Dynamic data-driven risk control method for health insurance claim settlement

Abstract

The invention discloses a dynamic data-driven risk control method for health insurance claim settlement, and the method comprises the steps: collecting and preprocessing claim settlement text data, and generating a text corpus; normal claim settlement samples are screened based on compliance labels, potential Dirichlet distribution topic modeling is carried out, and a topic probability feature library is constructed; comparing by a K-nearest neighbor algorithm, and determining K nearest normal claim settlement samples; calculating an anomaly score, comparing the anomaly score with a preset threshold value, and performing anomaly recognition and early warning; dynamic acquisition, data updating and regular parameter adjustment are carried out to form a risk control closed loop. According to the invention, automatic anomaly detection and risk early warning of the whole process of health insurance claim settlement are realized, and the intelligence and precision level of claim settlement risk control is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent risk control technology, and in particular to a dynamic data-driven risk control method for health insurance claims. Background Technology

[0002] Against the backdrop of rapid growth in health insurance claims, insurance companies face multiple challenges, including a large volume of claims, complex data types, and strong hidden risks. Existing claims risk control technologies primarily rely on rule engines and static feature analysis, often depending on expert-defined judgment rules or a limited amount of structured data features. While these technologies achieved initial screening and detection of some abnormal claims in the early stages, they suffer from significant shortcomings when dealing with unstructured data such as text-based medical documents, medical records, and claims explanations with complex semantic relationships. These shortcomings include weak modeling capabilities, poor knowledge transferability, and difficulty in dynamically adapting to business changes. Traditional risk control methods typically lack deep semantic mining and feature modeling of claims applications, failing to effectively identify high-risk behavioral patterns hidden within claims materials. Complex risks such as repeated claims, malicious collusion, or disguised excessive medical reimbursement are difficult to detect in real time. Existing methods often employ single static thresholds and judgment mechanisms dominated by human experience, which easily leads to false positives and false negatives. This increases the manual review burden on claims departments and negatively impacts customer experience and business compliance.

[0003] With the continuous development of data-driven risk control and artificial intelligence technologies, some claims risk identification methods based on statistical learning and machine learning are gradually being applied to the health insurance field. However, current mainstream solutions mostly focus on risk identification based on structured features, lacking multi-dimensional modeling and dynamic anomaly detection mechanisms for unstructured text semantics. As a result, the models are difficult to adapt to the rapid changes in high-frequency and new types of abnormal applications in actual claims scenarios.

[0004] Therefore, how to provide dynamic data-driven risk control methods for health insurance claims is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose a dynamic data-driven risk control method for health insurance claims. This invention fully combines latent Dirichlet distribution topic modeling and the K-nearest neighbor algorithm to perform deep semantic feature mining and intelligent risk identification on text data in the health insurance claims process. It describes in detail the anomaly detection and adaptive risk control process based on topic probability distribution, which has the advantages of accurate risk identification, strong dynamic adaptability and high degree of risk control automation.

[0006] The dynamic data-driven risk control method for health insurance claims according to embodiments of the present invention includes:

[0007] Collect text data generated during the health insurance claims process, including historical claims text data and claims text data to be analyzed, and preprocess the text data to generate a text corpus;

[0008] Based on the compliance review tags of historical claims text data, normal claims samples are screened, and topic modeling is performed on the text corpus through latent Dirichlet distribution to obtain the topic probability distribution vector corresponding to each claims text. A topic probability distribution feature library for normal claims samples is constructed, and corresponding topic probability distribution feature vectors are generated for the claims text data to be analyzed.

[0009] The K-nearest neighbor algorithm is used to calculate the nearest neighbor distance between the feature vector of the topic probability distribution of the claim text data to be analyzed and the normal claim samples in the topic probability distribution feature library, and to determine the K nearest claim samples to the claim text data to be analyzed.

[0010] Calculate the anomaly score of the claim text data to be analyzed based on the feature vector of the topic probability distribution of K claim samples, preset the claim anomaly score threshold, compare the anomaly score with the claim anomaly score threshold, identify abnormal claim applications and trigger risk warnings;

[0011] The system dynamically collects new claims data and updates the text corpus, and regularly updates the relevant parameters of the potential Dirichlet distribution and the K-nearest neighbor algorithm to achieve a dynamic data-driven closed loop for health insurance claims risk control.

[0012] Optionally, the historical claims text data specifically includes medical diagnostic reports, medical records, claims instructions, and invoice item descriptions corresponding to closed health insurance claims, and the claims text data to be analyzed specifically includes medical diagnostic reports, medical records, claims instructions, and invoice item descriptions corresponding to current health insurance claims that require risk identification.

[0013] Optionally, the preprocessing of the text data specifically includes word segmentation, stop word removal, and semantic standardization.

