Insurance path determination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an insurance path determination method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring an initial insurance data set; determining an insurance path with a ring flow in the initial insurance data set as a non-standard path; determining a first solution strategy corresponding to each ring flow according to the distribution condition of each flow node in the non-standard path and the flow attribute of each ring flow in the non-standard path; determining a process operation guide corresponding to the insurance process according to the first solution strategy; obtaining a first insurance data set; and under the condition that the path number of the non-standard paths in the first insurance data set is greater than a preset number, updating a process operation guide according to the first insurance data set until the path number of the non-standard paths in a second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, and obtaining a standard insurance path. According to the embodiment of the invention, the determination efficiency of the insurance path can be improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a method and device for determining an insurance path, an electronic device, and a storage medium. Background Art

[0002] In the operation of insurance business, it is crucial to determine an accurate and efficient standard insurance path. A clear and reasonable standard insurance path can not only greatly improve the operational efficiency of business personnel and reduce operational errors, but also significantly shorten the waiting time for customers to be insured, greatly improve customer experience, and enhance the market competitiveness of insurance companies. However, as insurance business scenarios become more complex, the operational paths of business personnel between different nodes in the insurance process have become highly discrete. At this time, the insurance path determination methods in related technologies have exposed obvious shortcomings and low efficiency. The insurance path determination methods in related technologies mostly rely on manual experience to preset standard insurance paths, and lack consideration of the path deviations that are dynamically generated during the actual business execution process. In addition, manual processing is not only slow to respond, but also difficult to meet the needs of rapid business advancement, resulting in low efficiency in determining the insurance path. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a method and device for determining an insurance path, an electronic device and a storage medium, aiming to improve the efficiency of determining the insurance path.

[0004] To achieve the above-mentioned object, a first aspect of an embodiment of the present application provides a method for determining an insurance path, the method comprising: obtaining an initial insurance data set, the initial insurance data set comprising insurance paths of multiple business personnel executing insurance processes;

[0005] The insurance paths with loop processes in the initial insurance data set are determined as non-standard paths. The loop process is a cyclic path with repeated business operations in the non-standard path.

[0006] Determine a first solution strategy corresponding to each loop process based on the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path;

[0007] Based on the first solution strategy, determine the process operation guide corresponding to the insurance process;

[0008] Obtaining a first insurance data set, where the first insurance data set includes insurance paths of multiple business personnel executing the insurance process according to the process operation guide;

[0009] When the number of non-standard paths in the first insurance data set is greater than the preset number, the process operation guide of the first insurance data set is updated until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, and the standard insurance path is obtained.

[0010] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides an insurance path determination device, the device comprising:

[0011] A first acquisition module is configured to acquire an initial insurance data set, wherein the initial insurance data set includes insurance paths of multiple business personnel executing the insurance process;

[0012] A first determining module is configured to determine an insurance path having a loop process in the initial insurance data set as a non-standard path, wherein the loop process is a cyclic path having repeated business operations in the non-standard path;

[0013] a second determining module, configured to determine a first solution strategy corresponding to each loop process according to the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path;

[0014] A third determining module is configured to determine a process operation guide corresponding to the insurance application process according to the first solution strategy;

[0015] a second acquisition module, configured to acquire a first insurance application data set, wherein the first insurance application data set includes an insurance application path of the plurality of business personnel executing the insurance application process according to the process operation guide;

[0016] The fourth determination module is used to update the process operation guide according to the first insurance data set when the number of non-standard paths in the first insurance data set is greater than a preset number, until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, thereby obtaining a standard insurance path.

[0017] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0018] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0019] To achieve the above objectives, the fifth aspect of the embodiments of the present application proposes a computer program product. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device executes the method described in the first aspect above.

[0020] The insurance path determination method and device, electronic device, and storage medium proposed in this application, by acquiring an initial insurance data set, comprehensively covers all types of insurance path data generated by multiple business personnel in actual operational scenarios, laying a solid foundation for accurate analysis. Based on this initial insurance data set, it can quickly and accurately identify non-standard paths with loop processes, precisely locating repetitive business operation loops that lead to inefficiencies in complex insurance processes, avoiding potential omissions in traditional manual investigations and providing a clear direction for subsequent optimization. Furthermore, by performing a multi-dimensional analysis of the distribution of process nodes and loop process attributes in non-standard paths, a first solution strategy corresponding to each loop process can be quickly generated. Compared with traditional approaches that rely on manual experience, this significantly shortens the time required to identify the root cause of the problem and develop a solution strategy, significantly improving the response speed and accuracy of strategy development. After obtaining the first insurance data set executed according to the process operation guide, if the number of non-standard paths still exceeds the preset number, the process operation guide will be updated based on the first insurance data set. This iterative optimization mechanism continuously absorbs new data and new situations generated during business execution, dynamically optimizing the insurance path until the number of non-standard paths in the insurance data set meets the preset requirements, thereby obtaining a standard insurance path. This process eliminates the lag of manual processing and automatically iterates to determine the standard insurance path, ensuring that the standard insurance path can be quickly determined even in complex business scenarios, thereby improving the efficiency of insurance path determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of the insurance path determination method provided in an embodiment of the present application;

[0022] Figure 2 yes Figure 1 Flowchart of step S102 in FIG.

[0023] Figure 3 yes Figure 1 Flowchart of step S103 in FIG.

[0024] Figure 4 yes Figure 3 Flowchart of step S301 in FIG.

[0025] Figure 5 This is a schematic diagram of the structure of the insurance path determination device provided in an embodiment of the present application;

[0026] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0028] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0030] First, let’s analyze some of the terms used in this application:

[0031] Artificial intelligence (AI) is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. A branch of computer science, AI seeks to understand the essence of intelligence and create new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thinking. It also encompasses theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0032] Based on this, the embodiments of the present application provide a method and device for determining an insurance path, an electronic device, and a storage medium, aiming to improve the efficiency of determining the insurance path.

[0033] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0034] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0035] The insurance path determination method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The insurance path determination method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the insurance path determination method, etc., but is not limited to the above forms.

[0036] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0037] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0038] Figure 1 This is an optional flow chart of the insurance path determination method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0039] S101, obtaining an initial insurance data set, wherein the initial insurance data set includes insurance paths executed by multiple business personnel in the insurance process;

[0040] S102: Determine an insurance path with a loop process in the initial insurance data set as a non-standard path, where the loop process is a cyclic path with repeated business operations in the non-standard path;

[0041] S103, determining a first solution strategy corresponding to each loop process according to the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path;

[0042] S104: Determine a process operation guide corresponding to the insurance application process based on the first solution strategy;

[0043] S105: Acquire a first insurance application data set, where the first insurance application data set includes insurance application paths of the multiple business personnel executing the insurance application process according to the process operation guide;

[0044] S106, when the number of non-standard paths in the first insurance data set is greater than a preset number, the process operation guide is updated according to the first insurance data set until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, thereby obtaining a standard insurance path.

