Method and device for automatically processing service of life insurance counter

Abstract

The invention provides an automatic processing method and device for life insurance counter business, and relates to the technical field of control systems.The method comprises the steps that a multi-mode identity verification module authenticates the identity of a salesman, and after the identity passes, group insurance policy information under the name of the salesman is inquired and displayed; the OCR recognition and intelligent classification module scans and recognizes security or claim settlement application materials and automatically classifies the materials, and structured data is generated and presented to a confirmation interface; a two-channel mechanism of local code scanning authentication or background short message notification auditing is selected according to service types, and an application is automatically submitted to a core system after auditing is completed; polling verification is carried out on the application, if verification fails, verification is carried out again at the interval of 48 hours, and manual intervention is triggered after more than 10 times of verification fails; after the audit is passed, the unified output platform generates and prints a delivery order, and meanwhile, a service result is notified through a short message. According to the life insurance counter service automatic processing method, full-process automation of preservation and claim settlement services is realized, the processing efficiency is improved, and the manual intervention and operation error rate is reduced.

Description

Technical Field

[0001] This invention relates to the field of control system technology, and in particular to an automated processing method and apparatus for life insurance counter services. Background Technology

[0002] As a crucial terminal device in the financial insurance service sector, life insurance counter kiosks are widely used in core business scenarios such as policy management, policy maintenance, and claims services. With the deepening of digital transformation in the insurance business, traditional counter systems have evolved from simple paper form processing to a comprehensive technical system integrating identity verification, document scanning, OCR recognition, intelligent classification, and multi-system collaboration. Specifically, this technology covers the entire process from user authentication to business data processing, including key aspects such as human resource management system verification, core system interaction, and unified output platform integration. The combination of OCR recognition technology and intelligent classification algorithms has become a vital support for improving business efficiency. Among related technologies, the linkage mechanism between document scanning and information display, and the three-level linkage query of policy information, etc., have constructed the basic framework for automated processing of insurance business.

[0003] However, existing life insurance counter kiosks rely on manual completion of paper forms and manual review processes, which presents significant technical shortcomings. Specifically, policy maintenance requires manually filling out over 20 form fields, taking an average of 15 minutes, and the traditional review process takes 3-5 business days, resulting in customer satisfaction rates below 60%. In claims processing, the error rate for verifying unstructured documents such as medical invoices is as high as 12%, and manual intervention not only increases operating costs but may also lead to compliance risks. Due to the lack of intelligent decision engines and dynamic classification algorithms, existing systems require repetitive operations when handling policy maintenance for multiple insurance types, resulting in a high rate of business interruption. These technical limitations make it difficult to overcome bottlenecks in insurance business processing efficiency and pose systemic risks in terms of data consistency and operational security, severely hindering the improvement of the intelligence level of insurance services. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to provide an automated processing method for life insurance counter services.

[0006] The second objective of this invention is to provide an automated processing device for life insurance counter services.

[0007] The third objective of this invention is to provide an electronic device.

[0008] The fourth objective of this invention is to provide a computer-readable storage medium.

[0009] The fifth objective of this invention is to provide a computer program product.

[0010] To achieve the above objectives, a first aspect of the present invention provides a method for automating life insurance counter services, comprising: S1 verifies the identity of the salesperson through the multimodal identity verification module. After successful verification, the salesperson's group insurance policy information is queried and displayed. S2, based on the OCR recognition and intelligent classification module, scans, recognizes and automatically classifies the application materials for insurance preservation or claims, generates structured data and displays a confirmation interface; S3 employs a dual-channel mechanism, allowing users to choose between local QR code authentication and background SMS notification for review based on the business type. Once the review is completed, the system automatically submits the business application to the core system. S4 performs round-robin verification of submitted business applications. If the verification fails, it is repeated every 48 hours. After more than 10 failures, a manual intervention process is triggered. S5, after approval, generates and prints a delivery order through a unified output platform, and sends a business result notification via SMS platform.

[0011] Optionally, in S11, after the salesperson enters the salesperson number, the system queries the personnel management system through the interface to verify the salesperson information, including whether the employee number exists, whether the account status is normal, and whether the ID card number is correct. S12, after successful verification, the system calls the face recognition module to perform biometric comparison. After successful comparison, the user is taken to the policy query interface.

[0012] Optionally, in S21, the system dynamically prompts a list of documents to be scanned based on the case type (small medical insurance claim, regular claim) and extracts key fields such as document number, amount, and date through OCR recognition. S22: For files that fail to be recognized by OCR, the system automatically categorizes them into an unknown category folder and allows sales staff to delete invalid files through the recycle bin function.

[0013] Optionally, S31, during local verification, the system generates a QR code containing the employee number of the office staff through the centralized authentication system and displays it on the screen for scanning authentication. S32, during the background review, the system sends an SMS notification containing the case number and review link to the mobile phone number of the preset reviewer via the SMS platform.

[0014] Optionally, S41, the system uses an exponential backoff algorithm to control the polling interval. After the first failure, the interval is 48 hours, and after the nth failure, the interval is calculated according to the formula. Increasing hourly; S42: When the cumulative number of failures reaches 10, the system automatically marks the case as high priority and pushes it to the top of the office staff's to-do list.

[0015] Optionally, in S6, when a salesperson chooses to print a delivery slip, the system generates a voice command containing a pickup prompt through the voice synthesis module, and simultaneously displays a pickup countdown on the screen. After the countdown ends, the order outlet is automatically locked.

