Survey expense risk identification method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a survey expense risk identification method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining the accident information and survey expense information of a target case corresponding to a processed target survey task; taking the survey expense information as input, and generating a first survey expense risk identification result of the target case through a historical case comparison model; performing feature comparison on the survey expense information and the accident information to generate a second survey expense risk identification result of the target case; based on the first survey expense risk identification result and the second survey expense risk identification result, judging whether the survey expense of the target case has a risk or not; therefore, risk identification can be timely and accurately carried out on the survey cost of the target case.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence technology and financial technology, and in particular to a method, device, computer equipment and computer-readable storage medium for identifying risks of survey costs. Background Art

[0002] At present, with the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming to financial technology (Fintech), and the risk identification technology of survey cost is no exception. However, due to the security and real-time requirements of the financial industry, higher requirements are also placed on the risk identification technology of survey cost.

[0003] At present, in the routine audit process of insurance companies, claims-related work is always a node with high incidence of violations, especially in terms of company finances. As a necessary workflow for claims personnel to carry out their work, there will be behaviors of including personal expenses in the case investigation expenses and reimbursing them together. Insurance companies often discover this problem only during the audit stage, which requires auditors to compare and verify the information of the accident location on the expense reimbursement form to prevent fraud and fraud, conduct review and warning, and standardize the claims process. However, traditional risk detection methods for investigation expenses usually require manual participation, which has the risk of subjectivity and misjudgment, and requires manpower. In addition, since the fraud comes from internal personnel, the operation is concealed, and the subsequent verification is delayed, and the problem has already caused adverse effects when it is discovered. Therefore, this requires a timely and highly accurate detection mechanism to review the reimbursement link of the investigation expenses.

[0004] Based on this, how to provide a method, device, computer equipment and computer-readable storage medium for identifying risks of investigation costs, which can realize timely and accurate risk identification of investigation costs of target cases, is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a method, apparatus, computer equipment and computer-readable storage medium for identifying risks of investigation costs, aiming to solve the problem of how to timely and accurately identify the risks of investigation costs of target cases.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for identifying risks of survey costs, which includes:

[0008] Obtain the accident information and investigation fee information of the target cases corresponding to the completed target investigation tasks;

[0009] Taking the investigation fee information as input, generating a first investigation fee risk identification result of the target case through a historical case comparison model;

[0010] Performing feature comparison between the survey cost information and the risk information to generate a second survey cost risk identification result for the target case;

[0011] Based on the first investigation fee risk identification result and the second investigation fee risk identification result, it is determined whether there is a risk in the investigation fee of the target case.

[0012] In a second aspect, the present invention provides a survey cost risk identification device, which includes:

[0013] An acquisition module is used to obtain the accident information and investigation fee information of the target case corresponding to the completed target investigation task;

[0014] An input module, used to take the investigation fee information as input and generate a first investigation fee risk identification result of the target case through a historical case comparison model;

[0015] A comparison module, used for comparing the characteristics of the survey cost information with the risk information, and generating a second survey cost risk identification result of the target case;

[0016] A judgment module is used to judge whether there is a risk in the investigation fee of the target case based on the first investigation fee risk identification result and the second investigation fee risk identification result.

[0017] In a third aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the survey cost risk identification method as described above when executing the computer program.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the survey cost risk identification method as described above.

[0019] Compared with the prior art, the present invention provides a method, apparatus, computer equipment and computer-readable storage medium for identifying risks of survey costs, wherein the risk information and survey cost information of a target case corresponding to a target survey task that has been processed are obtained; the survey cost information is used as input, and a first survey cost risk identification result of the target case is generated through a historical case comparison model; the survey cost information is feature-matched with the risk information to generate a second survey cost risk identification result of the target case; based on the first survey cost risk identification result and the second survey cost risk identification result, it is determined whether there is a risk in the survey cost of the target case; thereby, the present invention can realize timely and accurate risk identification of the survey cost of the target case. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A schematic diagram of an application environment of a method for identifying risks of survey costs provided in one embodiment of the present invention.

