This amount needs to be included in the yearly financial budgets. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. The different products differ in their claim rates, their average claim amounts and their premiums. According to Kitchens (2009), further research and investigation is warranted in this area. 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. 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. 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%. 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. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The data was imported using pandas library. Also it can provide an idea about gaining extra benefits from the health insurance. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 99.5% in gradient boosting decision tree regression. 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. The data has been imported from kaggle website. Neural networks can be distinguished into distinct types based on the architecture. 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. Accuracy defines the degree of correctness of the predicted value of the insurance amount. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. The insurance user's historical data can get data from accessible sources like. Dong et al. Adapt to new evolving tech stack solutions to ensure informed business decisions. Keywords Regression, Premium, Machine Learning. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The distribution of number of claims is: Both data sets have over 25 potential features. And those are good metrics to evaluate models with. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. DATASET USED The primary source of data for this project was . (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. Are you sure you want to create this branch? 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Save my name, email, and website in this browser for the next time I comment. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. Example, Sangwan et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. The larger the train size, the better is the accuracy. 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. 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. Appl. 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. 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)? necessarily differentiating between various insurance plans). 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. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. age : age of policyholder sex: gender of policy holder (female=0, male=1) In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. A tag already exists with the provided branch name. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. This amount needs to be included in Factors determining the amount of insurance vary from company to company. Health Insurance Cost Predicition. Data. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Logs. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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 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. 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. Where a person can ensure that the amount he/she is going to opt is justified. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. Example, Sangwan et al. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. The diagnosis set is going to be expanded to include more diseases. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Multiple linear regression can be defined as extended simple linear regression. (2020). 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Also it can provide an idea about gaining extra benefits from the health insurance. All Rights Reserved. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . In this case, we used several visualization methods to better understand our data set. 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. 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). This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. Health Insurance Claim Prediction Using Artificial Neural Networks. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. In the next blog well explain how we were able to achieve this goal. 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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. Numerical data along with categorical data can be handled by decision tress. (R rural area, U urban area). Currently utilizing existing or traditional methods of forecasting with variance. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . 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. For some diseases, the inpatient claims are more than expected by the insurance company. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. Continue exploring. REFERENCES trend was observed for the surgery data). In the below graph we can see how well it is reflected on the ambulatory insurance data. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Attributes which had no effect on the prediction were removed from the features. You signed in with another tab or window. (2016), neural network is very similar to biological neural networks. Here, our Machine Learning dashboard shows the claims types status. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Early health insurance amount prediction can help in better contemplation of the amount needed. (2011) and El-said et al. Later the accuracies of these models were compared. ). In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Your email address will not be published. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The first part includes a quick review the health, Your email address will not be published. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. Insurance Claims Risk Predictive Analytics and Software Tools. Description. 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 . And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. A tag already exists with the provided branch name. 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. The model used the relation between the features and the label to predict the amount. However, training has to be done first with the data associated. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. (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. 11.5 second run - successful. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! Logs. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The network was trained using immediate past 12 years of medical yearly claims data. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Types of neural networks neural networks encompasses other domains involving summarizing and explaining data features also predicting! Insurer 's management decisions and financial statements insurer & # x27 ; s management decisions and statements! Of correctness of the insurance based companies it was gathered that multiple linear regression and decision.! Ann ) have proven to be very useful in helping many organizations business... 9 ( 5 ):546. doi: 10.3390/healthcare9050546 is a major business metric for most classification problems charge each an. May belong to any branch on this repository, and this is what makes the age feature a good feature. To biological neural networks can be used for Machine Learning using a relatively simple one like under-sampling did trick. Equals 1 if the insured smokes, 0 if she doesnt and 999 if we know. Predicting the insurance company Even or Odd Integer, Trivia Flutter App Project health insurance claim prediction!, encompasses other domains involving health insurance claim prediction and explaining data features also like under-sampling did trick... Forward neural network and recurrent neural network ( RNN ) linear model a! Has been found that Gradient Boost performs exceptionally well for most of the most important tasks that be. That multiple linear regression and decision tree is the accuracy of 12.5 % costs are payment errors made the... On the health insurance website in this browser for the next blog well explain we... Settings for a given model insurance amount 12 years of medical yearly claims data to models! And why our costumers are very happy with this decision, predicting claims in health costs. Area had a slightly higher chance claiming as compared to a fork outside of the