Method, device, electronic device, medium and product for identifying auto insurance fraud

Abstract

The embodiments of the present application provide a method, device, electronic device, medium and product for identifying auto insurance fraud, which belongs to the field of artificial intelligence technology. The method includes: inputting the information of each auto insurance claim case into a neural network model to obtain the selection probability of each claim action in each case; determining the target claim action based on the selection probability. Determine the reward value of the target claim action based on the target claim action and the reference claim action, and determine the sum of the various reward values ​​as the cumulative reward of the model. Determine the second expected gradient based on the cumulative reward and the first expected gradient. Finally, iteratively update the model parameters based on the second expected gradient to obtain an auto insurance fraud identification model. In this way, by inputting the information of each auto insurance claim case into the neural network model, obtaining the cumulative reward of the model, and updating the model parameters based on the expected gradient of the model parameters through reinforcement learning, the anti-fraud strategy of the model can be optimized and the accuracy of auto insurance fraud identification can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device, electronic device, medium, and product for identifying auto insurance fraud. Background Art

[0002] At present, traditional methods for identifying auto insurance fraud mainly focus on manually labeled structured data, or use traditional binary classification models to detect auto insurance fraud.

[0003] A binary classification model is a machine learning model used to classify data into two categories. The goal of a binary classification model is to predict the output label, usually labeled 0 and 1 or negative and positive, based on the input features.

[0004] However, when the number of samples of different categories in the data set varies greatly, the binary classification model will tend to predict the sample category with the majority number, resulting in low accuracy of the prediction results.

[0005] Therefore, how to improve the accuracy of identifying auto insurance fraud has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] The main purpose of the embodiments of the present application is to propose a method, device, electronic device, medium and product for identifying auto insurance fraud, aiming to improve the accuracy of identifying auto insurance fraud.

[0007] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for identifying auto insurance fraud, the method comprising:

[0008] Input the information of each auto insurance claim case into the neural network model respectively to obtain the selection probability of each claim action corresponding to each auto insurance claim case;

[0009] Determine the target claim action corresponding to each auto insurance claim case based on the selection probability of each claim action corresponding to each auto insurance claim case;

[0010] Determine the reward value of the target claim action based on the target claim action and the reference claim action corresponding to the target claim action;

[0011] The sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case is determined as the cumulative reward of the neural network model;

[0012] Determine a second expected gradient based on the cumulative reward and the first expected gradient, where the first expected gradient refers to the expected gradient of each target claim settlement action with respect to the parameters of the neural network model, and the second expected gradient refers to the expected gradient of the expected reward of the neural network model with respect to the parameters of the neural network model;

[0013] The parameters of the neural network model are updated according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining a vehicle insurance fraud behavior identification model, which is used to identify vehicle insurance fraud behavior.

[0014] In some embodiments, determining the second expected gradient based on the accumulated reward and the first expected gradient includes:

[0015] Multiply the cumulative reward by the expected gradient of each target claim action to obtain the expected gradient of the parameters of the neural network model corresponding to each car insurance claim case;

[0016] The expected gradients of the parameters of the neural network model corresponding to each auto insurance claim case are summed to obtain a second expected gradient.

[0017] In some embodiments, before determining the second expected gradient based on the accumulated reward and the first expected gradient, the identifying method includes:

[0018] Determine the log probability of the selection probability of the target claim action;

[0019] The gradient of the log probability is determined based on the chain rule to obtain the first expected gradient.

[0020] In some embodiments, updating the initial parameters of the neural network model according to the second expected gradient includes:

[0021] Determine the product of the second expected gradient and the learning rate of the neural network model;

[0022] The sum of the product and the parameters of the neural network model is determined as the updated parameters of the neural network model.

[0023] In some embodiments, updating the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining a vehicle insurance fraud behavior recognition model, includes:

[0024] Determine the current cumulative return of the neural network model based on the reward value of the target claim action corresponding to each auto insurance claim case and the discount factor of the neural network model;

[0025] When the current cumulative return is greater than the cumulative return before the update, reduce the learning rate;

[0026] If the current cumulative reward is less than the cumulative reward before the update, increase the learning rate.

[0027] In some embodiments, determining a reward value for a target claim action based on the target claim action and a reference claim action corresponding to the target claim action includes:

[0028] When the target claim action is the same as the reference claim action, the reward value is determined to be a positive number;

[0029] When the target claim action is different from the reference claim action, the reward value is determined to be a negative number.