[0014] Optionally, the compliance review tags based on historical claims text data are used to filter normal claims samples. The text corpus is then modeled using a latent Dirichlet distribution to obtain the topic probability distribution vector corresponding to each claims text. A topic probability distribution feature library for normal claims samples is constructed. For the claims text data to be analyzed, a corresponding topic probability distribution feature vector is generated, including:

[0015] The collected historical claims text data is filtered according to compliance review tags to obtain normal claims samples, and the normal claims samples and the claims text data to be analyzed are included in the text corpus.

[0016] Based on the aforementioned text corpus, we statistically analyzed the specialized terms related to disease names, treatment items, and insurance terms in the health insurance field, and constructed a field-specific glossary.

[0017] In the initialization process of the topic-word distribution of the latent Dirichlet distribution, a fixed prior probability value of topic is set for each word in the domain-specific vocabulary, and the domain-specific words are assigned to each topic based on the prior probability value of topic. The domain-specific vocabulary is used to impose prior constraints on the topic generation process of the latent Dirichlet distribution.

[0018] After completing the prior constraints on topic-word distribution, topic modeling is performed on the text corpus based on the latent Dirichlet distribution. Combining the content complexity of the text corpus with the sparsity of topic distribution, the perplexity index is used to dynamically adjust the number of topics k.

[0019] Based on the dynamically adjusted number of topics k, for each claim text data in the text corpus, topic inference is performed on the claim text data through the latent Dirichlet distribution. The posterior probability value of the claim text data assigned to each topic is statistically calculated, and the posterior probability values ​​corresponding to all topics are arranged in order according to the topic number to form the topic probability distribution feature vector of the claim text data.

[0020] Based on the tag information obtained through compliance review in historical claims text data, the topic probability distribution of normal claims samples is used as the optimization objective. A semi-supervised regularization term is designed. In the process of topic modeling based on the potential Dirichlet distribution, the tag information is used to guide parameter updates. The topic probability distribution of normal claims samples remains stable and meets the reasonable requirements of the health insurance business field.

[0021] For all normal claim samples, the topic probability distribution is inferred one by one to obtain the posterior probability value of each normal claim sample on all topics. The posterior probability values ​​of the same normal claim sample on each topic are arranged in the order of topic number to form the topic probability distribution feature vector of normal claim samples. The feature vectors are summarized according to the claim sample number to establish the topic probability distribution feature library of normal claim samples. The topic probability distribution feature library contains the topic probability distribution feature vector corresponding to each normal claim sample and the total number of all normal claim samples.

[0022] For the claims text data to be analyzed, the latent Dirichlet distribution method based on semi-supervised regularization to complete topic modeling is adopted. The topic probability distribution inference is performed on each claims text data to be analyzed, and the topic probability distribution feature vector of each claims text data to be analyzed is generated as the input feature of the K nearest neighbor algorithm.

[0023] Optionally, the step of using the K-nearest neighbor algorithm to calculate the nearest neighbor distance between the topic probability distribution feature vector of the claims text data to be analyzed and the normal claims samples in the topic probability distribution feature library, and determining the K nearest claims samples to the claims text data to be analyzed, includes:

[0024] For each claim text data to be analyzed, extract the theme probability distribution feature vector generated by potential Dirichlet distribution theme modeling;

[0025] Obtain the theme probability distribution feature vector of all normal claims samples from the theme probability distribution feature library of normal claims samples, and obtain the theme probability distribution feature vector of historical abnormal claims samples from the claims samples marked as abnormal by the compliance review label in historical claims text data;

[0026] Based on the topic probability distribution characteristics of normal claims samples and historical abnormal claims samples, the nearest neighbor discrimination accuracy is evaluated on the sample sets of normal claims samples and historical abnormal claims samples for three distance measurement functions: Euclidean distance, Manhattan distance and cosine distance. The distance measurement function with the highest accuracy is selected as the distance calculation method of the K-nearest neighbor algorithm.

[0027] Based on the ability of each theme component to distinguish anomalies in historical risk control tasks, weights are assigned to each theme component of the theme probability distribution feature vector to form a set of weighted coefficients.

[0028] Based on the thematic probability distribution characteristics of normal claims samples, the degree of fluctuation of each thematic component is statistically analyzed, and a dynamic offset correction factor is set for thematic components with large fluctuations.

[0029] Using a defined distance metric function, combined with a set of weighted coefficients and a dynamic offset correction factor, the weighted distance between the topic probability distribution feature vector of the claims text data to be analyzed and the topic probability distribution feature vector of each normal claims sample is calculated.

