increased, even if the risk score is not particularly high.

[0219] Interest rate adjustment: Interest rates are also determined based on risk scores. Users with high risk scores are considered relatively low risk and can receive lower interest rates. Users with low risk scores are considered high risk and can receive correspondingly higher interest rates. Different risk score ranges can be set, corresponding to different interest rates. Furthermore, adjustments can be made based on factors such as market interest rates and bank funding costs. For example, when market interest rates are low, even users with low risk scores may receive relatively low interest rates.

[0220] Improve approval accuracy: Multi-dimensional data evaluation: Through data embedding, we can collect a variety of user information, such as business operating data (including revenue, profit, tax payment, etc.), personal credit data (such as past loan records, repayment status, credit rating, etc.), behavioral data (operation behavior on the H5 page, stay time, etc.). Comprehensively integrating these multi-dimensional data to evaluate the user's credit status and repayment ability is more accurate and objective than relying solely on one or two pieces of information. For example, although a company's revenue has declined slightly recently, it has a good repayment record in the past, and it has carefully filled in the information on the H5 page and carefully reviewed the relevant terms. Combining this information can more accurately assess its risk level, rather than simply rejecting the loan or granting a lower loan amount based solely on the decline in revenue.

[0221] Dynamic, real-time analysis: Real-time data processing can promptly reflect a user's current status and changes. For example, if a company recently secured a large order or an individual's financial situation improved, this real-time information can be captured and incorporated into analysis, enabling more precise adjustments to loan amounts and interest rates. Compared to traditional periodic assessments or reliance on static data, this dynamic, real-time analysis can better adapt to market changes and dynamic fluctuations in user circumstances, ensuring that approval results are more aligned with the user's actual situation and improving accuracy.

[0222] Identifying Risk Signals: During real-time data processing and analysis, risk indicators and early warning mechanisms can be established to quickly identify potential risk signals. For example, if a user frequently inquires about loan limits or interest rates within a short period of time, this may indicate an urgent need for funds or other potential issues. If a company's financial indicators suddenly deteriorate, the system can promptly detect and conduct in-depth analysis, allowing for more cautious approval measures, such as reducing the limit or raising the interest rate. This effectively reduces the risk of default and improves the accuracy of approvals.

[0223] Improve approval efficiency:

[0224] Automated decision-making process: Automatically adjusts loan limits and interest rates based on data analysis, reducing manual intervention. In traditional approval processes, loan officers spend a significant amount of time collecting information, conducting analysis and judgment, and then determining loan limits and interest rates. This process is not only inefficient but also susceptible to human factors, such as loan officers' experience and subjective judgment. However, automated decision-making processes, once data collection is complete, quickly calculate and adjust based on pre-set algorithms and rules, significantly shortening approval times and improving overall efficiency.

[0225] Rapidly respond to market demand: The system can adjust in real time based on market conditions and user needs. For example, when market capital is tight, banks can use data analysis to promptly adjust interest rates to attract high-quality customers. Alternatively, when a particular industry is experiencing strong growth, the system can automatically offer more favorable credit lines and interest rates to businesses within that industry, quickly meeting market demand and improving business competitiveness. This also improves approval efficiency and avoids customer churn caused by slow approvals.

[0226] Optimizing resource allocation: By analyzing large amounts of data, approval resources can be more rationally allocated. Applications with lower risks and clear limits and interest rate adjustments can be quickly approved, while more manpower and time can be allocated to in-depth review of complex or higher-risk applications. This allows for more efficient use of the bank's approval resources and improves overall approval efficiency.

[0227] 1. Business optimization and decision support:

[0228] Product Improvement: The number of active users reflects a product's appeal and user stickiness. If the number of active users is low, user behavior data can be analyzed to identify the causes of user churn and targeted improvements can be made to product features, interface design, or user experience. For example, if a high user churn rate is detected at a particular step in the process, the process can be optimized to make it more streamlined and streamlined. The number of transacting users directly reflects the product's commercial value. By analyzing the behavioral characteristics, preferences, and needs of transacting users, loan product terms, loan limit ranges, interest rate settings, and other factors can be optimized to better meet user needs and enhance the product's market competitiveness.

[0229] Marketing Strategy Adjustment: The growth trend of registered users can be used to evaluate the effectiveness of marketing campaigns. If the number of registered users is growing slowly, adjustments to marketing channels, advertising strategies, or promotional campaigns may be necessary. For example, if data analysis indicates that a particular channel is generating high-quality registered users, investment in that channel can be increased. Changes in the number of active users and transacting users can help determine marketing priorities. If the number of active users is high but the number of transacting users is low, it may be necessary to strengthen conversion marketing for active users, such as offering exclusive offers and personalized loan plan recommendations, to improve transaction conversion rates.

[0230] 2. Risk Assessment and Management

[0231] Credit risk assessment: Combining data such as the number of registered users, active users, and transacting users can create a more comprehensive user profile to assist in credit assessment. For example, users with long-term activity and a history of transactions may be considered relatively low risk, while newly registered users or those with low activity levels may require more cautious credit review. Analyzing the repayment behavior and overdue payments of different user types can optimize risk scoring models and improve the accuracy of risk identification. For example, if a certain type of active user is found to have a low overdue rate, this user can be offered a more favorable interest rate or higher credit limit.