[0045] Steps S101 to S106 shown in the embodiment of the present application, by acquiring the initial insurance data set, comprehensively cover all types of insurance path data generated by multiple business personnel in actual operation scenarios, laying a solid foundation for accurate analysis. Based on the initial insurance data set, it is possible to quickly and accurately identify non-standard paths with loop processes, accurately locate the repetitive business operation loop paths that lead to inefficiency in complex insurance processes, avoid omissions that may occur in traditional manual investigations, and provide a clear direction for subsequent optimization. On this basis, by performing a multi-dimensional analysis of the distribution of process nodes and loop process attributes in non-standard paths, the first solution strategy corresponding to each loop process can be quickly generated. Compared with the traditional approach of relying on manual experience, the time for investigating the root cause of the problem and formulating a solution strategy is greatly shortened, and the response speed and accuracy of strategy formulation are significantly improved. After obtaining the first insurance data set executed in accordance with the process operation guide, if the number of non-standard paths is still greater than the preset number, the process operation guide will be updated according to the first insurance data set. This iterative optimization mechanism continuously absorbs new data and new situations generated during business execution, dynamically optimizing the insurance path until the number of non-standard paths in the insurance data set meets the preset requirements, thereby obtaining a standard insurance path. This process eliminates the lag of manual processing and automatically iterates to determine the standard insurance path, ensuring that the standard insurance path can be quickly determined even in complex business scenarios, thereby improving the efficiency of insurance path determination.

[0046] In step S101 of some embodiments, the initial insurance application dataset serves as the starting data source for the entire insurance application path determination method. This dataset is a collection of raw data accumulated during the actual business operations of insurance companies, resulting from their collection and organization. This data comprehensively records operational information about insurance business operations in various business scenarios over a specific time period. Its rich and diverse content covers all aspects of insurance application operations, serving as the fundamental data support for subsequent analysis and processing of insurance application paths.

[0047] Sales personnel refer to the numerous employees involved in insurance-related work during the daily operations of an insurance company. These personnel possess varying work experience, operational habits, and professional skill levels. When handling insurance business, their understanding of business rules and operational approaches can lead to different insurance application paths. For example, some personnel may be more adept at handling insurance business for a specific type of insurance, while others may be faster but less accurate at entering information. The actual operational data of multiple personnel is integrated into the initial insurance application dataset, enabling it to fully reflect the diversity and complexity of insurance application paths in actual business scenarios.

[0048] The insurance application process is a series of standardized operational steps and links established by insurance companies to complete customer insurance business. The insurance application process can cover multiple key links, from customer consultation, information collection, risk assessment, terms confirmation, premium calculation, to the final policy generation and delivery. Each link has corresponding business rules and operational requirements to ensure the compliance, accuracy, and efficiency of the insurance business. In actual business operations, the insurance application process may vary depending on factors such as insurance product type, customer needs, and regulatory requirements. When executing the insurance application process, business personnel need to strictly follow the prescribed steps. However, due to various reasons, deviations from the standard process may occur during actual operations.

[0049] The insurance path refers to the various business nodes, the sequence of connections between nodes, and the operational trajectory that business personnel actually navigate during the insurance process. It reflects the actual operational steps and process flow from start to finish. A complete insurance path includes detailed information such as the operator's operation time, operation content, and data input and output at each business node. Due to individual differences among business personnel and the complexity of business scenarios, different business personnel may generate different insurance paths when executing the same insurance process. Some of these paths conform to standard process specifications, while others may present various issues.

[0050] In a feasible implementation method of the present application, the insurance company can deeply connect with various relevant internal business systems (such as core business systems, customer relationship management systems, financial systems, etc.). With the help of data interface technology, according to the established data transmission protocol, data related to the insurance business is extracted from these systems on a regular or real-time basis. For example, detailed operation records of the insurance process are obtained from the core business system, including the operation time, operator, input and output data of each step; and basic customer information, historical insurance records, risk preferences and other data are obtained from the customer relationship management system. These data from different systems are integrated into a unified data storage platform, and after preliminary data cleaning and format conversion, an initial insurance data set is formed.

[0051] A logging module can also be deployed within the business system where sales personnel execute the insurance application process. This module records every key operation step, system response information, and user interaction data in real time during the business operation. For example, every time a salesperson clicks a button, enters specific information, or encounters an error or warning prompt, all of these will be recorded in detail. This log data is regularly collected and organized, converted into a structured data format using log parsing tools, and integrated with other relevant business data to ultimately construct an initial insurance application dataset encompassing the insurance application paths of multiple sales personnel.

[0052] In step S102 of some embodiments, a loop process refers to a repetitive business process segment within the insurance application process. This segment of the insurance application process exhibits an unusual loop. In a loop process, business processing does not proceed in a normal linear sequence, but rather "loops," causing operations to repeat themselves between certain steps and prevent smooth progress to the next stage. For example, between the customer information entry and review stages, unclear information verification rules may cause business personnel to repeatedly modify and submit information, leading to repeated entry and review. This repetitive process constitutes a loop process.

[0053] Repetitive business operations are the specific behavioral manifestations of a circular process. During the insurance application process, business personnel perform multiple, unnecessary, and identical operations for the same business link or multiple related business links. For example, repeatedly filling in the same basic customer information or repeatedly performing the same risk assessment steps. These repetitive operations not only waste business personnel's time and energy, but also increase the risk of operational errors, leading to confusion and delays in the insurance application process. The circular path is closely related to the concept of a circular process, emphasizing that the insurance application path structurally forms a closed, repetitive loop. From the perspective of a process node, a business operation starts at a certain node, goes through a series of operations, and then returns to a previous node, forming a path similar to a ring. This circular path disrupts the linear progression of the normal insurance application process, causing business processing to fall into an ineffective, repetitive cycle. It is a key factor that leads to inefficiency and non-compliance with standard specifications in the insurance application path.

[0054] A non-standard path, or non-standard insurance application path, is the opposite of the ideal, compliant standard insurance application path. Within the initial insurance application data set, any path containing loops is considered a non-standard path. These paths violate the design logic and business specifications of the standard insurance application process and often exhibit operational redundancy and inefficiency, hindering the smooth progress and efficiency of insurance application processing. These paths warrant subsequent analysis and optimization.

[0055] In a feasible implementation of the present application, a special rule engine can be constructed to pre-set a series of rules for identifying loop processes. For example, it is set that when the same business operation step is repeatedly executed more than a certain number of times (such as 3 times) within a specific time period (such as 30 minutes), and there is a specific logical association between the operation steps, it is determined that a loop process exists. The rule engine traverses each insurance path in the initial insurance data set, and checks and matches the business operation steps in the path one by one according to these rules. For example, in the insurance process, if the customer information entry step is submitted repeatedly within a short period of time, and does not enter the next key node (such as premium calculation) after each submission, the rule engine can identify the existence of a loop process in the insurance path based on the set rules, and then determine it as a non-standard path.

