JP6821885B1 - Artificial intelligence-based insurance claim detection device and method - Google Patents

Abstract

The detection device and method for unfair insurance claims will be disclosed. The insurance claim unfair claim detection device according to the embodiment of the present application is an unfair instruction / learning platform that inputs the characteristic variable and the normal claim data, and the data preprocessing unit that standardizes the insurance claim breakdown data and derives the characteristic variable. It can include an unfair claim detection unit that constructs a claim detection model and an unfair claim determination unit that determines whether or not the breakdown of new insurance claims is unreasonable based on the unfair claim detection model.

Description

The present application relates to an artificial intelligence-based insurance claim detection device and method.

The existing insurance fraud prevention system is a method of deriving rules based on the experience and knowledge of the examiner for insurance cases claimed based on business rules, and divides them into investigation target rules and investigation target exclusion rules. Is calculated. However, as insurance fraud becomes more intelligent and sophisticated, it requires continuous updates to new fraudulent claims patterns or fraud patterns.

Insurers also cannot neglect insurance fraud without taking any action against it, and can spend an infinite amount of investigative costs with the goal of completely revealing insurance fraud. Can not. Therefore, insurance companies need to reduce the amount of insurance money stolen by insurance fraud, while determining the level of research efforts to an appropriate level without spending excessive research costs.

The technique behind the present application is disclosed in Patent Document 1.

Korean Registered Patent Gazette No. 10-0862181

The present application is for solving the above-mentioned problems of the prior art, and provides an artificial intelligence-based insurance claim unfair claim detection device and method that provides a detection model capable of determining an unfair claim claim. The purpose is to do.

The present application is for solving the above-mentioned problems of the prior art, and provides an artificial intelligence-based insurance claim unfair claim detection device and method for learning insurance claim-related data and determining improperness for a new claim. The purpose is to provide.

However, the technical problem to be achieved by the embodiment of the present application is not limited to the above-mentioned technical problem, and other technical problems may exist.

However, the technical problem to be achieved by the embodiment of the present application is not limited to the above-mentioned technical problem, and other technical problems may exist.

As a technical means for achieving the above technical problems, the insurance claim unfair claim detection device according to the embodiment of the present application includes a data preprocessing unit for stylizing insurance claim breakdown data and deriving characteristic variables, and the above. An unfair claim detection unit that builds an unfair claim detection model based on teaching and learning that inputs characteristic variables and normal claim data, and an unfair judgment of whether or not the new insurance claim breakdown is unreasonable based on the unfair claim detection model. It can include a billing determination unit.

According to one embodiment of the present application, the insurance claim breakdown data further includes a database that records at least one of the insurance claim breakdown data, the characteristic variable, and the normal claim data, and the insurance claim breakdown data includes claim data, contract data, and payment. It can include at least one of data, insurance designer data, and customer data.

According to one embodiment of the present application, the unreasonable claim detection unit can construct the unreasonable claim detection model based on the classification / prediction algorithm of the teaching learning platform.

According to one embodiment of the present application, the new pattern analysis unit further includes a new pattern analysis unit, which classifies new patterns of unfair claims based on a non-teaching learning-based crowd algorithm that inputs the characteristic variable. The pattern classification unit, the discrimination model construction unit that constructs a new pattern discrimination model based on the decision algorithm of the teaching and learning platform, and the new pattern discrimination unit that discriminates the pattern type of the new request based on the new pattern discrimination model. Can include.

According to one embodiment of the present application, the unreasonable claim pattern classification unit crowds the characteristic variable into a plurality of billing patterns through the crowd algorithm based on the frequency of the characteristic variable, and determines the degree of separation between the communities of the claim pattern. Based on this, the new pattern can be detected.

According to one embodiment of the present application, the unreasonable claim pattern classification unit can classify the new pattern into a plurality of similar pattern groups based on the degree of similarity between the new patterns.

According to one embodiment of the present application, the unreasonable claim pattern classification unit is based on the number of characteristic variables having a threshold value or more set in advance for each of the characteristic variables among the characteristic variables linked with each of the new patterns. The risk level of the pattern group can be set.

According to one embodiment of the present application, the discriminative model construction unit learns a new pattern discrimination rule based on a decision-making algorithm that inputs the new pattern included in the pattern group according to the risk level, and learns the new pattern discrimination rule. The new pattern discrimination model including the discrimination rule can be constructed.

According to one embodiment of the present application, the new pattern discriminating unit determines which of the normal billing pattern, the unreasonable billing pattern, and the new pattern the new billing is based on the new pattern discriminating rule. be able to.

The method of detecting an unfair claim for insurance claims according to an embodiment of the present application is a stage of deriving a characteristic variable by stylizing the insurance claim breakdown data, and an unfair claim detection model of a teaching and learning platform that inputs the characteristic variable and normal claim data. It can include a stage of construction and a stage of determining whether or not the breakdown of new insurance claims is unreasonable based on the unfair claim detection model.

According to one embodiment of the present application, the insurance claim breakdown data further includes a step of recording at least one of the insurance claim breakdown data, the characteristic variable, and the normal claim data, and the insurance claim breakdown data includes claim data, contract data, and payment. It can include at least one of data, insurance designer data, and customer data.

According to one embodiment of the present application, at the stage of constructing the unreasonable claim detection model, the unreasonable claim detection model can be constructed based on the classification / prediction algorithm of the teaching learning platform.