[0232] Fraud risk prevention: By monitoring unusual growth in the number of registered users and sudden changes in user behavior patterns, potential fraud can be detected promptly. For example, if the number of registered users increases significantly within a short period of time, and if these users exhibit similar information or unusual behavior, this may indicate the risk of mass registration fraud. Changes in the number of transacting users can also serve as an early warning indicator of fraud risk. If a user suddenly engages in frequent transactions or the transaction amounts are unusual, further scrutiny may be required to verify the authenticity and legitimacy of their transactions.

[0233] 3. Resource Allocation and Planning

[0234] Human Resources: Based on fluctuations in the number of active and transacting users, human resources such as customer service and approval personnel can be rationally allocated. During peak business periods, additional staff can be added to ensure timely processing of user inquiries and loan applications. During slow business periods, staff training or business process optimization can be implemented to improve work efficiency.

[0235] Technical resource optimization: By analyzing data traffic and user behavior, we can optimize server configuration and network bandwidth to ensure system stability and responsiveness. For example, if we detect a high concentration of user traffic during a certain time period, we can increase server resources in advance to avoid system lag or crashes.

[0236] Based on changes in user needs and usage habits, timely adjust the direction of technology research and development to develop features and services that better meet user needs. For example, if user demand for mobile devices increases, you can increase the optimization and function expansion of mobile H5 pages.

[0237] 1. Data collection and analysis stage:

[0238] Comprehensively collect user behavior data: Use tracking to collect user behavior on H5 pages, including clicks, swipes, input, and dwell time. For example, record how long users spend on different pages to understand their attention to each aspect. Collect user device information, network status, and other information to analyze external factors that may affect user experience.

[0239] Deepen your data analysis: Analyze user behavior at key points in the application process. For example, examine how many users drop out midway through the application process. Reasons for this dropout could include complex form design, too many required fields, and so on. Analyze page transitions to understand whether users are following the expected process. If a large number of users choose an unexpected path after a certain page, this may indicate that the page's navigation isn't clear enough.

[0240] 2. Problem identification and optimization direction determination stage:

[0241] Identify the problem: Based on the data analysis results, identify the specific issues that affect the user experience. For example, if you find that users stay too long on a certain page and the bounce rate is high, the page may load slowly or the content may be difficult to understand.

[0242] Pay attention to issues that receive frequent user feedback, such as cumbersome application procedures and unclear information prompts.

[0243] Determine optimization direction: Slow loading speed: Optimize page resource loading strategy, compress images, reduce unnecessary script loading, etc. For complex form design: Simplify form content, reasonably arrange required and optional items, and provide clear filling instructions and examples.

[0244] If users are easily lost in the process, optimize page navigation and guidance, using clear button labels and process instructions.

[0245] 3. Optimization implementation and verification stage:

[0246] Implementation of optimization measures:

[0247] Based on the determined optimization direction, make corresponding adjustments and improvements to the H5 page. For example, redesign the form layout, improve page loading speed, optimize the display of prompt information, etc.

[0248] During the implementation process, ensure that the optimization measures do not introduce new problems, such as compatibility issues, functional anomalies, etc.

[0249] Verify optimization results: Conduct data tracking again to collect user behavior data on the optimized H5 page. Compare the data before and after optimization to evaluate the effectiveness of the optimization measures. If user dwell time is shortened, bounce rates are reduced, and application completion rates are increased, the optimization measures are effective. If the results are not significant, further analysis is needed to adjust the optimization plan.

[0250] Data application solutions:

[0251] Loan Approval Optimization: Application: Use data analysis results (such as risk scores and repayment ability scores) to optimize the loan approval process. Optimization Solution: For users with high risk scores, loans can be automatically approved or offered more favorable loan terms. For users with low risk scores, loans can be further reviewed or rejected.

[0252] Personalized service provision: Application: Provide personalized loan products and services based on user profiles and preferences. Optimization plan: Customize loan products for different user groups, such as providing low-interest loans for students and flexible repayment plans for small businesses.

[0253] Enhanced Risk Control: Application: Leverage data analytics to identify high-risk loans and implement preventative measures. Optimization: For identified high-risk loans, increase collateral requirements, adjust loan amounts or interest rates, or implement stricter repayment monitoring.

[0254] Marketing strategy adjustment: Application: Analyze user behavior and needs to optimize marketing strategies. Optimization plan: Develop targeted marketing activities based on user characteristics and behaviors to improve marketing conversion rates.

[0255] User Experience Improvement: Application: Improve user experience by analyzing user behavior on H5 pages. Optimization: Simplify the application process, optimize page layout, provide useful guidance and assistance, and reduce user churn.

[0256] Specific optimization plan:

[0257] Automated approval process: Access the bank's automatic approval system to automatically make approval decisions based on data analysis results.

[0258] Dynamic risk pricing: Dynamically adjust loan interest rates and limits based on risk scores to balance risk and return.

[0259] User behavior feedback loop: Establish a feedback mechanism to incorporate users’ repayment behavior and loan performance into data analysis and continuously optimize the model.

[0260] Data-driven marketing: Utilize data analysis results to implement precision marketing and improve marketing efficiency.