[0056] Historically labeled data (data that clearly distinguishes between standard and non-standard paths) can also be used to train supervised learning models, such as decision trees, random forests, or neural networks. During the training process, the model learns the characteristic patterns of the standard insurance path and the non-standard path with loop processes, including the sequence of business operation steps, time intervals, repetition frequency, and other features. After the training is completed, the insurance path data in the initial insurance data set is input into the trained model. The model predicts each path based on the learned characteristic patterns, determines whether it has a loop process, and then determines whether it is a non-standard path. For example, if the trained model finds that the frequency and sequence combination of the "risk assessment" and "re-confirmation of risk" steps in a certain insurance path conforms to the learned loop process characteristic pattern, it can be determined that the path is a non-standard path.

[0057] In step S103 of some embodiments, within the non-standard path, the insurance process consists of multiple different process nodes. The distribution of each process node refers to information such as their position within the entire insurance process and their relationships. The node location clearly identifies the stage of the insurance process for each process node, which helps understand the sequence of business operations.

[0058] Relationships describe the logical connections between process nodes, that is, how the completion of one node affects the opening of subsequent nodes. For example, the premium calculation node can only be entered after the risk assessment node is completed, because the premium amount is usually determined based on the risk assessment results.

[0059] The process attributes of a loop process are a description of the characteristics of the loop process itself, which may include loop node composition, number of loops, loop period, etc.

[0060] The loop node composition specifies the specific nodes that make up the loop process. For example, a loop process might consist of two nodes: "Supplement Documentation" and "Return if Approval Failed."

[0061] The number of loops counts the number of times a process repeats, reflecting the impact of that loop on insurance efficiency. A higher number of loops indicates a more severe problem and a greater impact on the overall insurance process.

[0062] The cycle time is the time it takes for a calculation loop process to complete one cycle. A long cycle time may indicate problems such as complex business operations or slow data processing.

[0063] The first solution strategy is a preliminary solution developed for each loop process, aiming to eliminate or reduce loop processes and optimize the insurance path. The first solution strategy is determined based on the distribution of each process node and the process attributes of each loop process. The specific strategy may vary depending on the situation. If it is found that the loop process is caused by unclear or unreasonable business rules, the first solution strategy may be to reorganize and optimize the business rules. For example, clarify the specific standards for document review to avoid business personnel repeatedly supplementing documents due to vague standards. If the loop process is caused by operational errors or lack of proficiency of business personnel, a corresponding training plan can be formulated to provide operational training to business personnel to improve their business level and operational proficiency.

[0064] In one possible implementation of the present application, a machine learning model can be constructed. A large amount of historical non-standard path data, including the distribution of each process node and the process attributes of the loop process, as well as corresponding successful resolution strategies, is collected as training samples. The model is trained using a suitable machine learning algorithm, such as a decision tree or random forest. During the training process, the model learns the features and patterns in the data so that it can predict appropriate resolution strategies based on new non-standard path data. Subsequently, the current non-standard path data is input into the trained model. Based on this learned knowledge, the model analyzes the distribution of each process node and the process attributes of the loop process and outputs a recommended resolution strategy for each loop process. For example, based on characteristics such as the number of loops, the cycle, and the nodes involved in the loop process, the model may determine whether the loop process is caused by a system failure or a business rule issue, and then provide a corresponding resolution strategy, such as fixing system vulnerabilities or adjusting business rules. Finally, the strategies output by the model can be manually reviewed and adjusted. While machine learning models can provide valuable recommendations, due to the complexity and specificity of the insurance business, business personnel must review and optimize the strategies based on actual conditions to ensure their feasibility and effectiveness.

[0065] In step S104 of some embodiments, the process operation guide is a detailed specification and operation manual developed based on the determined first solution strategy, used to guide the execution of the insurance application process. The process operation guide is an important document that translates the strategy for solving looping process problems into specific, executable steps. It covers the operation methods, sequence, precautions, and other content for each link in the insurance application process. Specifically, the process operation guide will clearly stipulate how business personnel should perform each key step in the insurance application process. For example, in the customer information entry process, if the first solution strategy is to optimize the entry interface and clarify the review standards, the process operation guide will detail the filling requirements for each field in the new entry interface, as well as the specific standards and operation methods for the review process. For areas where looping processes may occur, the process operation guide will specifically emphasize how to avoid similar repetitive business operation cycles. In general, the process operation guide is a comprehensive and detailed operational guidance document designed to help business personnel execute the insurance application process according to unified standards and specifications, reduce operational errors and process deviations, thereby improving the efficiency and quality of the insurance application process, and ultimately achieving the goal of a standardized insurance application path.

[0066] In one possible implementation of this application, a process operation guide template library can be established. The template library contains a common operational framework for each step of the insurance application process and examples of solutions to common problems. After obtaining a first solution strategy, an automated tool is used to analyze the strategy and extract key information. For example, if the first solution involves optimizing the "premium calculation" process and proposes introducing a new premium calculation model, the automated tool will identify the key information "new premium calculation model." Based on this key information, a matching template is selected from the template library and filled in and modified. The automated tool then fills in the corresponding information in the template, including the specific formulas and applicable conditions of the new premium calculation model, to generate a preliminary process operation guide. For special operational requirements that cannot be directly obtained from the template, the tool will prompt manual supplementation. Finally, the generated process operation guide is manually reviewed to ensure its accuracy and completeness. The reviewer checks whether the operational steps conform to business logic and whether there are any omissions or errors. After correcting any identified issues, the final process operation guide is generated.

[0067] In step S105 of some embodiments, the first insurance data set is a data set formed after the business personnel execute the insurance process according to the process operation guide determined in S104. The first insurance data set plays a key role in verifying the effectiveness of the process operation guide in the entire insurance path determination method. The first insurance data set contains the insurance path information generated by multiple business personnel executing the insurance process while following the process operation guide. The process operation guide is formulated based on the first solution strategy to solve the problem of loop processes in non-standard paths, with the aim of guiding business personnel to complete the insurance process in a more reasonable and efficient manner. The first insurance data set reflects whether the business personnel have truly followed the guidelines in actual operations, and whether the insurance process has been improved after following the guidelines. The first insurance data set is an actual test of the implementation effect of the process operation guide, and provides important data basis for subsequent judgments on whether the process operation guide needs to be updated and how to update it. It is an important intermediate product in the entire insurance path optimization process.

[0068] In the implementation of the present application, the method of obtaining the first insurance data set is basically the same as the method of obtaining the initial insurance data set. Please refer to the method of obtaining the initial insurance data set and will not be repeated here.