[0030] To achieve the above-mentioned objectives, a second aspect of an embodiment of the present application provides a device for identifying auto insurance fraud, the device comprising:

[0031] An input module is used to input the information of each auto insurance claim case into the neural network model to obtain the selection probability of each claim action corresponding to each auto insurance claim case;

[0032] The first determination module is used to determine the target claim action corresponding to each auto insurance claim case based on the selection probability of each claim action corresponding to each auto insurance claim case;

[0033] A second determination module is used to determine the reward value of the target claim action based on the target claim action and the reference claim action corresponding to the target claim action;

[0034] A third determination module is used to determine the sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case as the cumulative reward of the neural network model;

[0035] a fourth determination module, configured to determine a second expected gradient based on the accumulated reward and the first expected gradient, wherein the first expected gradient refers to the expected gradient of each target claim settlement action with respect to the parameters of the neural network model, and the second expected gradient refers to the expected gradient of the expected reward of the neural network model with respect to the parameters of the neural network model;

[0036] An updating module is used to update the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining a vehicle insurance fraud behavior identification model, which is used to identify vehicle insurance fraud behavior.

[0037] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0038] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0039] To achieve the above-mentioned purpose, the fifth aspect of the embodiment of the present application proposes a computer program product. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device executes the method for identifying auto insurance fraud as described above.

[0040] The present application proposes a method, device, electronic device, medium, and product for identifying auto insurance fraud. The method inputs information from each auto insurance claim case into a neural network model to obtain the selection probabilities of each claim action corresponding to each auto insurance claim case. Based on the selection probabilities of each claim action corresponding to each auto insurance claim case, the target claim action corresponding to each auto insurance claim case is determined. The reward value for the target claim action is then determined based on the target claim action and the reference claim action corresponding to the target claim action. The sum of the reward values ​​for the target claim actions corresponding to each auto insurance claim case is then determined as the cumulative reward of the neural network model. A second expected gradient is determined based on the cumulative reward and the first expected gradient. Finally, the parameters of the neural network model are updated based on the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining an auto insurance fraud identification model. The auto insurance fraud identification model is used to identify auto insurance fraud. In this way, by inputting the information of each auto insurance claim case into the neural network model, the cumulative reward of the neural network model is obtained, and through reinforcement learning, the parameters of the neural network model are updated according to the expected gradient of the parameters of the neural network model, so that the neural network model can continuously update the parameters, optimize the anti-fraud strategy, and improve the accuracy of identifying auto insurance fraud behavior. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is one of the flow charts of the method for identifying auto insurance fraud provided in an embodiment of the present application;

[0042] Figure 2 yes Figure 1 Flowchart of step S105 in FIG.

[0043] Figure 3 yes Figure 1 Flowchart of step S106 in FIG.

[0044] Figure 4 This is the second flow chart of the method for identifying auto insurance fraud provided in an embodiment of the present application;

[0045] Figure 5 This is a schematic diagram of the structure of a device for identifying auto insurance fraud provided in an embodiment of the present application;

[0046] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0050] First, let’s analyze some of the terms used in this application:

[0051] Artificial intelligence (AI) is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. A branch of computer science, AI seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thinking. It also encompasses the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0052] Information Extraction: A text processing technology that extracts specified types of entity, relationship, event, and other factual information from natural language text and forms structured data output. Information extraction is a technology that extracts specific information from text data. Text data is composed of some specific units, such as sentences, paragraphs, and chapters. Text information is composed of some small specific units, such as characters, words, phrases, sentences, paragraphs, or a combination of these specific units. Extracting noun phrases, names, place names, etc. from text data is all text information extraction. Of course, the information extracted by text information extraction technology can be of various types.

[0053] Based on this, the embodiments of the present application provide a method, device, electronic device, medium and product for identifying auto insurance fraud, aiming to improve the accuracy of identifying auto insurance fraud.

[0054] The methods, devices, electronic devices, media, and products for identifying auto insurance fraud provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the identification method in the embodiments of the present application is described.

[0055] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0056] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0057] The method for identifying auto insurance fraud provided in the embodiment of the present application relates to the field of artificial intelligence technology. The method for identifying auto insurance fraud provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for identifying auto insurance fraud, etc., but is not limited to the above forms.

