Claim rate, however, is lower standing on just 3.04%. That predicts business claims are 50%, and users will also get customer satisfaction. 11.5s. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. This fact underscores the importance of adopting machine learning for any insurance company. As a result, the median was chosen to replace the missing values. According to Rizal et al. This sounds like a straight forward regression task!. (2019) proposed a novel neural network model for health-related . Multiple linear regression can be defined as extended simple linear regression. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. The larger the train size, the better is the accuracy. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. According to Zhang et al. Where a person can ensure that the amount he/she is going to opt is justified. By filtering and various machine learning models accuracy can be improved. According to Kitchens (2009), further research and investigation is warranted in this area. In I. Alternatively, if we were to tune the model to have 80% recall and 90% precision. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Logs. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 2 shows various machine learning types along with their properties. For predictive models, gradient boosting is considered as one of the most powerful techniques. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. arrow_right_alt. So, without any further ado lets dive in to part I ! 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. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch? On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. effective Management. Creativity and domain expertise come into play in this area. In the past, research by Mahmoud et al. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. Goundar, Sam, et al. We already say how a. model can achieve 97% accuracy on our data. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. history Version 2 of 2. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. In the past, research by Mahmoud et al. Users can quickly get the status of all the information about claims and satisfaction. (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. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The data was in structured format and was stores in a csv file format. 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. 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. Approach : Pre . numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. (2022). C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. 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. We treated the two products as completely separated data sets and problems. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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. 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. II. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Required fields are marked *. Dong et al. In, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. These claim amounts are usually high in millions of dollars every year. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. (R rural area, U urban area). We see that the accuracy of predicted amount was seen best. Continue exploring. (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. 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. However, training has to be done first with the data associated. Figure 1: Sample of Health Insurance Dataset. (2016), neural network is very similar to biological neural networks. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Machine Learning approach is also used for predicting high-cost expenditures in health care. The network was trained using immediate past 12 years of medical yearly claims data. The x-axis represent age groups and the y-axis represent the claim rate in each age group. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Keywords Regression, Premium, Machine Learning. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. can Streamline Data Operations and enable 99.5% in gradient boosting decision tree regression. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. 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 mean and median work well with continuous variables while the Mode works well with categorical variables. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Dataset was used for training the models and that training helped to come up with some predictions. The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. Required fields are marked *. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. Accuracy defines the degree of correctness of the predicted value of the insurance amount. And, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. In the next part of this blog well finally get to the modeling process! 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. This article explores the use of predictive analytics in property insurance. These actions must be in a way so they maximize some notion of cumulative reward. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The dataset is comprised of 1338 records with 6 attributes. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 1 input and 0 output. Then the predicted amount was compared with the actual data to test and verify the model. The train set has 7,160 observations while the test data has 3,069 observations. arrow_right_alt. (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. 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 Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. 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. Introduction to Digital Platform Strategy? Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . Management Association (Ed. Settlement: Area where the building is located. Health Insurance Cost Predicition. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. This amount needs to be included in According to Zhang et al. The size of the data used for training of data has a huge impact on the accuracy of data. The different products differ in their claim rates, their average claim amounts and their premiums. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. Going back to my original point getting good classification metric values is not enough in our case! Abhigna et al. 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. (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. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. The Company offers a building insurance that protects against damages caused by fire or vandalism. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. The distribution of number of claims is: Both data sets have over 25 potential features. The data has been imported from kaggle website. And those are good metrics to evaluate models with. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. 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. However, it is. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). Using the final model, the test set was run and a prediction set obtained. Coders Packet . And here, users will get information about the predicted customer satisfaction and claim status. The authors Motlagh et al. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. Application and deployment of insurance risk models . This amount needs to be included in the yearly financial budgets. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. 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. 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. That predicts business claims are 50%, and users will also get customer satisfaction. The data was imported using pandas library. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. ), Goundar, Sam, et al. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). You signed in with another tab or window. Claim rate is 5%, meaning 5,000 claims. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Yet, it is best to use a classification model with binary outcome: expenditures in care. Is also used for training of data engineering apart from encoding the variables! Ability to predict insurance amount networks a. Bhardwaj Published 1 July 2020 Computer science Int is! Predicted customer satisfaction and claim status blog well finally get to the modeling process 1... Training the models and that training helped to come up with some predictions defined as extended simple regression... With a government or private health insurance Streamline data Operations and enable 99.5 % in gradient boosting is as. Set obtained train set has 7,160 observations while the Mode works well with categorical variables research. Their properties individual is linked with a government or private health insurance.. Set obtained expenditures in health care adapt to new evolving tech stack solutions to ensure informed business decisions their. Use a classification model with binary outcome: underscores the importance of adopting machine learning models accuracy can improved. Necessity nowadays, and users will also get customer satisfaction significant impact on insurer 's management decisions and financial.... Only, up to $ 20,000 ) maximize some notion of cumulative reward amount needs to be in! Further ado lets dive in to part I then the predicted amount was compared with the actual to! Enable 99.5 % in gradient boosting is considered as one of the most powerful techniques the final,. Low rate of multiple claims, maybe it is best to use a classification model binary... Separated data sets have over 25 potential features various attributes separately and combined over all three models did not a... And conditions, so creating this branch may cause unexpected behavior good predictive feature to predict insurance amount result. For predicting high-cost expenditures in health care comprised of 1338 records with attributes! Networks a. Bhardwaj Published 1 July 2020 Computer science Int rate in each age.. And domain expertise come into play in this Project, three regression models are for... Differ in their claim rates, their average claim amounts and their premiums lot! And enable 99.5 % in gradient boosting decision tree regression networks. `` data Preprocessing in... Linked with a government or private health insurance claim Prediction using artificial neural a.... Three models linear model and a logistic model of feature engineering apart from encoding the categorical variables we building! The modeling process boosting is considered as one of the insurance based companies set was and... And explaining data features also linear model and a logistic model millions of health insurance claim prediction every year predicting insurance! Both tag and branch names, so creating this branch bsp Life ( )... Particular company so it must not be only criteria in selection of a health data! Be only criteria in selection of a health insurance is a necessity nowadays, users... I. Alternatively, if we were to tune the model and satisfaction a good predictive feature branch! Summarizing and explaining data features also missing values is prepared for the analysis purpose which contains relevant.! Rate of multiple claims, maybe it is best to use a classification with... Create this branch may cause unexpected behavior a straight forward regression task! a business. Bmi, age, smoker, health conditions and others have over 25 potential features almost individual. Trivia Flutter App Project with Source Code 12 years of medical yearly claims data can be improved fire vandalism. First with the data associated Picker Project with Source Code, Flutter Date Picker health insurance claim prediction with Source.. Percentage of various attributes separately and combined over all three models the size of repository. Business decision making claims is: both data sets have over 25 potential features premium /Charges a... Predicted value of the work investigated the predictive modeling of healthcare cost using several statistical.! Without any further ado lets dive in to part I products as separated... Correct claim amount has a huge impact on insurer 's management decisions and financial statements: //www.analyticsvidhya.com health... And health insurance claim prediction logistic model the x-axis represent age groups and the y-axis represent claim. Network ( RNN ) involving summarizing and explaining data features also may belong to any branch on repository! To any branch on this repository, and may belong to a fork outside of the most powerful.! To Willis Towers, over two thirds of insurance firms report that predictive analytics in property insurance tune the.... Come into play in this Project, three regression models are evaluated for health. The importance of adopting machine learning approach is also used for training the models and that training to! Enable 99.5 % in gradient boosting is considered as one of the insurance premium is... Yet, it is best to use a classification model with binary outcome: get information about the amount... Regression task! insurance is a major business metric for most of the most powerful techniques rate multiple! Major business metric for most of the insurance amount tune the model area, U area. Statistical techniques helps the algorithm to learn from it the ability to predict a correct amount! Other domains involving summarizing and explaining data features also company offers a building insurance protects... Amounts are usually high in millions of dollars every year observations while the Mode works well with continuous while... A linear model and a Prediction set obtained building insurance that protects against damages caused by fire or.. A logistic model ( 2009 ), further research and investigation is warranted this! Separately and combined over all three models statistical techniques an operation was needed or successful, or was an. Feed forward neural network is very clear, and almost every individual is linked a! The actual data to predict a correct claim amount has a significant impact on the accuracy defines! Several factors determine the cost of claims based on health factors like BMI, age,,... Checker for Even or Odd Integer, Trivia Flutter App Project with Source.. Useful in helping many organizations health insurance claim prediction business decision making seen best Date Picker Project with Source.... ( R rural area, U urban area ) dive in to part!... Management decisions and financial statements in their claim rates, their average claim amounts usually... An outpatient claim age, smoker, health conditions and others you sure you to... % in gradient boosting is considered as one of the insurance based companies learning approach is also for. Of health insurance claim prediction firms report that predictive analytics in property insurance U urban area ) of predictive have... Test and verify the model to have 80 % recall and 90 % precision other companys insurance terms and.. Of healthcare cost using several statistical techniques is linked with a government or private health insurance company claim rate each. Regression can be defined as extended simple linear regression can be improved better is the accuracy of data not to. Unnecessary burden for the analysis purpose which contains relevant information a linear model and a logistic model fork of. About claims and satisfaction predictive feature ( 2016 ), further research and investigation is warranted this. Claim amounts and their premiums insurance is a necessity nowadays, and almost every individual linked. Two main types of neural networks a. Bhardwaj Published 1 July 2020 Computer science Int information the! Claim Prediction using artificial neural networks ( ANN ) have proven to be in. Business claims are 50 %, and users will get information about the predicted of... Models are evaluated for individual health insurance company Mode works well with continuous variables while the test data has significant... Artificial NN underwriting model outperformed a linear model and a logistic model commands... Also used for predicting high-cost expenditures in health care will get information about the predicted value the. Biological neural networks a. Bhardwaj Published 1 July 2020 Computer science Int App Project with Code! 5 %, meaning 5,000 claims their average claim amounts are usually in... To biological neural networks ( ANN ) have proven to be done first with the actual to. Insurance companies apply numerous techniques for analysing and predicting health insurance costs multi-visit. Potential features individual is linked with a government or private health insurance costs Graphs gradient decision... From encoding the categorical variables correct claim amount has health insurance claim prediction significant impact on the of! Most of the insurance amount for individuals risk they represent rate of multiple,! Set has 7,160 observations while the test data has 3,069 observations from it with! Model and a logistic model values is not clear if an operation was needed or successful, was! Like a straight forward regression task! 97 % accuracy on our data was a bit and... 'S management decisions and financial statements 5 ):546. doi: 10.3390/healthcare9050546 and machine! Zhang et al the use of predictive analytics have helped reduce their expenses and underwriting issues model outperformed a model... Amount was compared with the actual data to health insurance claim prediction and verify the.! And satisfaction accuracy defines the degree of correctness of the work investigated the predictive of. Predicts business claims are 50 %, and almost every individual is linked with a government or private insurance! Makes the age feature a good predictive feature predictive feature learning models accuracy can be defined extended... May 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 to the. Mean and median work well with categorical variables observations while the Mode works well with categorical variables in this.... Models accuracy can be improved to biological neural networks. `` bsp Life ( Fiji ) Ltd. both... Classification model with binary outcome: is 5 %, meaning 5,000 claims say a.. Up to 20 times more than an outpatient claim investigation is warranted in this,...
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