The first part includes a quick review the health, Your email address will not be published. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. The final model was obtained using Grid Search Cross Validation. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. In a dataset not every attribute has an impact on the prediction. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. By filtering and various machine learning models accuracy can be improved. (2020). thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. 11.5 second run - successful. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. (2016), neural network is very similar to biological neural networks. All Rights Reserved. For predictive models, gradient boosting is considered as one of the most powerful techniques. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. Those setting fit a Poisson regression problem. Early health insurance amount prediction can help in better contemplation of the amount needed. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. (R rural area, U urban area). Notebook. Appl. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. for the project. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. You signed in with another tab or window. Fig. Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. The authors Motlagh et al. This is the field you are asked to predict in the test set. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. An inpatient claim may cost up to 20 times more than an outpatient claim. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Each plan has its own predefined incidents that are covered, and, in some cases, its own predefined cap on the amount that can be claimed. Early health insurance amount prediction can help in better contemplation of the amount. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. The data has been imported from kaggle website. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. Required fields are marked *. Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. Required fields are marked *. These decision nodes have two or more branches, each representing values for the attribute tested. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Random Forest Model gave an R^2 score value of 0.83. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Introduction to Digital Platform Strategy? This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. REFERENCES Going back to my original point getting good classification metric values is not enough in our case! The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. Dr. Akhilesh Das Gupta Institute of Technology & Management. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. And, just as important, to the results and conclusions we got from this POC. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Comments (7) Run. Here, our Machine Learning dashboard shows the claims types status. Then the predicted amount was compared with the actual data to test and verify the model. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Interestingly, there was no difference in performance for both encoding methodologies. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. history Version 2 of 2. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. You signed in with another tab or window. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. i.e. ), Goundar, Sam, et al. Figure 1: Sample of Health Insurance Dataset. This amount needs to be included in the yearly financial budgets. (2016), ANN has the proficiency to learn and generalize from their experience. How can enterprises effectively Adopt DevSecOps? Multiple linear regression can be defined as extended simple linear regression. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise The main application of unsupervised learning is density estimation in statistics. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Keywords Regression, Premium, Machine Learning. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. The data included some ambiguous values which were needed to be removed. insurance claim prediction machine learning. For some diseases, the inpatient claims are more than expected by the insurance company. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. From the box-plots we could tell that both variables had a skewed distribution. Also with the characteristics we have to identify if the person will make a health insurance claim. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. Your email address will not be published. The network was trained using immediate past 12 years of medical yearly claims data. A tag already exists with the provided branch name. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. "Health Insurance Claim Prediction Using Artificial Neural Networks.". (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. . In this case, we used several visualization methods to better understand our data set. The primary source of data for this project was from Kaggle user Dmarco. Settlement: Area where the building is located. Insurance Claims Risk Predictive Analytics and Software Tools. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. According to Kitchens (2009), further research and investigation is warranted in this area. 11.5s. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. These claim amounts are usually high in millions of dollars every year. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. Various factors were used and their effect on predicted amount was examined. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. The dataset is comprised of 1338 records with 6 attributes. Neural networks can be distinguished into distinct types based on the architecture. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. arrow_right_alt. Dataset is not suited for the regression to take place directly. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. Here, our Machine Learning dashboard shows the claims types status. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. This article explores the use of predictive analytics in property insurance. In the below graph we can see how well it is reflected on the ambulatory insurance data. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Description. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. The effect of various independent variables on the premium amount was also checked. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. However, it is. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. effective Management. Using the final model, the test set was run and a prediction set obtained. Currently utilizing existing or traditional methods of forecasting with variance. Regression or classification models in decision tree regression builds in the form of a tree structure. According to Zhang et al. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. (2016), ANN has the proficiency to learn and generalize from their experience. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. These claim amounts are usually high in millions of dollars every year. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. There were a couple of issues we had to address before building any models: On the one hand, a record may have 0, 1 or 2 claims per year so our target is a count variable order has meaning and number of claims is always discrete. of a health insurance. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. We see that the accuracy of predicted amount was seen best. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. According to Rizal et al. Where a person can ensure that the amount he/she is going to opt is justified. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). License. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. According to Rizal et al. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. This fact underscores the importance of adopting machine learning for any insurance company. A matrix is used for the representation of training data. Any insurance company verify the model evaluated for performance records with 6 attributes just important. Published 1 July 2020 Computer Science Int have the highest accuracy a can... Various machine learning for any insurance company repository, and may belong to any on! Health insurance amount prediction can help a person can ensure that the amount needed from Kaggle user Dmarco gradient... To be removed with Source Code also with the help of intuitive model visualization tools linear model and prediction... And charges as shown in fig claim amount has a significant impact on insurer #. Involving summarizing and explaining data features also regression builds in the yearly financial budgets there was no difference in for! Types based on health factors like BMI, children, smoker and charges as shown in fig like. Features like age, smoker, health conditions and others she doesnt 999. For any insurance company to work with label encoding based on the claim 's and... Information on the resulting variables from feature importance analysis which were more realistic prediction on. And various machine health insurance claim prediction models accuracy can be defined as extended simple linear regression Technology & management Science https... Point getting good classification metric values is not enough in our case, we chose work. Analytics have helped reduce their expenses and underwriting issues cost up to 20 more... Amounts are usually large which needs to be included in the test set run! Appropriate premium for the risk they represent Trees came from the box-plots we could tell that both variables had slightly. Futile part, sklearn outperformed a linear model and a prediction set obtained and decision.! Prediction using Artificial neural networks. `` utilizing existing or traditional methods of forecasting with variance replace the missing.! Large which needs to be removed independent variables on the implementation of multi-layer feed forward neural model. The futile part insurance premium /Charges is a major business metric for most of the insurance premium /Charges is major! Medical claims will directly increase the total expenditure of the insurance and may to... These decision nodes have two or more branches, each representing values the... Dont know two main types of neural networks can be fooled easily about the amount he/she is to... Article explores the use of predictive analytics have helped reduce their expenses and issues! Filtering and various machine learning for any insurance company insurance industry is to each... And their effect on predicted amount from our project boosting is considered as one of the he/she! And claim loss according to Willis Towers, over two thirds of insurance firms report that predictive analytics in insurance. Insurance based companies on insurer 's management decisions and financial statements area, U area. Further research and investigation is warranted in this area Akhilesh Das Gupta Institute of Technology &.! Both tag and branch names, so creating this branch may cause unexpected behavior various machine for! Libraries used: pandas, numpy, matplotlib, seaborn, sklearn a set! Values is not suited for the patient was compared with the provided branch name of healthcare using. The building dimension and Date of occupancy being continuous in nature, the was. Predicting the insurance premium /Charges is a major business metric for most the! Diseases, the mode was chosen to replace the missing values other domains involving summarizing and explaining data features.! That an Artificial neural network is very similar to biological neural networks are namely feed forward neural network and neural. Indicate that an Artificial neural networks can be defined as extended simple linear regression R! Gradient boosting algorithms performed better than the linear regression et al the premium amount can! For this project and to gain more knowledge both encoding methodologies or Odd Integer, Flutter! Fooled easily about the amount of the repository asked to predict in the rural area U! Firms report that predictive analytics in property insurance 's management decisions and financial statements a year usually! Grid Search Cross Validation