[0058] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0059] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0060] Figure 1 This is one of the flow charts of the method for identifying auto insurance fraud provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0061] In step S101, information of each auto insurance claim case is input into a neural network model to obtain the selection probability of each claim action corresponding to each auto insurance claim case.

[0062] In an embodiment of the present application, information on a car insurance claim case may include, but is not limited to, the insurer's personal information, vehicle information, policy information, claim history, accident type, and accident severity.

[0063] In some embodiments, the neural network model may use a multi-layer perceptron (MLP). MLP is an important neural network model that is composed of multiple perceptron layers, and is therefore also called a deep neural network.

[0064] In some embodiments, information about each auto insurance claim is input into a neural network model. The neural network model can output selection probabilities for each claim action corresponding to each auto insurance claim. In one example, the claim actions corresponding to each auto insurance claim can include claim settlement and claim rejection.

[0065] In some embodiments, information about each auto insurance claim can be input into the neural network model based on its corresponding time step. A time step can be understood as corresponding to a specific moment in the input data sequence. That is, each time step corresponds to the input of information about a single auto insurance claim.

[0066] Step S102 : determining a target claim action corresponding to each auto insurance claim case based on the selection probability of each claim action corresponding to each auto insurance claim case.

[0067] In this embodiment of the present application, the target claim action can be understood as the pending claim action selected by the neural network model. After the neural network model outputs the selection probabilities of each claim action corresponding to each auto insurance claim case, the target claim action corresponding to each auto insurance claim case can be determined based on the output selection probabilities of each claim action. In one example, the claim action with the highest selection probability can be determined as the target claim action.

[0068] Step S103: determining a reward value for the target claim settlement action based on the target claim settlement action and a reference claim settlement action corresponding to the target claim settlement action.

[0069] In this embodiment of the present application, the reference claim action corresponding to the target claim action can be understood as the actual claim action in a motor vehicle insurance claim case. The reward value can be understood as the feedback signal obtained by the neural network model at each time step (or state), which is usually a numerical value. The reward value can reflect the quality of the action taken by the neural network model in the current state.

[0070] In some embodiments, a reward value may be determined by determining whether a target claim action is consistent with a reference claim action corresponding to the target claim action. If the target claim action and the reference claim action corresponding to the target claim action are consistent, the reward value for the target claim action may be determined to be a positive number. If the target claim action and the reference claim action corresponding to the target claim action are inconsistent, the reward value for the target claim action may be determined to be a negative number.

[0071] In one example, the actual claim action for a car insurance claim is "claim settled." The claim information is fed into a neural network model, which outputs a 70% probability of a "claim settled" action and a 30% probability of a "claim denied" action. The "claim settled" action is determined as the target claim action. The target claim action is then compared with the actual claim action. If both are "claim settled," a reward of +10 is assigned to the target claim action.

[0072] In step S104, the sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case is determined as the cumulative reward of the neural network model.

[0073] In an embodiment of the present application, when training a neural network model, the neural network model is trained based on multiple auto insurance claims cases. Each target claim action corresponding to each auto insurance claim case corresponds to a reward value. After obtaining the reward value of the target claim action corresponding to each auto insurance claim case, the reward value of the target claim action corresponding to each auto insurance claim case is summed, and the obtained sum is determined as the cumulative reward of the neural network model. The cumulative reward can be understood as the sum of all rewards obtained by the neural network model after executing and completing a series of actions.

[0074] Step S105: Determine the second expected gradient based on the accumulated reward and the first expected gradient. The first expected gradient refers to the expected gradient of each target claim action with respect to the parameters of the neural network model, and the second expected gradient refers to the expected gradient of the expected reward of the neural network model with respect to the parameters of the neural network model.

[0075] In the embodiment of the present application, the first expected gradient can be understood as the expected gradient of each target claim action with respect to the parameters of the neural network model. The first expected gradient can be expressed as That is, in a given state s t When action a t The gradient of the logarithm of the probability density function of the policy parameter θ. t It can be understood as information about various car insurance claims. Action a t It can be understood as the target claim action. The strategy parameter θ can be understood as the parameter of the neural network model.

[0076] In some embodiments, the second expected gradient can be understood as the expected gradient of the expected reward of the neural network model with respect to the parameters of the neural network model. The second expected gradient can be expressed as in, It can be understood as the second expected gradient. T can be understood as the time period, and t can be understood as each time step within the time period. It can be understood as the first expected gradient. G t It can be understood as cumulative rewards.