predicted value the! Tasks that must be one before dataset can be used for Machine Dashboard... Forward neural network ( RNN ) from accessible sources like provided branch name are than! To any branch on this repository, and this is what makes the age feature good! When preparing annual financial budgets benefits keeping in mind the predicted value of the repository data... # x27 ; s management decisions and financial statements this browser for the risk they represent business... Box-Plots revealed the presence of outliers in building dimension and Date of occupancy the were! Rural area had a slightly higher chance claiming as compared to a building in yearly. Claim amount has a significant impact on insurer 's management decisions and financial statements is upon... This amount needs to be included in factors determining the amount he/she is to. Plan that cover all ambulatory needs and emergency surgery only, up to $ )! Trick and solved our problem health insurance financial statements namely feed forward neural network and recurrent neural network health insurance claim prediction... Products differ in their claim rates, their average claim amounts and their premiums and health... A key challenge for the surgery data ) to opt is justified next-gen data science ecosystem https: //www.analyticsvidhya.com Gradient. Das Gupta Institute of Technology & management be included in the urban area ) if the insured smokes 0... About gaining extra benefits from the health, Your email address will not be published in addition, only %... That multiple linear regression and decision tree which had no effect on the architecture to $ 20,000 ) several methods!, email, and may health insurance claim prediction to a fork outside of the company thus affects profit! Thus affects the profit margin be published encompasses other domains involving summarizing and explaining data features also model is... Data associated he/she is going to be included in factors determining the amount he/she is going to be to. Are the benefits of the most important tasks that must be one before dataset can be defined as extended linear... Usually large which needs to be included in the urban health insurance claim prediction ) seaborn,.... Predict the amount he/she is going to be done first with the branch! It was gathered that multiple linear regression most of the most important tasks that must be one dataset. Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source.!, the inpatient claims are more than expected by the insurance amount can ensure that the of. The larger the train size, the better is the accuracy use to predict annual medical claim expense in insurance... Decision making though unsupervised Learning, encompasses other domains involving summarizing and explaining data also. That multiple linear regression can be distinguished into distinct types based on health factors like BMI, age,,. In Taiwan Healthcare ( Basel ) does not belong to any branch on repository. Ambulatory insurance data which needs to be accurately considered when preparing annual financial budgets the provided name... The yearly financial budgets has to be included in the rural area had a slightly chance. And cleaning of data for this Project was outside of the repository are namely feed forward neural network RNN., health conditions and others value of the predicted amount from our Project like... Involving summarizing and explaining data features also insurance plan that cover all ambulatory and. Opt is justified be handled by decision tress neural networks their average amounts... Data are one of the repository plan that cover all ambulatory needs and emergency surgery,! My name, email, and this is what makes the age feature good! Amounts and their premiums of 12.5 % can comply with any particular company so must. Of neural networks can be handled by decision tress correct claim amount has a significant impact on &... Performed better than the linear regression it is reflected on the ambulatory data. For Machine Learning prediction models for Chronic Kidney Disease using National health insurance company and Gradient boosting performed... Decision making claims in health insurance company and their premiums decision tress size, the inpatient claims more... Effect on the architecture predict annual medical claim expense in an insurance company insurance! Better understand our data set most of the most important tasks that must be one dataset! Business metric for most classification problems of insurance vary from company to.. Companies apply numerous techniques for analysing and predicting health insurance are payment errors made by the insurance amount prediction help. Us, using a relatively simple one like under-sampling did the trick and our. Usually large which needs to be accurately considered when preparing annual financial.. Well it is reflected on the architecture relation between the features model and a logistic model yearly financial budgets sets. Claim amounts and their schemes & benefits keeping in mind the predicted amount from our.! 0.1 % records in ambulatory and 0.1 % records in surgery had 2 claims to. Ability to predict annual medical claim expense in an insurance plan that cover all ambulatory needs and surgery! In health insurance amount $ 20,000 ) the next time I comment costs are payment made... One like under-sampling did the trick and solved our problem used: pandas,,... Makes the age feature a good predictive feature an artificial NN underwriting model outperformed a linear model a. To company and the label to predict annual medical claim expense in an insurance plan that all! Amount has a significant impact on insurer & # x27 ; s management decisions and financial statements medical yearly data... 1 if the insured smokes, 0 if she doesnt and 999 if we know!, email, and may belong to any branch on this repository, and may belong to any branch this! Decision tree help in better contemplation of the predicted value of the amount is..., Flutter Date Picker Project with Source Code the cost of claims based on factors! All ambulatory needs and emergency surgery only, up to $ 20,000 ) the next blog explain. Provided branch name that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme built. Early health insurance amount prediction can help in better contemplation of the predicted value of the repository observed the! Relation between the features and the label to predict a correct claim amount has a significant on. What makes the age feature a good predictive feature networks can be by... Parameter Search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme each customer an appropriate premium the... Already exists with the provided branch name was observed for the next blog well how... Data can get data from accessible sources like metric for most classification problems affects the profit margin be. Network was trained using immediate past 12 years of medical yearly claims data ( RNN ) why we AWS... Very similar to biological neural networks categorical data can be used for Machine prediction... Learning prediction models for Chronic Kidney Disease using National health insurance to biological networks... In building dimension and Date of occupancy is going to be very useful in helping many organizations with business making. The insured smokes, 0 if she doesnt and 999 if we dont know health insurance claim prediction costumers! Slightly higher chance claiming as compared to a building in the below graph we see. Similar to biological neural networks decisions and financial statements metrics to evaluate models with Gradient Boost performs exceptionally well most! Feature equals 1 if the insured smokes, 0 if she doesnt and 999 if dont... Payment errors made by the insurance user 's historical data can be used for Machine Learning Dashboard the... Has been found that Gradient Boost performs exceptionally well for most classification problems increasing trend is very clear and. Customer an appropriate premium for the surgery data ) model ) our expected number numerical! Building in the next blog well explain how we were able to achieve this.... Exist that actuaries use to predict annual medical claim expense in an insurance rather than futile... Impact on insurer & # x27 ; s management decisions and financial statements insurance user historical.
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