[0030] Sort all weighted distances in ascending order and select the K nearest normal claim samples to form a preliminary candidate nearest neighbor sample set;

[0031] A consistency test is performed on the preliminary candidate nearest neighbor sample set. The weighted distance between each normal claim sample and the other normal claim samples in the preliminary candidate nearest neighbor sample set is calculated. If the weighted distance between a normal claim sample and other normal claim samples is greater than a preset distance deviation threshold, the normal claim sample is removed from the preliminary candidate nearest neighbor sample set. Only normal claim samples whose weighted distances are all within the threshold range are retained. Finally, the K nearest normal claim samples to the claim text data to be analyzed are determined.

[0032] Optionally, the step of calculating the anomaly score of the claim text data to be analyzed based on the feature vector of the topic probability distribution of K claim samples, setting a claim anomaly score threshold, comparing the anomaly score with the claim anomaly score threshold, identifying abnormal claim applications and triggering risk warnings includes:

[0033] For each piece of claim text data to be analyzed, obtain the topic probability distribution feature vectors of the K nearest normal claim samples;

[0034] For each nearest normal claim sample, a defined weighted distance metric is used to calculate the weighted distance between the topic probability distribution feature vector of the claim text data to be analyzed and the topic probability distribution feature vector of each normal claim sample, resulting in K weighted distance values.

[0035] Perform statistical analysis on the K weighted distance values, calculate the mean and standard deviation of the weighted distance, and at the same time calculate the average weighted distance between the claim text data to be analyzed and the K nearest normal claim samples;

[0036] Based on the calculated K weighted distance values, the average of all weighted distance values ​​is calculated, and the average of the K weighted distances is used as the anomaly score of the claim text data to be analyzed.

[0037] Set a threshold for abnormal claims scores. The threshold for abnormal claims scores can be determined based on empirical statistics of the weighted distance distribution of historical claims samples.

[0038] The abnormal score of the claim text data to be analyzed is compared with the claim abnormal score threshold. If the abnormal score is greater than the claim abnormal score threshold, it is determined to be an abnormal claim application, triggering the risk warning process.

[0039] For all claim text data that is determined to be abnormal, record the abnormality score and weighted distance statistical characteristics, generate a risk warning report and store it.

[0040] Optionally, the dynamic collection of new claims data and updating of the text corpus, and the periodic updating of relevant parameters of the latent Dirichlet distribution and the K-nearest neighbor algorithm, to achieve a dynamic data-driven closed loop for health insurance claims risk control, includes:

[0041] Dynamically collect newly added claim text data in health insurance claims business, including medical diagnosis reports, medical records, claim instructions and invoice item descriptions, and incorporate the newly added claim text data into a text corpus;

[0042] All claims text data in the text corpus are processed according to the preprocessing workflow, including word segmentation, stop word removal, and semantic standardization.

[0043] Regularly update the compliance review tags of the claims text data in the text corpus, and include newly added normal claims samples and abnormal claims samples into the corresponding datasets. In a timely manner, supplement the topic probability distribution feature library of normal claims samples and the feature library of historical abnormal claims samples.

[0044] For the updated text corpus, the potential Dirichlet distribution topic model is re-performed periodically, the topic probability distribution is inferred for all claims text data, the topic distribution parameters are optimized and the topic probability distribution feature vector of each sample is updated;

[0045] The topic component weighting coefficients, dynamic offset correction factors, and distance metric function parameters involved in the K-nearest neighbor algorithm are periodically re-evaluated and optimized.

[0046] Based on dynamically updated text corpora, potential Dirichlet distribution topic modeling results, and K-nearest neighbor algorithm parameters, we continuously conduct claims risk detection, abnormal application identification, and risk warning.

[0047] The beneficial effects of this invention are:

[0048] This invention enhances the ability of health insurance claims risk control systems to mine deep semantic features and detect anomalies in textual data by integrating latent Dirichlet distribution topic modeling with the K-nearest neighbor algorithm. Compared with traditional risk control methods that rely on rules and structured features, this invention automatically extracts topic probability distribution features from unstructured texts such as medical diagnosis reports, medical records, and claims instructions, achieving accurate characterization and discrimination of implicit risk patterns in claims content. The K-nearest neighbor algorithm overcomes the problems of distance failure and outlier interference in high-dimensional feature spaces through adaptive selection of distance metrics, introduction of topic component weighting, and dynamic offset correction mechanisms, thereby enhancing the sensitivity and accuracy of discrimination against complex claims anomalies.