[0261] User interface optimization: Based on user behavior data, optimize the design and functionality of H5 pages to improve user satisfaction and conversion rate.

[0262] Monitoring and early warning system: Implement a real-time monitoring and early warning system to promptly detect and address potential risks.

[0263] Continuous learning and model updates: Data analysis models are regularly evaluated and updated to adapt to market changes and changes in user behavior.

[0264] Through these optimization schemes, the technical solution of the present invention can more effectively utilize data analysis results, improve the efficiency of business processes, reduce operating costs, and at the same time improve risk management capabilities and customer service quality.

[0265] The specific risk assessment plan includes the following steps:

[0266] Data collection: When users submit loan applications through the H5 page, user behavior data and application information are collected.

[0267] Feature extraction: Extracting features from the collected data for risk assessment.

[0268] Model application: These features are input into the trained risk assessment model.

[0269] Score calculation: The model calculates a risk score that takes into account factors such as the user's credit history, financial status, and behavioral patterns.

[0270] Approval decision: Make loan approval decisions automatically or manually based on risk scores and preset approval criteria.

[0271] Feedback loop: Approval results and subsequent repayment behavior are fed back to the model for continuous optimization.

[0272] In this way, loan risks can be effectively assessed, approval efficiency and accuracy can be improved, and default risks can be reduced.

[0273] With the rapid development of internet technology, financial services are gradually shifting from offline to online. Online loan products, such as bank loan platforms, have become important financing tools for businesses. However, effectively collecting, analyzing, and utilizing data from the entire process of online loan products is key to improving product and service quality, optimizing user experience, and mitigating risks. Currently, data tracking technology within front-end H5 pages has become an important means of collecting user behavior data, but its application in the financial sector still has certain limitations and shortcomings.

[0274] This invention provides an information processing system for front-end H5 full-process data tracking based on a bank loan platform. Through refined data tracking design, it accurately captures and deeply analyzes user behavior throughout the entire process, providing strong support for product optimization, user experience enhancement, and risk control. The implementation of this invention can significantly improve the service quality and user experience of online loan products, reduce risks, and has broad application prospects and social value.

[0275] The purpose of this invention is to collect and integrate user behavior data and external user individual label data to supplement it, optimize the product, analyze the data and customize better operation strategies, combine multi-dimensional information when problems arise, correctly iterate the functions, maximize the product effect, improve marketing effects, and enhance risk control capabilities.

[0276] With the rapid development of mobile internet, more and more users are choosing to conduct financial transactions via mobile phones. As an online loan product, bank loan platforms need to collect real-time user behavior data throughout the application process to improve user experience and approval efficiency, enabling risk assessment and user profile building. However, traditional data collection methods often suffer from data loss and latency, failing to meet the real-time and accuracy requirements of bank loan platforms.

[0277] The present invention aims to provide an information processing system based on the front-end H5 full-process data embedding of the bank loan platform. Through refined data embedding design, it can achieve accurate capture and in-depth analysis of the user's full-process behavior in the front-end H5 page of the bank loan platform, providing strong support for product optimization, user experience improvement and risk control.

[0278] Objectives achieved:

[0279] (1) Build a global user data foundation. Complete the collection of user behavior through all channels, including the WeChat official account self-operated entrance, H5 marketing partner agency QR code submission, industrial bank APP, unified submission system, and integrated credit management system business process nodes, integrating external individual tags of corporate legal persons / shareholders / executives.

[0280] (2) User experience optimization. Optimize core user paths such as real-name authentication, credit application, credit application, and repayment application, analyze the reasons for process failure, reduce user friction, reduce user churn and customer complaints, and shorten user time.

[0281] (3) Improve the effectiveness of marketing operations. With the help of external user data, accurately identify the characteristics of the target audience and the activity of similar lending platforms. With the help of full-link channel interaction data analysis, optimize the channel delivery strategy, reduce customer acquisition costs, and improve customer acquisition efficiency. Combined with user activity, credit / repayment status, channel sources, etc., users are stratified and accurately recalled for users who have failed in the process, have been granted credit but have not withdrawn funds, failed to use credit, have good repayment status and have a credit balance. Supplement the labels of old users, including recent activity of similar banks / lending platforms, income status, asset status, family member status, normal mobile phone usage, etc., and launch multiple waves of personalized marketing awakening.

[0282] (4) Improved risk control capabilities. Identify abnormal user operation behaviors and frequencies through tracking. Supplement with external user data tags to enrich risk control scenarios, including normal user behavior judgment, such as regular social interaction, news browsing, car maintenance, live video, accounting, map navigation, etc. Judgment of high-risk users, such as high activity in blockchain and virtual currencies, changes in income levels, vehicle transactions, etc. Judgment of multiple loan risks, such as high activity of multiple installment loan products of the same type.

[0283] To achieve the above object, the present invention adopts the following technical solutions:

[0284] Embed data tracking code in the front-end H5 page of the bank loan platform to achieve real-time collection of user behavior. The data tracking code can collect user behavior data such as clicks, inputs, and sliding on the page and send it to the server in real time for processing.

[0285] Design an indicator system for data collection, including user personal information, behavioral data, device information, etc., to ensure the comprehensiveness and accuracy of the data.