[0077] Step S106, updating the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining a vehicle insurance fraud behavior recognition model, which is used to identify vehicle insurance fraud behavior.

[0078] In the embodiment of the present application, the goal of reinforcement learning is to maximize the cumulative reward. By calculating the expected gradient, the optimal strategy can be effectively found to maximize the reward obtained.

[0079] After obtaining the second expected gradient, the parameters of the neural network model can be updated according to the second expected gradient. In one example, the parameters of the neural network model can be updated using a gradient ascent method. The gradient ascent method is an optimization algorithm for finding a maximum value.

[0080] In some embodiments, the parameters of the neural network model may be iteratively updated based on the second expected gradient until the parameters of the neural network model meet an iteration termination condition, thereby obtaining a vehicle insurance fraud identification model. In one example, the iteration termination condition may be that the parameters of the neural network model meet a preset threshold for accuracy.

[0081] By updating the parameters of the neural network model, the probability of selecting high-reward actions can be increased and the probability of selecting low-reward actions can be reduced, thereby obtaining a more accurate auto insurance fraud behavior identification model.

[0082] In steps S101 to S106 shown in the embodiment of the present application, the selection probability of each claim action corresponding to each auto insurance claim case is obtained by inputting the information of each auto insurance claim case into the neural network model respectively; based on the selection probability of each claim action corresponding to each auto insurance claim case, the target claim action corresponding to each auto insurance claim case is determined. Then, the reward value of the target claim action is determined based on the target claim action and the reference claim action corresponding to the target claim action, and the sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case is determined as the cumulative reward of the neural network model. The second expected gradient is determined based on the cumulative reward and the first expected gradient. The first expected gradient refers to the expected gradient of each target claim action, and the second expected gradient refers to the expected gradient of the parameters of the neural network model. Finally, the parameters of the neural network model are updated according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining a car insurance fraud behavior recognition model, which is used to identify car insurance fraud behavior. In this way, by inputting the information of each auto insurance claim case into the neural network model, the cumulative reward of the neural network model is obtained, and through reinforcement learning, the parameters of the neural network model are updated according to the expected gradient of the parameters of the neural network model, so that the neural network model can continuously update the parameters, optimize the anti-fraud strategy, and improve the accuracy of identifying auto insurance fraud behavior.

[0083] See also Figure 2 In some embodiments, step S105 may include but is not limited to steps S201 to S202:

[0084] Step S201: Multiply the cumulative reward by the expected gradient of each target claim action with respect to the parameters of the neural network model to obtain the expected gradient of the parameters of the neural network model corresponding to each auto insurance claim case;

[0085] Step S202: summing the expected gradients of the parameters of the neural network model corresponding to each auto insurance claim case to obtain a second expected gradient.

[0086] Specifically, the cumulative reward can be understood as the sum of all rewards obtained by the neural network model after completing a series of actions. In other words, it is the sum of the reward values ​​of the target claim actions of each claim case.

[0087] In some embodiments, when calculating the second expected gradient, the cumulative reward is first multiplied by the expected gradient of each target claim action with respect to the parameters of the neural network model, that is, The expected gradient of the parameters of the neural network model corresponding to each auto insurance claim case is obtained. Then, the expected gradient of the parameters of the neural network model corresponding to each auto insurance claim case is summed to obtain a second expected gradient.

[0088] In some embodiments, the second desired gradient is expressed as in, It can be understood as the second expected gradient, N can be understood as the number of samples, that is, the number of car insurance claims, and T can be understood as the time period. It can be understood as the first expected gradient. G t It can be understood as cumulative rewards.

[0089] In this embodiment, by determining the second expected gradient based on the expected gradient of each target claim settlement action with respect to the parameters of the neural network model and the cumulative reward of the neural network model, the accuracy of determining the second expected gradient can be improved.

[0090] In some embodiments, before determining the second expected gradient based on the accumulated reward and the first expected gradient, the identifying method includes:

[0091] Determine the log probability of the selection probability of the target claim action;

[0092] The gradient of the log probability is determined based on the chain rule to obtain the first expected gradient.

[0093] Specifically, before determining the second expected gradient based on the cumulative reward and the first expected gradient, the first expected gradient needs to be calculated first. The first expected gradient can be understood as the expected gradient of each target claim action with respect to the parameters of the neural network model.