[0016] To achieve the above objectives, a second aspect of the present invention provides an automated processing device for life insurance counter services, comprising: The multimodal identity verification module is used to authenticate the identity of salespersons. After successful verification, the module can query and display the group insurance policy information under their name. The OCR recognition and intelligent classification module is used to scan, recognize, and automatically classify insurance or claim application materials based on the OCR recognition and intelligent classification module, generate structured data, and display a confirmation interface. The dual-channel review mechanism module is used to select either local QR code authentication review or background SMS notification review based on the business type. After the review is completed, the business application is automatically submitted to the core system. The round-robin verification and manual intervention module is used to perform round-robin verification on the submitted business applications. If the verification fails, the verification is repeated after 48 hours. After more than 10 failures, the manual intervention process is triggered. The delivery order generation and notification sending module is used to generate and print delivery orders through a unified output platform after approval, and to send business result notifications through an SMS platform.

[0017] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0018] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0019] To achieve the above objectives, a fifth aspect of the present invention provides a computer program product that, when executed by a processor, implements the method described in any one of the first aspects.

[0020] The technical solutions provided by the embodiments of the present invention bring at least the following beneficial effects: The methods, apparatus, electronic devices, and computer-readable storage media of the present invention significantly improve the processing efficiency of life insurance counter maintenance and claims business, reduce manual intervention and operational error rates, and achieve full-process automation and intelligent processing.

[0021] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating an automated processing method for life insurance counter services provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the operation flow of an automated processing method for life insurance counter services provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an automated processing device for life insurance counter services provided in an embodiment of the present invention. Detailed Implementation

[0023] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0024] To address the problems of cumbersome operations and low efficiency, embodiments of the present invention provide an automated processing method for life insurance counter services. Figure 1 This is a flowchart illustrating an automated processing method for life insurance counter services provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the operation flow of an automated processing method for life insurance counter services provided in an embodiment of the present invention. Figure 1 , Figure 2 As shown, the method includes the following steps: S1 verifies the identity of salespersons through a multimodal identity verification module. Once verified, the salesperson's group insurance policy information is queried and displayed.

[0025] Specifically, in some implementations, authenticating salespersons through a multimodal identity verification module is a key step in ensuring system security and operational compliance in this invention. This module integrates multiple verification methods, such as salesperson ID input, facial recognition, and centralized authentication system interface calls, to achieve multi-dimensional and high-precision identification and confirmation of salesperson identities.

[0026] From a technical implementation perspective, the salesperson first enters their salesperson ID on the login interface of the self-service terminal. The system communicates with the personnel management system to verify that the ID exists in the system and checks whether the account status is "normal" and whether the ID number matches. If the verification is successful, the system will trigger a facial recognition process, capturing the salesperson's facial image through the front-facing camera and comparing it with a local or cloud-based facial recognition algorithm. The facial recognition algorithm can be based on a deep learning model (such as ResNet, FaceNet, etc.) and determines whether it matches the registered salesperson's identity information within a preset threshold range. This process must meet the requirements of the ISO / IEC 30107-3 standard regarding facial recognition accuracy and false recognition rate, typically setting the false recognition rate (FAR) to be less than [a certain value]. The recognition rate (FRR) is less than 1%.

[0027] Regarding parameter settings, the system supports custom configuration of the verification timeout (e.g., ...). ), facial recognition confidence threshold (e.g.) The system includes features such as validating the format of the salesperson's ID input (e.g., [A-Za-z0-9]{6,12}) to ensure the stability and security of the verification process. Furthermore, the system supports integration with a centralized authentication system to generate a one-time QR code for secondary identity verification, further enhancing the real-time nature and anti-counterfeiting capabilities of the authentication process.

[0028] This step is widely used in practice for entry control in business processes such as group insurance policy inquiries, policy maintenance applications, and claims processing. Through multimodal authentication, the system can effectively prevent unauthorized operations and ensure the confidentiality and integrity of business data. At the same time, the module's efficient verification mechanism significantly shortens the login time for salespersons, improves overall business processing efficiency, and provides a reliable identity foundation for subsequent policy inquiries and business transactions.

[0029] Furthermore, S1 includes: S11. After the salesperson enters the salesperson number, the system queries the personnel management system through the interface to verify the salesperson information, including whether the employee number exists, whether the account status is normal, and whether the ID card number is correct.

[0030] Specifically, during the salesperson login verification process, the system interacts with the personnel management system (personnel management system) via an interface to verify the legality and validity of the salesperson's identity information. Specifically, the salesperson enters their salesperson number on the login interface of the self-service terminal. After receiving this number, the system calls a pre-defined interface service to initiate an authentication request to the personnel management system. This request includes the salesperson number as a unique identifier, used to check whether a corresponding employee record exists in the personnel management system.

[0031] At the technical implementation level, the system first authenticates the interface to ensure the legitimacy of the request source. Subsequently, the personnel management system returns verification results, including whether the employee ID exists, whether the account status is "normal," and whether the ID number matches the system record. The account status is determined based on preset status codes in the system, such as "01" for normal, "02" for disabled, and "03" for frozen. ID number verification uses a standard ID verification algorithm, including digit verification (18 digits), administrative division code verification, birth date format verification (YYYYMMDD), and check digit verification (last digit is a number or X), ensuring that the input information complies with the "Law of the People's Republic of China on Resident Identity Cards" and relevant industry standards.

[0032] In practical applications, this step is typically deployed in smart all-in-one machines at insurance company branches or authorized service points, serving as the first security checkpoint in the business processing flow. The salesperson's ID number can be entered via keyboard input or QR code scanning, and the system response time should be controlled within 500ms to ensure a good user experience. If verification fails, the system will return corresponding prompts based on the error type, such as "Employee ID does not exist," "Account status is abnormal," or "ID number does not match," and will log the failure for subsequent auditing.

[0033] The technical value of this step lies in its integration with the personnel management system, enabling real-time and accurate verification of salesperson identities. This effectively prevents unauthorized operations and identity theft, ensuring the security and compliance of business processes. Simultaneously, this verification mechanism provides the foundation for access control in subsequent policy inquiries, policy maintenance applications, and claims processing, making it a crucial component of the overall smart all-in-one system's security architecture.