According to one embodiment of the present application, the step of analyzing the new pattern further includes the step of analyzing the new pattern, and the step of analyzing the new pattern is based on the non-teaching learning-based crowd algorithm that inputs the characteristic variable. It can include a step of classifying, a step of constructing a new pattern discriminative model based on the decision-making algorithm of the teaching and learning platform, and a step of discriminating the pattern type of the new claim based on the new pattern discriminative model.

According to one embodiment of the present application, the step of classifying the new pattern of unfair claim is to crowd the characteristic variable into a plurality of billing patterns through the crowd algorithm based on the frequency of the characteristic variable, and crowd the claim pattern. The new pattern can be detected based on the degree of separation.

According to one embodiment of the present application, the step of classifying the new pattern of the unfair claim can classify the new pattern into a plurality of similar pattern groups based on the similarity between the new patterns.

According to one embodiment of the present application, the step of classifying the new pattern of the unfair claim is a characteristic variable having a threshold value or more set in advance for each of the characteristic variables among the characteristic variables linked with each of the new patterns. The risk level of the pattern group can be set based on the number.

According to one embodiment of the present application, at the stage of constructing the new pattern discrimination model, a new pattern discrimination rule is learned based on a decision-making algorithm that inputs the new pattern included in the pattern group according to the risk level. , The new pattern discrimination model including the new pattern discrimination rule can be constructed.

According to one embodiment of the present application, the step of discriminating the pattern type is to determine which of the normal billing pattern, the unreasonable billing pattern, and the new pattern the new billing is based on the new pattern discriminating rule. It can be determined.

The above-mentioned problem-solving means are merely exemplary and should not be construed with the intent of limiting the present application. In addition to the exemplary examples described above, there may be additional examples in the drawings and detailed description of the invention.

According to the above-mentioned problem-solving means of the present application, it is possible to provide an artificial intelligence-based detection device and method for unfair insurance claims, which provides a detection model capable of determining unfair claims for insurance claims.

The present application is for solving the above-mentioned problems of the prior art, and provides an artificial intelligence-based insurance claim unfair claim detection device and method for learning insurance claim-related data and determining improperness for a new claim. can do.

It is a drawing which illustrated the structure of the detection device of the insurance claim unfair claim by one Example of this application. It is a drawing which illustrated the structure of the new pattern analysis part of the detection device of the insurance claim unfair claim according to one Example of this application. It is a drawing which illustrated the example of the new pattern detection of the detection device of the insurance claim unfair claim by one Example of this application. It is a drawing which illustrated the example of the similar pattern group classification of the detection device of the insurance claim unfair claim by one Example of this application. It is a drawing which illustrated the evaluation index of the unfair claim detection model of the unfair claim detection device of the insurance claim unfair claim according to one Example of this application. It is a drawing which illustrated the performance comparison between algorithms of the detection device of an insurance claim unfair claim according to one Example of this application. It is a drawing which compared the stability and flexibility between algorithms of the detection device of unfair insurance claim according to one Example of this application. It is a drawing which illustrated the unfair claim detection model constructed by the Deep Learning algorithm of the insurance claim unfair claim detection device by one Example of this application. It is a drawing which illustrated the flow of the detection method of the insurance claim unfair claim by one Example of this application. It is a drawing which illustrated the flow of the new unfair claim pattern analysis of the method of detecting an unfair claim of insurance claim by one Example of this application.

In the following, examples of the present application will be described in detail so that a person having ordinary knowledge in the technical field to which the present application belongs can be easily carried out with reference to the attached drawings. However, the present application can be embodied in a variety of different forms and is not limited to the examples described herein. Then, in order to clearly explain the present application in the drawings, parts unrelated to the description are omitted, and similar parts are given similar drawing reference numerals throughout the specification.

In the entire specification of the present application, if one part is "connected" to another part, this is not only when it is "directly connected", but also "electrically" with another element in between. Including the case where it is "connected to".

Assuming that, in the entire specification of the present application, one member is located "above", "upper", "upper", "lower", "lower", "lower", this is This includes not only the case where one member is in contact with another member but also the case where another member exists between the two members.

In the entire specification of the present application, when a component is "included" in a certain part, it may include other components, rather than excluding other components, unless otherwise specified. It means that you can do it.

FIG. 1 is a drawing illustrating the configuration of an insurance claim unfair claim detection device according to an embodiment of the present application.

Referring to FIG. 1, the insurance claim unfair claim detection device 100 can include a data preprocessing unit 110, an unfair claim detection unit 120, an unfair claim determination unit 130, a new pattern analysis unit 140, and a database 150. The data preprocessing unit 110 can standardize the insurance claim breakdown data and derive characteristic variables.