[0022] Figure 2 A flowchart of a method for identifying risks of survey costs provided in accordance with an embodiment of the present invention.

[0023] Figure 3 A schematic diagram of a program module of a survey cost risk identification device provided in one embodiment of the present invention.

[0024] Figure 4 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention.

[0025] Figure 5 Another structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0028] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0029] As used in the present specification and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0030] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0031] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0032] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0033] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0034] An embodiment of the present invention provides a method for identifying risks of survey costs, which can be applied in the following situations: Figure 1In the application environment shown, the client and the server communicate through the network. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, personal digital assistants (PDAs) and other computer devices. The server can be an independent server or 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, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0035] See also Figure 2 An embodiment of the present invention provides a method for identifying risks of survey costs, wherein the method comprises the following steps:

[0036] S100, obtaining the accident information and investigation fee information of the target case corresponding to the completed target investigation task;

[0037] S200, taking the investigation fee information as input, generating a first investigation fee risk identification result of the target case through a historical case comparison model;

[0038] S300, performing feature comparison between the survey cost information and the risk information to generate a second survey cost risk identification result for the target case;

[0039] S400: Based on the first investigation fee risk identification result and the second investigation fee risk identification result, determine whether there is a risk in the investigation fee of the target case.

[0040] During specific implementation, the risk identification method for investigation expenses of this embodiment realizes timely and accurate identification of the investigation expense risk of the target case by integrating multiple steps. First, in step S100, by obtaining the risk information and investigation expense information of the target case, basic data is provided for the subsequent risk assessment of investigation expenses. Then, step S200 uses the historical case comparison model to compare the investigation expense information with the historical data to generate the first investigation expense risk identification result, which helps to identify the abnormality of the investigation expense. Step S300 further analyzes the consistency between the investigation expense information and the risk information through feature comparison, generates the second investigation expense risk identification result, and enhances the depth of investigation expense risk identification. Finally, step S400 combines the two investigation expense risk identification results, and then accurately determines whether the investigation expense of the target case is at risk. The entire process of the method of this embodiment not only improves the timeliness of investigation expense risk identification, but also enhances the accuracy of investigation expense risk identification through multi-angle analysis, and provides an effective investigation expense risk management tool for insurance companies.

[0041] Furthermore, in one embodiment, the method for identifying the risk of survey costs, wherein the step S100, obtaining the risk information and survey cost information of the target case corresponding to the completed target survey task, specifically comprises the steps of:

[0042] When the target survey task is completed, the initial risk information and initial survey fee information corresponding to the target case reported by the target surveyor are collected;

[0043] After the initial accident information and the initial investigation fee information are subjected to pre-processing operations of data verification, data cleaning, data formatting and data integration, the corresponding accident information and the investigation fee information are obtained.

[0044] In specific implementation, this embodiment realizes the rapid acquisition of basic case information by timely collecting the initial accident information and initial investigation fee information of the corresponding target case reported by the target surveyor after the target investigation task is processed. Subsequently, by preprocessing these initial information, including data verification, data cleaning, data formatting and data integration, they are converted into accurate and standardized accident information and investigation fee information, providing a reliable data basis for subsequent investigation fee risk analysis and assessment, thereby improving the efficiency and accuracy of risk management. This process not only speeds up the information flow, but also ensures data quality through preprocessing steps, laying a solid foundation for accurate risk control and decision support.

[0045] The specific implementation process of the steps in this embodiment is roughly as follows:

[0046] 1. Collect initial information:

[0047] After the target survey task is completed, the initial accident information and initial survey cost information of the target case are immediately obtained through the report submitted by the target surveyor or automatically collected by the system; this may include photos and videos of the accident scene, the written report of the target surveyor, and preliminary cost estimate information;

[0048] 2. Data verification and cleaning:

[0049] Verify the collected initial accident information and initial survey fee information to ensure the integrity and authenticity of the information; at the same time, clean the data to remove invalid, erroneous or duplicate data items to ensure the accuracy of subsequent analysis;

[0050] 3. Data formatting and integration:

[0051] Organize the cleaned data into a unified format, including converting unstructured data (such as text reports) into structured data (such as database entries) and integrating data from different sources and formats for further analysis and processing;

[0052] 4. Generate final information:

[0053] The accident information and survey cost information obtained after pre-processing operations are stored in the database, and this information will serve as the basic data for subsequent risk assessment and decision support.