[0286] The server processes and analyzes the collected data in real time to build user profiles and risk assessment models. Based on user behavior data, the server can analyze the user's loan needs, credit status, and other information to provide a basis for loan approval.

[0287] Based on data analysis results, the bank's loan platform's approval process and strategy can be optimized to improve the accuracy and efficiency of approvals. For example, loan amounts and interest rates can be automatically adjusted based on the user's risk score.

[0288] Module functions can be implemented through executable programs and code development. The relevant product function modules and functions are shown in Table 1 below.

[0289] Table 1 Related product function modules and function introduction

[0290]

[0291]

[0292]

[0293]

[0294] Based on data analysis results, the bank's loan platform's approval processes and strategies can be optimized to improve the accuracy and efficiency of approvals. For example, loan amounts and interest rates can be automatically adjusted based on the user's risk score. This approach enables personalized service and risk control for users, improving the accuracy and efficiency of loan approvals.

[0295] These include: Improvement of basic operations: The main analysis indicators include: number of active users, number of newly registered users, retention rate, number of logins, number of logged-in users, login duration, number of applications, credit amount, credit amount used, repayment amount, etc. The main analysis dimensions include: various channels (cooperating institutions) / operating systems / provinces / regions / application versions / functional types / user levels / product categories / time (day / week / month), etc. The analysis results can be visualized, such as Figure 3 The visual analysis results are shown.

[0296] Marketing evaluation improvements: Analytical metrics include: page views of the promotion landing page, number of unique visitors (UVs) to the landing page, bounce rate of the landing page, click / view depth of landing page elements / locations, number of new channel user registrations, registration conversion rate, and new device retention rate. Analytical dimensions include: promotion channel / promotion, keywords / promotion type / active cities / network type / browser type / operating system / time (daily / weekly / monthly), and high activity on similar lending platforms.

[0297] Functionality Improvement: Analytical metrics include: distribution of SMS verification functions, FaceID verification times, number of FaceID verification users, number of ID uploads, number of ID upload users, number of scan-and-pay use cases, number of scan-and-pay users, average usage of a function, retention rate of a function, page dwell time, bounce rate, and conversion time. Analytical dimensions include: channel (partner organization), operating system, province, region, app version, function type, user level, product category, and timeframe (daily, weekly, or monthly).

[0298] Improvements in business operations: The main analysis indicators include: number of clicks on the operation position, number of clickers on the operation position, average number of clicks on the operation position, number of clicks on different operation positions, number of clickers on different operation positions, average number of clicks on different operation positions, user usage path, conversion rate of the personal credit application process, conversion rate of the corporate credit application process, number of credit applications, number of users who applied for credit, number of successful credit submissions, number of users who successfully submitted credit, step conversion duration, conversion rate of the personal credit application process, conversion rate of the corporate credit application process, number of credit applications, number of users who applied for credit, number of successful credit submissions, number of users who successfully submitted credit (number of users withdrawing funds), number of failed credit submissions, number of users who failed credit submissions, distribution of reasons for failed credit submissions, number of repayment applications, number of users who applied for repayment, number of successful repayment submissions, number of users who successfully submitted repayments, number of failed repayment submissions, etc. The main analysis dimensions include: channels (partner institutions), operating systems, provinces, regions, application versions, functional types, product categories, time (day / week / month), etc.

[0299] The innovations of this patent are mainly reflected in the following aspects:

[0300] Front-end H5 full-process data embedding technology:

[0301] By embedding data tracking code in the front-end H5 page, real-time data collection of user behavior during the application process on the bank's loan platform is achieved. This technology can capture user clicks, input, swipe, and other behaviors, providing the bank with detailed data on user interactions.

[0302] Construction of a data collection indicator system: We have designed a comprehensive data collection indicator system that includes user personal information, behavioral data, device information, and more, ensuring the comprehensiveness and accuracy of the data. This indicator system facilitates more accurate analysis of user behavior and credit risk.

[0303] Real-time data processing and analysis: Servers process and analyze collected data in real time to build user profiles and risk assessment models. This real-time processing capability enables banks to quickly respond to market changes and user needs, improving decision-making efficiency.

[0304] Approval Process and Strategy Optimization: Based on data analysis results, the bank's loan platform's approval process and strategy are optimized to improve approval accuracy and efficiency. For example, loan amounts and interest rates can be automatically adjusted based on the user's risk score, achieving personalized service and risk control.

[0305] Digital transformation and improved user experience: This patented technology helps promote the digital transformation of banking services. By collecting and analyzing user data in real time, it improves user experience and service quality, thereby enhancing the competitiveness of banks.

[0306] Broad application prospects: The technical solution of the present invention is not only applicable to products of bank loan platforms, but can also be widely used in other financial business scenarios and various banking business scenarios, and has broad market prospects and application value.

[0307] The advantages of this invention include: real-time collection of user behavior data, ensuring its timeliness and accuracy. A comprehensive data indicator system is constructed, improving the accuracy of user profiling and risk assessment. Approval processes and strategies are optimized, increasing the efficiency and accuracy of loan approvals. This system can be widely applied in various financial and banking scenarios, demonstrating broad market prospects and application value.

