Insurance policy inspection method and device, computer device and storage medium

Abstract

The embodiment of the application belongs to the field of financial technology, and relates to a policy quality inspection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring policy data, extracting key policy basic information features, insured person features and risk type features from the policy data; constructing a risk assessment model based on the policy basic information features, the insured person features and the risk type features; performing policy risk assessment on the policy data based on an ant colony algorithm and the risk assessment model to obtain a risk distribution graph; extracting high-risk policies from the risk distribution graph, performing cluster analysis on the high-risk policies, and obtaining risk analysis information; and generating a policy quality inspection report based on the risk analysis information. The application can effectively and accurately perform risk assessment and analysis on policies, and generate a corresponding quality inspection report for display.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, specifically to the field of financial technology, and in particular to a policy quality inspection method, device, computer equipment and storage medium. Background Art

[0002] In insurance systems, insurance policies, as a core component of the business, carry critical information such as insurance liabilities, policyholder information, insured information, policy term, and premium amount. The quality of insurance policies is directly related to an insurance company's operating risk, customer satisfaction, and sustainable business development. Therefore, policy quality inspection has become an indispensable part of the insurance business. Its goal is to accurately identify high-risk, abnormal policies within massive amounts of policy data, providing an effective and reliable basis for subsequent risk analysis and resolution.

[0003] Traditional policy quality inspection methods rely primarily on manual labor, which involves manually reviewing policy information to identify potential risks. However, with the rapid development of the insurance business and the dramatic increase in the number of policies, manual quality inspection has gradually exposed problems such as inefficiency and severe waste of human resources. Manual quality inspection is not only time-consuming and labor-intensive, but also limited by individual experience and knowledge, making it difficult to ensure consistency and accuracy.

[0004] To overcome the limitations of traditional manual quality inspection and further improve the accuracy and efficiency of policy quality inspection, recent efforts have seen the direct use of machine learning and data mining methods to model raw policy data. However, this approach faces numerous challenges. First, policy data is highly dimensional and sparse, meaning it contains numerous features and attributes, many of which are not practical for risk identification. This increases the complexity and computational cost of model training. Second, feature engineering is a key step in machine learning and data mining, determining whether a model can effectively identify risk points. However, feature engineering for policy data is complex and tedious, requiring specialized knowledge and experience, making it a significant challenge for non-experts.

[0005] In summary, traditional policy quality inspection methods suffer from inefficiency and significant waste of human resources. Existing technologies that directly apply machine learning also have their own limitations. Therefore, efficiently and accurately identifying high-risk anomalies in policies has become a pressing technical challenge in the insurance industry. Summary of the Invention

[0006] The purpose of the embodiments of the present application is to propose a policy quality inspection method, device, computer equipment and storage medium to solve the problem of being unable to accurately and effectively assess and analyze the risks of insurance policies.

[0007] To solve the above technical problems, the present application provides a method for quality inspection of insurance policies, which adopts the following technical solutions:

[0008] Obtaining insurance policy data, and extracting key policy basic information features, insured person features, and insurance type features from the insurance policy data;

[0009] Building a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type;

[0010] Performing policy risk assessment on the policy data based on the ant colony algorithm and the risk assessment model to obtain a risk distribution map;

[0011] Extracting high-risk insurance policies from the risk distribution map, and performing cluster analysis on the high-risk insurance policies to obtain risk analysis information;

[0012] Generate a policy quality inspection report based on the risk analysis information.

[0013] Furthermore, the step of obtaining the policy data and extracting key policy basic information features, insured person features, and insurance type features from the policy data specifically includes:

[0014] Obtaining insurance policy data from a database, and extracting structured data and unstructured data from the insurance policy data;

[0015] Preprocessing the structured data and the unstructured data to obtain standard structured data and standard unstructured data;

[0016] Feature extraction is performed on the standard structured data and the standard unstructured data to obtain the basic information features of the insurance policy, the features of the insured, and the features of the insurance type.

[0017] Furthermore, the step of constructing a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type specifically includes:

[0018] Quantifying the basic information features of the insurance policy, the features of the insured, and the features of the insurance type to obtain quantitative features of the insurance policy, quantitative features of the insured, and quantitative features of the insurance type;

[0019] Constructing a policy complexity evaluation function, a risk exposure evaluation function, and an abnormality index evaluation function based on the policy quantitative characteristics, the insured quantitative characteristics, and the insurance type quantitative characteristics;

[0020] The policy complexity evaluation function, risk exposure evaluation function, and abnormality index evaluation function are integrated into the risk evaluation model.

[0021] Further, the step of performing policy risk assessment on the policy data based on the ant colony algorithm and the risk assessment model to obtain a risk distribution map specifically comprises:

[0022] extracting a policy risk assessment index from the policy data;

[0023] mapping the policy risk assessment index to a multi-dimensional space of the risk assessment model to construct an ant search space;

[0024] initializing ant colony algorithm parameters, searching in the ant search space according to the ant colony algorithm parameters to obtain pheromone concentration results, and taking the pheromone concentration results as a policy risk degree assessment result;

[0025] generating the risk distribution map according to the policy risk degree assessment result.

[0026] Further, the step of extracting high-risk policies from the risk distribution map and performing cluster analysis on the high-risk policies to obtain risk analysis information specifically comprises:

[0027] performing risk degree identification on the risk distribution map, and extracting the high-risk policies from the policy data according to the identification result;

[0028] performing feature extraction on the high-risk policies to obtain high-risk policy features;

[0029] performing cluster analysis on the high-risk policies according to a preset cluster algorithm and the high-risk policy features to obtain high-risk policy clusters of different categories;

[0030] performing problem classification on the high-risk policy clusters according to an association rule mining algorithm to generate a risk problem list;

[0031] performing reasoning analysis on the risk problem list according to a pre-established business knowledge base to obtain the risk analysis information.