It is obvious that the explanations described below include not only unfair claims for insurance claims but also unfair claims for subsidies, subsidies, security deposits, etc., but for convenience of explanation, the explanation will focus on unfair claims for insurance claims. .. The insurance claim breakdown data is, for example, at least one of insurance claim data, insurance or subsidy-related contract data, insurance payment data, insurance designer data, customer data, and insurance fraud detection result data. Can include one. The data pre-processing unit 110 can standardize the insurance claim breakdown data and derive characteristic variables. For example, in the case of customer data, stylizing insurance claim breakdown data means quantifying the income level of the customer, the number of hospitals visited by the customer, the number of reasons for illness applied by the customer, and the like. Further, the characteristic variable derived by the data preprocessing unit 110 is a variable that can have a quantified value in relation to the insurance claim, for example, a customer ID number and whether or not the insurance fraudster. , Number of duplicate applications with the same disease name, Number of contracts concluded, Maximum number of contracts per day, Number of securities approved for payment, Number of securities applied for payment, Number of securities concluded by customers, Number of claims for security insurance, Credit grade Number of changes, types of contracts subscribed, total number of visits to similar hospitals, number of reasons for illness applied by the customer, number of doctors the customer met, number of hospitals visited by the customer, total number of valid admissions / visits , The number of medical treatment subjects, the customer income level, the number of changes in FP (Financial Planner), the number of actual loss processing, and the number of fraudulent FP contracts can be included at least one of them. The database 150 can record at least one of the insurance claim breakdown data, the characteristic variable, and the normal claim data.

The unjustified claim detection unit 120 can construct an unjustified claim detection model of a teaching and learning platform that inputs the characteristic variable and normal claim data. Teaching learning means learning a model by utilizing pre-constructed learning data (training data). The unjustified claim detection unit 120 can construct the unreasonable claim detection model based on the classification / prediction algorithm of the teaching learning platform. By way of example, the classification / prediction algorithm can include at least one of a Random Forest algorithm, a SVM (support vector machine) algorithm, an Extra Tree algorithm, an XG Boost algorithm, and a Deep Learning algorithm. The Random Forest algorithm is an algorithm in which a large number of Decision Trees compose a Forest and averages each prediction result as one result variable, and the SVM algorithm divides the data at the largest boundary in the data distribution space. It is a non-probabilistic algorithm that determines the classification to which. The Extra Tree algorithm is similar to the Random forest, but the speed is faster than the Random forest. This is a boost algorithm applied to the tree. The Deep Learning algorithm is an algorithm that learns while adjusting the influence of the variable pattern on the result with a weighted value based on the multi-layered Neural Network.

The unjustified claim detection model can output the unjustified claim detection result by inputting the characteristic variable and the normal claim data. As an example, the unjustified claim detection model can output the unjustified claim detection result based on the similarity between the characteristic variable and the normal claim data. If the similarity is less than the preset threshold, the insurance claim linked with the characteristic variable is judged to be normal, and if the similarity is equal to or higher than the preset threshold, the insurance claim linked with the characteristic variable is judged to be normal. Can be judged as an unreasonable claim. The similarity can be calculated by at least one of a K-means clustering algorithm, a SOM (Self-Organizing-Maps) algorithm, and an EM & Canopy algorithm. The K-means clustering algorithm is a traditional classification technique in which a target population is repeatedly subdivided into K crowds based on the mean value (similarity) of distances, and the SOM algorithm is an artificial neural network. As a basis, it is a technique to learn the input pattern of the training set with a weighted value and crowd it. In addition, the EM & Canopy algorithm means a technique of updating and crowding parameter values through an iterative process from the one with the maximum possibility with a given initial value. The unjustified claim detection unit 120 can improve the accuracy of the unjustified claim detection model by repeatedly deriving the output by the input.

The unfair claim determination unit 130 can determine whether or not the new insurance claim breakdown is unreasonable based on the unfair claim detection model. If a new insurance claim breakdown is input to the unfair claim detection model constructed by the unfair claim detection unit 120, it is determined whether or not the new insurance claim breakdown is unreasonable based on the similarity as described above. Can be done.

FIG. 2 is a drawing illustrating the configuration of a new pattern analysis unit of the insurance claim unfair claim detection device according to the embodiment of the present application.

The insurance claim unfair claim detection device according to one embodiment of the present application can analyze a new pattern of unfair claims, that is, a new pattern, and determine a pattern type of a new insurance claim (new claim). With reference to FIG. 2, the new pattern analysis unit 140 can include an unreasonable claim pattern classification unit 141, a discrimination model construction unit 142, and a new pattern discrimination unit 143. The unfair claim pattern classification unit 141 can classify a new pattern of unfair claim based on a non-teaching learning-based crowd algorithm that inputs a characteristic variable. Non-teaching learning does not construct learning data, but means an algorithm that learns while analyzing or crowding the data itself. Since this is a known matter, a specific description thereof will be omitted. The unfair claim pattern classification unit 141 can crowd the characteristic variables into a plurality of claim patterns based on the frequency of the insurance claim breakdown data. As an example, the unfair billing pattern classification unit 141 may classify a new pattern of unfair billing based on at least one of a K-means clustering algorithm, a SOM (Self-Organizing-Maps) algorithm, and an EM & Coopy algorithm. it can.

FIG. 3 is a drawing illustrating an example of detecting a new pattern of an insurance claim unfair claim detection device according to an embodiment of the present application.

FIG. 3 shows billing patterns crowded according to the frequency of characteristic variables, and when the frequencies of characteristic variables belonging to different billing patterns are similar, they are classified by the same or similar color (blue) as shown in FIG. Can be done. That is, when the colors of the billing patterns are the same or similar, it can be said that the degree of separation between the communities is low. In addition, a claim pattern crowded with similar frequencies among characteristic variables is a claim pattern crowded based on the frequency of characteristic variables generated by a normal insurance claim, that is, a normal claim pattern rather than an unreasonable claim. You can judge.