[0034] S12, after successful verification, the system calls the face recognition module to perform biometric comparison. After successful comparison, the user is taken to the policy query interface.

[0035] Specifically, in some implementations, after successful verification, the system calls the facial recognition module to perform biometric comparison. Upon successful comparison, the user is directed to the policy query interface. This step, based on a multimodal identity authentication mechanism, combines the salesperson's ID with facial recognition technology to achieve dual verification of the operator's identity, thereby ensuring the security and compliance of business operations.

[0036] At the technical implementation level, the face recognition module employs deep learning-based feature extraction and comparison algorithms. It typically uses a convolutional neural network (CNN) to encode features from the acquired face images, generating face feature vectors (FaceEmbedding). The system compares the real-time acquired face images with standard face templates pre-registered in a centralized authentication system, calculating a similarity score. If the score exceeds a preset threshold (e.g., ...), the system determines the similarity score. If the match is successful, the process is considered complete, allowing the user to proceed to the next business process. This process typically takes place within [time period missing]. Completed within seconds, meeting the needs of real-time interaction.

[0037] In terms of parameters, the system sets a confidence threshold for face comparison. To balance the false recognition rate and the missed recognition rate. Simultaneously, the face recognition module supports liveness detection to prevent attacks from non-live subjects such as photos, videos, or 3D masks. The liveness detection accuracy should reach >99.5%, conforming to the ISO / IEC 30107-3 standard. Furthermore, the system supports face recognition under multi-angle and multi-lighting conditions to adapt to different counter environments.

[0038] In practical applications, this step is typically deployed in the smart all-in-one machines at insurance company branches. After the salesperson completes login verification, the system automatically uses the front-facing camera to capture their facial image and compares it with the salesperson's facial template stored in the centralized authentication system. If the comparison is successful, the system will redirect to the policy query interface, displaying a list of group insurance policies under the salesperson's name, achieving seamless integration of identity and permissions.

[0039] The technical advantage of this step lies in its ability to effectively prevent unauthorized users from impersonating sales representatives by introducing facial recognition technology, thereby enhancing system security and the compliance of business processing. Simultaneously, this step serves as a control node for the system's entry into core business processes, ensuring that subsequent operations such as policy inquiries, policy maintenance applications, and claims processing are conducted within the scope of legal authorization, providing a fundamental guarantee for the efficient operation of the entire intelligent counter system.

[0040] S2, based on the OCR recognition and intelligent classification module, scans, recognizes, and automatically classifies the application materials for policy maintenance or claims, generates structured data, and displays a confirmation interface.

[0041] Specifically, in some implementations, scanning, recognizing, and automatically classifying policy maintenance or claim application materials based on OCR recognition and intelligent classification modules, generating structured data, and displaying a confirmation interface is one of the core steps in this invention to improve business processing efficiency and intelligence. This step integrates high-precision optical character recognition (OCR) technology with deep learning-based document classification algorithms to automatically recognize and classify paper materials submitted by users, thereby reducing manual intervention and improving the accuracy and speed of data entry.

[0042] At the technical implementation level, the OCR recognition module employs image preprocessing and text detection models based on convolutional neural networks (CNNs), such as EAST or DBNet, to locate text regions within documents. Subsequently, a Transformer-based text recognition model (such as CRNN or PaddleOCR) is used to recognize characters in the scanned image, supporting the recognition and structured extraction of various document types (such as ID cards, bank cards, medical invoices, etc.). The intelligent classification module, based on multilayer perceptrons (MLPs) or graph neural networks (GNNs), automatically categorizes scanned documents into corresponding business document types, such as "identity certificates," "medical expense invoices," and "accident certificates," based on features extracted from the OCR, including text content, document format, and keyword matching.

[0043] In terms of performance parameters, the OCR recognition module should achieve an accuracy rate of over 95%, support scanning and recognizing A4 / A5 sized documents, and ideally have an image resolution of 300 dpi or higher to ensure recognition quality. The intelligent classification module should have a classification accuracy rate of no less than 90% and support the automatic classification of at least 10 common insurance business materials. The system response time should be controlled within 3 seconds to ensure smooth user interaction.

[0044] In application scenarios, this step is widely used in business processes such as group insurance policy maintenance applications and claims document submission. For example, in group insurance policy maintenance, after the salesperson scans change application forms, identity documents, and other materials, the system automatically identifies and categorizes them, displaying the structured data on the confirmation interface for the salesperson to check and correct. For documents that fail OCR recognition or are incorrectly categorized, the system provides a manual adjustment function to ensure the final accuracy of the data.

[0045] The technical benefits of this step are that, through automated identification and classification, it significantly reduces the workload of manual data entry and classification, improves business processing efficiency, and lowers the error rate. At the same time, the generation of structured data provides standardized data input for subsequent review, verification, and business processing, enhancing the system's intelligence and business closed-loop capabilities.

[0046] Furthermore, S2 includes: S21, the system dynamically prompts a list of documents to be scanned based on the case type (small medical insurance claim, regular claim), and extracts key fields such as document number, amount, and date through OCR recognition.

[0047] Specifically, in some implementations, the system dynamically prompts for a list of documents to be scanned based on the case type (such as small-amount medical insurance claims or routine claims), and extracts key fields such as document numbers, amounts, and dates through OCR recognition. This is one of the core steps in the intelligent business processing workflow of this invention. This step, based on image processing and natural language processing (NLP) technologies, combined with a business rule engine, enables automatic recognition and classification of user-submitted materials.