[0032] Further, the step of performing risk degree identification on the risk distribution map and extracting the high-risk policies from the policy data according to the identification result specifically comprises:

[0033] obtaining an ant colony algorithm path data set based on the risk distribution map, and preprocessing the ant colony algorithm path data set to obtain a standard path data set;

[0034] obtaining a preset risk policy feature index, and extracting abnormal premium ratio information, frequent claim frequency information, and multiple guarantee coverage information from the standard path data set according to the risk policy feature index;

[0035] Inputting the abnormal premium ratio information, frequent claims information, and multiple protection coverage information into a pre-trained risk identification model to obtain a risk level identification result;

[0036] The high-risk insurance policies are extracted from the insurance policy data according to the risk level identification result.

[0037] Furthermore, the step of generating a policy quality inspection report based on the risk analysis information specifically includes:

[0038] Obtaining a template extraction identifier, and extracting a quality inspection report template from a database according to the template extraction identifier;

[0039] Analyze the risk analysis information to obtain risk cause information, risk impact information, and risk solutions;

[0040] Optimizing the risk solution based on a decision tree algorithm to obtain an optimized risk solution;

[0041] Fill the risk cause information, the risk impact information, and the optimized risk solution into the quality inspection report template to generate the policy quality inspection report.

[0042] In order to solve the above technical problems, the present application also provides a policy quality inspection device, which adopts the following technical solutions:

[0043] A feature extraction module is used to obtain insurance policy data and extract key policy basic information features, insured person features, and insurance type features from the insurance policy data;

[0044] A model building module, configured to build a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type;

[0045] a risk assessment module, configured to perform a policy risk assessment on the policy data based on an ant colony algorithm and the risk assessment model to obtain a risk distribution map;

[0046] a risk analysis module, configured to extract high-risk policies from the risk distribution map and perform cluster analysis on the high-risk policies to obtain risk analysis information;

[0047] A report generation module is used to generate a policy quality inspection report based on the risk analysis information.

[0048] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0049] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the insurance policy quality inspection method as described in any one of the above items are implemented.

[0050] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0051] A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of the insurance policy quality inspection method as described in any one of the above items.

[0052] Compared with the prior art, the embodiments of the present application have the following major beneficial effects: by acquiring policy data, the embodiment extracts key policy basic information features, insured characteristics, and insurance type characteristics from the policy data; constructs a risk assessment model based on the policy basic information features, the insured characteristics, and the insurance type characteristics; performs policy risk assessment on the policy data based on an ant colony algorithm and the risk assessment model to obtain a risk distribution map; extracts high-risk policies from the risk distribution map and performs cluster analysis on the high-risk policies to obtain risk analysis information; and generates a policy quality inspection report based on the risk analysis information. This effectively achieves accurate and effective risk assessment and analysis of the policies, and generates a corresponding quality inspection report for presentation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0055] Figure 2 A flowchart of an embodiment of the insurance policy quality inspection method according to the present application;

[0056] Figure 3 yes Figure 2 A flowchart of a specific implementation of step S10;

[0057] Figure 4 yes Figure 2 A flowchart of a specific implementation of step S20;

[0058] Figure 5 yes Figure 2A flowchart of a specific implementation of step S30;

[0059] Figure 6 yes Figure 2 A flowchart of a specific implementation of step S40;

[0060] Figure 7 yes Figure 6 A flowchart of a specific implementation of step S401;

[0061] Figure 8 yes Figure 2 A flowchart of a specific implementation of step S50;

[0062] Figure 9 This is a schematic structural diagram of an embodiment of a policy quality inspection device according to the present application;

[0063] Figure 10 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0065] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to unrelated or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0066] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0067] like Figure 1As shown, system architecture 100 may include a terminal device 101, a network 102, and a server 103. Terminal device 101 may be a laptop computer 1011, a tablet computer 1012, or a mobile phone 1013. Network 102 is a medium for providing a communication link between terminal device 101 and server 103. Network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0068] The user can use the terminal device 101 to interact with the server 103 via the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0069] The terminal device 101 can be various electronic devices with a display screen and supporting web browsing. In addition to the laptop computer 1011, tablet computer 1012 or mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop computer and a desktop computer, etc.

[0070] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0071] It should be noted that the insurance policy quality inspection method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the insurance policy quality inspection device is generally set in the server / terminal device.

[0072] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0073] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for quality inspection of insurance policies according to the present application. The method for quality inspection of insurance policies includes the following steps:

[0074] Step S10: Obtaining insurance policy data, and extracting key insurance policy basic information features, insured person features, and insurance type features from the insurance policy data;

[0075] In this embodiment, the policy data can be obtained from the insurance company's database or file server. The policy data includes structured data and unstructured data. Key policy basic information features, insured person features, and insurance type features are extracted from the above structured data and unstructured data. The policy basic information features include: policy number, insurance type, insurance amount, insurance period, premium, insurance date, and payment method. The insured person features include: name, gender, age, occupation, health status (based on the health declaration in the unstructured data), and income level. The insurance type features include: insurance type (life insurance, car insurance, health insurance, etc.), insurance subtype (such as critical illness insurance and medical insurance in health insurance), risk level, coverage (such as hospitalization expenses and outpatient expenses in medical insurance), and specific terms (such as deductible and compensation ratio).