[0049] In practical applications, this invention dynamically collects new claims data and updates thematic modeling and anomaly detection parameters in real time, enabling the risk control model to self-learn and achieve closed-loop optimization. It supports flexible adjustments to risk control thresholds and strategies, continuously adapting to the rapid changes in health insurance claims. The system can automatically identify diverse and concealed high-risk claims, reducing the burden of manual review and the error rate, and improving overall claims service efficiency and risk management capabilities. Attached Figure Description

[0050] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0051] Figure 1 This is a flowchart of the dynamic data-driven risk control method for health insurance claims proposed in this invention;

[0052] Figure 2 This diagram illustrates the potential Dirichlet distribution topic modeling and claims text feature extraction for the dynamic data-driven risk control method for health insurance claims proposed in this invention. Detailed Implementation

[0053] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0054] refer to Figure 1 and Figure 2 Dynamic data-driven risk control methods for health insurance claims include:

[0055] Collect text data generated during the health insurance claims process, including historical claims text data and claims text data to be analyzed, and preprocess the text data to generate a text corpus;

[0056] Based on the compliance review tags of historical claims text data, normal claims samples are screened, and topic modeling is performed on the text corpus through latent Dirichlet distribution to obtain the topic probability distribution vector corresponding to each claims text. A topic probability distribution feature library for normal claims samples is constructed, and corresponding topic probability distribution feature vectors are generated for the claims text data to be analyzed.

[0057] The K-nearest neighbor algorithm is used to calculate the nearest neighbor distance between the feature vector of the topic probability distribution of the claim text data to be analyzed and the normal claim samples in the topic probability distribution feature library, and to determine the K nearest claim samples to the claim text data to be analyzed.

[0058] Calculate the anomaly score of the claim text data to be analyzed based on the feature vector of the topic probability distribution of K claim samples, preset the claim anomaly score threshold, compare the anomaly score with the claim anomaly score threshold, identify abnormal claim applications and trigger risk warnings;

[0059] The system dynamically collects new claims data and updates the text corpus, and regularly updates the relevant parameters of the potential Dirichlet distribution and the K-nearest neighbor algorithm to achieve a dynamic data-driven closed loop for health insurance claims risk control.

[0060] In this embodiment, the historical claims text data specifically includes medical diagnostic reports, medical records, claims explanations, and invoice item descriptions corresponding to closed health insurance claims, and the claims text data to be analyzed specifically includes medical diagnostic reports, medical records, claims explanations, and invoice item descriptions corresponding to current health insurance claims that require risk identification.

[0061] In this embodiment, the preprocessing of text data specifically includes word segmentation, stop word removal, and semantic standardization.

[0062] In this embodiment, the compliance review tags based on historical claims text data are used to screen normal claims samples. The text corpus is then modeled using a latent Dirichlet distribution to obtain the topic probability distribution vector corresponding to each claims text. A topic probability distribution feature library for normal claims samples is constructed. For the claims text data to be analyzed, a corresponding topic probability distribution feature vector is generated, including:

[0063] The collected historical claims text data is filtered according to compliance review tags to obtain normal claims samples, and the normal claims samples and the claims text data to be analyzed are included in the text corpus.

[0064] Based on the aforementioned text corpus, we statistically analyzed the specialized terms related to disease names, treatment items, and insurance terms in the health insurance field, and constructed a field-specific glossary.

[0065] In the initialization process of the topic-word distribution of the latent Dirichlet distribution, a fixed prior probability value of topic is set for each word in the domain-specific vocabulary, and the domain-specific words are assigned to each topic based on the prior probability value of topic. The domain-specific vocabulary is used to impose prior constraints on the topic generation process of the latent Dirichlet distribution.

[0066] After completing the prior constraints on topic-word distribution, topic modeling is performed on the text corpus based on the latent Dirichlet distribution. Combining the content complexity of the text corpus with the sparsity of topic distribution, the perplexity index is used to dynamically adjust the number of topics k.

[0067] Based on the dynamically adjusted number of topics k, for each claim text data in the text corpus, topic inference is performed on the claim text data through the latent Dirichlet distribution. The posterior probability value of the claim text data assigned to each topic is statistically calculated, and the posterior probability values ​​corresponding to all topics are arranged in order according to the topic number to form the topic probability distribution feature vector of the claim text data.

[0068] Based on the tag information obtained through compliance review in historical claims text data, the topic probability distribution of normal claims samples is used as the optimization objective. A semi-supervised regularization term is designed. In the process of topic modeling based on the potential Dirichlet distribution, the tag information is used to guide parameter updates. The topic probability distribution of normal claims samples remains stable and meets the reasonable requirements of the health insurance business field.

[0069] For all normal claim samples, the topic probability distribution is inferred one by one to obtain the posterior probability value of each normal claim sample on all topics. The posterior probability values ​​of the same normal claim sample on each topic are arranged in the order of topic number to form the topic probability distribution feature vector of normal claim samples. The feature vectors are summarized according to the claim sample number to establish the topic probability distribution feature library of normal claim samples. The topic probability distribution feature library contains the topic probability distribution feature vector corresponding to each normal claim sample and the total number of all normal claim samples.

[0070] For the claims text data to be analyzed, the latent Dirichlet distribution method based on semi-supervised regularization to complete topic modeling is adopted. The topic probability distribution inference is performed on each claims text data to be analyzed, and the topic probability distribution feature vector of each claims text data to be analyzed is generated as the input feature of the K nearest neighbor algorithm.