[0054] Through the above process, this embodiment can ensure that the initial information collected from the target surveyor is effectively converted into accurate and structured data that can be used for risk analysis and assessment, providing solid data support for subsequent risk management processes.

[0055] Furthermore, in one embodiment, the survey cost risk identification method, wherein the step S200, taking the survey cost information as input and generating the first survey cost risk identification result of the target case through the historical case comparison model, specifically comprises the steps of:

[0056] Constructing the historical case comparison model based on a decision tree algorithm, inputting the investigation fee information into the historical case comparison model, and generating a first initial investigation fee risk identification result of the target case through the historical case comparison model;

[0057] Performing content analysis on the first initial survey cost risk identification result to generate a first analysis result, and performing content adjustment on the first initial survey cost risk identification result based on the first analysis result to obtain the first survey cost risk identification result.

[0058] In specific implementation, this embodiment constructs a historical case comparison model by integrating a decision tree algorithm, and generates risk identification results in combination with the investigation fee information, thereby achieving an accurate assessment of the investigation fee risk of the target case. First, using the tree structure of the decision tree algorithm, the model can recursively split the data set and select the optimal features for risk assessment. Next, the investigation fee information is input into the model to generate the first initial investigation fee risk identification result. This step can make a preliminary judgment on the rationality of the current fee based on historical data. Then, by conducting an in-depth content analysis on the first initial investigation fee risk identification result, a first analysis result is generated, and the content of the first initial investigation fee risk identification result is adjusted accordingly, and finally a more accurate first investigation fee risk identification result is obtained. This process not only improves the accuracy of investigation fee risk identification, but also enhances the model's adaptability to new situations, ensuring the timeliness and effectiveness of investigation fee risk assessment. Through this method, potential investigation fee risks can be effectively identified and prevented, providing scientific decision support for insurance claims.

[0059] The specific implementation process of the steps in this embodiment is roughly as follows:

[0060] 1. Construct a historical case comparison model:

[0061] Use the decision tree algorithm to build a historical case comparison model based on historical case data. The decision tree creates a tree-like decision model by learning the patterns in the historical case data to predict the investigation fee risk of new cases. This step involves selecting appropriate features and labels, as well as setting the decision tree parameters, such as the maximum depth and minimum number of split samples.

[0062] 2. Enter the survey fee information:

[0063] Input the investigation cost information of the target case as input data into the historical case comparison model; this information may include the labor cost, material cost, transportation cost, etc. of the investigation, as well as other relevant information of the case, such as the type of accident, the extent of loss, etc.;

[0064] 3. Generate initial risk identification results:

[0065] Using the constructed historical case comparison model, the input investigation fee information is analyzed to generate the first initial investigation fee risk identification result of the target case; this result is a preliminary prediction of the investigation fee risk of the new case based on the learning of historical case data by the historical case comparison model, which may be a risk score or classification, indicating the risk level of the case;

[0066] 4. Analyze initial risk identification results:

[0067] Conduct a detailed content analysis of the generated first initial survey cost risk identification results, which may include interpretation, validation and evaluation of the model outputs; the purpose of the analysis is to understand the model's predictions and identify any possible biases or errors;

[0068] 5. Adjust the initial risk identification results:

[0069] According to the first analysis result, necessary content adjustments are made to the first initial investigation cost risk identification result; the adjustments may involve the identification and processing of risk score distribution and outliers, etc. This step ensures the accuracy and reliability of the risk identification result;

[0070] 6. Generate final risk identification results:

[0071] After adjustment, the final first inspection expense risk identification result is obtained; this result will be used for subsequent risk management and decision support, helping insurance companies to effectively identify and manage inspection expense risks;

[0072] Through the above process, the insurance company in this embodiment can more accurately identify and assess the risk of survey costs, thereby taking appropriate risk control measures, improving claims efficiency and reducing unnecessary expenses.