[0076] Step S20, constructing a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type;

[0077] In this embodiment, the risk assessment model is used to evaluate the risk level corresponding to the insurance policy by quantifying the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type, and constructing the insurance policy complexity assessment function, risk exposure assessment function, and abnormal indicator assessment function based on the quantified characteristics, thereby generating a risk assessment model that can effectively evaluate the complexity of the insurance policy, risk exposure, and abnormal indicators.

[0078] Step S30, performing policy risk assessment on the policy data based on the ant colony algorithm and the risk assessment model to obtain a risk distribution map;

[0079] In this embodiment, the risk distribution map is a graphical representation of the path search results obtained by an ant colony algorithm through a path search in the multidimensional space of the risk assessment model. The path search results correspond to the assessment results of the risk assessment model, that is, the risk results corresponding to the various information in the insurance policy. The Ant Colony Optimization (ACO) algorithm treats the various dimensions of the risk assessment model as coordinate axes in a multidimensional space, and the different risk points as points in this space. The ACO simulates the movement and path selection process of ants in the multidimensional space to find the optimal path from the starting point to the end point. The optimal path in the path search results can be regarded as the path with the lowest risk in the risk assessment model. By comparing the pheromone concentration (i.e., the risk level) on different paths, the risk level of each path can be assessed, and a risk distribution map can be constructed based on the risk level of each path.

[0080] Step S40, extracting high-risk policies from the risk distribution map, and performing cluster analysis on the high-risk policies to obtain risk analysis information;

[0081] In this embodiment, high-risk policies refer to policy data that has been determined to be high-risk after evaluation. This can be determined using indicators corresponding to high-risk policies. Risk analysis information is information about the risk causes, impacts, and solutions for high-risk policies, obtained through clustering and knowledge reasoning based on high-risk policies. By extracting corresponding information features from the path dataset corresponding to the ant colony algorithm, feature information used to identify high-risk policies is obtained. This feature information is then input into a pre-trained risk identification model to obtain a risk level identification result. The path dataset corresponding to the ant colony algorithm refers to the policy data used to perform path search within the risk assessment model using the ant colony algorithm.

[0082] Step S50: Generate a policy quality inspection report based on the risk analysis information.

[0083] In this embodiment, a policy quality inspection report is generated based on the information on the risk causes, impacts and solutions of high-risk policies in the risk analysis information, combined with a preset quality inspection report template. The content of the policy quality inspection report corresponds to risk cause information, risk impact information and optimized risk solution, to provide effective information support and reference for subsequent review.

[0084] This embodiment obtains insurance policy data, extracts key basic policy information features, insured characteristics, and insurance type characteristics from the policy data; constructs a risk assessment model based on the basic policy information features, the insured characteristics, and the insurance type characteristics; performs policy risk assessment on the policy data using an ant colony algorithm and the risk assessment model to obtain a risk distribution map; extracts high-risk policies from the risk distribution map and performs cluster analysis on the high-risk policies to obtain risk analysis information; and generates a policy quality inspection report based on the risk analysis information. This effectively achieves accurate and effective risk assessment and analysis of insurance policies, and generates corresponding quality inspection reports for presentation.

[0085] refer to Figure 3 In some optional implementations of this embodiment, step S10 includes the following steps:

[0086] Step S101, obtaining insurance policy data from a database, and extracting structured data and unstructured data from the insurance policy data;

[0087] In the present embodiment, the policy data can be extracted by matching the identification in the database through the policy data, and the matched policy data can be extracted to obtain. The structured data refers to the data with specific structure, and the part of the data usually exists in the form of table, such as policy number, insured person's name, date of birth, insurance amount, insurance period, etc. The unstructured data refers to the data without specific structure, and the part of the data such as policy description, insured person's health declaration, claim report and other text data, or picture, scanned copy and other multimedia data.

[0088] In step S102, the structured data and the unstructured data are preprocessed to obtain standard structured data and standard unstructured data.

[0089] In the present embodiment, the preprocessing of the structured data includes removing noise data: deleting invalid or abnormal values (such as age obviously exceeding the reasonable range, insurance amount with negative number, etc.); filling missing values: using interpolation method (such as mean, median, mode), prediction model or business rule to fill the missing values; data conversion: converting date, category type data into a format suitable for analysis (such as converting date into timestamp, converting category type data into numerical type code). The preprocessing of the unstructured data includes text data: using natural language processing tool for word segmentation; removing common but meaningless words for analysis (such as "of", "is"); reducing the word to its basic form (such as reducing "running" to "run"); sentiment analysis: evaluating the sentiment tendency (positive, negative, neutral) of the text. Multimedia data: such as image, the text information can be extracted by OCR (optical character recognition) technology, or image classification and recognition can be performed.

[0090] In step S103, the standard structured data and the standard unstructured data are feature extracted to obtain the policy basic information feature, the insured person feature and the insurance type feature.

[0091] In the present embodiment, the feature extraction of the standard structured data can be achieved by querying the required field information, and mapping the queried field information result to the corresponding feature variable, and converting the data, such as converting the date field to the standard format, and normalizing or discretizing the numerical field. The feature extraction of the standard unstructured data can be achieved by using natural language processing technology to process the text data such as word segmentation, standardization and entity naming, and then converting the text data to numerical vector by using Word2Vec and other text representation methods; the key features in the image such as shape, color and texture can be extracted by using image recognition algorithm.