[0048] At the technical implementation level, the system first receives the case type selected by the user through a front-end interactive interface. For example, in a group insurance claim application, the user can choose "minor medical insurance claim" or "regular claim." The system dynamically loads the corresponding document list template based on a pre-set business rule library. This template is stored in the back-end database and contains the types of documents required for scanning for different case types, along with their corresponding field extraction rules. For example, a minor claim might only require an ID card, medical invoice, and diagnosis certificate, while a regular claim might involve more materials, such as accident reports and bank account information.

[0049] In terms of performance metrics, the OCR recognition module employs deep learning-based text detection and recognition models (such as CRNN and Transformer architectures), achieving a recognition accuracy of over 98%. It supports various document types (such as ID cards, passports, and bank cards) and invoice formats. The system performs structured extraction of key fields (such as document numbers, amounts, and dates), with a field recognition accuracy of no less than 95%, and supports fuzzy matching and error correction mechanisms.

[0050] In application scenarios, this step is widely used in business processes such as group insurance policy maintenance and claims applications. For example, in a group insurance claims application, after the salesperson scans a medical invoice, the system automatically identifies fields such as the hospital visited, the total amount, and the invoice date, and compares the identification results with the user's input information to ensure data consistency. If OCR recognition fails or a field is missing, the system provides "re-scan" or "manual input" options to ensure the integrity of the process.

[0051] The technical benefits of this step lie in significantly improving the automation and accuracy of business processing. Through dynamic document checklist prompts, the system reduces the user's guesswork in material preparation, improving operational efficiency; OCR recognition and field extraction effectively reduce the error rate of manual data entry, improving data quality. Furthermore, this step provides a structured data foundation for subsequent image data confirmation, review processes, and core system interactions, making it a crucial link in achieving efficient processing by the intelligent counter all-in-one machine.

[0052] S22: For files that fail to be recognized by OCR, the system automatically categorizes them into an unknown category folder and allows sales staff to delete invalid files through the recycle bin function.

[0053] Specifically, in some implementations, when OCR recognition fails, the system automatically categorizes the file into an "Unknown Category Folder" and provides a "Recycle Bin" function for sales staff to delete invalid files. This step is technically implemented based on an exception handling mechanism for image recognition failures and user interaction design, aiming to improve the accuracy and efficiency of data processing.

[0054] At the technical implementation level, the system uses an OCR engine to extract text from scanned images. If recognition fails (e.g., due to blurry images, incorrect formatting, or incomplete content), the system marks the file as "unrecognized" and automatically categorizes it into an "unknown category folder." This folder serves as a temporary storage area to isolate files that failed to be recognized, preventing them from impacting subsequent business processes. After confirming the file is invalid, business personnel can delete it using the "Recycle Bin" function to avoid the accumulation of invalid data. The Recycle Bin supports multi-select deletion, single-entry deletion, and emptying operations, and provides a secondary confirmation mechanism before deletion to prevent accidental deletion.

[0055] At the parameter level, the criteria for judging OCR recognition failure typically include a recognition confidence level below a preset threshold (e.g., ...). ), insufficient length of recognized content (e.g.) The system can handle errors such as missing characters and missing key fields (e.g., ID number, name). After a recognition failure, the system records the file classification information in the log module for subsequent analysis and optimization of the OCR model. Furthermore, the storage period for files in the recycle bin is configurable; for example, files can be retained by default. Files that have not been processed within a day will be automatically deleted.

[0056] At the application level, this step is widely used in business processes such as policy maintenance applications and claims document submission. For example, in the group insurance claims process, if the OCR fails to correctly identify the invoice amount or hospital name after the agent scans a medical invoice, the system will automatically classify it into an unknown category and prompt the agent to manually confirm or delete invalid files. The recycle bin function allows agents to remove useless files after confirmation, ensuring the cleanliness and effectiveness of the image database.

[0057] The technical benefits of this step lie in its automatic classification and recycling mechanism, which effectively isolates invalid documents, improving the accuracy of data processing and the stability of system operation. Simultaneously, the recycle bin function provides sales staff with flexible document management tools, reducing manual intervention costs and improving overall business processing efficiency. This design has significant practical value in intelligent countertop all-in-one systems, especially in scenarios involving high concurrency and processing of multiple document types, where it can significantly optimize business processes.

[0058] S3 employs a dual-channel mechanism, allowing users to choose between local QR code authentication and background SMS notification for review, depending on the business type. Once the review is completed, the system automatically submits the business application to the core system.

[0059] Specifically, the "dual-channel mechanism of selecting local QR code authentication or background SMS notification for review based on business type, and automatically submitting the business application to the core system after review" in this invention is one of the key steps to achieve efficient business processing. This step, through an intelligent routing mechanism, combines a centralized authentication system and an SMS platform to achieve flexible configuration and automated connection of the review process, thereby improving business processing efficiency and ensuring operational compliance.

[0060] At the technical implementation level, the system first intelligently determines the review path based on the user's selected business type (such as policy maintenance, claims, etc.). For policy maintenance, the system will call the centralized authentication system to generate a QR code and display it on the interface for back-office staff to scan and authenticate using the E-operation platform. After scanning, the system obtains the staff member's employee ID and identity information through an interface and compares it with the whitelist configured in the backend to complete the permission verification. For claims, the system sends a review notification to the designated reviewer via SMS, including the business number, customer information, and review link, allowing the reviewer to complete the remote approval process on a mobile device.

[0061] Regarding parameter indicators, the system supports dynamic configuration of the review method. The review path selection logic is based on a preset business rule base, which includes fields such as business type, review level, and operation permissions. The SMS notification module must meet the ISO / IEC 20226-1 standard to ensure the real-time performance and security of information transmission. The SMS sending delay should be controlled within 5 seconds, and a resend mechanism should be supported, with a maximum of 3 retries at 10-second intervals. QR code generation must comply with the ISO / IEC 18004 standard and support QR Code Level M error correction to ensure stable recognition under different lighting and angles.