[0073] Furthermore, in one embodiment, the survey cost risk identification method, wherein the step S300, comparing the survey cost information with the risk information to generate a second survey cost risk identification result of the target case, specifically comprises the following steps:

[0074] Extracting features from the survey fee information and the accident information to obtain corresponding fee information features and accident information features;

[0075] Performing feature comparison between the expense information feature and the risk information feature according to the feature comparison strategy to generate a second initial investigation expense risk identification result for the target case;

[0076] Perform content analysis on the second initial survey cost risk identification result to generate a second analysis result, and perform content adjustment on the second initial survey cost risk identification result based on the second analysis result to obtain the second survey cost risk identification result.

[0077] In specific implementation, this embodiment realizes in-depth analysis and risk assessment of the relationship between survey cost information and accident information through three key links: feature extraction, feature comparison and result analysis and adjustment. First, by extracting features from survey cost information and accident information, the original data is converted into key features that can be used for analysis, thereby enhancing the processability and comparability of the data. Secondly, according to the feature comparison strategy, the cost information features are compared with the accident information features to generate the second initial survey cost risk identification result, which helps to reveal the potential inconsistency between the cost and the actual loss. Finally, by conducting in-depth content analysis on the second initial survey cost risk identification result and adjusting the content according to the second analysis result, a more accurate second survey cost risk identification result is obtained. This process not only improves the accuracy of survey cost risk identification, but also enhances the adaptability to new situations, ensures the timeliness and effectiveness of survey cost risk assessment, and provides scientific decision support for insurance claims.

[0078] The specific implementation process of the steps in this embodiment is roughly as follows:

[0079] 1. Feature extraction:

[0080] First, conduct in-depth analysis of the survey cost information and accident information to identify and extract key features. For survey cost information, possible features include total cost, distribution of various costs, time of cost occurrence, etc.; for accident information, possible features include accident type, loss extent, third parties involved, etc. This step requires data scientists or analysts to determine which features are most critical for risk assessment based on business knowledge and historical data.

[0081] 2. Feature comparison:

[0082] Next, according to the preset feature comparison strategy, the extracted expense information features are compared and analyzed with the accident information features; this strategy may include statistical methods, machine learning models or rule engines to identify anomalies or inconsistencies between expenses and accidents; for example, focus on identifying the time and location of the expense, whether it is consistent with the actual case, and whether the task execution time is reasonable.

[0083] 3. Generate initial risk identification results:

[0084] Based on the result of feature comparison, a second initial investigation fee risk identification result of the target case is generated; this result may be a risk score or classification, indicating the risk level of the case;

[0085] 4. Result analysis and adjustment:

[0086] Conduct a detailed content analysis of the second initial survey cost risk identification results, which may involve the distribution of risk scores and the identification of outliers; based on this analysis, determine whether the second initial survey cost risk identification results need to be adjusted, such as adjusting the distribution of risk scores or optimizing the weights of certain features;

[0087] 5. Generate final risk identification results:

[0088] Finally, the second initial investigation expense risk identification result is adjusted according to the second analysis result to obtain the final second investigation expense risk identification result; this result will more accurately reflect the investigation expense risk status of the target case, provide decision makers with more reliable investigation expense risk assessment information, and help them make more reasonable decisions;

[0089] Through the above process, this embodiment can ensure the scientificity and effectiveness of the investigation cost risk identification process, while improving the identification and management capabilities of the investigation cost risks.