[0071] In this embodiment, the step of using the K-nearest neighbor algorithm to calculate the nearest neighbor distance between the topic probability distribution feature vector of the claims text data to be analyzed and the normal claims samples in the topic probability distribution feature library, and to determine the K nearest claims samples to the claims text data to be analyzed, includes:

[0072] For each claim text data to be analyzed, extract the theme probability distribution feature vector generated by potential Dirichlet distribution theme modeling;

[0073] Obtain the theme probability distribution feature vector of all normal claims samples from the theme probability distribution feature library of normal claims samples, and obtain the theme probability distribution feature vector of historical abnormal claims samples from the claims samples marked as abnormal by the compliance review label in historical claims text data;

[0074] Based on the topic probability distribution characteristics of normal claims samples and historical abnormal claims samples, the nearest neighbor discrimination accuracy is evaluated on the sample sets of normal claims samples and historical abnormal claims samples for three distance measurement functions: Euclidean distance, Manhattan distance and cosine distance. The distance measurement function with the highest accuracy is selected as the distance calculation method of the K-nearest neighbor algorithm.

[0075] Based on the ability of each theme component to distinguish anomalies in historical risk control tasks, weights are assigned to each theme component of the theme probability distribution feature vector to form a set of weighted coefficients.

[0076] Based on the thematic probability distribution characteristics of normal claims samples, the degree of fluctuation of each thematic component is statistically analyzed, and a dynamic offset correction factor is set for thematic components with large fluctuations.

[0077] Using a defined distance metric function, combined with a set of weighted coefficients and a dynamic offset correction factor, the weighted distance is calculated between the topic probability distribution feature vector of the claims text data to be analyzed and the topic probability distribution feature vector of each normal claims sample:

[0078]

[0079] Where, d qj and d nj D respectively q and D n The component value of the j-th topic, δ j dist is the dynamic offset correction factor for the j-th topic component. w，δ (D q D n ) represents the weighted distance between the claims text data to be analyzed and the nth normal claims sample, k is the number of topics, and w j D is the weighting coefficient for the j-th topic component. q Let D be the feature vector of the topic probability distribution of the claims text data to be analyzed. n Let be the feature vector of the thematic probability distribution of the nth normal claim sample;

[0080] Sort all weighted distances in ascending order and select the K nearest normal claim samples to form a preliminary candidate nearest neighbor sample set;

[0081] A consistency test is performed on the preliminary candidate nearest neighbor sample set. The weighted distance between each normal claim sample and the other normal claim samples in the preliminary candidate nearest neighbor sample set is calculated. If the weighted distance between a normal claim sample and other normal claim samples is greater than a preset distance deviation threshold, the normal claim sample is removed from the preliminary candidate nearest neighbor sample set. Only normal claim samples whose weighted distances are all within the threshold range are retained. Finally, the K nearest normal claim samples to the claim text data to be analyzed are determined.

[0082] In this embodiment, the step of calculating the anomaly score of the claim text data to be analyzed based on the feature vector of the topic probability distribution of K claim samples, setting a claim anomaly score threshold, comparing the anomaly score with the claim anomaly score threshold, identifying abnormal claim applications and triggering risk warnings includes:

[0083] For each piece of claim text data to be analyzed, obtain the topic probability distribution feature vectors of the K nearest normal claim samples;

[0084] For each nearest normal claim sample, a defined weighted distance metric is used to calculate the weighted distance between the topic probability distribution feature vector of the claim text data to be analyzed and the topic probability distribution feature vector of each normal claim sample, resulting in K weighted distance values.

[0085] Perform statistical analysis on the K weighted distance values, calculate the mean and standard deviation of the weighted distance, and at the same time calculate the average weighted distance between the claim text data to be analyzed and the K nearest normal claim samples;

[0086] Based on the calculated K weighted distance values, the average of all weighted distance values ​​is calculated, and the average of the K weighted distances is used as the anomaly score of the claim text data to be analyzed.

[0087] Set a threshold for abnormal claims scores. The threshold for abnormal claims scores can be determined based on empirical statistics of the weighted distance distribution of historical claims samples.

[0088] The abnormal score of the claim text data to be analyzed is compared with the claim abnormal score threshold. If the abnormal score is greater than the claim abnormal score threshold, it is determined to be an abnormal claim application, triggering the risk warning process.

[0089] For all claim text data that is determined to be abnormal, record the abnormality score and weighted distance statistical characteristics, generate a risk warning report and store it.

[0090] In this embodiment, the dynamic collection of newly added claims data and updating of the text corpus, and the periodic updating of relevant parameters of the latent Dirichlet distribution and the K-nearest neighbor algorithm, to achieve a dynamic data-driven closed loop for health insurance claims risk control, include:

[0091] Dynamically collect newly added claim text data in health insurance claims business, including medical diagnosis reports, medical records, claim instructions and invoice item descriptions, and incorporate the newly added claim text data into a text corpus;

[0092] All claims text data in the text corpus are processed according to the preprocessing workflow, including word segmentation, stop word removal, and semantic standardization.