[0062] In application scenarios, this dual-channel mechanism is widely applicable to the processing of group insurance policy maintenance and claims applications at the counter's integrated kiosk. For example, in group insurance policy maintenance, after the salesperson completes document scanning and OCR recognition, the system automatically selects the review path based on the policy maintenance type, ensuring the compliance and efficiency of the review process. After the review is approved, the system calls the core system interface, encapsulates the business application data in JSON format, and submits it to the core system for further processing via HTTPS protocol.

[0063] The technical advantage of this step lies in the fact that, through a dual-channel mechanism of local QR code scanning and backend SMS verification, the system achieves flexible adaptation and efficient execution of the verification process, significantly reducing manual intervention and processing time, and improving the automation level and security of business processing. At the same time, this mechanism supports the traceability and access control of the verification process, enhancing the system's compliance and manageability.

[0064] Furthermore, S3 includes: S31. During local verification, the system generates a QR code containing the employee number of the office staff through the centralized authentication system and displays it on the screen for scanning and authentication.

[0065] Specifically, in the local review process, the system generates a QR code containing the employee's ID number through a centralized authentication system and displays it on the screen of the self-service kiosk for the staff to scan for identity verification. This step is technically implemented by integrating QR code encoding with an identity verification mechanism, aiming to improve the security and efficiency of the review process.

[0066] At the technical implementation level, the system first obtains the unique identification of the office staff from the centralized authentication system, typically an employee ID or worker number. This identification information is encoded using a standard QR code encoding algorithm (such as the QR Code ISO / IEC 18004 standard) to generate a QR code image containing encrypted fields. Structured data can be embedded in the QR code, for example... ,in This is the employee ID number for the office staff. This is the current timestamp. The QR code is a one-time random number to prevent replay attacks. After generation, the QR code is displayed in real time on the touchscreen of the all-in-one machine using a graphics rendering engine, ensuring clear and highly readable images.

[0067] In terms of parameters, the size of a QR code is typically set to... The pixel count is adjusted to accommodate scanning needs at different distances. The encoding level uses QR Code Level M or Level Q to ensure correct recognition even under partial obstruction or poor lighting conditions. The scanning device must support image quality inspection according to the ISO / IEC 15438 standard to guarantee the stability and accuracy of the scanning process.

[0068] In application scenarios, this step is primarily used for the local review process of group insurance policy maintenance or claims. After the salesperson submits an application, the system displays a QR code page, and the back-office staff uses the QR code scanning module in the E-operation system to authenticate. After successful authentication, the system compares the whitelist with the back-end management terminal via an interface to confirm that the salesperson has the necessary review authority, thus proceeding to the next step of business processing.

[0069] The technical advantage of this step lies in enabling rapid, contactless authentication of office staff using QR codes, avoiding the cumbersome process of traditionally entering employee IDs and passwords, thus improving review efficiency and security. Simultaneously, the integration of facial recognition and a whitelist mechanism ensures operational compliance and traceability of responsibility, representing a significant innovation in identity authentication and process control.

[0070] S32, during the background review, the system sends an SMS notification containing the case number and review link to the mobile phone number of the preset reviewer via the SMS platform.

[0071] Specifically, during the background review process, the system sends an SMS notification containing the case number and review link to the mobile phone number of a pre-selected reviewer via an SMS platform. This is one of the key communication mechanisms in the review process module of this invention. The technical implementation of this step is based on the interface integration between an enterprise-level SMS gateway and the business system, using standard HTTP / HTTPS protocols for data transmission to ensure the security and real-time nature of information transmission. The SMS content uses a structured text format, containing a case number (Case ID) and a review link (Review URL). The case number is a unique identifier automatically generated by the system, in the format PICC-CLAIM-YYYYMMDD-XXXX, used to uniquely identify each preservation or claim case. The review link is the URL address of the corresponding review page in the backend management system, supporting HTTPS encrypted access to ensure data security for reviewers when accessing remotely.

[0072] In some implementations, the SMS platform supports multi-carrier adaptation, including API interfaces from China Mobile, China Unicom, and China Telecom. The system automatically distributes SMS messages through configured routing policies. SMS sending latency is controlled within 5 seconds to ensure that reviewers receive notifications quickly after submitting business requests. SMS content length is limited to 140 characters and conforms to the GSM 7-bit encoding standard to ensure compatibility. The system also supports an SMS status callback mechanism, monitoring SMS delivery success in real time by receiving parameters such as `status_code` and `delivery_time` from the SMS platform. If delivery fails, a resend mechanism is triggered, with a maximum of 3 retries at 10-minute intervals.

[0073] In practical applications, this step primarily serves to enable back-end reviewers to quickly obtain information on cases awaiting review in a remote environment, thereby improving review efficiency. Reviewers can directly access the back-end management system by clicking the link in the text message to view case details and complete the review process. This mechanism effectively reduces the time cost and information omission risk of traditional manual notification methods, achieving automation and remote access to the review process. It is a significant manifestation of this invention's ability to improve business processing efficiency and intelligence.

[0074] S4 performs round-robin verification of submitted business applications. If the verification fails, it is repeated every 48 hours. After more than 10 failures, a manual intervention process is triggered.

[0075] Specifically, the step in this invention of "performing round-robin verification of submitted business applications, repeating verification after 48 hours if verification fails, and triggering manual intervention after more than 10 failures" is a key fault-tolerance and process control mechanism designed to ensure business data consistency and system stability when handling complex businesses such as policy maintenance or claims. This step, based on asynchronous verification and retry strategies, combined with system status monitoring and manual intervention triggering logic, enables automatic processing of business applications and abnormal responses.