[0308] The information processing system in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0309] (1) The technical solution of the present invention solves the following technical problems through the front-end H5 full-process data embedding solution:

[0310] Process Efficiency and Automation: The bank has achieved rapid processing of the entire process, from customer application to bank approval, in as little as seven minutes. This efficiency improvement is due to the application of front-end data tracking technology, which makes the entire process highly automated and reduces manual intervention.

[0311] Data-driven and decision-supported: Through data tracking technology, banks can collect and analyze customer behavior data and leverage big data models to support decision-making. For example, a bank can use GPS systems to understand the utilization rate of a customer's equipment and combine this with other data for analysis to determine whether to grant a loan.

[0312] Optimized user experience: Data tracking technology helps banks optimize the customer experience. Customers can complete all processes, including identity verification, information entry, loan application, and approval, on their mobile phones. This convenience significantly improves user satisfaction.

[0313] Risk Control and Business Management: Patented technologies also help banks achieve advancements in risk control and business management. By monitoring and analyzing data in real time, banks can better assess and manage loan risks while improving the speed and accuracy of business processing.

[0314] In traditional financial business processes, especially loan application and approval processes, the following methods are usually used:

[0315] Paper document processing: Traditional methods usually involve a large amount of paper documents, including loan application forms, identification documents, financial statements, etc. These documents need to be filled out manually, signed, and submitted to the bank by mail or in person.

[0316] Manual review: Bank staff manually review the documents submitted by applicants to check their completeness and accuracy. This process is often time-consuming and prone to errors.

[0317] Face-to-face service: In the traditional process, customers may need to go to a bank branch in person for consultation, submit application materials, and complete other related procedures.

[0318] Communication by phone or email: During the application process, communication between customers and banks is mainly conducted by phone or email, which may result in untimely information delivery or inefficient communication.

[0319] Simple data collection: While some traditional systems may include basic electronic data collection capabilities, these systems often lack in-depth analysis of user behavior and comprehensive monitoring of the entire application process.

[0320] Independent system operation: Different business links may be processed using different systems, such as application entry system, approval system, post-loan management system, etc. These systems may lack effective data exchange and integration.

[0321] Compared to these traditional methods, the patented front-end H5 full-process data tracking technology of this invention provides a more efficient, automated, and data-driven approach. Through this technology, banks can achieve rapid approvals, targeted marketing, optimized user experience, and improve overall business processing capabilities.

[0322] (2) The drawbacks and shortcomings of traditional technologies in handling loan applications and approval processes mainly include:

[0323] Inefficiency: Paper-based and manual processes are often time-consuming. Each application requires manual review, which can slow processing and increase customer wait times. High error rates: Manual processing is prone to errors, whether from data entry errors or omissions during document review, which can lead to process delays or poor decisions. High costs: Maintaining paper documents, storage space, and human resources are costly. In addition, processing errors and rework add additional costs. Poor user experience: Customers must visit the bank in person or communicate via email or phone, which can be cumbersome and inconvenient, affecting the overall customer experience. Limited data analysis and decision support: Traditional methods often lack in-depth data analysis and mining, which limits banks' ability to use data to optimize products and services and improve decision-making quality. Information silos: Disparate systems often lack effective data sharing and integration, resulting in information silos and inefficient data utilization. Security issues: Paper documents are easily lost, damaged, or tampered with, and traditional data storage systems may lack adequate security measures to protect sensitive information. Lack of transparency: Traditional processes may lack transparency, making it difficult for customers to track application status, which can lead to a decline in trust. Poor adaptability and scalability: As business volume increases or the market changes, traditional systems may struggle to adapt quickly to new demands, resulting in poor scalability. Environmental impact: The extensive use of paper documents has a negative impact on the environment and is inconsistent with sustainable development requirements. Therefore, to overcome these drawbacks and shortcomings, financial institutions are increasingly adopting digital and automated solutions, such as front-end H5 full-process data tracking technology, to improve efficiency, reduce costs, optimize user experience, and enhance data analysis and decision support capabilities.

[0324] (3) Solve the drawbacks and shortcomings of traditional technologies by:

[0325] Digital processing of the entire loan application process: By transferring the entire loan application process to the H5 platform, the entire process from application to approval is digitized, reducing the use of paper documents and improving processing speed.

[0326] Data tracking and real-time monitoring: Implementing data tracking on H5 pages can collect user behavior data in real time, providing banks with instant business insights, thereby optimizing processes and improving user experience.

[0327] Automated approval process: Utilize collected data and combine it with machine learning algorithms to automate loan approval, reduce manual intervention, lower error rates, and improve approval efficiency.

[0328] User-friendly interface design: H5 technology supports user-friendly interface design, making the loan application process more intuitive and convenient, and improving the user's operating experience.

[0329] Data integration and analysis: By integrating data from different systems, information silos are broken down, enabling banks to conduct more comprehensive data analysis and risk assessment.

[0330] Enhanced security: Advanced encryption technology and security protocols are used to ensure the security of user data transmission and storage on the H5 platform.

[0331] Cost savings: Reduced costs for paper document processing, storage, and manual review, and further reduced operating costs through automated processes.

