TW202345078A - Server for predicting claim amount - Google Patents

Abstract

A server for predicting claim amount is provided. The server includes a storage medium, a transceiver and a processor. The storage medium stores an artificial intelligence model. The transceiver is communicatively connected to a user electronic device. The processor is coupled to the storage medium and the transceiver, wherein the processor receives an insurance policy and a medical certificate from the user electronic device through the transceiver; the processor inputs the insurance policy and the medical certificate into the artificial intelligence model to predict a claim amount; the processor transmits the claim amount to the user electronic device through the transceiver.

Description

Server for predicting claim amounts

This disclosure is about a server used to predict claim amounts.

Currently, for a specific policy, because the policy terms in the policy are often too complex/trivial, users are usually unable to know the claim amount that can be obtained from this specific policy and a specific diagnosis (for example, including a diagnosis certificate/surgery level, doctor’s diagnosis) . For example, users cannot clearly know the claim amount that can be obtained by performing Level 1 medical surgery and/or the claim amount that can be obtained by performing Level 2 medical surgery based on the policy terms in the policy. Therefore, users will have difficulty choosing from numerous insurance policies and/or numerous medical certificates. Based on this, a server for predicting the claim amount is needed.

The server disclosed in this disclosure for predicting the claim amount includes a storage medium, a transceiver and a processor. The storage medium stores the artificial intelligence model. The transceiver is communicatively connected to the user electronic device. The processor is coupled to the storage medium and the transceiver, wherein the processor receives the insurance policy and the diagnosis certificate from the user electronic device through the transceiver; the processor inputs the insurance policy and the diagnosis certificate into the artificial intelligence model to predict the claim amount; the processor transmits the claim settlement through the transceiver The amount is transferred to the user's electronic device.

In order to make the above features and advantages of the present disclosure more obvious and understandable, embodiments are given below and described in detail with reference to the attached drawings.

FIG. 1 is a schematic diagram of a server 100 for predicting claim amounts according to an embodiment of the present disclosure. The server 100 may include a storage medium 130, a transceiver 110, and a processor 120.

The storage medium 130 is, for example, any type of fixed or removable random access memory (random access memory, RAM), read-only memory (read-only memory, ROM), or flash memory (flash memory). , hard disk drive (HDD), solid state drive (SSD) or similar components or a combination of the above components, and are used to store multiple modules or various application programs that can be executed by the processor 120 . The storage medium 130 can store artificial intelligence models. The artificial intelligence model in this embodiment may include, but is not limited to, Time Delay Neural Network (TDNN), Maximum Entropy, Support Vector Machine, and Gradient Boosting Decision Tree), Convolutional Neural Network (CNN) or Long Short Term Memory Networks (LSTM). The purpose of the artificial intelligence model will be explained later.

The transceiver 110 transmits and receives signals in a wireless or wired manner. The transceiver 110 can be communicatively connected to the user electronic device 200 .

The processor 120 is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose micro control unit (MCU), microprocessor, digital signal processing Digital signal processor (DSP), programmable controller, application specific integrated circuit (ASIC), graphics processing unit (GPU), image signal processor (ISP) ), image processing unit (IPU), arithmetic logic unit (ALU), complex programmable logic device (CPLD), field programmable gate array (field programmable gate array) , FPGA) or other similar components or a combination of the above components. The processor 120 may be coupled to the storage medium 130 and the transceiver 110 .

In order to improve the accuracy of the subsequent artificial intelligence model in predicting the claim amount, the processor 120 can pre-train the artificial intelligence model using a training data set including multiple training policies, multiple training medical certificates, and multiple training claim amounts. Specifically, the "multiple training policies" here can be, "Training policy 1 corresponding to user identification 1, including policy clause 1" and "Training policy 2 corresponding to user identification 2, including policy clause 2" " and other multiple training policies, and the "multiple training medical certificates" here can be, "Training medical certificate 1 corresponding to user identification 1, including diagnosis certificate/surgery level 1" and "Corresponding to user identification 2 , including multiple training medical certificates such as diagnosis certificate/surgery level 2 training medical certificate 2, and the "multiple training claim amounts" here can be, "After user identification 1 obtains training medical certificate 1, through training policy 1 Training claim amount obtained 1" and "Training claim amount 2 obtained through training policy 2 after user identification 2 obtains training diagnosis certificate 2". In other words, the aforementioned multiple training insurance policies, multiple training medical certificates, and multiple training claim amounts may be obtained by the server 100 from different users (user identifications) in advance.

