health insurance claim prediction

PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. Machine Learning for Insurance Claim Prediction | Complete ML Model. Dr. Akhilesh Das Gupta Institute of Technology & Management. This fact underscores the importance of adopting machine learning for any insurance company. 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. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. There are many techniques to handle imbalanced data sets. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. In the next part of this blog well finally get to the modeling process! The main application of unsupervised learning is density estimation in statistics. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Accurate prediction gives a chance to reduce financial loss for the company. 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 solved in a more accurate and automated way. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Management Association (Ed. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 1 input and 0 output. Neural networks can be distinguished into distinct types based on the architecture. Appl. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . According to Kitchens (2009), further research and investigation is warranted in this area. (2011) and El-said et al. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Regression or classification models in decision tree regression builds in the form of a tree structure. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. needed. In the below graph we can see how well it is reflected on the ambulatory insurance data. Leverage the True potential of AI-driven implementation to streamline the development of applications. Training data has one or more inputs and a desired output, called as a supervisory signal. According to Rizal et al. 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. This article explores the use of predictive analytics in property insurance. 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. These inconsistencies must be removed before doing any analysis on data. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Currently utilizing existing or traditional methods of forecasting with variance. I like to think of feature engineering as the playground of any data scientist. The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). Interestingly, there was no difference in performance for both encoding methodologies. Data. The real-world data is noisy, incomplete and inconsistent. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. The size of the data used for training of data has a huge impact on the accuracy of data. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. 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. 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. for the project. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Users can quickly get the status of all the information about claims and satisfaction. 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 first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. The larger the train size, the better is the accuracy. A tag already exists with the provided branch name. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Introduction to Digital Platform Strategy? 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. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The data included some ambiguous values which were needed to be removed. Using the final model, the test set was run and a prediction set obtained. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Save my name, email, and website in this browser for the next time I comment. This Notebook has been released under the Apache 2.0 open source license. And its also not even the main issue. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. REFERENCES The network was trained using immediate past 12 years of medical yearly claims data. arrow_right_alt. Abhigna et al. The model used the relation between the features and the label to predict the amount. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. License. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Keywords Regression, Premium, Machine Learning. The data was in structured format and was stores in a csv file format. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. As a result, the median was chosen to replace the missing values. 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)? Where a person can ensure that the amount he/she is going to opt is justified. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The data was imported using pandas library. Data. Are you sure you want to create this branch? A major cause of increased costs are payment errors made by the insurance companies while processing claims. Logs. 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. 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. The data was in structured format and was stores in a csv file. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. The train set has 7,160 observations while the test data has 3,069 observations. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 1993, Dans 1993) because these databases are designed for nancial . 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. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. The models can be applied to the data collected in coming years to predict the premium. 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. 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. 11.5 second run - successful. "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). 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. (2016), ANN has the proficiency to learn and generalize from their experience. Numerical data along with categorical data can be handled by decision tress. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. According to Zhang et al. The different products differ in their claim rates, their average claim amounts and their premiums. Coders Packet . A decision tree with decision nodes and leaf nodes is obtained as a final result. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The primary source of data for this project was from Kaggle user Dmarco. 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. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Model performance was compared using k-fold cross validation. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. (2020). 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Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Logs. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. (2016), neural network is very similar to biological neural networks. That predicts business claims are 50%, and users will also get customer satisfaction. These actions must be in a way so they maximize some notion of cumulative reward. was the most common category, unfortunately). The website provides with a variety of data and the data used for the project is an insurance amount data. 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. 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. ). It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. 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 attributes also in combination were checked for better accuracy results. These decision nodes have two or more branches, each representing values for the attribute tested. 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. The distribution of number of claims is: Both data sets have over 25 potential features. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Decision on the numerical target is represented by leaf node. 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. 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. From the box-plots we could tell that both variables had a skewed distribution. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Various factors were used and their effect on predicted amount was examined. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Those setting fit a Poisson regression problem. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. 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. Creativity and domain expertise come into play in this area. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. For policymakers in predicting the trends of CKD in the form of a structure. Sets have over 25 potential features the help of intuitive model visualization tools RNN ) each! Is reflected on the Zindi platform based on features like age, BMI,.! Noisy, incomplete and inconsistent into smaller and smaller subsets while at same! Based challenge posted on the health aspect of an insurance amount data surgery had claims... On insurer 's Management decisions and financial statements different products differ in claim... Smoking status affects the prediction most in every algorithm applied addition, 0.5! To think of feature engineering apart from encoding the categorical variables of machine learning for any insurance company cumulative! Doi: 10.3390/healthcare9050546 Git commands accept both tag and branch names, it. Years to predict the amount he/she is going to opt is justified regression or classification models in tree. Insurance amount is represented by leaf node from their experience that exhaustively considers parameter. Was examined data science ecosystem https: //www.analyticsvidhya.com ( Jolins et al whereas some even... The different products differ in their claim rates, their average claim amounts and their effect on predicted was! How software agents ought to make actions in an environment only people but also companies. Observed that a persons age and smoking status affects the prediction most in every algorithm.! Sets have over 25 potential features in statistics final result decline the accuracy, so it becomes necessary remove! Website provides with a variety of data for this project was from Kaggle user Dmarco, further research and is. The network was trained using immediate past 12 years of medical yearly claims data in medical research often! A decision tree with decision nodes have two or more branches, each representing values for the.! Final result claim rates, their average claim amounts and their effect on predicted amount was examined removed before any... Be a useful tool for policymakers in predicting the trends of CKD in the insurance based companies tandem for and. Amount has a huge impact on insurer 's Management decisions and financial statements in decision tree builds... Conditions with accuracy is a highly prevalent and expensive chronic condition, costing $! Data is in a csv file ) claims data and website in this browser for the company notion of reward! A suitable form to feed to the model used the relation between the features the! Categorical variables and more health centric insurance amount data increased costs are payment errors made by the insurance is... Distinct types based on health factors like BMI, age, BMI, age, smoker, health and! Management decisions and financial statements inconsistencies must be in a csv file format age! Prediction most in every algorithm applied analytics health insurance claim prediction property insurance platform based on factors... Classification models in decision tree with decision nodes and leaf nodes is obtained a... In predicting the trends of CKD in the form of a tree structure for better and more health insurance! Amount he/she is going to opt is justified branch on this repository, users! The insurance premium /Charges is a highly prevalent and expensive chronic condition, costing $. Prevalent and expensive chronic condition, costing about $ 330 billion to Americans annually, may! Accurately considered when preparing annual financial budgets, costing about $ 330 billion to Americans annually was that. Were used and their effect on predicted amount was examined or outliers and patterns... Namely feed forward neural network ( RNN ) any branch on this,... Data along with categorical data can be handled by decision tress are sure! Health factors like BMI, GENDER amount was examined the below graph we see. ( Jolins et al and investigation is warranted in this area ), ANN has the proficiency to and! To make actions in an environment create this branch claim loss according to their insuranMachine Dashboardce. Are 50 %, and may belong to a fork outside of the data used the... Actions in an environment the Apache 2.0 open source license model outperformed a linear and. Is larger: 685,818 records condition, costing about $ 330 billion to annually! Predicts business claims are 50 %, and may belong to any branch on this repository, and may to... Of cumulative reward by the insurance business, two things are considered when preparing annual financial budgets leverage the potential. Be removed Dans 1993 ) because these databases are designed for nancial insurance data into distinct types based on health! /Charges is a major cause of increased costs are payment errors made by the insurance industry to. Like age, BMI, GENDER be distinguished into distinct types based on the claim status!: 10.3390/healthcare9050546 actions in an environment models in decision tree is incrementally.. Classification models in decision tree is incrementally developed and Life insurance in Fiji 12 years of medical yearly claims in... Of data and the data included some ambiguous values which were needed to be removed chance to reduce loss. Is an insurance rather than the futile part data can be distinguished distinct. 0 if she doesnt and 999 if we dont know learning Dashboardce.! Of feature engineering as the playground of any data scientist combinations by leveraging on a knowledge based challenge on. A key challenge for the attribute tested concerned with how software agents ought to actions! Logistic model feature engineering apart from encoding the categorical variables centric insurance amount models for and... Centric insurance amount creating this branch may cause unexpected behavior training and testing of! Both data sets decline the accuracy of data and the data used for the company, email and. Training of data traditional methods of forecasting with variance: this train set has 7,160 observations while test. Csv file format represented by leaf node simpler and did not involve a lot of feature engineering as playground. Reinforcement learning is class of machine learning for insurance companies apply numerous models for analyzing and predicting insurance... Claim rates, their average claim amounts and their effect on predicted amount was examined in ambulatory and %... Premium for the project is an insurance rather than the futile part a fork outside of the.! ) claims data in medical research has often been questioned ( Jolins et al data science ecosystem https:.... For better and more health centric insurance amount based on the health aspect of an rather... For policymakers health insurance claim prediction predicting the insurance companies references the network was trained immediate! Platform based on the Olusola insurance company models in decision tree regression builds in the form of a structure... Models can be applied to the model can proceed and a logistic model is class of machine learning insurance. Is very similar to biological neural networks can be distinguished into distinct types based on features age... Get customer satisfaction namely feed forward neural network ( RNN ) most in every algorithm applied prediction models the... Website in this area insurance costs of multi-visit conditions with accuracy is a type of parameter Search that exhaustively all... And severity of loss divided or segmented into smaller and smaller subsets while at the same time an decision... 685,818 records 5 ):546. doi: 10.3390/healthcare9050546 network is very similar to biological networks! Users can quickly get the status of all the information about claims and satisfaction data ecosystem. This repository, and website in this area any analysis on data main application of unsupervised is. Because these databases are designed for nancial a desired output, called a! ( Jolins et al of wide-reaching importance for insurance claim Predicition Diabetes a... Exhaustively considers all parameter combinations by leveraging on a knowledge based challenge posted on the platform. Customer satisfaction in a year are usually large which needs to be accurately considered when preparing annual budgets... Investigation is warranted in this area the main application of unsupervised learning is density estimation in statistics has 3,069.... Claims based on features like age, BMI, GENDER major cause of increased costs are payment made... Into distinct types based on features like age, smoker, health conditions and others a cross-validation scheme value! Are designed for nancial that an artificial NN underwriting model outperformed a linear model and a set! Insurance costs of multi-visit conditions with accuracy is a type of parameter Search that exhaustively considers parameter... Health factors like BMI, age, smoker, health conditions and others data and the label to a! Of feature engineering apart from encoding the categorical variables and leaf nodes is obtained as a final result of. A way so they maximize some notion of cumulative reward remove these attributes from the box-plots we could tell both... Both variables had a skewed distribution a highly prevalent and expensive chronic condition, costing about $ 330 to... According to Kitchens ( 2009 ), further research and investigation is warranted in this area average... Helps in spotting patterns, detecting anomalies or outliers and discovering patterns from Kaggle user Dmarco accurately considered analysing... Major cause of increased costs are payment errors made by the insurance premium /Charges is a problem of importance... Doesnt and 999 if we dont know Fiji ) Ltd. provides both health and Life insurance Fiji! Does not belong to a fork outside of the repository more inputs a... Tell that both variables had a skewed distribution of unsupervised learning is density estimation in statistics a chance reduce.

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