[0090] Furthermore, in one embodiment, the survey cost risk identification method, wherein the step S400, based on the first survey cost risk identification result and the second survey cost risk identification result, determines whether the survey cost of the target case has a risk, specifically comprises the steps of:

[0091] Formulate a risk identification result judgment strategy based on the historical case data and business rules corresponding to the target case, and determine a risk judgment threshold based on the risk identification result judgment strategy;

[0092] comparing the first survey expense risk identification result and the second survey expense risk identification result with the risk judgment threshold;

[0093] When the first investigation expense risk identification result and / or the second investigation expense risk identification result exceeds the risk judgment threshold, it is determined that there is a risk in the investigation expense of the target case.

[0094] Furthermore, the survey cost risk identification method further includes the following steps: when the first survey cost risk identification result and / or the second survey cost risk identification result exceeds the risk judgment threshold, it is determined that the survey cost of the target case is at risk:

[0095] Trigger an alarm prompt, and send the alarm content, the accident information of the target case and the investigation fee information to the processing terminal of the superior personnel, so that the superior personnel can review the investigation fee of the target case;

[0096] Receive the review result returned by the processing terminal, and output and / or save the review result according to the instruction.

[0097] Furthermore, the survey cost risk identification method, wherein after comparing the first survey cost risk identification result and the second survey cost risk identification result with the risk judgment threshold, specifically further comprises the steps of:

[0098] When both the first investigation expense risk identification result and the second investigation expense risk identification result do not exceed the risk judgment threshold, it is determined that there is no risk in the investigation expense of the target case;

[0099] According to the instructions, the funds are remitted to the designated account according to the fee amount in the investigation fee information of the target case.

[0100] In specific implementation, this embodiment realizes the automated assessment and processing of the investigation cost risk of the target case by comprehensively using the risk identification result judgment strategy, threshold comparison, alarm prompt and review process. First, by determining the risk judgment threshold, a clear standard is provided for the risk assessment of the investigation cost. Then, the first investigation cost risk identification result and the second investigation cost risk identification result are compared with the risk judgment threshold to automatically determine whether there is a risk in the investigation cost of the target case, and trigger an alarm prompt when the first investigation cost risk identification result and / or the second investigation cost risk identification result exceeds the risk judgment threshold, and notify the superior to review to ensure the accuracy of the risk judgment. At the same time, by receiving the review results and outputting or saving them according to the instructions, a closed loop of risk management is realized. In addition, when the first investigation cost risk identification result and the second investigation cost risk identification result do not exceed the risk judgment threshold, the system will automatically process the payment according to the amount in the investigation cost information, thereby improving the efficiency and accuracy of the investigation cost processing. Overall, this process of the method in this embodiment not only improves the automation and accuracy of survey cost risk identification, but also enhances the responsiveness and decision-making support of survey cost risk management through timely alarm and review mechanisms, providing insurance companies with an efficient and reliable survey cost risk control solution.

[0101] The specific implementation process of the steps in this embodiment is roughly as follows:

[0102] 1. Determine the risk judgment threshold:

[0103] According to the historical case data and business rules corresponding to the target case, formulate a risk identification result judgment strategy, and determine the risk judgment threshold accordingly; this threshold is the critical point for evaluating the risk of investigation costs, and the results exceeding this value will be considered high risk;

[0104] 2. Compare risk identification results with thresholds:

[0105] Compare the first and second survey cost risk identification results with the risk judgment threshold; this step is automated, and the system will automatically identify whether any result exceeds the preset threshold;

[0106] 3. Risk determination and warning:

[0107] If any of the investigation fee risk identification results exceeds the risk judgment threshold, the system will automatically determine that the investigation fee of the target case is at risk and trigger an alarm prompt; and send the alarm content together with the risk information and investigation fee information of the target case to the processing terminal of the superior personnel;

[0108] 4. Review and result processing:

[0109] After receiving the alarm, the superior personnel will review the investigation fee of the target case and return the review result; the system receives the review result and outputs the result (such as notifying relevant personnel) and / or saves it (such as recording it in the case file) according to the instruction;

[0110] 5. Treatment when risk does not exist:

[0111] If the two investigation fee risk identification results do not exceed the risk judgment threshold, the system will determine that there is no risk in the investigation fee of the target case; then, according to the instructions and the fee amount in the investigation fee information, the system will automatically process the payment and remit the funds to the designated account;

[0112] Of course, when the review result in step 4 above is that there is no risk in the investigation fee of the target case, the system will automatically process the payment according to the instruction and the fee amount in the investigation fee information, and remit the funds to the designated account;

[0113] Through the above process, this embodiment can realize the automated assessment, warning, review and payment processing of the risk of survey costs, thereby improving the efficiency and accuracy of survey cost risk management, while ensuring the rationality and safety of survey costs.