[0076] At the technical implementation level, after receiving a business application, the system first calls the core system interface to verify the legality of the business data, including policy status, applicant identity consistency, and document completeness. If the verification fails, the system records the failure status in the local database and sets a retry counter. The initial value is 0. The system uses a scheduled task mechanism, which executes the task every 48 hours (i.e., ...). Automatically trigger a verification retry process until... After each verification failure, the counter... Increment by 1, that is .like If the system determines that the exception has failed, it will automatically trigger a manual intervention process, push the business application to the backend management system, and send a notification to the designated office staff via SMS platform to ensure that the problem is handled in a timely manner.

[0077] At the parameter level, the system is configured with a retry interval of 48 hours to avoid putting pressure on the core system due to frequent requests, while ensuring the timeliness of business data. The maximum number of retries is set to 10. Once the threshold is exceeded, automatic retry will cease, and the system will proceed to manual processing. The system supports asynchronous verification mechanisms to ensure the continuity and stability of business processing.

[0078] At the application level, this step is widely used in business processes such as group insurance policy maintenance applications and claims document submissions. For example, in a policy maintenance application, if verification fails due to a delay in core system data synchronization, the system will automatically retry after 48 hours to avoid business interruption due to temporary issues. If multiple failures occur, the system will automatically push the application to a manual review queue to ensure that the business is eventually processed.

[0079] The technical advantage of this step lies in its ability to effectively address temporary data anomalies or API call failures through a round-robin verification mechanism, thereby improving the fault tolerance and automation level of business processing. Simultaneously, by setting a maximum number of retries, the system ensures both automated processing efficiency and timely manual intervention in abnormal situations, thus enhancing the overall reliability of business processing and user experience.

[0080] Furthermore, S4 includes: S41, the system uses an exponential backoff algorithm to control the polling interval. After the first failure, the interval is 48 hours, and after the nth failure, the interval is calculated according to the formula. Increasing hourly.

[0081] Specifically, in some implementations, the system employs an exponential backoff algorithm to control the polling interval, addressing situations where policy maintenance or claims applications fail verification after their initial submission. This algorithm, by setting an incremental retry interval, effectively avoids resource waste and network congestion caused by frequent failed requests, while ensuring the continuity and stability of business processing.

[0082] Specifically, when the system is in the first If the check still fails in the next test, the polling interval time According to the formula Increasing hourly. Among them, Indicates the number of failures. Indicates the first Waiting time after the first failure. System settings after the first failure. The interval was 48 hours; after the second failure, The interval time becomes The algorithm operates on an exponential growth model, resulting in a high retry frequency initially, which gradually decreases as the number of failures increases. This provides a sufficient time window for system recovery, preventing repeated failures caused by transient faults or temporary system anomalies.

[0083] In terms of implementation, the system records the timestamp and number of failures for each verification failure through a background task scheduling module. The system calculates the interval for the next submission based on the formula above. This module is typically implemented using a scheduled task framework (such as Quartz or Spring Task) to ensure precise time control and task scalability. The system also sets a maximum of 10 retries. If the 10th attempt fails, a manual intervention mechanism is triggered, sending a prominent notification to back-office staff to ensure the business process does not hang indefinitely due to automatic retry failures.

[0084] In practical applications, this step is mainly used to handle abnormal submission scenarios in key business processes such as new policy maintenance or claims applications. For example, in a group insurance policy maintenance application, if the core system returns a verification failure, the system will automatically enter an indexed backoff retry mechanism to ensure that subsequent verification can be completed in a timely manner after the system recovers, while avoiding excessive pressure on the core system.

[0085] Through this exponential backoff algorithm, the system achieves intelligent control over the retry mechanism, improving the system's fault tolerance and stability, reducing the risk of business interruption caused by system anomalies, and thus enhancing the overall reliability of business processing and user experience.

[0086] S42: When the cumulative number of failures reaches 10, the system automatically marks the case as high priority and pushes it to the top of the office staff's to-do list.

[0087] Specifically, in some implementations, when the system detects that the cumulative number of failures has reached a preset threshold (e.g., 10 times), it automatically marks the case as high priority and pushes it to the top of the back-office staff's to-do list. This step, based on the system's real-time monitoring mechanism for business processing status, combined with task scheduling strategies and priority management algorithms, enables rapid response and processing of abnormal cases.

[0088] From a technical implementation perspective, the system dynamically tracks the processing status of policy maintenance or claims applications using an event-driven architecture. Each time processing fails, the system records the failure event and updates the case status fields, such as `case_status` and `failure_count`. When `failure_count` reaches 10 times, the system triggers high-priority marking logic, setting the case's priority field `priority_level` to "high" and pushing the case to the back-office staff's task queue via a message queue (such as RabbitMQ or Kafka), inserting it at the head of the queue to ensure priority processing. This process complies with the system reliability and response time requirements of the ISO / IEC 25010 standard.

[0089] At the parameter level, the system sets a failure threshold of 10 times. This value is derived from statistical analysis of historical business data, aiming to balance the system's automated processing capabilities with the necessity of manual intervention. The system also supports configurable settings, allowing administrators to dynamically adjust this threshold through backend configuration files or database fields (such as `max_retry_count`). After a case is prioritized, the system inserts it into the back-office staff's to-do list via the `task_queue` module, setting the `task_position` field to "top" to ensure its priority display in the task list.

[0090] In application scenarios, this mechanism is suitable for business processes that require multiple rounds of automatic verification, such as policy maintenance applications and claims document submissions. For example, in group insurance policy maintenance applications, if the system fails to submit to the core system multiple times within 48 hours, this high-priority processing mechanism is automatically triggered to ensure timely intervention by back-office staff and avoid case backlog or processing delays. This step plays a key role in exception handling and process optimization within the system, effectively improving the system's fault tolerance and response efficiency in complex business scenarios.