On the other hand, in the case of a billing pattern crowded with a characteristic variable having a similar frequency and a characteristic variable having a different frequency, as shown in FIG. 3, it can be classified by a color (red) different from the above-mentioned billing pattern. .. Since such a billing pattern is different from the frequency of the characteristic variable of the normal billing pattern (for example, when the number of FP changes is relatively large compared to the characteristic variable of the normal billing pattern), the new pattern of unfair billing It can be and can be detected by the unjustified claim pattern classification unit 141.

FIG. 4 is a drawing illustrating an example of similar pattern group classification of the insurance claim unfair claim detection device according to one embodiment of the present application.

The unjustified claim pattern classification unit 141 can classify the new pattern into a plurality of similar pattern groups based on the degree of similarity between the new patterns. Since the similarity between new patterns can be calculated based on the crowd algorithm described above, duplicate description will be omitted. FIG. 4 illustrates an example of classification into seven similar pattern groups based on the degree of similarity between new patterns. It is self-evident that the number of similar pattern groups can vary depending on the degree of similarity between new patterns. In addition, the unfair claim pattern classification unit 141 determines the risk level of the pattern group based on the number of characteristic variables linked to each of the new patterns and which is equal to or higher than the threshold value set in advance for each of the characteristic variables. Can be set. As an example, as shown in FIG. 4, the risk level can be classified into a caution group, a low risk group, a medium risk group, and a high risk group, but the risk level is not limited thereto. That is, in the case of a pattern group classified as a high-risk group, it can be said that there is a high possibility that the pattern group includes a new pattern having many characteristic variables above the threshold value.

The discriminative model construction unit 142 can construct a new pattern discriminative model based on the decision-making algorithm of the teaching and learning platform. Teaching learning means learning a model by utilizing pre-constructed learning data (training data). Further, the decision-making algorithm can be, for example, a Decision Tree algorithm, but is not limited thereto.

The discriminative model construction unit 142 can learn a new pattern discriminative rule based on a decision-making algorithm that inputs the billing pattern classified into a pattern group in which a risk level is set. In addition, the discriminative model building unit 130 can build a new unjustified claim pattern discriminative model including a new pattern discriminative rule. In the above-mentioned new pattern classification based on non-teaching learning, new patterns can be classified simply based on frequency, but it is unknown which variable was used for classification. Therefore, a new pattern discrimination rule can be learned based on the decision-making algorithm of the teaching learning platform.

The new pattern discrimination rule means a rule that can discriminate a billing pattern as a new pattern based on a characteristic variable equal to or higher than a threshold value. Further, such a new pattern discrimination rule can be integrated and constructed as a new pattern discrimination model by the discrimination model construction unit 142. It is self-evident that the new pattern of the pattern group set as the high-risk group has a relatively high probability of having a characteristic variable equal to or higher than a preset threshold value as compared with other risk groups. However, even if it is a pattern group of a low-risk group, there is a possibility that it has a characteristic variable that can be unfairly claimed (that is, a characteristic variable that is equal to or higher than a preset threshold value). A new pattern discrimination rule can be learned by inputting a new pattern included in a group. In addition, the importance of characteristic variables can be taken into consideration when learning new pattern discrimination rules. That is, among various characteristic variables, characteristic variables with a high risk of unfair claim can have a relatively high importance, and the importance of such characteristic variables is taken into consideration when learning a new pattern discrimination rule. Can be done. For example, among the characteristic variables, the total number of visits to a similar hospital can be given a higher importance than the number of doctors the customer has met. The discriminative model construction unit 142 can construct a more accurate new pattern discriminative model by considering the importance of the characteristic variable.

The new pattern discrimination unit 143 can discriminate the pattern type of the new request based on the new pattern discriminative model. The new pattern discriminating unit 143 can discriminate which of the normal billing pattern, the unreasonable billing pattern, and the new pattern the new billing is based on the new pattern discriminating rule. In other words, by inputting the characteristic variable of the new billing into the constructed new pattern discriminative model, the billing pattern is judged to be normal, the pattern is judged by the unfair billing, or the new pattern of the new unfair billing is used. It is possible to determine the type of pattern. In addition, when the new claim is determined as a pattern determined as an unreasonable claim and a new pattern of an unreasonable claim, a characteristic variable acting as a factor (that is, a characteristic variable) for determining an unreasonable claim can be detected. As an example, when the distribution of health insurance is large in new claims, the age of the examinee is in the mid-20s, and the characteristic variable is that there are many women, the initial examination and the re-examination show a high distribution. , Can be judged as a pattern of unfair claims to be noted. In addition, when the rate of return visits is relatively higher than that of the first visit, it can be judged as a pattern of unreasonable claims before treatment, treatment, and surgery.

FIG. 5 is a drawing illustrating an evaluation index of an unfair claim detection model of an insurance claim unfair claim detection device according to an embodiment of the present application.

The performance of the unjustified claim detection model depends on the classification / prediction algorithm that is the basis for model construction. With reference to FIG. 5, the evaluation index of the unjustified claim detection model uses Accuracy (accuracy), Precison (precision), and Recall (recall rate), but in the actual unjustified claim detection, the emphasis is on improving Y Recall. Put. Accuracy (accuracy) can be expressed as (TP (True Positive) + TN (True Negative)) / Total, which is the ratio of positive detection in which the result detected by the unjustified claim detection model matches the actual result, and is close to 100. It can be interpreted as a good indicator. Precision (precision) can be expressed as (TP) / (TP + FP (False Positive)) in the correct detection ratio, which is also an unreasonable actual result among the results of detecting that the unjustified claim detection model is unreasonable. , 1-Precison can represent the false positive rate.