In addition, in order to retrieve the keywords (meanings) of the aforementioned multiple training policies and multiple training medical certificates, the processor 120 may perform natural language processing (NLP) on the multiple training policies and multiple training medical certificates, to train artificial intelligence models.

FIG. 2 is a flowchart of a method for predicting a claim amount according to an embodiment of the present disclosure. The method for predicting a claim amount can be implemented by the server 100 shown in FIG. 1 .

Please refer to Figure 1 and Figure 2. In step S201, when the user wishes to know the claim amount A that can be obtained from a specific policy A and a specific medical certificate A from the server 100, the user can operate the user electronic device 200 to transmit the policy A and the medical certificate A to the server 100. . In other words, the processor 120 may receive the policy A and the medical certificate A from the user electronic device 200 through the transceiver 110 .

Next, in step S202, the processor 120 may input the policy A and the medical certificate A into the artificial intelligence model to predict (out) the claim amount A. Furthermore, in order to retrieve the keywords (meanings) of policy A and medical certificate A, the processor 120 may perform natural language processing on policy A and medical certificate A.

After obtaining the predicted claim amount A, in step S203, the processor 120 may transmit the claim amount A to the user electronic device 200 through the transceiver 110.

After the user uses the above steps S201 to S203 to obtain the claim amount A, the user can also use the policy B and the medical certificate B to perform the above steps S201 to S203 again to obtain the claim amount B. In other words, users can use this to compare the claim amounts obtained from different policies and/or medical certificates to compare each policy (insurance product) and/or compare each medical certificate (for example, to determine the level of surgery the user wants to perform) the goal of.

To sum up, the disclosed server for predicting the claim amount can predict the claim amount using the user's insurance policy and medical certificate. Based on this, the user can choose from many insurance policies and/or many medical certificates based on the claim amount predicted by the server for predicting the claim amount. For users, in addition to comparing various insurance policies (insurance products), they can also compare various medical certificates (for example, to determine the level of surgery the user wants to perform), thereby improving the accuracy of the user's selection of insurance policies and/or medical certificates. and convenience.

Although the disclosure has been disclosed above through embodiments, they are not intended to limit the disclosure. Anyone with ordinary knowledge in the technical field may make slight changes and modifications without departing from the spirit and scope of the disclosure. Therefore, The scope of protection of this disclosure shall be determined by the scope of the appended patent application.

100: Server used to predict claim amounts 110:Transceiver 120: Processor 130:Storage media 200: User electronic device S201, S202, S203: steps

FIG. 1 is a schematic diagram of a server for predicting claim amounts according to an embodiment of the present disclosure. FIG. 2 is a flowchart of a method for predicting a claim amount according to an embodiment of the present disclosure.

100: Server used to predict claim amounts

110:Transceiver

120: Processor

130:Storage media

200: User electronic device

Claims (5)

A server for predicting claim amounts, including: Storage media to store artificial intelligence models; a transceiver for communication connection to the user's electronic device; and a processor coupled to the storage medium and the transceiver, wherein The processor receives an insurance policy and a medical certificate from the user electronic device through the transceiver; The processor inputs the policy and the medical certificate into the artificial intelligence model to predict the claim amount; The processor transmits the claim amount to the user electronic device through the transceiver. A server for predicting claim amounts as described in request 1, wherein The processor performs natural language processing on the insurance policy and the medical certificate. The server for predicting claim amounts as described in claim 1, wherein the processor trains the artificial intelligence model using a training data set including a plurality of training policies, a plurality of training medical certificates, and a plurality of training claim amounts. The server for predicting claim amount as described in claim 3, wherein the first training policy among the plurality of training policies corresponds to user identification, and the first training diagnosis certificate in the plurality of training diagnosis books corresponds to Based on the user identification, and a first training claim amount among the plurality of training claim amounts corresponds to the user identification. A server for predicting claim amounts as described in request 3, wherein The processor performs natural language processing on the plurality of training policies and the plurality of training medical certificates.

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