[0092] This embodiment obtains insurance policy data from a database and extracts structured data and unstructured data from the insurance policy data; preprocesses the structured data and the unstructured data to obtain standard structured data and standard unstructured data; and performs feature extraction on the standard structured data and the standard unstructured data, thereby effectively obtaining the basic information features of the insurance policy, the insured person features, and the insurance type features that characterize each information in the insurance policy, so as to facilitate the subsequent construction and processing of the risk assessment model.

[0093] refer to Figure 4 In some optional implementations of this embodiment, step S20 includes the following steps:

[0094] Step S201, performing dimensional feature quantification on the basic information features of the insurance policy, the features of the insured, and the features of the insurance type to obtain quantitative features of the insurance policy, quantitative features of the insured, and quantitative features of the insurance type;

[0095] In this embodiment, quantifying the basic information features of insurance policies includes: converting categorical features such as policy type and insurance term into numerical features through encoding (e.g., one-hot encoding or label encoding); standardizing or normalizing continuous features such as premium amount to ensure comparability between different features. Quantifying insured person features includes: converting categorical features such as gender into numerical features, such as 1 for male and 0 for female; segmenting or normalizing continuous features such as age; utilizing text mining techniques to convert text descriptions such as health status into numerical features, such as disease severity scores; and constructing risk level assessment indicators based on factors such as the insured's occupation and income level. Quantifying insurance type features includes: categorizing and encoding insurance type types; constructing numerical indicators reflecting the risk characteristics of insurance types based on their coverage and compensation conditions; and analyzing historical data to calculate statistical indicators such as the average claim ratio and accident rate for each insurance type as the basis for quantifying insurance type risk.

[0096] Step S202: constructing a policy complexity evaluation function, a risk exposure evaluation function, and an abnormality index evaluation function based on the policy quantitative characteristics, the insured quantitative characteristics, and the insurance type quantitative characteristics;

[0097] In this embodiment, multiple dimensional features, such as policy type, policy term, premium amount, insured characteristics, and insurance type characteristics, are integrated into a comprehensive index through methods such as weighted summation, principal component analysis (PCA), and cluster analysis to construct a quantitative index of policy complexity. A policy complexity assessment function is then generated based on this quantitative index. Machine learning algorithms, such as logistic regression and decision trees, are used to predict and assess risk exposure based on factors such as the insured's age, health status, and occupation, as well as the coverage and compensation conditions of the insurance type, to construct a quantitative index of risk exposure. A risk exposure assessment function is then generated based on this quantitative index. Anomaly detection is performed using statistical methods (such as Z-scores and boxplots) or machine learning algorithms (such as isolation forests and one-class support vector machines) to identify abnormal features in policies, insureds, or insurance types, such as abnormally high or low premiums or abnormal insured age. This allows the construction of an anomaly index assessment function for detecting similar anomalies in new policies.

[0098] Step S203: Integrate the policy complexity assessment function, risk exposure assessment function, and abnormality index assessment function into the risk assessment model.

[0099] In this embodiment, the policy complexity assessment function, the risk exposure assessment function, and the abnormality index assessment function are integrated into the same model to generate a risk assessment model. In the risk assessment model, each function is set and corresponds to a different weight.

[0100] This embodiment quantifies the dimensional features of the basic information features of the policy, the insured features, and the insurance type features to obtain quantitative features of the policy, the insured features, and the insurance type features; constructs a policy complexity assessment function, a risk exposure assessment function, and anomaly index assessment function based on the quantitative features of the policy, the insured features, and the insurance type features; and integrates the policy complexity assessment function, the risk exposure assessment function, and the anomaly index assessment function into the risk assessment model. This effectively constructs a risk assessment model that effectively assesses the risk level of policy data based on the basic information features of the policy, the insured features, and the insurance type features, thereby ensuring the accuracy and effectiveness of the risk assessment and facilitating subsequent policy risk assessment processing.

[0101] refer to Figure 5 In some optional implementations of this embodiment, step S30 includes the following steps:

[0102] Step S301, extracting insurance policy risk assessment indicators from the insurance policy data;

[0103] In this embodiment, the policy data may include basic policy information (such as policy number, policyholder information, insured information, insurance type, insurance term, premium amount, etc.), historical claims records, the insured's health status, occupation, income level, etc. Policy risk assessment indicators include the insured's age, gender, health status score, occupational risk level, insurance type risk level, historical claims ratio, etc. Policy risk assessment indicators can be extracted from the policy data using pre-set risk assessment indicator extraction rules.

[0104] Step S302: Mapping the policy risk assessment indicators to the multidimensional space of the risk assessment model to construct an ant search space;

[0105] In this embodiment, the extracted risk assessment indicators are mapped to the multidimensional space of the risk assessment model, where each dimension in the multidimensional space represents a risk assessment indicator. Based on the scope and number of the risk assessment indicators, a suitable search space is defined, and the space is used as the search domain of the ant colony algorithm to effectively construct the ant search space.

[0106] Step S303, initializing ant colony algorithm parameters, searching in the ant search space according to the ant colony algorithm parameters, obtaining pheromone concentration results, and using the pheromone concentration results as the policy risk assessment results;

[0107] In this embodiment, initializing the ant colony algorithm parameters includes setting the number of ants in the colony, which needs to be adjusted based on the scale and complexity of the actual policy data; setting the initial pheromone concentration, which determines the initial preference of the algorithm's search; setting the pheromone volatility coefficient, which determines the rate at which the pheromone decays over time and affects the algorithm's exploration and utilization capabilities; and setting parameters used to calculate ant transition probabilities, such as the heuristic factor and the pheromone importance factor. This algorithm randomly generates initial ant positions in the ant search space, where each position represents a set of risk assessment indicator values. Based on the risk assessment indicator value at the current position, the pheromone concentration, and the transition probability calculation parameters, the ant's transition probability to the next position is calculated. Based on the transition probability, the next search position is selected and the ant's position information is updated. The search continues until the ant reaches the risk point, and the pheromone concentration corresponding to the path is obtained.

