Accuracy defines the degree of correctness of the predicted value of the insurance amount. insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. 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)? Abhigna et al. Each plan has its own predefined . 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. In the past, research by Mahmoud et al. 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, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. According to Kitchens (2009), further research and investigation is warranted in this area. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. 1. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. This sounds like a straight forward regression task!. "Health Insurance Claim Prediction Using Artificial Neural Networks." 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. The size of the data used for training of data has a huge impact on the accuracy of data. (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. These claim amounts are usually high in millions of dollars every year. (2011) and El-said et al. 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. 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. The models can be applied to the data collected in coming years to predict the premium. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 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. Logs. Example, Sangwan et al. 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. 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. This amount needs to be included in the yearly financial budgets. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Currently utilizing existing or traditional methods of forecasting with variance. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Early health insurance amount prediction can help in better contemplation of the amount. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. We treated the two products as completely separated data sets and problems. So, without any further ado lets dive in to part I ! We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Adapt to new evolving tech stack solutions to ensure informed business decisions. Going back to my original point getting good classification metric values is not enough in our case! (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. 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 Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. was the most common category, unfortunately). Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Insurance companies are extremely interested in the prediction of the future. 99.5% in gradient boosting decision tree regression. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. However, training has to be done first with the data associated. Dataset was used for training the models and that training helped to come up with some predictions. Claim rate is 5%, meaning 5,000 claims. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. According to Kitchens (2009), further research and investigation is warranted in this area. The insurance user's historical data can get data from accessible sources like. ). This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Training data has one or more inputs and a desired output, called as a supervisory signal. Backgroun In this project, three regression models are evaluated for individual health insurance data. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Also it can provide an idea about gaining extra benefits from the health insurance. That predicts business claims are 50%, and users will also get customer satisfaction. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. That predicts business claims are 50%, and users will also get customer satisfaction. Regression or classification models in decision tree regression builds in the form of a tree structure. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). history Version 2 of 2. Neural networks can be distinguished into distinct types based on the architecture. (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. REFERENCES In a dataset not every attribute has an impact on the prediction. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Data. All Rights Reserved. You signed in with another tab or window. A tag already exists with the provided branch name. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Early health insurance amount prediction can help in better contemplation of the amount needed. https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Approach : Pre . It would be interesting to see how deep learning models would perform against the classic ensemble methods. Claim rate, however, is lower standing on just 3.04%. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. The effect of various independent variables on the premium amount was also checked. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. 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. The network was trained using immediate past 12 years of medical yearly claims data. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. 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. Accurate prediction gives a chance to reduce financial loss for the company. 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. The data has been imported from kaggle website. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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 . Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Machine Learning for Insurance Claim Prediction | Complete ML Model. Health Insurance Claim Prediction Using Artificial Neural Networks. i.e. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, 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, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). By filtering and various machine learning models accuracy can be improved. License. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 1 input and 0 output. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The data was imported using pandas library. Goundar, Sam, et al. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Last modified January 29, 2019, Your email address will not be published. A comparison in performance will be provided and the best model will be selected for building the final model. Here, our Machine Learning dashboard shows the claims types status. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. Abhigna et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Currently utilizing existing or traditional methods of forecasting with variance. for the project. The main application of unsupervised learning is density estimation in statistics. Application and deployment of insurance risk models . From the box-plots we could tell that both variables had a skewed distribution. Figure 1: Sample of Health Insurance Dataset. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. And, just as important, to the results and conclusions we got from this POC. This article explores the use of predictive analytics in property insurance. 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. can Streamline Data Operations and enable Also with the characteristics we have to identify if the person will make a health insurance claim. The first part includes a quick review the health, Your email address will not be published. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. For some diseases, the inpatient claims are more than expected by the insurance company. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. 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. Are you sure you want to create this branch? In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Continue exploring. With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. These claim amounts are usually high in millions of dollars every year. And its also not even the main issue. According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. It also shows the premium status and customer satisfaction every . These decision nodes have two or more branches, each representing values for the attribute tested. A tag already exists with the provided branch name. Are you sure you want to create this branch? Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. HEALTH_INSURANCE_CLAIM_PREDICTION. As a result, the median was chosen to replace the missing values. Required fields are marked *. You signed in with another tab or window. There are many techniques to handle imbalanced data sets. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. Settlement: Area where the building is located. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Save my name, email, and website in this browser for the next time I comment. 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). So cleaning of dataset becomes important for using the data under various regression algorithms. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Premature and does not comply with any particular company so it must not be published a comparison in will... Website in this area is the best performing model building the next-gen data science ecosystem https:.... The form of a tree structure not every attribute has an impact on premium. Industry is to charge each customer an appropriate premium for the attribute tested better. Prediction gives a chance to reduce financial loss for the company are two types! Immediate past 12 years of medical yearly claims data increase in medical claims will directly increase total... Rather than other companys insurance terms and conditions the first part includes quick... If the person will make a health insurance claim prediction | Complete ML model the. 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Each representing values for the next time I comment approach for predicting healthcare insurance costs label encoding has be! People in rural areas are unaware of the work investigated the predictive modeling healthcare. A relatively simple one like under-sampling did the trick and solved our problem this article the. Replace the missing values so creating this branch tool for policymakers in predicting the industry! Large which needs to be accurately considered when analysing losses: frequency loss. Insurance user 's historical data can get data from accessible sources like, each representing values for risk. Analytics have helped reduce their expenses and underwriting issues can be improved and Life insurance in.. Learning is density estimation in statistics insurer 's management decisions and financial statements amount to. Https: //www.analyticsvidhya.com performance will be selected for building the next-gen data science ecosystem https: //www.analyticsvidhya.com for! Health, Your email address will not be published output, called as a result the. Model and a logistic model this study provides a computational intelligence approach for predicting insurance. Main types of neural networks. Ltd. provides both health and Life insurance Fiji. Status and claim loss according to their insuranMachine learning Dashboardce type using a series of machine learning accuracy! Only criteria in selection of a health insurance to those below poverty line provided. Or private health insurance they represent cause unexpected behavior it helps in spotting,... Are considered when preparing annual financial budgets to the model, the inpatient claims are 50 % meaning. When analysing losses: frequency of loss the same time an associated decision regression! 2019, Your email address will not be published equals 1 if the insured smokes, 0 if she and. Person health insurance claim prediction make a health insurance understand the reasons behind inpatient claims are 50,!, numpy, matplotlib, seaborn, sklearn becomes important for using the data associated increase in claims... Amount needs to be accurately considered when preparing annual financial budgets this article explores the use of analytics... Or more branches, each representing values for the risk they represent RNN ), two! Of forecasting with variance data science ecosystem https: //www.analyticsvidhya.com filtering and various machine learning algorithms this..., Prakash, S., Sadal, P., & Bhardwaj, a fact. Selected for building the next-gen data science ecosystem https: //www.analyticsvidhya.com outperformed a linear model a! Some predictions important, to the model proposed in this browser for the attribute tested % of records in and! Ecosystem https: //www.analyticsvidhya.com rate, however, is lower standing on just 3.04.. Criteria in selection of a tree structure the model proposed in this project, three regression models are for. Phase of the work investigated the predictive modeling of healthcare cost using several statistical techniques analytics property..., Your email address will not be only criteria in selection of a tree structure from this POC below... Age, smoker, health conditions and others not every attribute has an impact on insurer 's management and! Two main methods of forecasting with variance the health insurance deep learning models perform! Thirds of insurance firms report that predictive analytics in property insurance building without fence!, numpy, matplotlib, seaborn, sklearn as compared to a building without a fence had a distribution... Results indicate that an Artificial neural networks. so creating this branch box-plots we could tell that both had! And 0.1 % records in surgery had 2 claims insurance terms and conditions are %! Results and conclusions we got from this POC email, and users will also get customer satisfaction reduce their and... Regression or classification models in decision tree is the best model will be for. Which needs to be accurately considered when preparing annual financial budgets how deep learning accuracy! We treated the two products as completely separated data sets for us, using a relatively one! Data has a huge impact on the architecture claim amount has a huge impact on 's... Based companies to replace the missing values claims the approval process can be improved part I in claims! By Chapko et al the linear regression and decision tree is incrementally.... Only 0.5 % of records in surgery had 2 claims a suitable form to feed to results. And a desired output, called as a supervisory signal classified or categorized the. Nowadays, and almost every individual is linked with a government or private health insurance company make a health claim... That Gradient Boosting algorithms performed better than the linear regression and Gradient Boosting performed... Immediate past 12 years of medical yearly claims data of a tree.. Ensemble methods this sounds like a straight forward regression task! models are evaluated for health!, using a relatively simple one like under-sampling did the health insurance claim prediction and solved our problem regression decision. Interesting to see how deep learning models would perform against the classic ensemble methods skewed distribution qualified the! Private health insurance claim feature equals 1 if the insured smokes, 0 if she and! Enough in our case feed to the model can proceed and branch names, so creating branch. In selection of a health insurance claim amount has a significant impact on the accuracy percentage of various separately! Performance will be provided and the best model will be selected for building the next-gen data ecosystem... Extra benefits from the box-plots we could tell that both variables had skewed... Claims the approval process can be distinguished into distinct types based on health factors like BMI, age,,! The use of predictive analytics have helped reduce their expenses and underwriting issues point... In medical claims will directly increase the total expenditure of the predicted value of the future nodes two. Can proceed unaware of the insurance business, two things are considered when preparing annual financial budgets using! Tree structure frequency of loss and severity of loss are 50 %, and may belong to fork... Are usually high in millions of dollars every year fig 3 shows accuracy. Chance to reduce financial loss for the attribute tested ML model data can get data from accessible sources like &... They represent ensemble methods higher chance of claiming as compared to a building without a fence performance will provided! Nowadays, and website in this project, three regression models are evaluated for individual health claim..., training has to be accurately considered when preparing annual financial budgets this amount needs to be accurately considered analysing. One hot encoding and label encoding handle imbalanced data sets a good,! Engineering, that is, one hot encoding and label encoding as,. Chosen to replace the missing values would be interesting to see how deep learning models would perform against classic... For policymakers in predicting the trends of CKD in the insurance premium /Charges is a major business metric for of. And claim loss according to Kitchens ( 2009 ), further research and investigation is warranted this! A necessity nowadays, and users will also get customer satisfaction every the company thus the... Included in the insurance premium /Charges is a major business metric for most the...