The Recall can be expressed as (TP) / (TP + FN (False Negative)) by the correct detection ratio that accurately detects that the unreasonable claim detection model is unjustified in the total unjustified claim. -Recall can represent the unexplored rate.

FIG. 6 is a drawing illustrating a performance comparison between algorithms of an insurance claim unfair claim detection device according to an embodiment of the present application.

FIG. 6 is a drawing comparing the results output by inputting characteristic variables linked with the pattern group of the high-risk group into the unfair billing detection model constructed by the Random Forest algorithm, the SVM algorithm, the Extra Tree algorithm, and the XFBest algorithm, respectively. Is. The high accuracy of the fraudulent billing detection model constructed by each algorithm is also important, but if Y Recall, which can detect the actual fraudulent billing, is considered more importantly, it is constructed by the Random Forest algorithm and the Extra Tree algorithm. It can be confirmed that the unjustified claim detection model exhibits relatively superior performance compared to other algorithms.

FIG. 7 is a drawing comparing the inter-algorithm stability and flexibility of the insurance claim unfair claim detection device according to the embodiment of the present application.

FIG. 7a is a diagram diagram of the unreasonable claim detection model constructed by the Random Forest algorithm, and FIG. 7b is a diagram diagram of the unjustified claim detection model constructed by the Deep Learning algorithm. The unfair billing detection model constructed by the Random Forest algorithm is classified based on the conditions, for example, "a * billing amount + b * number of doctors + c * number of consultations", or billing amount> a, doctor. If the number> b and the number of consultations> c, it is classified as "abnormal" (that is, unreasonable claim). The unfair billing detection model constructed by the Deep Learning algorithm is a method of finding a combination of (billing amount, number of doctors, number of consultations) corresponding to an abnormal (unfair billing) state, and is a Convolutional Neural Network (CNN) of the Deep Learning algorithm. Is suitable for image processing, and Recurrent Neural Network (RNN) has characteristics suitable for time series processing. The unfair claim detection model built with the Random Forest algorithm is suitable for applying variables with linear relationships to known rules, but has the disadvantage of being less accurate with more related variables, while Deep. The unfair claim detection model constructed by the learning algorithm can learn the combination of variables as a pattern, discover a new pattern by itself, calculate the possibility of anomaly for any pattern, and is accurate for many variables. It is characterized by being able to make high-level predictions. Therefore, it can be said that the unreasonable claim detection model constructed by the Deep Learning algorithm is more stable and flexible than the unjustified claim detection model constructed by the Random Forest algorithm.

On the other hand, the performance of the Deep Learning algorithm may deteriorate when the number of learnings, that is, the number of iterations for calculating the output by the input is small. As an example, the number of characteristic variables associated with high-risk group pattern groups may be relatively smaller than the number of characteristic variables associated with other risk group pattern groups. In such a case, the unjustified claim detection model constructed by the Deep Learning algorithm may have a reduced ability to discriminate unreasonableness. Therefore, when the number of characteristic variables is less than a preset number, the unreasonable claim detection unit 120 improves the Y Recall of the unjustified claim detection model constructed by the Deep Learning algorithm by increasing the unreasonable rate through Under Learning. Can be made to.

FIG. 8 is a drawing illustrating an unfair claim detection model constructed by the Deep Learning algorithm of the insurance claim unfair claim detection device according to the embodiment of the present application.

With reference to FIG. 8, the unreasonable claim detection model constructed by the Deep Learning algorithm does not clearly exhibit its performance when learning is not sufficiently performed as described above, but its performance improves as the learning is repeated. It has the characteristic of being done. In addition, since the difference in performance varies greatly depending on the structure of the Hidden Layer of the Deep Learning algorithm, the performance of the unfair billing detection model constructed by the Deep Learning algorithm is optimized by constructing an unfair claim detection model having various Layer structures. Can be transformed into.

FIG. 9 is a drawing illustrating the flow of a method for detecting an unfair insurance claim according to an embodiment of the present application, and FIG. 10 is a new unfair claim pattern analysis of a method for detecting an unfair insurance claim according to an embodiment of the present application. It is a drawing which illustrated the flow.

The new unfair claim pattern analysis method shown in FIGS. 9 and 10 can be performed by the insurance claim unfair claim detection device described with reference to FIGS. 1 to 8 described above. Therefore, even if the content is omitted below, the content described for the insurance claim unfair claim detection device through FIGS. 1 to 8 can be similarly applied to FIGS. 9 and 10.

Referring to FIG. 9, in step S910, the data preprocessing unit 110 can stylize the insurance claim breakdown data and derive characteristic variables. The insurance claim breakdown data is, for example, at least one of insurance claim data, insurance or subsidy-related contract data, insurance payment data, insurance designer data, customer data, and insurance fraud detection result data. Can include one. For example, in the case of customer data, stylizing insurance claim breakdown data means quantifying the income level of the customer, the number of hospitals visited by the customer, the number of reasons for illness applied by the customer, and the like. The database 150 can record at least one of the insurance claim breakdown data, the characteristic variable, and the normal claim data.