[0093] Regularly update the compliance review tags of the claims text data in the text corpus, and include newly added normal claims samples and abnormal claims samples into the corresponding datasets. In a timely manner, supplement the topic probability distribution feature library of normal claims samples and the feature library of historical abnormal claims samples.

[0094] For the updated text corpus, the potential Dirichlet distribution topic model is re-performed periodically, the topic probability distribution is inferred for all claims text data, the topic distribution parameters are optimized and the topic probability distribution feature vector of each sample is updated;

[0095] The topic component weighting coefficients, dynamic offset correction factors, and distance metric function parameters involved in the K-nearest neighbor algorithm are periodically re-evaluated and optimized.

[0096] Based on dynamically updated text corpora, potential Dirichlet distribution topic modeling results, and K-nearest neighbor algorithm parameters, we continuously conduct claims risk detection, abnormal application identification, and risk warning.

[0097] Example 1:

[0098] To verify the feasibility of this invention in practice, it was applied to the claims center of an insurance company. The total number of claims each month was constantly increasing, and the insurance company needed to manually review a large amount of textual materials, such as diagnostic certificates, hospital records, claims instructions, and medical invoices. Faced with complex and diverse unstructured text data, human reviewers needed to invest a significant amount of time in initial screening and multiple rounds of review, which easily led to untimely identification of high-risk claims, missed double claims, and delays in reviewing some abnormal cases. The claims center reviewed an average of about 730 claims per month, with an average review period of about 2.5 days per claim, and a manual review rate of 14.2%. Statistics from January to June of the year showed that 41 high-risk claims were identified through a combination of automatic detection and manual review, but the accuracy rate of manual review in identifying high-risk cases was only 70.7%, the false positive rate for anomaly detection was 3.2%, the missed detection rate was 2.4%, the claims complaint rate was high, and customer feedback on claims processing timeliness and experience was poor.

[0099] In January 2025, the insurance company officially launched the health insurance claims dynamic data-driven risk control system of this invention. All newly submitted claims text data are automatically collected and stored in the system. The system performs word segmentation, stop word removal, and standardization on the text materials, and uniformly incorporates them into topic modeling analysis. Based on the latent Dirichlet distribution method, the system generates a topic probability distribution feature vector for each claims text and performs multi-dimensional comparison with the feature library of historical normal claims samples. The K-nearest neighbor algorithm introduces feature weighting, dynamic correction, and anomaly removal strategies in distance calculation, making it more sensitive to high-risk topics. The anomaly score of each claims application is automatically compared with a preset claim anomaly score threshold. High-risk cases are immediately alerted and pushed to the manual review team, achieving a combination of automatic screening and key manual verification. The system supports periodic dynamic collection of new claims data and automatic updates to the text corpus, topic modeling parameters, and K-nearest neighbor discrimination parameters.

[0100] Table 1 Comparison of the effects of the health insurance claims risk control system before and after its application

[0101]

[0102] As shown in Table 1, the method of this invention has brought significant improvements in efficiency and risk control in actual health insurance claims processing. After the system went live, the total number of claims increased from 5,120 to 5,380, but the average review time was significantly shortened from 2.5 days to 1.1 days, indicating a marked improvement in claims processing speed and enhancing the customer's claims experience. The number of automatically detected high-risk cases increased from 41 to 54, demonstrating the system's stronger ability and wider coverage in identifying high-risk cases. The accuracy rate of high-risk cases also improved significantly, from 70.7% to 88.9%, indicating that the intelligent risk control method reduced the probability of misjudgments and omissions.

[0103] The manual review rate decreased from 14.2% to 2.7%, significantly enhancing the accuracy and intelligence of the system's automatic screening and reducing the workload of manual reviewers. The false alarm rate for anomaly detection decreased from 3.2% to 0.8%, and the false negative rate also decreased from 2.4% to 0.3%, indicating that the method of this invention not only improves the sensitivity of risk warnings but also reduces the occurrence of false alarms and false negatives. The system has achieved remarkable results in improving the efficiency of health insurance claims processing, risk control capabilities, and customer service experience, fully demonstrating the practical value of data-driven intelligent risk control in actual health insurance claims.