[0108] Step S304: generating the risk distribution map according to the policy risk assessment result.

[0109] In this embodiment, the policy risk assessment results correspond to the pheromone concentration results of the above-mentioned multiple paths. The policy risk assessment results reflect the risk situation of each policy. The policy risk assessment results are combined with visualization tools to generate a risk distribution map that effectively reflects the risk distribution of the policy data.

[0110] This embodiment extracts insurance policy risk assessment indicators from the insurance policy data; maps the insurance policy risk assessment indicators to the multidimensional space of the risk assessment model to construct an ant search space; initializes ant colony algorithm parameters, searches the ant search space based on the ant colony algorithm parameters to obtain pheromone concentration results, and uses the pheromone concentration results as the insurance policy risk assessment results; and generates the risk distribution map based on the insurance policy risk assessment results. This effectively generates a risk distribution map based on the insurance policy data that effectively reflects the specific distribution of information risk, facilitating subsequent analysis based on the risk distribution map.

[0111] Continue to refer Figure 6 In some optional implementations of this embodiment, step S40 includes the following steps:

[0112] Step S401, identifying the risk level of the risk distribution map, and extracting the high-risk insurance policies from the insurance policy data based on the identification result;

[0113] In this embodiment, a high-risk policy refers to policy data in which the abnormal premium ratio information, frequent claims information, and multiple protection coverage information are higher than the preset judgment standards. Among them, the abnormal premium ratio information, frequent claims information, and multiple protection coverage information are judged based on the pre-trained risk identification model. The abnormal premium ratio information, frequent claims information, and multiple protection coverage information correspond to different weights respectively, and the weighted total score is used to comprehensively judge whether the policy data is a high-risk policy.

[0114] Step S402: extract features of the high-risk insurance policy to obtain high-risk insurance policy features.

[0115] In this embodiment, high-risk policy features may include the insured's age, gender, health status, occupation, insurance type, insurance term, premium amount, etc. The extracted high-risk policy features may be processed by missing value filling, outlier processing, feature scaling, etc. to ensure the quality and consistency of the feature data and facilitate subsequent feature application.

[0116] Step S403: performing cluster analysis on the high-risk insurance policies according to a preset clustering algorithm and the characteristics of the high-risk insurance policies to obtain high-risk insurance policy clusters of different categories;

[0117] In this embodiment, the preset clustering algorithm adopts the K-means algorithm. The application of the K-means algorithm includes the following algorithm parameter settings: according to the characteristics of high-risk policies and business requirements, the number of clusters K is pre-set. The selection of the K value can be determined by trial and error, elbow method or silhouette coefficient method; K initial centroids are randomly selected, and these centroids will serve as the starting point of clustering. Iterative clustering process: Calculate the distance between each high-risk policy feature vector and the K centroids, and assign each policy to the cluster with the nearest centroid; Recalculate the centroid of each cluster, which is usually the mean of all policy feature vectors in the cluster; Repeat the above two steps until the position of the centroid no longer changes significantly or reaches the preset number of iterations, and output the policy clustering results at this time as high-risk policy clusters of different categories.

[0118] Step S404: classify the high-risk policy cluster according to the association rule mining algorithm to generate a risk problem list;

[0119] In this embodiment, the association rule mining algorithm uses the Apriori algorithm to discover frequent itemsets and association rules in the data. Appropriate support and confidence thresholds can be set to screen for meaningful association rules. By analyzing these association rules, characteristics or factors associated with high-risk policies are identified, resulting in association rule mining results. Based on the association rule mining results, issues within the high-risk policy cluster are categorized into categories such as policyholder fraud, poor insured health, excessively high insurance premiums, and excessively long insurance periods. This generates a risk issue list, listing the specific issues and related policies within each category.

[0120] Step S405 , performing reasoning analysis on the risk problem list according to a pre-established business knowledge base to obtain the risk analysis information.

[0121] In this embodiment, the pre-established business knowledge base is a database containing insurance business knowledge, including information such as insurance clauses, compensation policies, industry regulations, historical cases, and more. For each issue in the risk list, the business knowledge base is combined with a knowledge reasoning algorithm to perform reasoning analysis to identify the root cause of the issue, potential risk points, and possible solutions or preventative measures, thereby generating risk analysis information.

[0122] The embodiment performs risk degree identification on the risk distribution map, extracts the high-risk insurance policies from the insurance policy data according to the identification result, performs feature extraction on the high-risk insurance policies to obtain high-risk insurance policy features, performs clustering analysis on the high-risk insurance policies according to a preset clustering algorithm and the high-risk insurance policy features to obtain high-risk insurance policy clusters of different categories, performs problem classification on the high-risk insurance policy clusters according to an association rule mining algorithm to generate a risk problem list, and performs reasoning analysis on the risk problem list according to a pre-established business knowledge base, thereby effectively obtaining risk analysis information of associated analysis on high-risk insurance policy set business knowledge to facilitate generation of an insurance policy quality inspection report according to the risk analysis information.

[0123] With reference to the foregoing Figure 7 In some optional implementation manners of the embodiment, step S401 includes the following steps.