[0069] In step S106 of some embodiments, the updated process operation guide is obtained after analyzing the first insurance data set and revising and improving the original process operation guide based on the problems reflected therein. The first insurance data set shows the actual situation of business personnel executing the insurance process according to the original process operation guide. If the number of non-standard paths is greater than the preset number, it means that the original process operation guide may have deficiencies, such as some operating steps are not clear enough, and the solution strategy fails to effectively eliminate the loop process. Based on the first insurance data set, adjustments are made to the unreasonable or inapplicable parts of the original process operation guide, such as further clarifying the operating specifications, optimizing business rules, and supplementing methods for dealing with special situations, so as to form an updated process operation guide that is more in line with actual business needs and can better guide business personnel to execute the insurance process.

[0070] The second insurance data set is a collection of insurance path data generated after business personnel execute the insurance process according to the updated process operation guide. It serves as a basis for verifying the effectiveness of the implementation of the updated process operation guide. By comparing the first and second insurance data sets, one can intuitively see the improvement in the insurance process after the updated process operation guide. If the updated process operation guide is effective, the number of non-standard paths in the second insurance data set should decrease. Similar to the first insurance data set, the second insurance data set contains detailed operational information of multiple business personnel when executing the insurance process, such as the operation time, operation content, and flow sequence of business nodes at each link, providing data support for determining whether the standard insurance path has been achieved.

[0071] The standard insurance path is the ultimate goal of the entire insurance path determination method. It is an ideal insurance process path obtained through multiple iterations and verifications during the continuous optimization of process operation guidelines. When the number of non-standard paths in the second insurance data set obtained according to the updated process operation guidelines is less than or equal to the preset number, the insurance path is considered to meet the standard. The standard insurance path is characterized by high efficiency, standardization, and non-redundant operations. It eliminates factors such as loops that lead to insurance inefficiency, ensures the smooth completion of insurance business within a reasonable time, and guarantees the accuracy and compliance of business operations, providing a better service experience for insurance companies and customers.

[0072] In a feasible implementation method of the present application, first, the number of non-standard paths in the first insured data set is counted. Then, the number of non-standard paths obtained by counting is compared with the preset number. If the number of non-standard paths in the first insured data set is greater than the preset number, it means that the current process operation guide has failed to effectively solve the problems in the insurance process and needs to be updated. The basis for updating the process operation guide is an in-depth analysis of the first insured data set. During the analysis process, focus on the distribution of each process node in the non-standard path in the first insured data set and the specific attributes of the loop process. Through analysis, find out the reasons why non-standard paths still appear in the first insured data set, such as whether the operating steps are not clear enough, there are loopholes in the business rules, or there are defects in the system functions.

[0073] According to the analysis results, the process operation guide is modified and improved in a targeted manner. After the process operation guide is updated, the business personnel are organized to execute the insurance process again according to the updated process operation guide, so as to obtain the second insurance data set. The method of obtaining the second insurance data set is similar to S105, and this application will not go into details. After obtaining the second insurance data set, the number of non-standard paths therein is counted again and compared with the preset number. If the number of non-standard paths in the second insurance data set is still greater than the preset number, it means that the updated process operation guide still needs to be further optimized, and the above-mentioned analysis of the data set, updating of the process operation guide and acquisition of the new insurance data set are repeated, and the optimization is continuously iterated.

[0074] Once the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, the insurance process is considered to have met the standards and a standard insurance path has been established. The establishment of a standard insurance path means that the insurance process is more efficient and standardized, effectively reducing unnecessary duplication of operations and improving the efficiency and quality of insurance business processing.

[0075] In one embodiment, if Figure 2As shown, the insurance path with a loop process in the initial insurance data set is determined as a non-standard path, including:

[0076] S201: For any insurance path in the initial insurance data set, construct a directed graph corresponding to the insurance path, using the business process nodes in the insurance path as vertices and the operation sequences between the business process nodes in the insurance path as edges;

[0077] S202 : For each directed graph corresponding to the insurance path, if there is a loop in the directed graph, determine that the insurance path corresponding to the directed graph is a non-standard path.

[0078] Optionally, in this embodiment of the present application, a vertex refers to a business process node in each insurance application path in the initial insurance application data set, which is treated as an independent point. These points are the vertices in the directed graph. Each vertex represents a key link in the insurance application process, such as customer information entry, risk assessment, premium calculation, contract signing, etc. Each link corresponds to a vertex.

[0079] Edges connect vertices in a directed graph and represent the sequence of operations between business process nodes in the insurance application path. In other words, edges clearly define the flow of business operations from one node to another. For example, an edge from the "Customer Information Entry" vertex to the "Risk Assessment" vertex indicates that after completing customer information entry in the insurance application process, the next step is the risk assessment. Edges not only determine the order of business operations but also reflect the logical relationship between each link in the insurance application process, demonstrating how business progresses from one stage to the next.

[0080] The directed graph fully presents the overall picture of the insurance path. Through it, we can intuitively observe the sequence, mutual relationship and overall direction of each link in the insurance process, and clearly show the operation path of business personnel when executing the insurance process.

[0081] A loop is a path in a directed graph that starts at a vertex, passes through a series of vertices along the edges, and then returns to the starting vertex. In the insurance application process, a loop represents a cyclical path with repeated business operations. When a loop exists in a directed graph, it means that the insurance application path contains a return operation. This is because loops are usually caused by certain conditions or operations in the process, causing the business process to not proceed in the normal order, but instead return to a previously processed node, thus forming a loop.

[0082] For example, consider an insurance application process that should proceed in the order of "fill out the application form → submit for review → review and approval → generate the policy." However, if an issue is discovered during the review phase, the business personnel will need to revisit the application form, resulting in a return operation from the "review" node back to the "fill out the application form" node. If this return operation is not properly handled, causing the business process to continuously cycle between "fill out the application form" and "review," a loop will form in the corresponding directed graph. Therefore, a loop can be seen as the result of ineffective control or improper use of the return operation. It reflects potential errors or irregularities in the insurance application process. By analyzing the loop, we can identify and resolve the return operation issues to optimize the insurance application process and ensure its smooth and standardized operation.

[0083] Optionally, in a specific implementation of the present application, the directed graph corresponding to each constructed insurance path is checked. Determining whether there is a loop in the directed graph is a key step. A depth-first search algorithm can be used. When executing the depth-first search algorithm, traversal starts from a vertex of the directed graph and marks the vertices that have been visited. During the traversal process, if a vertex that has been marked and is not the parent node of the current vertex is encountered, it means that a loop exists. For example, during the traversal process, starting from vertex A, passing through vertices B and C, and then returning to vertex A, this indicates that there is a loop in the directed graph.

[0084] Once a loop is determined in a directed graph, the corresponding insurance path can be determined to be a non-standard path. This is because a loop in a directed graph indicates a cyclical path with repeated business operations within the insurance process, which means a loop exists. By definition, an insurance path with a loop is a non-standard path. Through the above steps, insurance paths with loops in the initial insurance data set can be accurately identified and determined to be non-standard paths.