In step S920, the unreasonable claim detection unit 120 can construct an unreasonable claim detection model of the teaching learning base that inputs the characteristic variable and the normal claim data. The unjustified claim detection unit 120 can construct the unreasonable claim detection model based on the classification / prediction algorithm of the teaching learning platform. By way of example, the classification / prediction algorithm can include at least one of a Random Forest algorithm, an SVM (support vector machine) algorithm, an Extra Tree algorithm, an XG Boost algorithm, and a Deep Learning algorithm.

The unjustified claim detection model can output the unjustified claim detection result by inputting the characteristic variable and the normal claim data. As an example, the unjustified claim detection model can output the unjustified claim detection result based on the similarity between the characteristic variable and the normal claim data. If the similarity is less than the preset threshold value, the insurance claim linked with the characteristic variable is judged to be normal, and if the similarity is equal to or higher than the preset threshold value, the insurance claim linked with the characteristic variable is unreasonable. It can be judged as a claim. The similarity can be calculated by at least one of a K-means clustering algorithm, a SOM (Self-Organizing-Maps) algorithm, and an EM & Canopy algorithm.

In step S930, the unfair claim determination unit 130 can determine whether or not the new insurance claim breakdown is unreasonable based on the unfair claim detection model. If a new insurance claim breakdown is input to the unfair claim detection model constructed by the unfair claim detection unit 120, it is determined whether or not the new insurance claim breakdown is unreasonable based on the similarity as described above. be able to.

Explaining the flow of the new unfair claim pattern analysis with reference to FIG. 10, in step S1010, the data preprocessing unit 110 can standardize the insurance claim breakdown data and derive characteristic variables. Since this can be understood in the same manner as the data preprocessing unit 110 described in step S910 described above, duplicate description will be omitted.

In step S1020, the unjustified claim pattern classification unit 141 can classify a new unjustified claim pattern based on a non-teaching learning-based crowd algorithm that takes a characteristic variable as an input. The unfair claim pattern classification unit 141 can crowd the characteristic variables into a plurality of claim patterns based on the frequency of the insurance claim breakdown data. As an example, the unfair billing pattern classification unit 141 may classify a new pattern of unfair billing based on at least one of a K-means clustering algorithm, a SOM (Self-Organizing-Maps) algorithm, and an EM & Coopy algorithm. it can. The unjustified claim pattern classification unit 141 can crowd the characteristic variable into a plurality of billing patterns through the crowd algorithm based on the frequency of the characteristic variable. That is, the claim pattern can be a community of homogeneous characteristic variables. In addition, the new pattern can be detected based on the degree of separation between the crowds of the billing pattern, that is, the distance between the crowds.

Further, the unreasonable claim pattern classification unit 141 can classify the new pattern into a plurality of similar pattern groups based on the degree of similarity between the new patterns. Since the similarity between new patterns can be calculated based on the crowd algorithm described above, duplicate description will be omitted.

In step S1030, the discriminative model building unit 142 can build a new pattern discriminative model based on the decision-making algorithm of the teaching and learning platform. Teaching learning means learning a model by utilizing pre-constructed learning data (training data). Further, the decision-making algorithm can be, for example, a Decision Tree algorithm, but is not limited thereto. The discriminative model construction unit 130 can learn a new pattern discriminative rule based on a decision-making algorithm that inputs the billing pattern classified into a pattern group in which a risk level is set. Further, the discriminative model construction unit 142 can construct a new unjustified claim pattern discriminative model including a new pattern discriminative rule. The importance of characteristic variables can be taken into account when learning new pattern discrimination rules. That is, among various characteristic variables, characteristic variables with a high risk of unfair claim can have a relatively high importance, and the importance of such characteristic variables is taken into consideration when learning a new pattern discrimination rule. Can be done. For example, among the characteristic variables, the total number of visits to a similar hospital can be given a higher importance than the number of doctors the customer has met. The discriminative model construction unit 142 can construct a more accurate new pattern discriminative model by considering the importance of the characteristic variable.

In step S1040, the new pattern discrimination unit 143 can discriminate the pattern type of the new claim based on the new pattern discriminative model. The new pattern discriminating unit 143 can discriminate which of the normal billing pattern, the unreasonable billing pattern, and the new pattern the new billing is based on the new pattern discriminating rule. In other words, by inputting the characteristic variable of the new billing into the constructed new pattern discriminative model, the billing pattern is judged to be normal, the pattern is judged to be unreasonable billing, or the new pattern of new unjustified billing is used. It is possible to determine the type of pattern. In addition, when the new claim is determined as a pattern determined as an unreasonable claim and a new pattern of an unreasonable claim, a characteristic variable acting as a factor (that is, a characteristic variable) for determining an unreasonable claim can be detected.

According to one embodiment of the present application, the method of detecting an unfair insurance claim can be embodied in a program instruction form that can be performed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be those specially designed and configured for the present invention, or may be those known and usable by those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and floptic discs. ) Includes magnetic-optical media such as), and hardware devices specially configured to store and perform program instructions such as ROM, RAM, flash memory, etc. Is done. Examples of program instructions include not only machine language code as produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above can be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

The above description of the present application is for illustration purposes only, and a person having ordinary knowledge of the technical field to which the present application belongs may use other specific forms without changing the technical idea or essential features of the present application. It can be seen that it can be easily deformed. Therefore, it should be understood that the examples described above are exemplary in all respects and are not limiting. For example, each component described in the single type may be carried out in a distributed manner, and similarly, the components described in a distributed manner may be carried out in a combined form.