[0114] It can be seen from the above method embodiments that the method for identifying the risk of survey expenses provided by the present invention includes: obtaining the risk information and survey expense information of the target case corresponding to the target survey task that has been processed; using the survey expense information as input, and generating a first survey expense risk identification result of the target case through a historical case comparison model; performing feature comparison between the survey expense information and the risk information, and generating a second survey expense risk identification result of the target case; based on the first survey expense risk identification result and the second survey expense risk identification result, determining whether there is a risk in the survey expense of the target case. In this way, the method of the present invention can realize timely and accurate risk identification of the survey expense of the target case.

[0115] It can be understood that the survey cost risk identification method provided by the embodiment of the present invention can be applied to survey cost risk identification scenarios related to the field of financial technology. Specifically, the survey cost risk identification method provided by the present invention can be integrated into the corresponding application of the insurance institution to achieve timely and accurate risk identification of the survey costs of the target case, thereby improving the risk management level of the insurance institution.

[0116] It should be understood that, although the present application provides method operation steps as described in the embodiments or flowcharts, more or less operation steps may be included based on conventional or non-creative labor, and these operation steps are not necessarily performed in sequence according to the embodiment or flowchart. The order of steps listed in the embodiment or flowchart is only one way of executing the order of many steps, and does not represent the only execution order. It should be noted that there is not necessarily a certain order between the above steps. A person of ordinary skill in the art can understand from the description of the embodiment of the present invention that in different embodiments, the above steps may have different execution orders, that is, they may be executed in parallel, or they may be executed in exchange, etc. Moreover, at least a part of the steps in the embodiment or flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but may be executed in turn, alternately or synchronously with other steps or at least a part of the sub-steps or stages of other steps.

[0117] Based on the above method embodiment, please refer to Figure 3 Another embodiment of the present invention further provides a survey cost risk identification device, wherein the device comprises:

[0118] The acquisition module 11 is used to obtain the accident information and investigation fee information of the target case corresponding to the completed target investigation task;

[0119] An input module 12, configured to take the investigation fee information as input and generate a first investigation fee risk identification result of the target case through a historical case comparison model;

[0120] A comparison module 13, used for comparing the characteristics of the survey cost information with the risk information, and generating a second survey cost risk identification result of the target case;

[0121] The judgment module 14 is used to judge whether there is a risk in the investigation fee of the target case based on the first investigation fee risk identification result and the second investigation fee risk identification result.

[0122] Furthermore, in one embodiment, in the survey cost risk identification device, the acquisition module 11 is specifically used to:

[0123] When the target survey task is completed, the initial risk information and initial survey fee information corresponding to the target case reported by the target surveyor are collected;

[0124] After the initial accident information and the initial investigation fee information are subjected to pre-processing operations of data verification, data cleaning, data formatting and data integration, the corresponding accident information and the investigation fee information are obtained.

[0125] Furthermore, in one embodiment, in the survey cost risk identification device, the input module 12 is specifically used to:

[0126] Constructing the historical case comparison model based on a decision tree algorithm, inputting the investigation fee information into the historical case comparison model, and generating a first initial investigation fee risk identification result of the target case through the historical case comparison model;

[0127] Performing content analysis on the first initial survey cost risk identification result to generate a first analysis result, and performing content adjustment on the first initial survey cost risk identification result based on the first analysis result to obtain the first survey cost risk identification result.