[0332] Environmentally friendly: It reduces dependence on paper documents, helps protect the environment, and is in line with the concept of sustainable development.

[0333] Flexibility and scalability: The flexibility of the H5 platform enables banks to quickly adjust and expand services based on market demand, improving business adaptability.

[0334] (4) Specifically, the following are several key points of the technical solution of the present invention to solve the drawbacks and shortcomings:

[0335] Fast approval: Through the H5 platform's real-time data collection and automated approval, loan approval time is greatly shortened from traditional days or even weeks to as fast as 7 minutes.

[0336] Decision support: Leveraging big data analysis and machine learning, loan decisions are made based on user behavior data, improving the accuracy and efficiency of decisions.

[0337] User experience: Users can complete the entire loan process on their mobile phones without having to go to the bank, which simplifies the operation steps and improves satisfaction.

[0338] Risk control: By monitoring and analyzing data in real time, banks can better manage loan risks and ensure business stability.

[0339] (5) The specific analysis methods of the technical solution of the present invention for real-time processing and analysis of the collected data by the data processing and analysis module on the back-end server may include the following aspects:

[0340] User Behavior Analysis: 1) Clickstream Analysis: Tracks user click behavior and analyzes user interaction patterns on the H5 page, such as which buttons are clicked and which pages are visited. 2) Path Analysis: Analyzes the user's path from entering the app to completing the loan application, identifying common user behavior paths and potential churn points.

[0341] Data Mining: 1) Feature Extraction: Extracting key features from collected data, such as user personal information, device information, and behavioral patterns. 2) Pattern Recognition: Using machine learning algorithms to identify patterns in user behavior, such as which behaviors are correlated with loan approval rates.

[0342] Machine Learning Models: 1) Risk Scoring Model: Using machine learning techniques such as random forests and gradient boosting, we build risk scoring models based on user data for automated approval processes. 2) Risk Prediction Model: This predicts loan default risk and helps banks decide whether to approve loan applications.

[0343] Real-time monitoring and early warning: 1) Anomaly detection: Real-time monitoring of data streams, using anomaly detection algorithms to identify abnormal behavior and trigger early warning mechanisms. 2) Performance indicator monitoring: Monitoring performance indicators of the loan approval process, such as approval speed and approval rate.

[0344] Statistical Analysis: 1) Conversion Rate Analysis: Calculate the conversion rate for key steps, such as visit to application and application to approval. 2) A / B Testing Analysis: Test different page layouts or process designs to determine which approach is most effective.

[0345] Big data analysis: 2) User segmentation: Divide users into different groups based on their behavioral characteristics, credit status, etc. to achieve precision marketing. 2) Trend analysis: Analyze market trends and changes in user needs to provide data support for product iteration and market strategy.

[0346] Data visualization: 2) Real-time reports: Generate real-time reports and present analysis results in charts to facilitate quick decision-making by management. 3) Dashboard: Create management dashboards to centrally display key business indicators and monitoring results.

[0347] These analytical methods combined can provide in-depth business insights for bank operations, optimize user experience, improve approval efficiency, reduce operating costs, and effectively control risks.

[0348] (6) The process of building a user profile and risk assessment model is roughly as follows:

[0349] Build user portraits: 1) Data collection: Collect basic information about users, such as age, gender, occupation, etc. Obtain user transaction behavior data, browsing behavior, click data, etc. 2) Feature engineering: Extract useful features from the collected data, such as consumption habits, repayment ability, social activities, etc. Clean, transform and normalize the features. 3) User grouping: Use clustering algorithms (such as K-means) to group users and find groups with similar characteristics. Define labels for each group, such as "high-income group", "student group", etc. 4) Portrait construction: Build a detailed portrait of each user based on the characteristics and clustering results. User portraits may include multiple dimensions, such as demographic characteristics, consumption characteristics, credit characteristics, etc.

[0350] Building a risk assessment model: 1) Data preparation: Collect historical loan data, including loan amounts, repayment status, and overdue records. Combined with user profile data, prepare a complete dataset for modeling. 2) Model selection: Select an appropriate machine learning algorithm, such as logistic regression, decision tree, or random forest. 3) Feature selection: Use feature importance analysis to select features with the greatest impact on risk assessment. 4) Model training: Train the model using the training dataset and adjust model parameters for optimal performance. Evaluate the model's accuracy and generalization capabilities through methods such as cross-validation. 5) Model validation: Test the model using the validation dataset to ensure its effectiveness in real-world applications.

[0351] Subsequent use: 1) Use of user portraits: Personalized services: Provide personalized loan products and services based on user portraits. Marketing strategies: Develop precise marketing strategies based on the characteristics of different user groups. User experience optimization: Improve the design and functionality of the H5 page based on user portraits to enhance user experience. 2) Use of risk assessment models: Automatic approval: When a user submits a loan application, the risk assessment model is automatically used for approval to improve approval efficiency. Risk control: Identify high-risk loan applications and take measures to reduce potential default risks. Decision support: Provide data support to bank managers to help make more informed loan decisions. In this way, banks can manage loan business more effectively, improve service quality, and reduce operating costs and risks. The server can analyze user loan needs, credit status and other information based on user behavior data.