[0104] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic data-driven risk control method for health insurance claims, characterized in that, include: Collect text data generated during the health insurance claims process, including historical claims text data and claims text data to be analyzed, and preprocess the text data to generate a text corpus; Based on the compliance review tags of historical claims text data, normal claims samples are screened, and topic modeling is performed on the text corpus through latent Dirichlet distribution to obtain the topic probability distribution vector corresponding to each claims text. A topic probability distribution feature library for normal claims samples is constructed, and corresponding topic probability distribution feature vectors are generated for the claims text data to be analyzed. The K-nearest neighbor algorithm is used to calculate the nearest neighbor distance between the feature vector of the topic probability distribution of the claim text data to be analyzed and the normal claim samples in the topic probability distribution feature library, and to determine the K nearest claim samples to the claim text data to be analyzed. Calculate the anomaly score of the claim text data to be analyzed based on the feature vector of the topic probability distribution of K claim samples, preset the claim anomaly score threshold, compare the anomaly score with the claim anomaly score threshold, identify abnormal claim applications and trigger risk warnings; The system dynamically collects new claims data and updates the text corpus, and regularly updates the relevant parameters of the potential Dirichlet distribution and the K-nearest neighbor algorithm to achieve a dynamic data-driven closed loop for health insurance claims risk control.

2. The dynamic data-driven risk control method for health insurance claims according to claim 1, characterized in that, The historical claims data specifically includes medical diagnostic reports, medical records, claims explanations, and invoice item descriptions corresponding to closed health insurance claims. The claims data to be analyzed specifically includes medical diagnostic reports, medical records, claims explanations, and invoice item descriptions corresponding to current health insurance claims that require risk identification.

3. The dynamic data-driven risk control method for health insurance claims according to claim 1, characterized in that, The preprocessing of text data specifically includes word segmentation, stop word removal, and semantic standardization.

4. The dynamic data-driven risk control method for health insurance claims according to claim 1, characterized in that, The compliance review tags based on historical claims text data are used to filter normal claims samples. The text corpus is then modeled using a latent Dirichlet distribution to obtain the topic probability distribution vector corresponding to each claims text. A topic probability distribution feature library for normal claims samples is constructed. For the claims text data to be analyzed, corresponding topic probability distribution feature vectors are generated, including: The collected historical claims text data is filtered according to compliance review tags to obtain normal claims samples, and the normal claims samples and the claims text data to be analyzed are included in the text corpus. Based on the aforementioned text corpus, we statistically analyzed the specialized terms related to disease names, treatment items, and insurance terms in the health insurance field, and constructed a field-specific glossary. In the initialization process of the topic-word distribution of the latent Dirichlet distribution, a fixed prior probability value of topic is set for each word in the domain-specific vocabulary, and the domain-specific words are assigned to each topic based on the prior probability value of topic. The domain-specific vocabulary is used to impose prior constraints on the topic generation process of the latent Dirichlet distribution. After completing the prior constraints on topic-word distribution, topic modeling is performed on the text corpus based on the latent Dirichlet distribution. Combining the content complexity of the text corpus with the sparsity of topic distribution, the perplexity index is used to dynamically adjust the number of topics k. Based on the dynamically adjusted number of topics k, for each claim text data in the text corpus, topic inference is performed on the claim text data through the latent Dirichlet distribution. The posterior probability value of the claim text data assigned to each topic is statistically calculated, and the posterior probability values ​​corresponding to all topics are arranged in order according to the topic number to form the topic probability distribution feature vector of the claim text data. Based on the tag information obtained through compliance review in historical claims text data, the topic probability distribution of normal claims samples is used as the optimization objective. A semi-supervised regularization term is designed. In the process of topic modeling based on the potential Dirichlet distribution, the tag information is used to guide parameter updates. The topic probability distribution of normal claims samples remains stable and meets the reasonable requirements of the health insurance business field. For all normal claim samples, the topic probability distribution is inferred one by one to obtain the posterior probability value of each normal claim sample on all topics. The posterior probability values ​​of the same normal claim sample on each topic are arranged in the order of topic number to form the topic probability distribution feature vector of normal claim samples. The feature vectors are summarized according to the claim sample number to establish the topic probability distribution feature library of normal claim samples. The topic probability distribution feature library contains the topic probability distribution feature vector corresponding to each normal claim sample and the total number of all normal claim samples. For the claims text data to be analyzed, the latent Dirichlet distribution method based on semi-supervised regularization to complete topic modeling is adopted. The topic probability distribution inference is performed on each claims text data to be analyzed, and the topic probability distribution feature vector of each claims text data to be analyzed is generated as the input feature of the K nearest neighbor algorithm.