[0094] In some embodiments, the first desired gradient can be expressed as The logarithmic probability of the target claim action selection probability. Then, the gradient of the logarithmic probability is determined based on the chain rule to obtain the first expected gradient. θ (α t |s t ) can be understood as the strategy π in a given state s t Next select action a t The probability of . When calculating, given the state s t It can be understood as information about car insurance claims. Action a t This is the target action corresponding to the car insurance claim case. θ (α t |s t ) Take the logarithm and get logπ θ (α t |s t ). After taking the logarithm, the convenience of calculation can be improved. Then logπ θ (α t |s t ) to find the gradient, we can get the first expected gradient

[0095] In this embodiment, by taking the logarithm of the selection probability of the target claim settlement action and then calculating the first expected gradient, the convenience of calculation can be improved.

[0096] See also Figure 3 In some embodiments, step S106 may include but is not limited to steps S301 to S302:

[0097] Step S301: determining the product of the second expected gradient and the learning rate of the neural network model;

[0098] Step S302: The sum of the product and the parameters of the neural network model is determined as the updated parameters of the neural network model.

[0099] Specifically, when updating the initial parameters of the neural network model based on the second expected gradient, a gradient ascent method can be used. The gradient ascent method aims to maximize the objective function by iteratively updating the parameters. The gradient ascent method focuses on finding the maximum value of the function. The gradient ascent method is often used to optimize a function, such as optimizing the likelihood function or other loss function in machine learning.

[0100] In some embodiments, the learning rate of the neural network model is first determined. The learning rate determines the pace of each update. In one example, if the learning rate is too small, convergence will be slow; in another example, if the learning rate is too large, it may cause parameter oscillation or non-convergence.

[0101] In some embodiments, after determining the learning rate of the neural network model, the product of the second expected gradient and the learning rate of the neural network model can be determined, and the sum of the product and the initial parameters can be determined as the updated parameters of the neural network model.

[0102] In some embodiments, updating the parameters of the neural network model using the gradient ascent method can be expressed as: Among them, θ can be understood as the parameters of the neural network model after updating. θ0 can be understood as the parameters of the neural network model before updating. α can be understood as the learning rate of the neural network model. It can be understood as the second expected gradient.

[0103] In this embodiment, by using the gradient ascent method to update the parameters of the neural network model, the accuracy of the parameter update can be improved.

[0104] In some embodiments, updating the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining a vehicle insurance fraud behavior recognition model, includes:

[0105] Determine the current cumulative return of the neural network model based on the reward value of the target claim action corresponding to each auto insurance claim case and the discount factor of the neural network model;

[0106] When the current cumulative return is greater than the cumulative return before the update, reduce the learning rate;

[0107] If the current cumulative reward is less than the cumulative reward before the update, increase the learning rate.

[0108] Specifically, the learning rate determines the size of each update. If the learning rate is too small, convergence will be slow; if it is too large, it may cause parameter oscillation or non-convergence. Therefore, it is necessary to adjust the learning rate of the neural network model in real time during the parameter update process.

[0109] In some embodiments, the current cumulative reward of the neural network model can be determined based on the reward value of the target claim action corresponding to each auto insurance claim case and the discount factor of the neural network model. The cumulative reward can be understood as the sum of the rewards of all future time steps from the current moment.

[0110] In some embodiments, the current cumulative reward can be expressed as Among them, Gt It can be expressed as cumulative rewards. γ can represent the discount factor of the neural network model. In one example, the discount factor γ ranges from [0, 1). t can be understood as a time step. k can be understood as the number of reward values ​​for the target claim action corresponding to the car insurance claim case.

[0111] In some embodiments, the learning rate can be adjusted by comparing the current cumulative reward with the reward before the update. If the current cumulative reward is greater than the cumulative reward before the update, the learning rate is reduced; if the current cumulative reward is less than the cumulative reward before the update, the learning rate is increased.

[0112] In some embodiments, the learning rate can also be automatically adjusted by setting the loss function to a negative policy gradient.

[0113] In this embodiment, by adjusting the learning rate in real time during the updating process of the parameters of the neural network model, the accuracy of the updating of the parameters of the neural network model can be improved.

[0114] In some embodiments, determining a reward value for a target claim action based on the target claim action and a reference claim action corresponding to the target claim action includes:

[0115] When the target claim action is the same as the reference claim action, the reward value is determined to be a positive number;

[0116] When the target claim action is different from the reference claim action, the reward value is determined to be a negative number.