[0085] In these optional embodiments, the insurance path is converted into a directed graph, with business process nodes as vertices and operation sequences as edges, allowing the complex insurance process to be presented in an intuitive graphical form, making it easier to understand and analyze. By determining whether there are loops in the directed graph to determine non-standard paths, insurance paths with repeated business operation cycles can be accurately identified. Such paths often lead to inefficient processes and are prone to errors. This allows for the rapid identification of problematic insurance paths, providing clear goals for subsequent optimization of the insurance process and the development of reasonable solution strategies, effectively improving the standardization and efficiency of insurance business, reducing unnecessary resource waste, and ensuring the smooth operation of insurance business.

[0086] In one embodiment, if Figure 3As shown, the first solution strategy corresponding to each loop process is determined according to the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path, including:

[0087] S301, determining a ring type corresponding to each ring process according to the distribution of each process node in the non-standard path and the process attributes of each ring process in the non-standard path;

[0088] S302: Determine a first solution strategy corresponding to each ring process according to the ring type corresponding to each ring process.

[0089] Optionally, in an embodiment of the present application, the loop type is a loop process category divided according to the distribution of each process node in the non-standard path and the process attributes of each loop process when analyzing the non-standard path. The loop process can be divided into different types. For example, from the triggering cause, there may be rule-type loops caused by unclear business rules, such as vague audit standards that cause the business to cycle between "submit for review-return for modification if the audit fails"; there are also operational loops caused by operational errors or unskilled operation of business personnel, such as repeated entry of wrong information. In terms of loop complexity, there may be simple two-node loops, as well as complex loops involving multiple nodes. By determining the loop type, the essential characteristics of the loop process can be grasped more accurately, providing a basis for the subsequent formulation of a targeted first solution strategy.

[0090] In these optional embodiments, by analyzing process node distribution and loop attributes to determine loop types, complex and diverse loop processes can be categorized and sorted, clarifying the nature of the problem. Formulating a primary solution strategy based on loop type ensures targeted solutions, avoiding blind attempts and making the strategy more targeted. This categorized approach helps quickly identify the root cause of problems, efficiently resolve loop issues within the insurance application process, and improve standardization and efficiency of insurance processing.

[0091] In one embodiment, if Figure 4 As shown, the ring type corresponding to each ring process is determined according to the distribution of each process node in the non-standard path and the process attributes of each ring process in the non-standard path, including:

[0092] S401: Performing a feature extraction operation on each loop process in each non-standard path to obtain feature information corresponding to each loop process; the feature extraction operation comprises: performing multi-dimensional quantification on the distribution of each process node in the non-standard path corresponding to the loop process and the process attributes of the loop process, and converting the quantified distribution into feature information;

[0093] S402 , classifying each loop process according to the feature information corresponding to each loop process using a preset clustering algorithm to determine the loop type corresponding to each loop process; wherein the similarity between the feature information of loop processes belonging to the same loop type is greater than a preset threshold.

[0094] Optionally, in an embodiment of the present application, multi-dimensional quantification refers to numerically representing the distribution of each process node in the non-standard path corresponding to the ring process and the process attributes of the ring process from multiple different aspects. The distribution of process nodes can be quantified from dimensions such as node position, mutual relationship, and frequency of occurrence; the process attributes of the ring process can be quantified from dimensions such as loop node composition, number of cycles, and cycle period. For example, the frequency of node occurrence is represented by a specific number, and the cycle period is converted into a time value. Through multi-dimensional quantification, the originally abstract process information can be converted into specific data, which is convenient for subsequent analysis.

[0095] Feature information is the result of multi-dimensional quantification. It is a digital description of the loop process, including the specific values ​​of the loop process in each quantitative dimension. These values ​​reflect the characteristics and essence of the loop process and serve as the basis for its classification and analysis.

[0096] A pre-defined clustering algorithm is a pre-defined algorithm used to classify data. For example, this algorithm might be K-means clustering or hierarchical clustering. Clustering algorithms use a distance metric to group similar loop processes into the same category.

[0097] Similarity measures the degree of similarity between the feature information of two loop processes. The closer the values ​​of the feature information of two loop processes on each quantitative dimension, the higher their similarity. When the similarity between the feature information of two loop processes exceeds a preset threshold, the two loop processes are considered to belong to the same loop type. For example, if the values ​​of two loop processes in terms of node distribution, number of loops, and cycle period are very similar, their similarity is high and they may be classified as the same loop type.

[0098] Optionally, in a specific implementation of the present application, first, a feature extraction operation is performed on each ring process in each non-standard path. First, the distribution of each process node in the non-standard path corresponding to the ring process is sorted out, and at the same time, the process attributes of the ring process are analyzed. Then, the above information is quantified in multiple dimensions and converted into feature information that can be processed by a computer. After completing the feature extraction, taking K-means clustering as an example, the algorithm first randomly selects the feature information of K ring processes as the initial clustering centers, and then calculates the distance from the feature information of other ring processes to these centers, assigns them to the category where the nearest center is located, and then recalculates the center of each category, and repeats the process of allocating and calculating the center until the set number of iterations is reached or the cluster center no longer changes.

[0099] During the clustering process, the ring type is determined by comparing the similarity between the feature information of the ring processes. Similarity can be measured by calculating the cosine similarity or Manhattan distance between the feature information vectors. When the similarity between the feature information of two ring processes exceeds a preset threshold, they are classified as the same ring type. For example, if the preset threshold is 0.8, if the cosine similarity of the feature information of two ring processes reaches 0.85, they are considered to belong to the same ring type. Through continuous iterative clustering, the ring type corresponding to each ring process is ultimately determined, providing a basis for the subsequent development of targeted solution strategies.

[0100] In these optional embodiments, feature extraction is performed through multi-dimensional quantification, transforming complex process information into analyzable features, providing a clear data foundation for subsequent classification. Using a pre-set clustering algorithm to categorize loop processes, it is possible to quickly and automatically identify loop process categories with similar characteristics. This similarity-based classification approach helps accurately identify the characteristics of different loop process types, providing a basis for the subsequent development of targeted solutions, thereby improving the efficiency of resolving non-standard routing issues and optimizing the insurance application process.

[0101] In one embodiment, determining the first solution strategy corresponding to each ring flow according to the ring type corresponding to each ring flow includes:

[0102] For any ring type, obtain a process data set corresponding to the ring type, the process data set including the ring processes of the same ring type in the non-standard path;

[0103] Determining the cause of the ring process of the ring type according to the process attributes of each ring process in the process data set; the process attributes include at least one of the position distribution of each service node in the ring process in the entire insurance path and the path length of the ring process;

[0104] A target strategy matching the cause is obtained from a preset strategy database, and a first solution strategy corresponding to a ring process of the same ring type is determined as the target strategy; the preset strategy database includes a correspondence between multiple causes and multiple target strategies.

[0105] Optionally, in an embodiment of the present application, the process data set is for a certain ring type, and includes a collection of all ring process related information belonging to this ring type in a non-standard path. This information can be used to analyze the characteristics and rules of the ring process of this ring type.