5. The dynamic data-driven risk control method for health insurance claims according to claim 1, characterized in that, The K-nearest neighbor algorithm is used to calculate the nearest neighbor distance between the topic probability distribution feature vector of the claims text data to be analyzed and the normal claims samples in the topic probability distribution feature library, and to determine the K nearest claims samples to the claims text data to be analyzed, including: For each claim text data to be analyzed, extract the theme probability distribution feature vector generated by potential Dirichlet distribution theme modeling; Obtain the theme probability distribution feature vector of all normal claims samples from the theme probability distribution feature library of normal claims samples, and obtain the theme probability distribution feature vector of historical abnormal claims samples from the claims samples marked as abnormal by the compliance review label in historical claims text data; Based on the topic probability distribution characteristics of normal claims samples and historical abnormal claims samples, the nearest neighbor discrimination accuracy is evaluated on the sample sets of normal claims samples and historical abnormal claims samples for three distance measurement functions: Euclidean distance, Manhattan distance and cosine distance. The distance measurement function with the highest accuracy is selected as the distance calculation method of the K-nearest neighbor algorithm. Based on the ability of each theme component to distinguish anomalies in historical risk control tasks, weights are assigned to each theme component of the theme probability distribution feature vector to form a set of weighted coefficients. Based on the thematic probability distribution characteristics of normal claims samples, the degree of fluctuation of each thematic component is statistically analyzed, and a dynamic offset correction factor is set for thematic components with large fluctuations. Using a defined distance metric function, combined with a set of weighted coefficients and a dynamic offset correction factor, the weighted distance between the topic probability distribution feature vector of the claims text data to be analyzed and the topic probability distribution feature vector of each normal claims sample is calculated. Sort all weighted distances in ascending order and select the K nearest normal claim samples to form a preliminary candidate nearest neighbor sample set; A consistency test is performed on the preliminary candidate nearest neighbor sample set. The weighted distance between each normal claim sample and the other normal claim samples in the preliminary candidate nearest neighbor sample set is calculated. If the weighted distance between a normal claim sample and other normal claim samples is greater than a preset distance deviation threshold, the normal claim sample is removed from the preliminary candidate nearest neighbor sample set. Only normal claim samples whose weighted distances are all within the threshold range are retained. Finally, the K nearest normal claim samples to the claim text data to be analyzed are determined.

6. The dynamic data-driven risk control method for health insurance claims according to claim 1, characterized in that, The process of calculating anomaly scores for the claim text data to be analyzed based on the feature vectors of the topic probability distribution of K claim samples, setting a predefined threshold for claim anomaly scores, comparing the anomaly scores with the threshold, identifying abnormal claim applications, and triggering risk warnings includes: For each piece of claim text data to be analyzed, obtain the topic probability distribution feature vectors of the K nearest normal claim samples; For each nearest normal claim sample, a defined weighted distance metric is used to calculate the weighted distance between the topic probability distribution feature vector of the claim text data to be analyzed and the topic probability distribution feature vector of each normal claim sample, resulting in K weighted distance values. Perform statistical analysis on the K weighted distance values, calculate the mean and standard deviation of the weighted distance, and at the same time calculate the average weighted distance between the claim text data to be analyzed and the K nearest normal claim samples; Based on the calculated K weighted distance values, the average of all weighted distance values ​​is calculated, and the average of the K weighted distances is used as the anomaly score of the claim text data to be analyzed. Set a threshold for abnormal claims scores. The threshold for abnormal claims scores can be determined based on empirical statistics of the weighted distance distribution of historical claims samples. The abnormal score of the claim text data to be analyzed is compared with the claim abnormal score threshold. If the abnormal score is greater than the claim abnormal score threshold, it is determined to be an abnormal claim application, triggering the risk warning process. For all claim text data that is determined to be abnormal, record the abnormality score and weighted distance statistical characteristics, generate a risk warning report and store it.

7. The dynamic data-driven risk control method for health insurance claims according to claim 1, characterized in that, The dynamic collection of new claims data and updating of the text corpus, along with the periodic updating of relevant parameters of the latent Dirichlet distribution and the K-nearest neighbor algorithm, realize a dynamic data-driven closed-loop health insurance claims risk control system, including: Dynamically collect newly added claim text data in health insurance claims business, including medical diagnosis reports, medical records, claim instructions and invoice item descriptions, and incorporate the newly added claim text data into a text corpus; All claims text data in the text corpus are processed according to the preprocessing workflow, including word segmentation, stop word removal, and semantic standardization. Regularly update the compliance review tags of the claims text data in the text corpus, and include newly added normal claims samples and abnormal claims samples into the corresponding datasets. In a timely manner, supplement the topic probability distribution feature library of normal claims samples and the feature library of historical abnormal claims samples. For the updated text corpus, the potential Dirichlet distribution topic model is re-performed periodically, the topic probability distribution is inferred for all claims text data, the topic distribution parameters are optimized and the topic probability distribution feature vector of each sample is updated; The topic component weighting coefficients, dynamic offset correction factors, and distance metric function parameters involved in the K-nearest neighbor algorithm are periodically re-evaluated and optimized. Based on dynamically updated text corpora, potential Dirichlet distribution topic modeling results, and K-nearest neighbor algorithm parameters, we continuously conduct claims risk detection, abnormal application identification, and risk warning.

Images