[0117] Specifically, the reward value can be understood as the feedback signal received by the neural network model at each time step (or state), typically a numerical value. The reward value reflects the quality of the neural network model's actions in the current state. A time step can be understood as corresponding to a specific moment in the input data sequence. In other words, each time step corresponds to the input of information about a car insurance claim.

[0118] In some embodiments, if the target claim action is the same as the reference claim action, indicating that the claim action selected by the system is consistent with the actual reference claim handling process for the motor insurance claim case, a positive reward value may be determined, thereby increasing the probability of the selection. If the target claim action is different from the reference claim action, indicating that the claim action selected by the system is inconsistent with the actual reference claim handling process for the motor insurance claim case, a negative reward value may be determined, thereby decreasing the probability of the selection.

[0119] In this embodiment, by setting different reward values ​​according to different situations, the selection probability of each claim action can be adjusted in a timely manner, thereby making the parameter update of the neural network model more accurate.

[0120] See also Figure 4 , is the second flow chart of the method for identifying auto insurance fraud provided in the embodiment of the present application. Figure 4 As shown, the method for identifying auto insurance fraud includes the following steps:

[0121] Step S401, status definition: obtaining key information required in the auto insurance claim process.

[0122] Step S402, MLP network prediction: Based on the state input, the model outputs the probability distribution of the action.

[0123] Step S403: Action execution: perform corresponding operations according to the action (claim settlement / claim rejection) selected by the model.

[0124] Step S404, reward feedback: feedback the reward value based on the actual results as a basis for subsequent optimization.

[0125] Step S405, strategy update: adjust the MLP network parameters through the strategy update step of reinforcement learning to optimize future decisions.

[0126] In this specific embodiment, states and actions are first defined. States can be understood as containing all key information in the insurance claims risk control problem, such as the insurer's personal information, vehicle information, policy information, claim history, accident type, accident severity, etc. MLP can solve states that are both continuous (such as the insurer's renewal years, premium increases caused by past claims, vehicle age, etc.) or discrete (such as vehicle model, accident type, whether the number of claims exceeds 3, whether driving on holidays, etc.). Actions can be understood as defining all possible actions that the model can take, such as claim settlement or rejection, changes in claim amount, etc. (continuous or discrete).

[0127] In this specific embodiment, the policy network can use the REINFORCE algorithm from the policy gradient algorithm, with an MLP serving as the policy network. The input is the state, and the output is the probability distribution over each possible action. For example, for car insurance (claim paid or denied), the output is two neurons, representing the probability of each action. The expected reward after each action is also set. For example, the reward value given for car insurance (claim paid or denied) is: -10 for a claim paid, +10 for a claim denied.

[0128] In this specific embodiment, the expected reward (i.e., the second expected gradient in this application) can be maximized by calculating the gradient of each time step and multiplying it by the corresponding advantage function or cumulative reward, and then calculating the expectation over all time steps. This expected gradient can be used to update the parameters of the policy function to improve the policy. That is, the probability of selecting high-reward actions is increased and the probability of selecting low-reward actions is reduced, thereby improving the policy and maximizing the overall reward.

[0129] In this specific embodiment, in reinforcement learning, an "agent" refers to an intelligent entity that makes decisions and takes actions within an environment to achieve a specific goal. After the agent runs a data set and selects its final action and receives the corresponding reward, it compares the final action with the annotated answer and increases the probability of all actions that match the final action, while decreasing the probability of actions that do not match the final action. If the agent determines that a claim is rejected, and the annotated data also indicates a rejection, the probability of all agents selecting the action that would result in a rejection in that state is increased.

[0130] In this specific embodiment, the neural network model can be trained on the Aether platform, deployed on the risk control platform, and called during the auto insurance claims process (reporting, investigation, and damage assessment), and the caller is the auto insurance claims payer.

[0131] See also Figure 5 , is a schematic diagram of the structure of a device for identifying auto insurance fraud provided in an embodiment of the present application. The device for identifying auto insurance fraud can implement the above-mentioned method for identifying auto insurance fraud. The device includes:

[0132] Input module 501, for inputting information of each auto insurance claim case into the neural network model to obtain the selection probability of each claim action corresponding to each auto insurance claim case;

[0133] The first determination module 502 is configured to determine a target claim action corresponding to each motor vehicle insurance claim case based on the selection probability of each claim action corresponding to each motor vehicle insurance claim case;

[0134] The second determining module 503 is used to determine the reward value of the target claim action according to the target claim action and the reference claim action corresponding to the target claim action;

[0135] The third determining module 504 is configured to determine the sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case as the cumulative reward of the neural network model;

[0136] A fourth determining module 505 is configured to determine a second expected gradient based on the accumulated reward and the first expected gradient, wherein the first expected gradient refers to the expected gradient of each target claim settlement action, and the second expected gradient refers to the expected gradient of the parameters of the neural network model;

[0137] The updating module 506 is used to update the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining a car insurance fraud behavior identification model, which is used to identify car insurance fraud behavior.

