Commercial insurance intelligent service system and method based on cloud computing

Abstract

The invention discloses a commercial insurance intelligent service system and method based on cloud computing, and belongs to the technical field of insurance science and technology and big data intelligent processing. The method comprises the following steps: S1, acquiring basic information, health medical data, travel records, consumption behaviors and social credit data of an insurance customer, and uploading the basic information, the health medical data, the travel records, the consumption behaviors and the social credit data to a cloud computing platform for unified storage and preprocessing; s2, based on a distributed data cleaning and feature extraction module, performing standardization, desensitization and risk feature modeling on the customer multi-source data; s3, performing personalized risk assessment and insurance premium calculation on the customer by using an insurance intelligent pricing model; s4, realizing automatic processing of insurance, claim settlement, anti-fraud and supervision compliance in combination with block chain evidence storage and intelligent contract technologies; and S5, insurance policy management, risk prompt, claim settlement tracking and value-added financial services are provided for the client through the intelligent customer service and the mobile terminal application. According to the invention, through combination of cloud computing, artificial intelligence and a block chain technology, intelligence, personalization and high efficiency of commercial insurance services are realized, the operation cost can be reduced, the customer experience can be improved, and the real-time monitoring and control capability of insurance companies on risks can be enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of insurance technology and big data processing, and particularly relates to a commercial insurance intelligent service system and method based on cloud computing. BACKGROUND

[0002] With the acceleration of the digital transformation of the insurance industry, traditional insurance has problems such as low efficiency, complicated manual operation, high fraud risk and insufficient customer experience in pricing, claims settlement and risk control.

[0003] For example:

[0004] 1. Traditional premium pricing is based on big data statistics, which cannot fine-tune the pricing for individual differences, resulting in overpricing for low-risk customers and underpricing for high-risk customers;

[0005] 2. The claims settlement process usually involves manual review, which is time-consuming and has information asymmetry and fraud;

[0006] 3. Customers lack intelligent guidance in the process of insurance, renewal and claims settlement, and the experience is poor.

[0007] The development of cloud computing, big data, artificial intelligence and blockchain technology provides new possibilities for the intelligent transformation of the insurance industry. How to effectively combine the above technologies to build an extensible, secure and intelligent commercial insurance service system has become a technical problem that needs to be solved. SUMMARY

[0008] The purpose of the present application is to provide a commercial insurance intelligent service system and method based on cloud computing, which solves the problems of low efficiency and opacity in traditional insurance mode through data intelligent processing, risk modeling, intelligent pricing and automatic claims settlement, and improves customer experience and risk management ability

[0009] To achieve the above purpose, the present application adopts the following technical solutions:

[0010] S1, build an insurance data management platform based on cloud computing architecture, support multi-source data collection and real-time calculation;

[0011] S2, design intelligent risk modeling and pricing module, realize personalized premium calculation;

[0012] S3, introduce blockchain and smart contract technology to ensure the credibility and automation of the insurance policy and claims settlement process;

[0013] S4, integrate intelligent customer service and mobile application to improve customer service level.

[0014] Compared with the prior art, the present application has the following advantages:

[0015] Support multi-source data integration and real-time calculation;

[0016] Provide personalized and dynamic risk assessment and pricing;

[0017] Improve transparency and anti-fraud capabilities through blockchain technology;

[0018] Realize the intelligent and automated whole process of claim settlement;

[0019] Significantly improve customer service experience and operational efficiency of insurance companies. BRIEF DESCRIPTION OF DRAWINGS

[0020] Fig. 1 The overall flowchart of the method of the present application;

[0021] Fig. 2 The system architecture diagram of the present application;

[0022] Fig. 3 The application scenario diagram of the present application in the application of insurance, claim settlement and customer service interaction. DETAILED DESCRIPTION

[0023] Please refer to Figs. 1-3 In an embodiment of the present application, a business insurance intelligent service method based on cloud computing includes:

[0024] Step S1: Data collection and uploading

[0025] Through API interface, IoT device and third-party data platform, the multi-source data of the customer is obtained, including: medical health data (such as physical examination report, electronic medical record), financial data (such as consumption record, credit score), travel data (such as vehicle driving trajectory), occupation information and historical claim record. After encryption transmission, the data is uploaded to the cloud platform.

[0026] Step S2: Data preprocessing

[0027] Use distributed computing framework (such as Spark, Hadoop) to clean the data, remove outliers and redundant data, and perform desensitization operation on sensitive information.

[0028] Step S3: Risk feature modeling

[0029] Based on the cloud feature modeling module, the health risk factors (disease history, BMI, etc.), behavior risk factors (travel frequency, driving habits, etc.), financial risk factors (consumption structure, loan record, etc.) and occupation risk factors (working environment, job stability) are extracted.

[0030] Step S4: Intelligent pricing

[0031] Adopting a method combining deep learning model and Bayesian network, customer risk is modeled in multiple dimensions, personalized risk score is output, and premium amount is dynamically adjusted to realize fine pricing.