The scope of the present application is represented by the scope of claims, which will be described later rather than the above detailed description, and the meaning and scope of the scope of claims and all modified or modified forms derived from the concept of equality thereof are the scope of the present application. Should be interpreted as being included in.
[Item 1]
In the insurance claim unfair claim detection device
A data pre-processing unit that standardizes insurance claim breakdown data and derives characteristic variables,
The unfair billing detection unit that builds the unfair billing detection model of the teaching and learning platform that inputs the above characteristic variables and normal billing data,
An insurance claim unfair claim detection device including an unfair claim determination unit that determines whether or not a new insurance claim breakdown is unreasonable based on the above unfair claim detection model.
[Item 2]
It further includes a database that records at least one of the insurance claim breakdown data, the characteristic variable and the normal claim data.
The insurance claim unfair claim detection device according to item 1, wherein the insurance claim breakdown data includes at least one of claim data, contract data, payment data, insurance designer data, and customer data.
[Item 3]
The above unjustified claim detection department
The insurance claim unfair claim detection device according to item 1, which constructs the unfair claim detection model based on the classification / prediction algorithm of the teaching and learning platform.
[Item 4]
Including a new pattern analysis department
The above new pattern analysis department
An unfair billing pattern classification unit that classifies new patterns of unfair billing based on a non-teaching learning-based crowd algorithm that inputs the above characteristic variables,
A discriminative model construction unit that builds a new pattern discriminative model based on the decision-making algorithm of the teaching and learning platform,
The device for detecting an unfair insurance claim according to item 1, which includes a new pattern discrimination unit that discriminates a pattern type of a new claim based on the new pattern discriminative model.
[Item 5]
The above unfair billing pattern classification department
The characteristic variables are crowded into a plurality of billing patterns through the crowd algorithm based on the frequency of the characteristic variables.
The device for detecting an unfair insurance claim according to item 4, which detects the new pattern based on the degree of separation between communities of the claim pattern.
[Item 6]
The above unfair billing pattern classification department
The device for detecting unfair insurance claims according to item 5, which classifies the new patterns into a plurality of similar pattern groups based on the degree of similarity between the new patterns.
[Item 7]
The above unfair billing pattern classification department
Item 6 wherein the risk level of the pattern group is set based on the number of characteristic variables linked to each of the new patterns and which are equal to or larger than the preset threshold value for each of the characteristic variables. Insurance claim unfair claim detection device.
[Item 8]
The above discriminative model construction unit
A new pattern discrimination rule is learned based on a decision-making algorithm that inputs the new pattern included in the pattern group according to the risk level, and the new pattern discrimination model including the new pattern discrimination rule is constructed. The detection device for unfair insurance claims according to item 7.
[Item 9]
The new pattern discriminating unit is
The insurance claim unfair claim detection device according to item 8, which determines which of the normal claim pattern, the unfair claim pattern, and the new pattern the new claim is based on the new pattern determination rule. ..
[Item 10]
In the method of detecting unfair insurance claims
At the stage of stylizing insurance claim breakdown data and deriving characteristic variables,
At the stage of constructing an unfair billing detection model of the teaching learning platform that inputs the above characteristic variables and normal billing data,
A method for detecting an unfair insurance claim, including a step of determining whether or not the breakdown of a new insurance claim is unreasonable based on the above-mentioned unfair claim detection model.
[Item 11]
Further including the step of recording at least one of the insurance claim breakdown data, the characteristic variable and the normal claim data.
The method for detecting an unfair insurance claim according to item 10, wherein the insurance claim breakdown data includes at least one of claim data, contract data, payment data, insurance designer data, and customer data.
[Item 12]
The stage of constructing the above unjustified claim detection model is
The method for detecting an unfair insurance claim according to item 10, wherein the unfair claim detection model is constructed based on the classification / prediction algorithm of the teaching and learning base.
[Item 13]
Including the stage of analyzing new patterns
The stage of analyzing the above new pattern is
At the stage of classifying new patterns of unfair billing based on the non-teaching learning-based crowd algorithm that inputs the above characteristic variables,
At the stage of constructing a new pattern discriminative model based on the decision-making algorithm of the teaching and learning platform,
The method for detecting an unfair insurance claim according to item 12, which includes a step of discriminating a pattern type of a new claim based on the new pattern discriminative model.
[Item 14]
The stage of classifying the new pattern of unfair billing is
The characteristic variables are crowded into a plurality of billing patterns through the crowd algorithm based on the frequency of the characteristic variables.
The method for detecting an unfair insurance claim according to item 13, which detects the new pattern based on the degree of separation between communities of the claim pattern.
[Item 15]
The stage of classifying the new pattern of unfair billing is
The method for detecting an unfair insurance claim according to item 14, wherein the new pattern is classified into a plurality of similar pattern groups based on the degree of similarity between the new patterns.
[Item 16]
The stage of classifying the new pattern of unfair billing is
Item 15. The item 15 in which the risk level of the pattern group is set based on the number of characteristic variables linked to each of the new patterns and which is equal to or higher than the preset threshold value for each of the characteristic variables. How to detect unfair insurance claims.
[Item 17]
The stage of constructing the above new pattern discrimination model is
A new pattern discrimination rule is learned based on a decision-making algorithm that inputs the new pattern included in the pattern group according to the risk level, and the new pattern discrimination model including the new pattern discrimination rule is constructed. The method for detecting an unfair insurance claim according to item 16.
[Item 18]
The stage for determining the above pattern type is
The method for detecting an unfair insurance claim according to item 17, which determines which of the normal claim pattern, the unfair claim pattern, and the new pattern the new claim is based on the new pattern determination rule. ..
[Item 19]
A computer-readable recording medium on which a program for executing the method of any one of items 10 to 18 is recorded.