[0138] The specific implementation of the vehicle insurance fraud identification device is basically the same as the specific embodiment of the above-mentioned vehicle insurance fraud identification method, and will not be repeated here.

[0139] The present application also provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method for identifying auto insurance fraud. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0140] See also Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0141] The processor 601 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0142] The memory 602 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called by the processor 601 to execute the method for identifying auto insurance fraud in the embodiments of this application.

[0143] Input / output interface 603, used to implement information input and output;

[0144] Communication interface 604, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0145] Bus 605 , which transmits information between various components of the device (e.g., processor 601 , memory 602 , input / output interface 603 , and communication interface 604 );

[0146] The processor 601 , the memory 602 , the input / output interface 603 and the communication interface 604 are connected to each other in communication within the device via a bus 605 .

[0147] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for identifying auto insurance fraud.

[0148] An embodiment of the present application also provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the above-mentioned method for identifying auto insurance fraud.

[0149] The present application proposes a method, device, electronic device, medium, and product for identifying auto insurance fraud. The method inputs information from each auto insurance claim case into a neural network model to obtain the selection probabilities of each claim action corresponding to each auto insurance claim case. Based on the selection probabilities of each claim action corresponding to each auto insurance claim case, the target claim action corresponding to each auto insurance claim case is determined. The reward value for the target claim action is then determined based on the target claim action and the reference claim action corresponding to the target claim action. The sum of the reward values ​​for the target claim actions corresponding to each auto insurance claim case is then determined as the cumulative reward of the neural network model. A second expected gradient is determined based on the cumulative reward and the first expected gradient. Finally, the parameters of the neural network model are updated based on the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining an auto insurance fraud identification model. The auto insurance fraud identification model is used to identify auto insurance fraud. In this way, by inputting the information of each auto insurance claim case into the neural network model, the cumulative reward of the neural network model is obtained, and through reinforcement learning, the parameters of the neural network model are updated according to the expected gradient of the parameters of the neural network model, so that the neural network model can continuously update the parameters, optimize the anti-fraud strategy, and improve the accuracy of identifying auto insurance fraud behavior.

[0150] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0151] The embodiments of the present application provide a method, device, electronic device, medium, and product for identifying auto insurance fraud. The method inputs the information of each auto insurance claim case into a neural network model to obtain the selection probability of each claim action corresponding to each auto insurance claim case; determines the target claim action corresponding to each auto insurance claim case based on the selection probability of each claim action corresponding to each auto insurance claim case. Then, the reward value of the target claim action is determined based on the target claim action and the reference claim action corresponding to the target claim action, and the sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case is determined as the cumulative reward of the neural network model. The second expected gradient is determined based on the cumulative reward and the first expected gradient. The first expected gradient refers to the expected gradient of each target claim action, and the second expected gradient refers to the expected gradient of the parameters of the neural network model. Finally, the parameters of the neural network model are updated according to the second expected gradient until the parameters of the neural network model meet the iteration termination condition, thereby obtaining an auto insurance fraud identification model. The auto insurance fraud identification model is used to identify auto insurance fraud. In this way, by inputting each auto insurance claim case into the neural network model, the cumulative reward of the neural network model is obtained, and through reinforcement learning, the parameters of the neural network model are updated according to the expected gradient of the parameters of the neural network model, so that the neural network model can continuously update the parameters, optimize the anti-fraud strategy, and improve the accuracy of identifying auto insurance fraud behavior.