[0032] Step S5: Blockchain and smart contract

[0033] Through blockchain, the whole process of insurance and claim settlement is stored, and smart contract automatically triggers compensation when the agreed conditions are met, thereby reducing manual intervention and fraud.

[0034] Step S6: Intelligent customer service and service output

[0035] Through natural language processing models such as BERT and GPT, intelligent interaction with customers is realized, including insurance consultation, policy management, risk tips, claim tracking and financial value-added services. As a further improvement of the technical solution: in S2, the data preprocessing method is:

[0036] S201: Abnormality detection and elimination

[0037] Abnormal points are identified through statistical methods and machine learning methods. For example, for any attribute feature If it deviates from the mean by more than the standard deviation threshold, it is determined to be abnormal:

[0038] |x i -μ|>kσ

[0039] Where μ is the mean, σ is the standard deviation, and k is a threshold constant (usually 2-3).

[0040] S202: Missing value filling

[0041] For features with missing values, multiple methods are used to complete them, for example:

[0042] Numerical features: mean filling, K-nearest neighbor interpolation or regression prediction can be used:

[0043]

[0044] Where, Indicates the feature values of the K nearest neighbors of the sample.

[0045] S203: Data standardization

[0046] In order to eliminate the influence of dimension, the invention normalizes or standardizes numerical features:

[0047] Min-Max normalization:

[0048]

[0049] Z-score standardization:

[0050]

[0051] S204: Data de-sensitization

[0052] Sensitive information (such as ID number, mobile phone number, bank card number, etc.) is encrypted or hashed, and common methods include:

[0053] Hash de-sensitization:

[0054] ID' = Hash(ID)

[0055] S205: Feature vectorization and uniform format conversion

[0056] Convert structured data, semi-structured data (such as JSON), unstructured data (such as text, image features) into a uniform vector form, and input into the subsequent modeling module. For example, text features are represented as vectors through TF-IDF or Word2Vec.

[0057] As a further improvement of the technical solution: in S3, the method of risk modeling is: S301: Feature classification and grouping

[0058] The pre-processed data is divided into four categories of risk factors:

[0059] · Health risk factors: BMI, blood pressure, blood sugar, medical history, physical examination report, etc.

[0060] · Behavioral risk factors: travel frequency, driving habits, exercise habits, smoking and drinking habits;

[0061] · Financial risk factors: consumption structure, debt ratio, credit score, asset situation;

[0062] · Professional risk factors: job stability, work environment risk index (such as miners, drivers are higher than office workers).

[0063] Each type of factor forms a feature vector:

[0064] X = {X health , X behavior , X finance , X career}

[0065] S302: Feature selection and dimension reduction

[0066] Use information gain, mutual information (Mutual Information), principal component analysis (PCA) and other methods to remove redundant features, reduce dimensionality and improve model efficiency.

[0067] For example, mutual information formula:

[0068]

[0069] Where X is the input feature, Y is the risk label.

[0070] S303: Risk score model construction

[0071] The present application adopts a hybrid modeling method, combining statistical models and machine learning models:

[0072] Logistic Regression: used to calculate the customer's basic risk probability:

[0073] Bayesian network model: used to model the dependence between risk factors

[0074] Deep neural network (DNN): model high-dimensional nonlinear relationships to improve prediction accuracy. The output is the customer's risk score:

[0075] S304: Risk level division

[0076] Finally, the risk probability and risk score output by the above models are combined to divide customers into different risk levels:

[0077] Low risk (0~0.3): low premium, high discount coefficient;

[0078] Medium risk (0.3~0.7): moderate premium, with certain risk warnings;

[0079] High risk (0.7~1.0): high premium, may require additional conditions or rejection.

[0080] RiskLevel = α·P(Y|X) + (1-α)·Score

[0081] Where α is a weight parameter used to balance the results of statistical models and machine learning models S305: Model dynamic update

[0082] The present application supports online learning and model updating mechanism, that is, when new customer data (such as new medical examination data or the latest credit record) is uploaded to the system, the model can be automatically retrained or fine-tuned, realizing dynamic update of risk assessment.

[0083] As a further improvement of the technical solution: in S4, the intelligent pricing method is:

[0084] Assuming that the customer's risk feature modeling result is a comprehensive risk level

[0085] RiskLevel∈[0,1], the base premium is composed of the following parts:

[0086] Premium=Base×(1+λ·RiskLevel)

[0087] Where: Base: the standard base premium of the insurance product; λ: risk adjustment coefficient, set according to the pricing strategy of the insurance company; RiskLevel: risk level score from S3 risk characteristic modeling.

[0088] S402: Multi-dimensional risk correction coefficient

[0089] For different dimensions of risk factors, set the correction coefficient:

[0090] Premium′=Premium×(1+α1H+α2B+α3F+α4C)

[0091] Where:

[0092] H: health risk factor score; B: behavior risk factor score; F: financial risk factor score; C: professional risk factor score;

[0093] α1, α2, α3, α4, corresponding risk weight parameters, obtained by training historical claim data.