Claims (9)

In the insurance claim unfair claim detection device
A data pre-processing unit that standardizes insurance claim breakdown data and derives characteristic variables,
An unfair billing detection unit that constructs an unfair billing detection model of a teaching and learning platform that inputs the characteristic variables and normal billing data,
An unfair claim judgment unit that determines whether or not the breakdown of new insurance claims is unreasonable based on the unfair claim detection model .
The unfair billing pattern classification unit that classifies new patterns of unfair billing based on the crowd algorithm of the non-teaching learning base that inputs the characteristic variables, and the discriminative model that builds a new pattern discriminative model based on the decision algorithm of the teaching learning base. Including a construction unit and a new pattern analysis unit including a new pattern discrimination unit that discriminates a pattern type of a new claim based on the new pattern discrimination model.
The unfair claim detection unit
The unjustified claim detection model was constructed based on the classification / prediction algorithm of the teaching and learning base, and
The unfair billing pattern classification unit
The characteristic variables are crowded into a plurality of billing patterns through the crowd algorithm based on the frequency of the characteristic variables.
The new pattern is detected based on the degree of separation between communities of the billing pattern.
The new patterns are classified into a plurality of similar pattern groups based on the degree of similarity between the new patterns.
Among the characteristic variables linked with each of the new patterns, the risk level of the pattern group is set based on the number of characteristic variables equal to or higher than the preset threshold value for each of the characteristic variables.
The characteristic variable is a variable that can have a quantified value in relation to an insurance claim, and the characteristic variable includes the number of duplicate applications for the same disease name and the number of disease reasons applied by the customer. , Insurance claim unfair claim detection device. It further includes a database that records at least one of the insurance claim breakdown data, the characteristic variable, and the normal claim data.
The insurance claim unfair claim detection device according to claim 1, wherein the insurance claim breakdown data includes at least one of claim data, contract data, payment data, insurance designer data, and customer data. The discriminative model construction unit
A new pattern discrimination rule is learned based on a decision-making algorithm that inputs the new pattern included in the pattern group according to the risk level, and the new pattern discrimination model including the new pattern discrimination rule is constructed. The detection device for unfair insurance claims according to claim 1 or 2 . The new pattern discriminating unit is
The detection of unfair insurance claims according to claim 3 , which determines which of the normal claim pattern, the unfair claim pattern, and the new pattern the new claim is based on the new pattern determination rule. apparatus. In the method of detecting unfair insurance claims
The stage where the data pre-processing department standardizes the insurance claim breakdown data and derives the characteristic variables,
The stage where the unfair billing detection unit constructs the unfair billing detection model of the teaching and learning platform that inputs the characteristic variables and normal billing data,
The stage where the Unfair Claim Judgment Department determines whether or not the new insurance claim breakdown is unreasonable based on the unfair claim detection model ,
The new pattern analysis unit classifies new patterns of unfair claims based on the non-teaching learning platform crowd algorithm that inputs the characteristic variable, constructs a new pattern discrimination model based on the teaching learning platform decision-making algorithm, and describes the above. Including the stage of discriminating the pattern type of new billing based on the new pattern discriminative model.
The stage of constructing the unfair claim detection model is
The unjustified claim detection model was constructed based on the classification / prediction algorithm of the teaching and learning base, and
The step of determining the pattern type of the new billing is
The characteristic variables are crowded into a plurality of billing patterns through the crowd algorithm based on the frequency of the characteristic variables.
The new pattern is detected based on the degree of separation between communities of the billing pattern.
The new patterns are classified into a plurality of similar pattern groups based on the degree of similarity between the new patterns.
Among the characteristic variables linked with each of the new patterns, the risk level of the pattern group is set based on the number of characteristic variables equal to or higher than the preset threshold value for each of the characteristic variables.
The characteristic variable is a variable that can have a quantified value in relation to an insurance claim, and the characteristic variable includes the number of duplicate applications for the same disease name and the number of disease reasons applied by the customer. , How to detect unfair insurance claims. Further including the step of recording at least one of the insurance claim breakdown data, the characteristic variable and the normal claim data.
The method for detecting an unfair insurance claim according to claim 5 , wherein the insurance claim breakdown data includes at least one of claim data, contract data, payment data, insurance designer data, and customer data. The stage of constructing the new pattern discrimination model is
A new pattern discrimination rule is learned based on a decision-making algorithm that inputs the new pattern included in the pattern group according to the risk level, and the new pattern discrimination model including the new pattern discrimination rule is constructed. The method for detecting an unfair insurance claim according to claim 5 or 6 . The step of discriminating the pattern type is
The detection of unfair insurance claims according to claim 7 , which determines which of the normal claim pattern, the unfair claim pattern, and the new pattern the new claim is based on the new pattern determination rule. Method. Any order of the program to execute the one of methods in a computer according to claim 5 to 8.