[0128] Furthermore, in one embodiment, in the survey cost risk identification device, the comparison module 13 is specifically used to:

[0129] Extracting features from the survey fee information and the accident information to obtain corresponding fee information features and accident information features;

[0130] Performing feature comparison between the expense information feature and the risk information feature according to the feature comparison strategy to generate a second initial investigation expense risk identification result for the target case;

[0131] Perform content analysis on the second initial survey cost risk identification result to generate a second analysis result, and perform content adjustment on the second initial survey cost risk identification result based on the second analysis result to obtain the second survey cost risk identification result.

[0132] Furthermore, in one embodiment, in the survey cost risk identification device, the judgment module 14 is specifically used to:

[0133] Formulate a risk identification result judgment strategy based on the historical case data and business rules corresponding to the target case, and determine a risk judgment threshold based on the risk identification result judgment strategy;

[0134] comparing the first survey expense risk identification result and the second survey expense risk identification result with the risk judgment threshold;

[0135] When the first investigation expense risk identification result and / or the second investigation expense risk identification result exceeds the risk judgment threshold, it is determined that there is a risk in the investigation expense of the target case.

[0136] Furthermore, the survey cost risk identification device, wherein when the first survey cost risk identification result and / or the second survey cost risk identification result exceeds the risk judgment threshold, it is determined that the survey cost of the target case has a risk, and specifically further includes:

[0137] Trigger an alarm prompt, and send the alarm content, the accident information of the target case and the investigation fee information to the processing terminal of the superior personnel, so that the superior personnel can review the investigation fee of the target case;

[0138] Receive the review result returned by the processing terminal, and output and / or save the review result according to the instruction.

[0139] Furthermore, the survey cost risk identification device, wherein after comparing the first survey cost risk identification result and the second survey cost risk identification result with the risk judgment threshold, specifically further comprises:

[0140] When both the first investigation expense risk identification result and the second investigation expense risk identification result do not exceed the risk judgment threshold, it is determined that there is no risk in the investigation expense of the target case;

[0141] According to the instructions, the funds are remitted to the designated account according to the fee amount in the investigation fee information of the target case.

[0142] It should be noted that in the embodiment of the device of the present invention, the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the aforementioned method embodiment part and will not be repeated here.

[0143] Based on the above method embodiment, another embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the functions or steps on the service side of the survey cost risk identification method in any of the above method embodiments are implemented.

[0144] Based on the above method embodiment, another embodiment of the present invention further provides a computer device, which may be a client, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the functions or steps on the client side of the survey cost risk identification method in any of the above method embodiments are implemented.

[0145] Those skilled in the art will understand that Figure 4 and Figure 5 The structural schematic diagram shown in the figure is only a schematic diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0147] Among them, the memory includes a readable storage medium, an internal memory, etc., wherein the internal memory can be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium can be a hard disk of a computer device, and in other embodiments, it can also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory can also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory can also be used to temporarily store data that has been output or is to be output.

[0148] Based on the above method embodiments, another embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the survey cost risk identification method in any of the above method embodiments is implemented. The computer-readable storage medium may be non-volatile or volatile.

[0149] It should be noted that the above-mentioned functions or steps that can be implemented by the computer-readable storage medium or computer device, and the technical effects brought about by the functions / steps, can be found in the relevant descriptions in the aforementioned method embodiments. To avoid repetition, they will not be described one by one here.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM). The disclosed memory components or memories of the operating environments described herein are intended to comprise one or more of these and / or any other suitable types of memory.

[0151] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, in the embodiment of the device of the present invention, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. 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.

[0152] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0153] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, 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.

[0154] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] It should be noted that if software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for illustration and do not represent actual use. The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for identifying risks of survey costs, characterized in that: include: Obtain the accident information and investigation fee information of the target cases corresponding to the completed target investigation tasks; Taking the investigation fee information as input, generating a first investigation fee risk identification result of the target case through a historical case comparison model; Performing feature comparison between the survey cost information and the risk information to generate a second survey cost risk identification result for the target case; Based on the first investigation fee risk identification result and the second investigation fee risk identification result, it is determined whether there is a risk in the investigation fee of the target case.