[0091] In terms of technical effectiveness, this step, by introducing a failure count threshold and priority scheduling strategy, significantly improved the efficiency of handling abnormal cases and reduced business delays caused by system failures in automatic processing. Simultaneously, this mechanism enhanced the system's intelligence level, providing clearer task priority guidance for back-office staff, thereby improving the overall automation level and service quality of business processing.

[0092] S5, after approval, generates and prints a delivery order through a unified output platform, and sends a business result notification via SMS platform.

[0093] Specifically, after approval, the system generates and prints a delivery slip through a unified output platform, and simultaneously sends a business result notification to the salesperson or relevant reviewer via SMS. This step is the final stage of the entire business process, and its technical implementation relies on the collaborative work of the data interaction mechanism between systems, the document generation engine, and the communication module.

[0094] At the technical implementation level, the unified output platform employs a PDF document generation engine, dynamically rendering settlement statement content based on a preset template structure and business data. The system extracts approved business data from the core business system, including policy number, policy type, changes, and reviewer information, and synchronizes the data via a RESTful API or message queue. During document generation, the system supports single-record printing or batch printing, and the print format must conform to the layout and content requirements for insurance endorsements and settlement statements in the "Insurance Business Document Format Specification" (ISO / IEC 21827-3). After printing, the system uploads the print record to the core system to ensure consistency between business data and physical documents.

[0095] Regarding parameters, the system is set to print a maximum of 30 pages; exceeding this limit will result in a printing failure message to prevent hardware malfunctions due to excessively large documents. Each policy number's settlement statement can be printed a maximum of 5 times to prevent duplicate printing and resource waste. The SMS platform uses the industry-standard SMPP protocol or HTTP interface, supporting templated SMS content configuration, such as "[PICC Life Insurance] Your group insurance policy maintenance application has been approved, and the settlement statement has been printed. Please pick it up promptly." SMS sending delays are controlled within 3 seconds to ensure real-time information delivery.

[0096] In application scenarios, this step is widely used in group insurance policy maintenance, claims applications, and other business scenarios. For example, in group insurance policy maintenance, after approval, the system automatically generates and prints an approval form, and simultaneously notifies back-office staff or sales representatives via SMS, ensuring a closed-loop process. In claims scenarios, the review results are provided instantly via SMS, facilitating follow-up by sales representatives.

[0097] The technical benefits of this step lie in achieving real-time visualization and information synchronization of business results through standardized document generation and multi-channel notification mechanisms, thereby improving the compliance and efficiency of business processing. Simultaneously, the introduction of SMS notifications enhances business transparency, reduces manual communication costs, and provides strong support for subsequent business tracking and customer service.

[0098] The life insurance counter service automation processing method of this invention can realize the intelligent and automated processing of life insurance counter services, significantly improve the operational efficiency and accuracy of complex services such as policy maintenance and claims, and reduce manual intervention and error rate.

[0099] S6: When a salesperson selects to print a delivery order, the system generates a voice command containing a pickup prompt through the voice synthesis module, and simultaneously displays a pickup countdown on the screen. After the countdown ends, the order printing port is automatically locked.

[0100] Specifically, when a salesperson selects to print a delivery slip, the system generates a voice command containing a pickup prompt via a speech synthesis module, and simultaneously displays a pickup countdown on the screen. After the countdown ends, the slip printing port is automatically locked. This step is a crucial link in achieving closed-loop management of business processes in this invention, ensuring timely delivery of business processing results and the safety of equipment operation.

[0101] At the technical implementation level, upon receiving the "print delivery slip" command, the system first invokes the text-to-speech (TTS) module to convert the preset prompt text (such as "Please collect the delivery slip within 30 seconds") into a speech signal. The TTS module supports multilingual output, with a sampling rate typically set to 16 kHz and encoding in PCM or AAC format to ensure speech clarity and smooth playback. The voice prompt is played synchronously through the all-in-one machine's built-in speaker, ensuring that sales personnel receive immediate reminders to collect the slip during the operation.

[0102] Simultaneously, the system displays a countdown timer on the user interface (UI). The countdown time is configurable, typically set between 30 and 60 seconds, with the specific time determined by the system administrator based on the business scenario and device performance. The countdown uses a decrementing numerical method, refreshing the interface once per second. The remaining time is updated in the following way, where This is the initial countdown time. The time increment is 1 second. After the countdown ends, the system automatically closes the output port by controlling the mechanical locking device (such as an electromagnetic lock or a stepper motor driven gate) to prevent the document from being lost or accidentally taken by others.

[0103] In terms of parameters, the speech synthesis module must support real-time synthesis of text no longer than 256 characters, with a response time of less than 500 ms. The countdown module must have high-precision timing capabilities, with an error controlled within ±100 ms to ensure the accuracy of time prompts. The output port locking mechanism should have rapid response capabilities, with a locking action time of no more than 2 seconds, and support status feedback to ensure that the system can accurately determine whether locking has been successful.

[0104] In application scenarios, this step is widely used in the final delivery stage of business processes such as policy maintenance and claims settlement. For example, after a group insurance policy maintenance application is completed, the system automatically generates and prints a settlement statement. The salesperson must complete the operation within a limited time before picking it up. The system uses both voice prompts and a countdown timer to ensure the standardization and security of the operation.

[0105] The technical effect of this step is that, through the collaborative prompting mechanism of voice and countdown, it effectively improves the operational efficiency of salespersons and the security of file management, avoids the problem of file retention or loss due to operation delays, and enhances the intelligence of the system and user experience. It is one of the important innovations of this invention in business closed-loop management.

[0106] The life insurance counter service automation method of this invention effectively reminds users to pick up the documents in time by combining voice synthesis and visual countdown prompts when the salesperson selects to print the settlement statement, so as to avoid the documents being left behind or lost, and further improves the automation level of counter service processing and user experience.