[0124] In step S4011, an ant colony algorithm path data set is obtained based on the risk distribution map, and the ant colony algorithm path data set is preprocessed to obtain a standard path data set.

[0125] In the embodiment, the ant colony algorithm path data set refers to insurance policy data searched by applying an ant colony algorithm in a risk assessment model. The preprocessing includes data cleaning (removing invalid paths and correcting erroneous data) and data standardization (risk value standardization and path length standardization), and the standard path data set is effectively obtained by performing the foregoing preprocessing on the ant colony algorithm path data set.

[0126] In step S4012, a preset risk insurance policy feature index is obtained, and abnormal premium ratio information, frequent claim frequency information, and multiple guarantee coverage degree information are extracted from the standard path data set according to the risk insurance policy feature index.

[0127] In this embodiment, the preset risk policy characteristic indicators include abnormal premium ratio, frequent claims number, and multiple protection coverage, wherein the abnormal premium ratio refers to the ratio of the premium amount exceeding the normal range, the frequent claims number refers to the number of claims occurring within a certain period exceeding the preset threshold, and the multiple protection coverage refers to whether the types and scope of protection provided in the policy are excessively overlapping or insufficient. The abnormal premium ratio information is extracted by searching for fields related to premiums in the standard path data set, and calculating the premium ratio of each policy, that is, the ratio of the premium of the policy to the average premium or the preset premium threshold, and comparing the calculated premium ratio with the preset ratio threshold to determine whether the premium ratio is abnormal. The frequent claims number information is extracted by searching for fields related to claims in the standard path data set, and counting the number of claims within a certain time window, and determining whether the number of claims exceeds the preset number threshold to determine whether the number of claims is frequent. The extraction of multiple protection coverage information is carried out by searching for fields related to the type and scope of protection in the standard path dataset, analyzing the type and scope of protection provided, identifying whether there is excessive overlap or insufficient coverage, and recording the multiple protection coverage information based on the analysis results, including the specific content of excessive coverage and insufficient coverage.

[0128] Step S4013: Input the abnormal premium ratio information, frequent claims information, and multiple protection coverage information into a pre-trained risk identification model to obtain a risk level identification result;

[0129] In this embodiment, the pre-trained risk identification model can employ a random forest model. This model is trained and optimized using historical sample data, enabling it to be used for risk assessment tasks involving high-risk insurance policies. The risk level identification results include risk level and risk probability. In this embodiment, the risk levels include primary, secondary, and tertiary risk. The risk probability corresponds to the probability that each policy belongs to a particular risk level, providing explanatory risk assessment information.

[0130] Step S4014: extract the high-risk insurance policies from the insurance policy data based on the risk level identification result.

[0131] In this embodiment, level one risk is low-level risk, level two risk is medium-level risk, and level three risk is high-level risk. By identifying and extracting insurance policies with a risk level identification result of level three risk from the insurance policy data, high-risk insurance policies can be effectively obtained.

[0132] This embodiment obtains an ant colony algorithm path dataset based on the risk distribution map and preprocesses the ant colony algorithm path dataset to obtain a standard path dataset; obtains preset risk policy characteristic indicators, and extracts abnormal premium ratio information, frequent claims information, and multiple protection coverage information from the standard path dataset based on the risk policy characteristic indicators; inputs the abnormal premium ratio information, frequent claims information, and multiple protection coverage information into a pre-trained risk identification model to obtain a risk level identification result; and extracts the high-risk policies from the policy data based on the risk level identification result. This allows for accurate extraction of high-risk policies that meet the preset risk policy characteristic indicator requirements from the policy data, facilitating subsequent feature extraction processing.

[0133] Continue to refer Figure 8 In some optional implementations of this embodiment, step S50 includes the following steps:

[0134] Step S501: obtaining a template extraction identifier, and extracting a quality inspection report template from a database according to the template extraction identifier;

[0135] In this embodiment, the template extraction identifier is a unique identifier corresponding to the quality inspection report template. A matching query is performed in the database based on the template extraction identifier to query and extract the quality inspection report template.

[0136] Step S502: parse the risk analysis information to obtain risk cause information, risk impact information, and risk solution;

[0137] In this embodiment, risk analysis information includes risk cause information, risk impact information, and risk solutions. Risk cause information refers to the various causes and factors that lead to the occurrence of risk events. For high-risk policies, risk causes involve multiple aspects, such as the personal characteristics of the policyholder or insured (such as age, health status, occupation, etc.), the design of policy terms (such as insurance amount, insurance period, compensation conditions, etc.), and changes in the external environment (such as economic cycles, adjustments to laws and regulations, natural disasters, etc.). Risk impact information refers to the possible consequences and impacts after the occurrence of a risk event. For high-risk policies, risk impacts include economic losses (such as increased compensation amounts, reduced premium income, etc.), decreased customer satisfaction (such as increased complaints due to improper compensation handling), and damaged brand image (such as negative public opinion caused by media exposure of risk events). Risk solutions refer to measures and methods developed to reduce the probability of risk occurrence and mitigate the impact of risks based on identified risk causes and impacts. For high-risk policies, risk solutions include adjusting policy terms (such as raising the compensation threshold, shortening the insurance period, etc.), strengthening risk control (such as improving underwriting standards, strengthening risk monitoring, etc.), and optimizing business processes (such as improving claims processing efficiency, strengthening customer service, etc.).