[0106] The distribution of each business node within the entire insurance path represents the specific positioning of the loop process within its respective insurance path. This not only clarifies the specific stage of the loop process within the insurance process, such as the loop process occurring after customer information is entered and before premium calculation, but also precisely defines the logical sequence of each business node within the loop process and the other nodes in the insurance path, as well as their relative position within the overall insurance path process.

[0107] The path length of a loop process refers to a metric such as the number of process nodes or the time it takes to reach the end node, starting from the starting node, passing through a series of business nodes. This path length can reflect the complexity of the loop process and potential problems. If the path length of a loop process is too long, it may indicate unnecessary duplication of operations or inappropriate process settings, leading to the occurrence of loop processes.

[0108] The causes are derived from an in-depth analysis of the process attributes of each loop process in the process dataset. By studying the location distribution of each business node in the loop process throughout the insurance path and attributes such as the loop process path length, combined with business knowledge and practical operational experience, the root causes leading to the emergence of this type of loop process are identified. These causes can include unclear business rules, irrational operational processes, system functional defects, and human operational errors. Accurately determining the cause is a key prerequisite for developing an effective solution strategy. Only by clarifying the root cause of the problem can targeted measures be taken to resolve the loop process problem.

[0109] The preset strategy database can be a pre-established and maintained database system. It stores the correspondence between multiple causes and target strategies. This database is built through a variety of methods, including analysis of extensive historical data, expert experience, and feedback from actual business operations. When faced with specific process issues, the preset strategy database can quickly search for matching target strategies based on the analyzed causes. This database significantly improves the efficiency and accuracy of identifying resolution strategies, eliminating the tedious process of re-strategizing each time.

[0110] Target strategies are specific solutions drawn from a pre-set strategy database and tailored to the causes of loop processes. These strategies are designed to address specific causes and effectively resolve loop process issues of that type. These strategies can include specific measures such as adjusting business rules, optimizing operational processes, improving system functionality, and training personnel.

[0111] In one specific implementation of this application, the first step is to obtain a process dataset corresponding to the loop type. For each loop type, it is necessary to filter out all loop processes belonging to that loop type from the non-standard path. This can be achieved using the loop type label previously determined during feature extraction and classification. In actual operation, a database query statement can be used to extract the corresponding loop process data from the database storing non-standard path data based on the loop type identifier, thereby forming a process dataset corresponding to the loop type.

[0112] Next, determine the cause of the loop process based on the process data set. The process attributes mainly consider the position distribution of each business node in the loop process in the entire insurance path and the path length of the loop process. When analyzing the location distribution of business nodes, check whether the business nodes in the loop process are in the key links of the insurance process, or whether there are problems such as node position reversal and redundancy. Regarding the path length of the loop process, if the path is too long, it may mean that there are unnecessary repeated operations or unreasonable process settings. By quantitatively analyzing and comparing these process attributes, combined with business knowledge and experience, find out the root cause of the loop process, such as unclear business rules, operational process design defects, or system function abnormalities.

[0113] Finally, the target policy is retrieved from the preset policy database. After determining the cause of the loop process, this cause is used as a query condition to search for matches in the preset policy database. For example, if analysis indicates that the loop process is caused by unclear business rules, the database is used to identify target policies that address this situation, such as reorganizing and clarifying business rules or developing detailed operational guidelines. The target policy found is then determined as the first resolution strategy for loop processes of the same loop type.

[0114] In these optional embodiments, through the above steps, it is possible to accurately match appropriate solution strategies from the preset strategy database for loop processes of different types of loops according to their characteristics and causes, providing an effective method for optimizing the insurance process and eliminating loop processes.

[0115] In one embodiment, after determining the process operation guide corresponding to the insurance application process according to the solution strategy and before obtaining the first insurance application data set, the method further includes:

[0116] When a business person executes an insurance application process, obtaining the historical insurance application data of the business person;

[0117] Determine, based on the historical insurance data and the process operation guide, an operation manual for the business personnel to execute the insurance process;

[0118] The operation manual is recommended to the business personnel so that the business personnel can perform the insurance process according to the operation manual.

[0119] The obtaining of the first insurance data set includes:

[0120] The first insurance data set is determined according to the insurance paths of the insurance process executed by the multiple business personnel according to their respective corresponding operation manuals.

[0121] Optionally, in this embodiment of the present application, historical insurance application data refers to various relevant data records generated by agents during past insurance application processes. This data covers a range of operational information when agents handle insurance applications, including but not limited to operation time, operation steps, customer information involved, insurance product type selected, various forms completed, problems encountered, and solutions. It provides a detailed record of agents' past insurance application work. By analyzing this data, we can understand agents' operational habits, common errors, and approaches to handling different types of insurance applications, providing a basis for developing personalized operation manuals.

[0122] An operations manual is a document designed to guide sales personnel through the insurance application process, based on their historical insurance application data and process operation guidelines. It is tailored to each salesperson, integrating their previous insurance application practices and experience, as well as general process operation guidelines developed specifically for the insurance application process. The operations manual details the specific steps, operating standards, and precautions that sales personnel should follow when executing the insurance application process. It aims to help sales personnel complete the insurance application process more accurately and efficiently, reduce errors and problems caused by non-standard operations or unfamiliarity with the process, and ensure that the insurance application process proceeds smoothly according to the established optimization plan.

[0123] Optionally, in one specific implementation of the present application, historical insurance application data is first obtained when a salesperson performs the insurance application process. This process can be accomplished through a data storage system and logging functionality. In the business system, each time a salesperson performs an insurance application, the system automatically records relevant information, such as the operation time, operation steps, involved customer information, and any problems encountered. This historical data can be extracted from the system's database, organized, and filtered to obtain complete and accurate historical insurance application data for the salesperson.

[0124] Next, based on the historical insurance data obtained and the established process operation guidelines, an operating manual for sales personnel to execute the insurance process is determined. Analyze historical insurance data to identify problems, habits, and characteristics that sales personnel have encountered in past operations. For example, some sales personnel may be prone to errors when entering customer information, or they may have unique operating habits in the insurance process for specific types of insurance products. Combined with the process operation guidelines, personalized adjustments and supplements are made to the general process operation guidelines to address these issues and characteristics. If a sales person is found to frequently overlook key information during risk assessment, the important information and key operational points for that step are specifically emphasized in the operation manual, thereby generating a unique operation manual for each sales person.

[0125] The generated manual is then recommended to the business personnel. This can be sent via the business system's push notification function, internal office software, or email. Necessary instructions and training are also provided to ensure that the personnel understand the manual's content and how to use it.

[0126] Finally, the first insurance application dataset is obtained. As multiple sales personnel execute the insurance application process according to their respective operating manuals, the system records their insurance application paths again. This information is collected and consolidated, removing duplicate and invalid data. The data is then organized according to a unified format and standards, ultimately forming the first insurance application dataset. This dataset can be used for subsequent analysis and evaluation to verify the effectiveness of the operating manuals and process guidelines.