[0107] To achieve the above embodiments, the present invention also proposes an automated processing device for life insurance counter services. Figure 3 This is a schematic diagram of the structure of an automated processing device for life insurance counter services provided in an embodiment of the present invention. Figure 3 As shown, the device includes: The multimodal identity verification module 100 is used to authenticate the identity of salespersons through the multimodal identity verification module. After successful verification, the salesperson's group insurance policy information is queried and displayed. The OCR recognition and intelligent classification module 200 is used to scan, recognize, and automatically classify insurance or claim application materials based on the OCR recognition and intelligent classification module, generate structured data, and display a confirmation interface. The dual-channel review mechanism module 300 is used to select either local QR code authentication review or background SMS notification review based on the business type. After the review is completed, the business application is automatically submitted to the core system. The 400-module round-robin verification and manual intervention module is used to perform round-robin verification on submitted business applications. If the verification fails, the verification is repeated every 48 hours. After more than 10 failures, the manual intervention process is triggered. The delivery order generation and notification sending module 500 is used to generate and print delivery orders through a unified output platform after approval, and to send business result notifications through an SMS platform.

[0108] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0109] To implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0110] To implement the above embodiments, the present invention also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0111] To implement the above embodiments, the present invention also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0112] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0113] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0114] This invention is intended to provide implementation schemes for users to selectively prevent the use or access to personal information data. That is, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0115] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0116] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0117] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0118] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0119] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0120] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0121] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0122] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0123] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0124] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for automating the processing of life insurance front office transactions, the method comprising the steps of: Comprise: ​ S1, the identity of the salesman is authenticated through the multi-modal identity authentication module, the information of the group insurance policy under his name is inquired and displayed after verification; S2, the scanned identification and automatic classification of the claim or claim application materials are carried out based on the OCR recognition and intelligent classification module, the structured data is generated and the confirmation interface is displayed; S3, according to the business type, the dual-channel mechanism of local code scanning authentication audit or background SMS notification audit is selected, and after the audit is completed, the business application is automatically submitted to the core system; S4, the submitted business application is checked, if the check fails, the check is repeated every 48 hours, and if it fails more than 10 times, the manual intervention process is triggered; S5, after the audit is passed, the delivery sheet is generated and printed through the unified output platform, and the business result notification is sent through the SMS platform.

2. The method of claim 1, wherein, The identity of the salesman is authenticated through the multi-modal identity authentication module, the information of the group insurance policy under his name is inquired and displayed after verification further comprises: S11, the salesman inputs the salesman number, the system queries the personnel management system through the interface to verify the salesman information, including whether the work number exists, whether the account status is normal and whether the ID number is correct; S12, after verification, the system calls the face recognition module for biological feature comparison, and enters the policy inquiry interface after successful comparison.

3. The method of claim 1, wherein, The scanned identification and automatic classification of the claim or claim application materials are carried out based on the OCR recognition and intelligent classification module, the structured data is generated and the confirmation interface is displayed further comprises: S21, the system dynamically prompts the file list to be scanned according to the case type (small medical insurance claim, regular claim) of claim or claim, and extracts the key fields such as certificate number, amount and date through OCR recognition; S22, for the files failed in OCR recognition, the system automatically classifies them into unknown folder, and allows the salesman to delete invalid files through the recycle bin function.

4. The method of claim 1, wherein, According to the business type, the dual-channel mechanism of local code scanning authentication audit or background SMS notification audit is selected, and after the audit is completed, the business application is automatically submitted to the core system further comprises: S31, when local audit, the system generates a two-dimensional code containing the work number of the internal staff through the centralized authentication system, and displays it on the screen for code scanning authentication; S32, when background audit, the system sends a SMS notification containing the case number and audit link to the mobile phone number of the preset audit personnel through the SMS platform.

5. The method of claim 1, wherein, The submitted business application is checked, if the check fails, the check is repeated every 48 hours, and if it fails more than 10 times, the manual intervention process is triggered further comprises: S41, the system adopts exponential backoff algorithm to control the wheel patrol interval, the interval is 48 hours after the first failure, and the interval time after the nth failure is according to the formula Hourly increase; S42, when the cumulative failure number reaches 10 times, the system automatically marks the case as high priority and pushes it to the top of the internal staff to-do list.

6. The method of claim 1, wherein, Further comprising: S6, when the salesman selects to print the delivery sheet, the system generates a voice instruction containing a take-away prompt through the voice synthesis module, and synchronously displays the take-away countdown on the screen, and automatically locks the output port after the countdown is over.

7. An apparatus for automated processing of life insurance front office transactions, characterized by: Comprise: Multi-modal identity authentication module, for authenticating the identity of the salesman through the multi-modal identity authentication module, and inquiring and displaying the group insurance policy information under his name after verification; An OCR recognition and intelligent classification module is configured to scan and recognize and automatically classify security or claim settlement application materials based on the OCR recognition and intelligent classification module, generate structured data, and display a confirmation interface; A dual-channel audit mechanism module is configured to select a dual-channel mechanism of local code scanning authentication audit or background SMS notification audit according to a business type, complete the audit, and automatically submit a business application to a core system; A round-patrol verification and manual intervention module is configured to perform round-patrol verification on a submitted business application, repeatedly verify the business application at intervals of 48 hours if the verification fails, and trigger a manual intervention process after 10 failures. A delivery note generation and notification sending module is configured to generate a delivery note and print the delivery note through a unified output platform after the audit is passed, and send a business result notification through an SMS platform.

8. An electronic device, comprising: It comprises a processor and a memory connected in communication with the processor. The memory stores computer execution instructions. The processor executes the computer execution instructions stored in the memory to implement the method of any one of claims 1-6. The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It comprises a computer program which, when executed by the processor, implements the method of any one of claims 1-6.

10. A computer program product, characterised in that, ​

Images