[0138] Step S503, optimizing the risk solution based on a decision tree algorithm to obtain an optimized risk solution;

[0139] In this embodiment, a decision tree algorithm, using CART, can be used to optimize risk solutions. By examining the branches and nodes of the decision tree, key factors and paths leading to high-risk policies can be identified. These paths reflect the probability and impact of risk under different feature combinations. Based on the analysis results of the decision tree, targeted risk solution optimization strategies can be developed. For example, for certain specific solutions, it may be necessary to adjust policy terms, strengthen risk control measures, or optimize business processes.

[0140] Step S504: Fill the risk cause information, the risk impact information, and the optimized risk solution into the quality inspection report template to generate the policy quality inspection report.

[0141] In this embodiment, the quality inspection report template is preset with corresponding filling positions, filling methods and typesetting methods for risk cause information, risk impact information and optimized risk solutions. The risk cause information, risk impact information and optimized risk solutions are filled into the quality inspection report template according to the filling positions, filling methods and typesetting methods to generate a policy quality inspection report for subsequent analysis and processing.

[0142] In this embodiment, a corresponding policy quality inspection report can also be generated for the non-high-risk policies in the risk distribution map. The policy quality inspection report corresponding to the non-high-risk policy may include the policy risk situation. The policy risk situation may be the risk distribution situation of the policy. By generating a policy quality inspection report corresponding to the non-high-risk policy, the risk situation of the non-high-risk policy can be visualized for subsequent analysis.

[0143] This embodiment obtains a template extraction identifier and extracts a quality inspection report template from a database based on the template extraction identifier; parses the risk analysis information to obtain risk cause information, risk impact information, and risk solutions; optimizes the risk solutions based on a decision tree algorithm to obtain an optimized risk solution; and then enters the risk cause information, risk impact information, and optimized risk solutions into the quality inspection report template to generate the policy quality inspection report. This effectively integrates risk cause information, risk impact information, and risk solutions to generate a policy quality inspection report, thereby improving the reliability of the policy quality inspection report.

[0144] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0145] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0146] Further references Figure 9 , as a response to the above Figure 1 In order to realize the method shown in the figure, the present application provides an embodiment of a device for quality inspection of insurance policies. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0147] like Figure 9 As shown, the insurance policy quality inspection device 800 of this embodiment includes: a feature extraction module 601, a model construction module 602, a risk assessment module 603, a risk analysis module 604, and a report generation module 605. Among them:

[0148] Feature extraction module 601, used to obtain insurance policy data and extract key policy basic information features, insured person features, and insurance type features from the insurance policy data;

[0149] A model building module 602 is used to build a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type;

[0150] A risk assessment module 603 is configured to perform a risk assessment on the policy data based on an ant colony algorithm and the risk assessment model to obtain a risk distribution map;

[0151] a risk analysis module 604 for extracting high-risk policies from the risk distribution map and performing cluster analysis on the high-risk policies to obtain risk analysis information;

[0152] The report generation module 605 is used to generate a policy quality inspection report based on the risk analysis information.

[0153] This embodiment, by employing the aforementioned policy quality inspection device, can acquire policy data, extract key policy basic information features, insured characteristics, and insurance type characteristics from the policy data; construct a risk assessment model based on the policy basic information features, the insured characteristics, and the insurance type characteristics; perform policy risk assessment on the policy data using an ant colony algorithm and the risk assessment model to obtain a risk distribution map; extract high-risk policies from the risk distribution map and perform cluster analysis on the high-risk policies to obtain risk analysis information; and generate a policy quality inspection report based on the risk analysis information. This effectively achieves accurate and effective risk assessment and analysis of policies, and generates a corresponding quality inspection report for presentation.

[0154] To solve the above technical problems, the present application also provides a computer device. Figure 10 , Figure 10 This is a basic structural block diagram of the computer device in this embodiment.

[0155] The computer device 7 includes a memory 71, a processor 72, and a network interface 73 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 7 with components 91-73, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0156] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0157] The memory 71 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the computer device 7. Of course, the memory 71 may also include both the internal storage unit of the computer device 7 and its external storage device. In this embodiment, the memory 71 is generally used to store the operating system and various application software installed on the computer device 7, such as computer-readable instructions for the insurance policy quality inspection method. In addition, the memory 71 can also be used to temporarily store various types of data that have been output or are to be output.

[0158] In some embodiments, the processor 72 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 72 is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 72 is used to execute computer-readable instructions or process data stored in the memory 71, such as computer-readable instructions for executing the insurance policy quality inspection method.

[0159] The network interface 73 may include a wireless network interface or a wired network interface. The network interface 73 is generally used to establish a communication connection between the computer device 7 and other electronic devices.

[0160] By employing the aforementioned computer device, this embodiment can acquire insurance policy data, extract key basic policy information features, insured person features, and insurance type features from the policy data; construct a risk assessment model based on the basic policy information features, the insured person features, and the insurance type features; perform policy risk assessment on the policy data using an ant colony algorithm and the risk assessment model to obtain a risk distribution map; extract high-risk policies from the risk distribution map and perform cluster analysis on the high-risk policies to obtain risk analysis information; and generate a policy quality inspection report based on the risk analysis information. This effectively achieves accurate and effective risk assessment and analysis of insurance policies, and generates corresponding quality inspection reports for presentation.

[0161] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned insurance policy quality inspection method.