[0352] (7) Scoring Type: In the technical solution of the present invention, scoring may include the following types: Loan Demand Score: This score reflects the user's demand for a loan and may be based on factors such as the user's behavior patterns, application information, historical loan records, etc. Credit Status Score: This score assesses the user's credit risk and is usually based on the user's repayment record, credit history, financial status, etc.

[0353] In addition to the above scores, the following other possible scores are available: Repayment Ability Score: This assesses a user's ability to repay a loan, potentially taking into account income, balance sheet, and other factors. Fraud Risk Score: This assesses the authenticity of user-submitted information and is used to detect potential fraud. Comprehensive Score: This combines multiple of the above scoring dimensions to provide users with a comprehensive assessment.

[0354] These scoring results can be used in the following ways: Automated approval: Automatically deciding whether to approve a loan application based on the scoring results. Risk pricing: Adjusting the loan interest rate or loan amount based on the risk score. Personalized service: Providing customized loan products based on the loan demand score.

[0355] (8) The impact of the method of providing the basis for loan approval on the approval process is mainly reflected in the following aspects:

[0356] 1) Improved Approval Efficiency: Automated Processing: By collecting and analyzing user behavioral data in real time, we can automatically assess the credit status and repayment ability of loan applicants. Automated scoring models can quickly provide approval recommendations, reducing manual review time and significantly improving approval efficiency.

[0357] 2) Enhanced Approval Accuracy: Data-Driven Decision-Making: Leveraging big data and machine learning, the approval process no longer relies solely on human judgment, but instead relies on analysis based on massive amounts of data and complex algorithms. This data-driven decision-making approach can more accurately identify potential high-quality and high-risk customers.

[0358] 3) Risk Control Optimization: Refined Management: Data tracking technology enables banks to collect more diverse information, such as user browsing behavior and operating habits. This information facilitates more refined risk assessments. By analyzing this data, banks can better identify and prevent fraud and reduce loan default risks.

[0359] 4) Approval Process Transparency: Process Monitoring: Patented technology allows banks to monitor every step of the approval process in real time, ensuring transparency and fairness. Management can review approval status and decision-making basis at any time, facilitating oversight and adjustment of approval strategies.

[0360] 5) Specific ways of impact: Preliminary screening: After the user submits the application, the system will immediately conduct a preliminary screening based on preset rules and models to quickly exclude applications that do not meet the requirements. Risk scoring: The user's risk score directly affects the approval result. Users with high risk scores may receive faster approval and more favorable loan terms. Behavioral analysis: Analyze the user's behavioral patterns on the H5 page, such as whether they repeatedly check the loan conditions, whether they are hesitant, etc. These behavioral characteristics can be used as reference factors for approval. Dynamic adjustment: The approval model can dynamically adjust the approval standards according to market conditions and bank strategies to ensure the flexibility and adaptability of the approval process. Instant feedback: The system can provide users with instant approval feedback, and can provide improvement suggestions or alternatives for rejected applications.

[0361] (9) Loan amount adjustment method: Data collection and analysis: Collect users' basic information, financial status, historical credit records, behavioral data, etc. Analyze this data to evaluate users' repayment ability, credit risk, etc. Risk scoring model: Use machine learning algorithms to train risk scoring models based on user data. The risk scoring model outputs a score that reflects the user's credit rating. Loan amount rule setting: Determine the loan amount range corresponding to different credit ratings based on risk scores and preset rules. Rules may include factors such as credit score and loan amount mapping table, risk tolerance, etc. Dynamic adjustment: Dynamically adjust loan amount rules based on market conditions, bank risk preferences and policy orientations. For specific user groups or market activities, temporary loan amount adjustment strategies can be implemented. User feedback and adjustment: Adjust the loan amount of individual users based on user feedback and loan performance.

[0362] (10) Interest rate adjustment methods: Risk pricing model: Establish a risk pricing model that takes into account factors such as the user's credit risk, market interest rates, and bank costs. The model will calculate a risk-based loan interest rate. Interest rate rule setting: Set interest rate rules to correspond risk scores to interest rate levels. Rules may include interest rate ranges for different credit ratings, fixed markups, or floating ratios. Market interest rate linkage: Consider market interest rate changes, such as changes in the benchmark interest rate, and adjust the loan interest rate accordingly. Personalized pricing: Provide personalized interest rate pricing based on factors such as the user's risk score, loan history, and relationship with the bank. Promotions and offers: Provide promotional interest rates or preferential conditions during specific periods or for specific user groups.

[0363] Specific adjustment process: Application submission: Users submit loan applications through the H5 interface. Data evaluation: The server collects and analyzes user data, including behavioral data and risk scores. Loan limit and interest rate determination: Based on the risk score and pre-set rules, the system automatically determines the loan limit and interest rate. Approval feedback: The approval result (including loan limit and interest rate) is fed back to the user. User confirmation: The user accepts or rejects the loan terms.

[0364] In this way, the technical solution of the present invention can achieve dynamic and personalized adjustment of loan amounts and interest rates to adapt to the risk conditions and market environment of different users, while optimizing the bank's loan business efficiency and risk management.

[0365] It should be noted that the contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

[0366] Figure 4 It is a structural diagram of an information processing method based on a bank loan platform according to an embodiment of the present invention.