2. The method for identifying survey cost risks according to claim 1, characterized in that: The obtaining of the accident information and investigation fee information of the target case corresponding to the completed target investigation task includes: When the target survey task is completed, the initial risk information and initial survey fee information corresponding to the target case reported by the target surveyor are collected; After the initial accident information and the initial investigation fee information are subjected to pre-processing operations of data verification, data cleaning, data formatting and data integration, the corresponding accident information and the investigation fee information are obtained.

3. The method for identifying survey cost risks according to claim 1, characterized in that: The step of taking the investigation fee information as input and generating the first investigation fee risk identification result of the target case through a historical case comparison model includes: Constructing the historical case comparison model based on a decision tree algorithm, inputting the investigation fee information into the historical case comparison model, and generating a first initial investigation fee risk identification result of the target case through the historical case comparison model; Performing content analysis on the first initial survey cost risk identification result to generate a first analysis result, and performing content adjustment on the first initial survey cost risk identification result based on the first analysis result to obtain the first survey cost risk identification result.

4. The method for identifying survey cost risks according to claim 1, characterized in that: The feature comparison between the survey cost information and the risk information to generate a second survey cost risk identification result for the target case includes: Extracting features from the survey fee information and the accident information to obtain corresponding fee information features and accident information features; Performing feature comparison between the expense information feature and the risk information feature according to the feature comparison strategy to generate a second initial investigation expense risk identification result for the target case; Perform content analysis on the second initial survey cost risk identification result to generate a second analysis result, and perform content adjustment on the second initial survey cost risk identification result based on the second analysis result to obtain the second survey cost risk identification result.

5. The method for identifying survey cost risks according to any one of claims 1 to 4, characterized in that: The determining whether there is a risk in the investigation fee of the target case based on the first investigation fee risk identification result and the second investigation fee risk identification result includes: Formulate a risk identification result judgment strategy based on the historical case data and business rules corresponding to the target case, and determine a risk judgment threshold based on the risk identification result judgment strategy; comparing the first survey expense risk identification result and the second survey expense risk identification result with the risk judgment threshold; When the first investigation expense risk identification result and / or the second investigation expense risk identification result exceeds the risk judgment threshold, it is determined that there is a risk in the investigation expense of the target case.

6. The method for identifying survey cost risks according to claim 5, characterized in that: When the first investigation expense risk identification result and / or the second investigation expense risk identification result exceeds the risk judgment threshold, after determining that the investigation expense of the target case is at risk, the method further includes: Trigger an alarm prompt, and send the alarm content, the accident information of the target case and the investigation fee information to the processing terminal of the superior personnel, so that the superior personnel can review the investigation fee of the target case; Receive the review result returned by the processing terminal, and output and / or save the review result according to the instruction.

7. The method for identifying survey cost risks according to claim 5, characterized in that: After comparing the first investigation expense risk identification result and the second investigation expense risk identification result with the risk judgment threshold, the method further includes: When both the first investigation expense risk identification result and the second investigation expense risk identification result do not exceed the risk judgment threshold, it is determined that there is no risk in the investigation expense of the target case; According to the instructions, the funds are remitted to the designated account according to the fee amount in the investigation fee information of the target case.

8. A survey cost risk identification device, characterized in that: include: An acquisition module is used to obtain the accident information and investigation fee information of the target case corresponding to the completed target investigation task; An input module, used to take the investigation fee information as input and generate a first investigation fee risk identification result of the target case through a historical case comparison model; A comparison module, used for comparing the characteristics of the survey cost information with the risk information, and generating a second survey cost risk identification result of the target case; A judgment module is used to judge whether there is a risk in the investigation fee of the target case based on the first investigation fee risk identification result and the second investigation fee risk identification result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for identifying risks of survey costs as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying risks of survey costs as described in any one of claims 1 to 7 is implemented.

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