[0162] By using the aforementioned computer-readable storage medium, this embodiment can acquire insurance policy data, extract key basic policy information features, insured person features, and insurance type features from the policy data; construct a risk assessment model based on the basic policy information features, the insured person features, and the insurance type features; perform policy risk assessment on the policy data using an ant colony algorithm and the risk assessment model to obtain a risk distribution map; extract high-risk policies from the risk distribution map and perform cluster analysis on the high-risk policies to obtain risk analysis information; and generate a policy quality inspection report based on the risk analysis information. This effectively achieves accurate and effective risk assessment and analysis of insurance policies, and generates corresponding quality inspection reports for presentation.

[0163] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0164] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A policy quality inspection method, characterized in that: The steps include: Obtaining insurance policy data, and extracting key policy basic information features, insured person features, and insurance type features from the insurance policy data; Building a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type; Performing policy risk assessment on the policy data based on the ant colony algorithm and the risk assessment model to obtain a risk distribution map; Extracting high-risk insurance policies from the risk distribution map, and performing cluster analysis on the high-risk insurance policies to obtain risk analysis information; Generate a policy quality inspection report based on the risk analysis information; The step of constructing a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type specifically includes: Quantifying the basic information features of the insurance policy, the features of the insured, and the features of the insurance type to obtain quantitative features of the insurance policy, quantitative features of the insured, and quantitative features of the insurance type; Constructing a policy complexity evaluation function, a risk exposure evaluation function, and an abnormality index evaluation function based on the policy quantitative characteristics, the insured quantitative characteristics, and the insurance type quantitative characteristics; Integrating the policy complexity assessment function, risk exposure assessment function, and abnormality index assessment function into the risk assessment model; The step of extracting high-risk policies from the risk distribution map and performing cluster analysis on the high-risk policies to obtain risk analysis information specifically includes: Identifying the risk level of the risk distribution map, and extracting the high-risk insurance policies from the insurance policy data based on the identification result; Extracting features of the high-risk insurance policy to obtain high-risk insurance policy features; Performing cluster analysis on the high-risk insurance policies according to a preset clustering algorithm and the characteristics of the high-risk insurance policies to obtain high-risk insurance policy clusters of different categories; Classify the problems of the high-risk policy cluster according to the association rule mining algorithm and generate a risk problem list; Performing reasoning analysis on the risk problem list based on a pre-established business knowledge base to obtain the risk analysis information; The step of generating a policy quality inspection report based on the risk analysis information specifically includes: Obtaining a template extraction identifier, and extracting a quality inspection report template from a database according to the template extraction identifier; Analyze the risk analysis information to obtain risk cause information, risk impact information, and risk solutions; Optimizing the risk solution based on a decision tree algorithm to obtain an optimized risk solution; Fill the risk cause information, the risk impact information, and the optimized risk solution into the quality inspection report template to generate the policy quality inspection report.

2. The policy quality inspection method according to claim 1, characterized in that: The step of obtaining the policy data and extracting key policy basic information features, insured person features, and insurance type features from the policy data specifically includes: Obtaining insurance policy data from a database, and extracting structured data and unstructured data from the insurance policy data; Preprocessing the structured data and the unstructured data to obtain standard structured data and standard unstructured data; Feature extraction is performed on the standard structured data and the standard unstructured data to obtain the basic information features of the insurance policy, the features of the insured, and the features of the insurance type.

3. The policy quality inspection method according to claim 1, characterized in that: The step of performing policy risk assessment on the policy data based on the ant colony algorithm and the risk assessment model to obtain a risk distribution map specifically includes: extracting insurance policy risk assessment indicators from the insurance policy data; Mapping the policy risk assessment indicators to the multidimensional space of the risk assessment model to construct an ant search space; Initializing ant colony algorithm parameters, searching in the ant search space according to the ant colony algorithm parameters, obtaining pheromone concentration results, and using the pheromone concentration results as policy risk assessment results; The risk distribution map is generated based on the policy risk assessment results.

4. The policy quality inspection method according to claim 1, characterized in that: The step of identifying the risk level of the risk distribution map and extracting the high-risk insurance policies from the insurance policy data according to the identification result specifically includes: Acquire an ant colony algorithm path dataset based on the risk distribution map, and preprocess the ant colony algorithm path dataset to obtain a standard path dataset; Obtaining preset risk policy characteristic indicators, and extracting abnormal premium ratio information, frequent claims information, and multiple protection coverage information from the standard path data set based on the risk policy characteristic indicators; Inputting the abnormal premium ratio information, frequent claims information, and multiple protection coverage information into a pre-trained risk identification model to obtain a risk level identification result; The high-risk insurance policies are extracted from the insurance policy data according to the risk level identification result.

5. A policy quality inspection device, characterized in that: The insurance policy quality inspection device is used to implement the insurance policy quality inspection method according to any one of claims 1 to 4, and the insurance policy quality inspection device includes: A feature extraction module is used to obtain insurance policy data and extract key policy basic information features, insured person features, and insurance type features from the insurance policy data; A model building module, configured to build a risk assessment model based on the basic information characteristics of the insurance policy, the characteristics of the insured, and the characteristics of the insurance type; a risk assessment module, configured to perform a policy risk assessment on the policy data based on an ant colony algorithm and the risk assessment model to obtain a risk distribution map; a risk analysis module, configured to extract high-risk policies from the risk distribution map and perform cluster analysis on the high-risk policies to obtain risk analysis information; A report generation module is used to generate a policy quality inspection report based on the risk analysis information.

6. A computer device, characterized in that: It includes a memory and a processor, the memory stores computer-readable instructions, and the processor implements the steps of the insurance policy quality inspection method as described in any one of claims 1 to 4 when executing the computer-readable instructions.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the insurance policy quality inspection method according to any one of claims 1 to 4.

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