[0152] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0153] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0154] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0155] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0156] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0157] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0158] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0159] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0162] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for identifying auto insurance fraud, characterized in that: The identification method comprises: Inputting information of each auto insurance claim case into the neural network model respectively to obtain the selection probability of each claim action corresponding to each auto insurance claim case; determining a target claim action corresponding to each of the auto insurance claim cases based on a selection probability of each claim action corresponding to each of the auto insurance claim cases; Determining a reward value for the target claim settlement action according to the target claim settlement action and a reference claim settlement action corresponding to the target claim settlement action; The sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case is determined as the cumulative reward of the neural network model; Determining the logarithmic probability of the selection probability of the target claim action; Determining the gradient of the log probability based on the chain rule to obtain a first expected gradient; Determining a second expected gradient based on the cumulative reward and the first expected gradient, wherein the first expected gradient refers to the expected gradient of each target claim settlement action with respect to the parameters of the neural network model, and the second expected gradient refers to the expected gradient of the expected reward of the neural network model with respect to the parameters of the neural network model; updating the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet an iteration termination condition, thereby obtaining a vehicle insurance fraud behavior identification model, wherein the vehicle insurance fraud behavior identification model is used to identify vehicle insurance fraud behavior; The determining of the second expected gradient according to the accumulated reward and the first expected gradient includes: Multiplying the cumulative reward by the expected gradient of each target claim action with respect to the parameters of the neural network model to obtain the expected gradient of the parameters of the neural network model corresponding to each auto insurance claim case; The expected gradients of the parameters of the neural network model corresponding to each of the auto insurance claim cases are summed to obtain the second expected gradient.

2. The identification method according to claim 1, characterized in that Updating the initial parameters of the neural network model according to the second expected gradient includes: Determining a product of the second expected gradient and a learning rate of the neural network model; The sum of the product and the parameters of the neural network model is determined as the updated parameters of the neural network model.

3. The identification method according to claim 2, characterized in that The updating of the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet an iteration termination condition to obtain a vehicle insurance fraud behavior identification model includes: Determining a current cumulative reward of the neural network model based on the reward value of the target claim action corresponding to each of the auto insurance claim cases and the discount factor of the neural network model; When the current cumulative reward is greater than the cumulative reward before the update, reducing the learning rate; When the current cumulative reward is less than the cumulative reward before the update, the learning rate is increased.

4. The identification method according to claim 1, wherein: The determining the reward value of the target claim settlement action according to the target claim settlement action and the reference claim settlement action corresponding to the target claim settlement action includes: When the target claim settlement action is the same as the reference claim settlement action, determining the reward value to be a positive number; When the target claim settlement action is different from the reference claim settlement action, the reward value is determined to be a negative number.

5. A device for identifying auto insurance fraud, characterized in that: The identification device comprises: An input module, configured to input information of each auto insurance claim case into the neural network model to obtain a selection probability of each claim action corresponding to each auto insurance claim case; A first determination module is configured to determine a target claim settlement action corresponding to each of the motor vehicle insurance claim settlement cases based on a selection probability of each of the claim settlement actions corresponding to each of the motor vehicle insurance claim settlement cases; A second determining module is configured to determine a reward value of the target claim settlement action based on the target claim settlement action and a reference claim settlement action corresponding to the target claim settlement action; A third determination module is used to determine the sum of the reward values ​​of the target claim actions corresponding to each auto insurance claim case as the cumulative reward of the neural network model; a fourth determination module, configured to determine a logarithmic probability of the selection probability of the target claim settlement action, determine a gradient of the logarithmic probability based on the chain rule to obtain a first expected gradient, and determine a second expected gradient based on the cumulative reward and the first expected gradient, wherein the first expected gradient refers to the expected gradient of each target claim settlement action with respect to the parameters of the neural network model, and the second expected gradient refers to the expected gradient of the expected reward of the neural network model with respect to the parameters of the neural network model; an updating module, configured to update the parameters of the neural network model according to the second expected gradient until the parameters of the neural network model meet an iteration termination condition, thereby obtaining a vehicle insurance fraud behavior identification model, wherein the vehicle insurance fraud behavior identification model is used to identify vehicle insurance fraud behavior; The fourth determining module is further configured to: Multiplying the cumulative reward by the expected gradient of each target claim action with respect to the parameters of the neural network model to obtain the expected gradient of the parameters of the neural network model corresponding to each auto insurance claim case; The expected gradients of the parameters of the neural network model corresponding to each of the auto insurance claim cases are summed to obtain the second expected gradient.

6. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method for identifying auto insurance fraud as described in any one of claims 1 to 4 when executing the computer program.

7. 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 automobile insurance fraud according to any one of claims 1 to 4 is implemented.

8. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for identifying auto insurance fraud as described in any one of claims 1 to 4.

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