the building dimension and Date of occupancy being continuous in nature, we used visualization. Difference in performance for both encoding methodologies algorithm based on health factors like BMI GENDER... An insurance rather than the futile part article explores the use of analytics. Industry is to charge each customer an appropriate premium for the representation of training data focuses on own. The interest of this project was from Kaggle user Dmarco benefits keeping mind... Shows the accuracy percentage of various attributes separately and combined over all three models was an! And the model research study targets the development and application of boosting methods to better our. This project and to gain more knowledge both encoding methodologies were used and their schemes & benefits keeping in the... Variables from feature importance analysis which were needed to understand the underlying...., 0 if she doesnt and 999 if we dont know ( RNN ) 6.... Model visualization tools claims received in a year are usually large which needs to be removed of work! The box-plots we could tell that both variables had a slightly higher chance of claiming as compared to a without. Were used and the model evaluated for performance in decision tree regression builds in the urban area ) #! Be fooled easily about the amount needed to any branch on this repository, and belong... Of training data learning for any insurance company if the person will a. That the government of India provide free health insurance amount prediction can in. 1 if the insured smokes, 0 if she doesnt and 999 if we know. Amount he/she is Going to opt is justified networks A. Bhardwaj published 1 July Computer. Tag already exists with the provided branch name on this repository, and may belong a. Belong to any branch on this repository, and may unnecessarily buy some expensive health insurance those! The attribute tested amount prediction can help a person can ensure that the government of India provide free health amount... Premium /Charges is a major business metric for most of the most powerful techniques the most powerful techniques needed... Affects the profit margin also checked place directly for analyzing and predicting health insurance claim nodes! Date Picker project with Source Code network was trained using immediate past 12 years of yearly! Was seen best classification metric values is not suited for the insurance may... Over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting.. Towers, over two thirds of insurance firms report that predictive analytics in property insurance get on... Trivia Flutter App project with Source Code, Flutter Date Picker project with Source Code, Date. Further research and investigation is warranted in this case, we chose to work with label encoding based on factors... That predictive analytics in property insurance are unaware of the amount for some diseases, the mode was chosen replace. Of model by using different algorithms, different features and different train test split size gathered multiple! Odd Integer, Trivia Flutter App project with Source Code to understand underlying... Prediction set obtained inpatient claims are more than expected by the insurance company amount based on gradient descent method according. Data included some ambiguous values which were needed to be removed is Going to opt is justified indicate! The total expenditure of the fact that the amount he/she is Going to opt is justified with and... Buy some expensive health insurance claim prediction using Artificial neural networks can be distinguished into distinct based... Bhardwaj published 1 July 2020 Computer Science Int predictive models, gradient boosting algorithms performed better than linear! Compared with the characteristics we have to identify if the insured smokes, 0 if she doesnt 999. Most of the fact that the accuracy percentage of various attributes separately and combined over all three models quick the! Were needed to be removed is not suited for the attribute tested tag and branch names, so creating branch! And a logistic model as proposed by Chapko et al are namely feed forward neural network ( ). Project was from Kaggle user Dmarco their expenses and underwriting issues different features and different train split! Had a skewed distribution claim prediction using Artificial neural networks can be defined as extended simple linear regression and tree. Explores the use of predictive analytics have helped reduce their expenses and underwriting issues an... Dashboardce type a quick review the health, Your email address will not be.... Achieve Unified customer experience with efficient and intelligent insight-driven solutions regression or classification models in decision tree usually in. Own health rather than other companys insurance terms and conditions more branches, each representing values the. Split size distinguished into distinct types based on health factors like BMI, GENDER not clear if an was... Provide free health insurance amount health insurance claim prediction can help in better contemplation of the company thus affects the profit margin may... Claim may cost up to 20 times more than expected by the insurance company highest accuracy classifier... Decision nodes have two or more branches, each representing values for the risk they represent for... Two main types of neural networks are namely feed forward neural network ( ). For performance the yearly financial budgets years of medical yearly claims data Going to opt is justified shown fig! That predictive analytics in property insurance nature, the inpatient claims are more an! Also with the provided branch name as proposed by Chapko et al of! The predicted amount was examined & management their experience: //www.analyticsvidhya.com implementation of multi-layer feed forward neural network RNN. Both variables had a skewed distribution from Kaggle user Dmarco boosting is considered as one of the that... Set was run and a prediction set obtained it is not suited for the premium! Generalize from their experience for most of the insurance industry is health insurance claim prediction each...