[0127] In these optional embodiments, by acquiring the salesperson's historical insurance application data, their operational characteristics and problems can be identified. Combined with the process operation guide, a personalized operation manual can be generated, tailored to individual work habits and reducing the probability of operational errors. The manual is then recommended to salespersons, encouraging them to follow the standard procedures and ensuring standardization of the insurance application process. Ultimately, based on the manual's execution, a first insurance application dataset is determined, providing accurate data support for subsequent process optimization, forming a virtuous cycle of "analysis-optimization-implementation-reanalysis," continuously improving the quality and efficiency of insurance operations.

[0128] In one embodiment, when the number of non-standard paths in the first insurance data set is greater than a preset number, updating the process operation guide according to the first insurance data set until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, thereby obtaining a standard insurance path, includes:

[0129] When the number of non-standard paths in the first insurance data set is greater than a preset number, determining a second solution strategy corresponding to each loop process in the first insurance data set;

[0130] The process operation guide is updated according to the second solution strategy to obtain the updated process operation guide; until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, the standard insurance path is determined according to the updated process operation guide.

[0131] Optionally, in an embodiment of the present application, the second solution strategy is a solution determined for each loop process in the first insurance data set when the number of non-standard paths in the first insurance data set is greater than a preset number. The second solution strategy is a strategy formulated based on the analysis of the first insurance data set to solve the loop process problems existing therein, and is intended to update the process operation guide. It should be noted that the first solution strategy first acts on the insurance process to determine the initial process operation guide. If the number of non-standard paths in the first insurance data set obtained by subsequently executing the insurance process according to the operation guide does not meet the requirements, that is, it is greater than the preset number, the second solution strategy will be formulated based on the analysis of the first insurance data set to further optimize the process operation guide. In other words, the second solution strategy is a deepening and supplement to the first solution strategy.

[0132] Optionally, in a specific implementation of the present application, when the number of non-standard paths in the first insurance data set is greater than a preset number, it means that there are still many irregularities in the current insurance process, and the process operation guide needs to be iteratively updated through the following steps to obtain a standard insurance path. First, a second solution strategy is determined for each ring process in the first insurance data set. In terms of specific operations, all non-standard paths in the first insurance data set are first sorted out to find out the ring processes therein. Just like determining the ring type before, the distribution of business nodes in the ring process, path length, loop trigger conditions and other process attributes are deeply analyzed again, and the specific reasons for the generation of each ring process are determined in combination with business logic and actual operation scenarios. Based on these reasons, a targeted second solution strategy is formulated for each ring process.

[0133] Next, update the process operation guide based on the second solution strategy. Incorporate the second solution strategy for each loop process into the existing process operation guide, and modify, supplement, or adjust the corresponding sections of the guide. For example, if the second solution strategy for a loop process is to simplify a redundant process, then the process operation guide should clearly state the simplified operation steps, relevant requirements, and changes to the business nodes involved. If the solution strategy involves adding new business rules, then the guide should detail the rule content, applicable scenarios, and execution methods, thereby creating an updated process operation guide.

[0134] Then, the insurance business is carried out in accordance with the updated process operation guide, and the insurance paths generated by the business personnel executing the insurance process are recorded, thereby forming a second insurance data set. The number of non-standard paths in the second insurance data set is continuously monitored. If the number of non-standard paths is still greater than the preset number, it means that the current update has not achieved the ideal effect. The above process of determining the second solution strategy and updating the process operation guide is repeated again, and continuous iterative optimization is carried out. When the number of non-standard paths in the second insurance data set is less than or equal to the preset number, it indicates that the insurance process at this time has basically eliminated non-standard issues and meets the standard requirements. The insurance path corresponding to the current updated process operation guide can be determined as the standard insurance path, which will be used to guide the standardized execution of insurance business in the future.

[0135] In these optional embodiments, by determining a second solution strategy when the first insurance data set contains an excessive number of non-standard paths, loop process issues can be precisely addressed, providing specific direction for process optimization. Updating the process operation guidelines based on this strategy can better align them with actual business conditions and enhance operability. Continuous iteration until the non-standard paths in the second insurance data set meet the standards can continuously optimize the process, reduce irregular operations, and improve insurance efficiency and quality. Ultimately, a standard insurance path is determined, providing a standardized template for subsequent business operations and ensuring the stable and efficient operation of insurance business.

[0136] It should be noted that the various optional implementation methods introduced in the embodiments of the present application can be implemented in combination with each other or separately if they do not conflict with each other, and the embodiments of the present application do not limit this.

[0137] In order to facilitate understanding of the motor control method provided in the above embodiment, the above insurance path determination method is described below using a specific scenario embodiment.

[0138] For example, in one insurance company's medical insurance underwriting business, analysis of the underwriting process's trajectory data (i.e., the underwriting dataset) revealed significant discrepancies between the process paths executed by different business personnel when handling medical insurance underwriting, with significant deviations from the standard path. This resulted in significant operational difficulties for new employees when learning the underwriting process, leading to frequent process errors. The time between submitting an insurance application and the policy taking effect was lengthy, dampening customer enthusiasm. Furthermore, non-standard paths resulted in confusing underwriting data formats, severely hindering the digitalization and intelligentization of processes such as intelligent underwriting and risk assessment.

[0139] To address these issues, the insurance company first studied massive amounts of historical insurance application data and discovered that when executing the standard process, sales personnel often encountered rollbacks at points such as health declaration and medical history verification. For example, if a customer's health information was incomplete or their medical history conflicted with the insurance terms, sales personnel would need to repeatedly communicate with the customer to confirm. The company then meticulously organized the insurance application data, extracting key information such as process nodes, operation sequence, and rollback links.

[0140] Next, based on the collated data features, the K-means algorithm was used to classify different insurance application paths. Non-standard paths with similar characteristics were grouped together, for example, paths that resulted in process loops due to health disclosure issues were grouped together. Subsequently, an in-depth analysis of different types of non-standard paths was conducted to determine the corresponding loop type for each loop and identify the causes of the loops, such as an unreasonable health disclosure questionnaire design or unclear underwriting rules. Targeted strategies were then matched from the pre-set policy database for remediation, such as optimizing the health disclosure questionnaire and refining the underwriting rules.

[0141] Then, based on the transformed insurance application trajectory data, they reanalyzed it using a classification algorithm to verify the effectiveness of the governance. Through multiple rounds of analysis, classification, and rectification, the insurance application process was continuously optimized, and the number of non-standard paths was gradually reduced. Ultimately, a standardized insurance application path was obtained, achieving standardization of the insurance application trajectory. This not only reduced the difficulty of business operations, but also significantly shortened the medical insurance application process, improved customer satisfaction, and laid the foundation for the subsequent implementation of digital services such as intelligent underwriting and accurate risk assessment.