[0094] S403: Dynamic pricing mechanism

[0095] The application introduces the time dimension, supports pricing adjustment based on user dynamic data (such as real-time health monitoring data, consumption record update).

[0096] For example, assuming that the risk level of the user at time t is RiskLevel, then the dynamic premium is:

[0097] Premium t =Base×(1+λ·RiskLevel t )

[0098] When the user's lifestyle improves (such as increased exercise, improved health indicators), the risk score decreases, and the system can dynamically reduce the premium to encourage healthy behavior of the user.

[0099] S404: Machine learning pricing model

[0100] In practical applications, the application supports a nonlinear pricing model based on big data training:

[0101] Premium=f θ (X)

[0102] Where,

[0103] X is the customer multi-dimensional feature vector (from S3), fθ is the trained machine learning model (such as GradientBoosting, deep neural network DNN, XGBoost, etc.), and θ is the model parameter.

[0104] The model can capture the nonlinear interaction between risk factors, and is more accurate than traditional linear pricing.

[0105] S405: Pricing transparency and explainability

[0106] To meet regulatory compliance, the invention also generates a pricing explanation report when outputting personalized premiums, including: the contribution of each dimension risk factor; the difference between the current premium and the standard premium; and the premium change prediction after simulating "several improvement scenarios" (such as reducing BMI and improving credit score). For example, by using ShapleyValue or LIME method, the model prediction results can be explained:

[0107] Contribution(X i )=f(X)-f(X\{X i})

[0108] The implementation effect of the invention is:

[0109] · Insurance companies can conduct dynamic risk assessment based on real-time data, improving profitability;

[0110] · Customers can obtain more fair and transparent insurance services, improving user satisfaction;

[0111] ● Claims process automation, shortening the claims cycle and reducing disputes;

[0112] ● Blockchain technology ensures data tamper-proofing, enhancing industry supervision and compliance.

Claims

1. A cloud-based intelligent service method for commercial insurance, characterized in that, Includes the following steps: o Step S1: Obtain multi-source heterogeneous data of insurance customers, and encrypt and upload the data to the cloud computing platform for unified storage; Step S2: Preprocess the multi-source heterogeneous data, including data cleaning, standardization, and desensitization operations; Step S3: Based on the preprocessed data, extract the customer's multidimensional risk characteristic indicators; Step S4: Use the intelligent pricing model to conduct a personalized risk assessment for the customer and generate a differentiated premium quote; Step S5: Based on blockchain technology, the policy and claims process are stored and notarized, and smart contracts are used to automate claims processing and anti-fraud judgment. Step S6: Use the intelligent customer service system to provide customers with insurance product recommendations, risk analysis reports, and claims progress feedback.

2. The method according to claim 1, characterized in that, The multi-source heterogeneous data includes at least three of the following: medical and health data, travel data, financial consumption data, occupational information, credit records, and historical claims records.

3. The method according to claim 1, characterized in that, The data preprocessing in step S2 is implemented through a distributed computing framework, including outlier detection, missing value imputation, noise data removal, and data standardization.

4. The method according to claim 1, characterized in that, The multidimensional risk characteristic indicators extracted in step S3 include health risk factors, behavioral risk factors, financial risk factors, and occupational risk factors.

5. The method according to claim 4, characterized in that: The health risk factors mentioned include medical history, physical examination indicators, and family medical history; The behavioral risk factors mentioned include driving behavior, travel frequency, and exercise habits; The financial risk factors mentioned above include income level, debt situation, and consumption structure; The occupational risk factors mentioned include occupational category, work environment, and job stability.

6. The method according to claim 1, characterized in that, The intelligent pricing model in step S4 uses a combination of deep learning models and Bayesian networks to output customer risk scores and dynamically adjust premiums.

7. The method according to claim 1, characterized in that, In step S5, the smart contract automatically triggers the compensation process when the preset compensation conditions are met, and identifies and warns against abnormal compensation behavior.

8. The method according to claim 1, characterized in that, The intelligent customer service system in step S6 is based on a natural language processing model, supports voice and text interaction, and provides services such as insurance consultation, policy management, claims application and progress inquiry.

9. The method according to claim 8, characterized in that, The intelligent customer service system also integrates an emotion recognition module to identify user emotions and adjust response strategies accordingly.

10. A cloud-based intelligent service system for commercial insurance, characterized in that, include: The data acquisition module is used to obtain customer data from multiple sources and upload it to the cloud platform; The data preprocessing module is used to clean, standardize, and de-identify the raw data; The feature modeling module is used to extract risk feature indicators from preprocessed data; The intelligent pricing module is used to output personalized premium quotes based on risk characteristics; The blockchain and smart contract module is used to realize policy storage, automatic claims processing, and anti-fraud detection; The intelligent customer service module is used to provide customer interaction and service output.

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