[0367] like Figure 4 As shown, an embodiment of the present invention provides an information processing method based on a bank loan platform, and the information processing method based on the bank loan platform includes: S101: determining a data tracking plan based on the business process and user behavior characteristics of the front-end H5 page of the bank loan platform. The business process includes the loan application process. S102: collecting user data in real time according to the data tracking plan, and sending the user data to the back-end server. User data includes user behavior data and application information. Behavioral data includes: user browsing path, dwell time and click events. Application information includes: application time, loan amount, loan term, repayment method, personal information: user's name, age and gender, business information: business size and business information, credit history: historical loan records and historical repayment records. S103: receiving, processing and analyzing user data to determine data analysis results. S104: optimizing the loan approval process based on the data analysis results includes: adjusting the loan amount and interest rate, making loan approval decisions, so as to speed up the loan approval process. For details, please refer to the specific implementation method of the above system, which will not be repeated here.

[0368] See also Figure 5 , Figure 5 Schematic diagram of the hardware structure of the computer device according to the embodiment of the present invention. Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Figure 5 In the example, a processor 10 is used. Processor 10 may be a central processing unit (CPU), a network processor (NPU), or a combination thereof. Memory 20 stores instructions executable by at least one processor 10, causing at least one processor 10 to implement the methods described in the above embodiments. The computer device also includes a communication interface 30 for communicating with other devices or a communication network.

[0369] The present invention also provides a computer-readable storage medium. The methods according to the embodiments of the present invention described above can be implemented in hardware, firmware, or as computer code that can be recorded on a storage medium, or downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The methods shown in the above embodiments are implemented.

[0370] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0371] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. An information processing system based on a bank loan platform, characterized in that: include: The data tracking design module is used to determine the data tracking plan based on the business process and user behavior characteristics of the front-end H5 page of the bank loan platform; The business process includes a loan application process; The data collection module is configured in the front-end H5 page and is used to collect user data in real time according to the data embedding scheme determined by the data embedding design module, and send the user data to the back-end server; The user data includes user behavior data and application information; The behavioral data includes: the user's browsing path, dwell time, and click events; the application information includes: application time, loan amount, loan term, repayment method, personal information (user's name, age, and gender), business information (business size and operating information), credit history (historical loan records and historical repayment records); A data processing and analysis module, configured on the back-end server, for receiving, processing and analyzing the user data to determine a data analysis result; The result application module includes a loan approval optimization unit, which is used to optimize the loan approval process according to the data analysis results, including: adjusting the loan amount and interest rate, making loan approval decisions, so as to speed up the loan approval process.

2. The system according to claim 1, wherein: The data embedding design module includes: A tracking point demand determination unit is used to determine tracking point demand based on the business process and user behavior characteristics of the front-end H5 page of the bank loan platform; the tracking point demand includes key nodes and data fields corresponding to the key nodes; The tracking solution design unit is used to determine the tracking location according to the key nodes in the tracking requirements, determine the data type according to the data fields corresponding to the key nodes, and design the corresponding collection frequency.

3. The system according to claim 1, wherein: The data acquisition module includes: A data collection unit is used to collect user data in real time according to the data embedding scheme determined by the data embedding design module; A data encryption unit, configured to encrypt the user data to obtain encrypted user data; A data compression unit, used to compress the encrypted user data to obtain compressed user data; The data transmission unit is used to send the compressed user data to the back-end server.

4. The system according to claim 1, wherein: The data processing and analysis module includes: A data processing unit, configured to receive the user data and perform data processing on the user data to obtain processed user data; the data processing includes: preprocessing and feature extraction; The data analysis unit is used to analyze the user data after data processing to obtain the data analysis result.

5. The system according to claim 4, characterized in that The data analysis unit comprises: A model building unit, used to build a risk assessment model; The model application unit is used to input the processed user data as input data into the risk assessment model to obtain the risk score corresponding to the user; the risk score is used to characterize the user's credit risk and loan risk.

6. The system according to claim 5, characterized in that The loan approval optimization unit is specifically used to adjust the loan amount and interest rate according to the risk score corresponding to the user, and automatically or manually make loan approval decisions based on the risk score corresponding to the user and preset approval standards.

7. The system according to claim 5, characterized in that The data analysis unit further includes: The user portrait construction unit is used to analyze the user data after data processing and construct a user portrait.

8. An information processing method based on a bank loan platform, characterized in that: include: Determine the data embedding plan based on the business process and user behavior characteristics of the bank loan platform's front-end H5 page; The business process includes a loan application process; Collect user data in real time according to the data embedding solution, and send the user data to the back-end server; The user data includes user behavior data and application information; The behavioral data includes: the user's browsing path, dwell time, and click events; the application information includes: application time, loan amount, loan term, repayment method, personal information (user's name, age, and gender), business information (business size and operating information), credit history (historical loan records and historical repayment records); Receive, process and analyze the user data to determine data analysis results; Optimizing the loan approval process based on the data analysis results includes adjusting the loan amount and interest rate, and making loan approval decisions to speed up the loan approval process.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the information processing method based on the bank loan platform as described in claim 8 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the information processing method based on the bank loan platform described in claim 8.

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