[0142] See also Figure 5 The present application also provides an insurance path determination device that can implement the above-mentioned insurance path determination method. The device includes:

[0143] A first acquisition module is configured to acquire an initial insurance data set, wherein the initial insurance data set includes insurance paths of multiple business personnel executing the insurance process;

[0144] A first determining module is configured to determine an insurance path having a loop process in the initial insurance data set as a non-standard path, wherein the loop process is a cyclic path having repeated business operations in the non-standard path;

[0145] a second determining module, configured to determine a first solution strategy corresponding to each loop process according to the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path;

[0146] A third determining module is configured to determine a process operation guide corresponding to the insurance application process according to the first solution strategy;

[0147] a second acquisition module, configured to acquire a first insurance application data set, wherein the first insurance application data set includes an insurance application path of the plurality of business personnel executing the insurance application process according to the process operation guide;

[0148] The fourth determination module is used to update the process operation guide according to the first insurance data set when the number of non-standard paths in the first insurance data set is greater than a preset number, until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, thereby obtaining a standard insurance path.

[0149] The specific implementation of the insurance path determination device is basically the same as the specific embodiment of the above-mentioned insurance path determination method, and will not be repeated here.

[0150] The present application also provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described insurance route determination method. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0151] See also Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0152] The processor 601 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0153] The memory 602 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called by the processor 601 to execute the insurance path determination method of the embodiments of this application.

[0154] Input / output interface 603, used to implement information input and output;

[0155] Communication interface 604, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0156] Bus 605 , which transmits information between various components of the device (e.g., processor 601 , memory 602 , input / output interface 603 , and communication interface 604 );

[0157] The processor 601 , the memory 602 , the input / output interface 603 and the communication interface 604 are connected to each other in communication within the device via a bus 605 .

[0158] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned insurance path determination method.

[0159] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0160] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0161] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0162] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0163] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0164] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0165] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0167] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0169] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0170] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for determining an insurance path, characterized in that: The method comprises: Acquire an initial insurance data set, where the initial insurance data set includes insurance paths of multiple business personnel executing the insurance process; Determine the insurance path with a loop process in the initial insurance data set as a non-standard path, wherein the loop process is a cyclic path with repeated business operations in the non-standard path; Determining a first solution strategy corresponding to each loop process according to the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path; Determine the process operation guide corresponding to the insurance process according to the first solution strategy; Acquire a first insurance application data set, where the first insurance application data set includes insurance application paths of the multiple business personnel executing the insurance application process according to the process operation guide; When the number of non-standard paths in the first insurance data set is greater than a preset number, the process operation guide is updated according to the first insurance data set until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, thereby obtaining a standard insurance path.

2. The method according to claim 1, characterized in that The step of determining the insurance path with a loop process in the initial insurance data set as a non-standard path includes: For any insurance path in the initial insurance data set, a directed graph corresponding to the insurance path is constructed with the business process nodes in the insurance path as vertices and the operation sequences between the business process nodes in the insurance path as edges; For the directed graph corresponding to each of the insurance paths, if there is a loop in the directed graph, it is determined that the insurance path corresponding to the directed graph is a non-standard path.

3. The method according to claim 1, characterized in that Determining a first solution strategy corresponding to each loop process according to the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path includes: Determining the ring type corresponding to each ring process according to the distribution of each process node in the non-standard path and the process attributes of each ring process in the non-standard path; According to the ring type corresponding to each ring process, a first solution strategy corresponding to each ring process is determined.

4. The method according to claim 3, characterized in that Determining the ring type corresponding to each ring process according to the distribution of each process node in the non-standard path and the process attributes of each ring process in the non-standard path includes: Performing a feature extraction operation on each loop process in each non-standard path to obtain feature information corresponding to each loop process; the feature extraction operation is to quantify the distribution of each process node in the non-standard path corresponding to the loop process and the process attributes of the loop process in multiple dimensions and convert them into feature information; According to the characteristic information corresponding to each ring process, a preset clustering algorithm is used to classify each ring process and determine the ring type corresponding to each ring process; wherein the similarity between the characteristic information of ring processes belonging to the same ring type is greater than a preset threshold.

5. The method according to claim 3, characterized in that The determining of the first solution strategy corresponding to each ring flow according to the ring type corresponding to each ring flow includes: For any ring type, obtain a process data set corresponding to the ring type, the process data set including the ring processes of the same ring type in the non-standard path; Determining the cause of the ring process of the ring type according to the process attributes of each ring process in the process data set; the process attributes include at least one of the position distribution of each service node in the ring process in the entire insurance path and the path length of the ring process; A target strategy matching the cause is obtained from a preset strategy database, and a first solution strategy corresponding to a ring process of the same ring type is determined as the target strategy; the preset strategy database includes a correspondence between multiple causes and multiple target strategies.

6. The method according to claim 1, characterized in that After determining the process operation guide corresponding to the insurance process according to the solution strategy and before obtaining the first insurance data set, the method further includes: When a business person executes an insurance application process, obtaining the historical insurance application data of the business person; Determine, based on the historical insurance data and the process operation guide, an operation manual for the business personnel to execute the insurance process; Recommending the operation manual to the business personnel so that the business personnel can perform the insurance application process according to the operation manual; The obtaining of the first insurance data set includes: The first insurance data set is determined according to the insurance paths of the insurance process executed by the multiple business personnel according to their respective corresponding operation manuals.

7. The method according to claim 1, characterized in that When the number of non-standard paths in the first insurance data set is greater than a preset number, updating the process operation guide according to the first insurance data set until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, thereby obtaining a standard insurance path, including: When the number of non-standard paths in the first insurance data set is greater than a preset number, determining a second solution strategy corresponding to each loop process in the first insurance data set; The process operation guide is updated according to the second solution strategy to obtain the updated process operation guide; until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, the standard insurance path is determined according to the updated process operation guide.

8. An insurance path determination device, characterized in that: The device comprises: A first acquisition module is configured to acquire an initial insurance data set, wherein the initial insurance data set includes insurance paths of multiple business personnel executing the insurance process; A first determining module is configured to determine an insurance path having a loop process in the initial insurance data set as a non-standard path, wherein the loop process is a cyclic path having repeated business operations in the non-standard path; a second determining module, configured to determine a first solution strategy corresponding to each loop process according to the distribution of each process node in the non-standard path and the process attributes of each loop process in the non-standard path; A third determining module is configured to determine a process operation guide corresponding to the insurance application process according to the first solution strategy; a second acquisition module, configured to acquire a first insurance application data set, wherein the first insurance application data set includes an insurance application path of the plurality of business personnel executing the insurance application process according to the process operation guide; The fourth determination module is used to update the process operation guide according to the first insurance data set when the number of non-standard paths in the first insurance data set is greater than a preset number, until the number of non-standard paths in the second insurance data set obtained according to the updated process operation guide is less than or equal to the preset number, thereby obtaining a standard insurance path.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the insurance path determination method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the insurance path determination method according to any one of claims 1 to 7 is implemented.

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