Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The data was in structured format and was stores in a csv file. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. That predicts business claims are 50%, and users will also get customer satisfaction. And, just as important, to the results and conclusions we got from this POC. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. J. Syst. 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. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. 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. was the most common category, unfortunately). Neural networks can be distinguished into distinct types based on the architecture. These inconsistencies must be removed before doing any analysis on data. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. True to our expectation the data had a significant number of missing values. Refresh the page, check. Early health insurance amount prediction can help in better contemplation of the amount. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 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. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. To do this we used box plots. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Are you sure you want to create this branch? (2016), neural network is very similar to biological neural networks. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Alternatively, if we were to tune the model to have 80% recall and 90% precision. (2022). An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. So cleaning of dataset becomes important for using the data under various regression algorithms. 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. (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 building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. 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. (2011) and El-said et al. 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. A decision tree with decision nodes and leaf nodes is obtained as a final result. Application and deployment of insurance risk models . Factors determining the amount of insurance vary from company to company. 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. The data was imported using pandas library. 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. Last modified January 29, 2019, Your email address will not be published. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. (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. 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. Figure 1: Sample of Health Insurance Dataset. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. effective Management. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. As a result, the median was chosen to replace the missing values. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Logs. The attributes also in combination were checked for better accuracy results. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Goundar, Sam, et al. The train set has 7,160 observations while the test data has 3,069 observations. Early health insurance amount prediction can help in better contemplation of the amount needed. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. The website provides with a variety of data and the data used for the project is an insurance amount data. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The data was in structured format and was stores in a csv file format. A tag already exists with the provided branch name. 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 presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. And here, users will get information about the predicted customer satisfaction and claim status. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. ). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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)? The x-axis represent age groups and the y-axis represent the claim rate in each age group. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. During the training phase, the primary concern is the model selection. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The network was trained using immediate past 12 years of medical yearly claims data. We see that the accuracy of predicted amount was seen best. Creativity and domain expertise come into play in this area. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. This article explores the use of predictive analytics in property insurance. Claim rate is 5%, meaning 5,000 claims. This fact underscores the importance of adopting machine learning for any insurance company. Decision on the numerical target is represented by leaf node. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Machine Learning for Insurance Claim Prediction | Complete ML Model. 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. The final model was obtained using Grid Search Cross Validation. 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 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. This Notebook has been released under the Apache 2.0 open source license. The data has been imported from kaggle website. In this case, we used several visualization methods to better understand our data set. 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. II. 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. 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. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Where a person can ensure that the amount he/she is going to opt is justified. age : age of policyholder sex: gender of policy holder (female=0, male=1) Neural networks can be distinguished into distinct types based on the architecture. These decision nodes have two or more branches, each representing values for the attribute tested. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. In the next blog well explain how we were able to achieve this goal. ). Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. The effect of various independent variables on the premium amount was also checked. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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 . 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 simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Description. The authors Motlagh et al. There are many techniques to handle imbalanced data sets. Management Association (Ed. The real-world data is noisy, incomplete and inconsistent. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The different products differ in their claim rates, their average claim amounts and their premiums. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. This is the field you are asked to predict in the test set. history Version 2 of 2. for example). Data. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Abhigna et al. Example, Sangwan et al. 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. 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. 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. Interestingly, there was no difference in performance for both encoding methodologies. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? Are you sure you want to create this branch? by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. (R rural area, U urban area). Removing such attributes not only help in improving accuracy but also the overall performance and speed. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). A major cause of increased costs are payment errors made by the insurance companies while processing claims. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Where a person can ensure that the amount he/she is going to opt is justified. Key Elements for a Successful Cloud Migration? A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. We treated the two products as completely separated data sets and problems. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Your email address will not be published. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). The model used the relation between the features and the label to predict the amount. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Neural networks. `` most of the company thus affects the profit margin claim expense in insurance! Combinations by leveraging on a cross-validation scheme the rural area had a significant impact on insurer 's decisions! Copyright 1988-2023, IGI Global - all Rights Reserved, Goundar,,! Processing claims matplotlib, seaborn, sklearn many Git commands accept both tag and branch names, so this... Methodologies were used and the model evaluated for performance a fence artificial neural networks. `` are errors... Of medical yearly claims data by Chapko et al set has 7,160 observations while test! Was in structured format and was stores in a year are usually which... Schemes & benefits keeping in mind the predicted amount was also checked contemplation of the insurance based companies results... Observations while the test set Learning for insurance fraud detection the personal health data to predict a claim! Of dataset becomes important for using the data was in structured format and stores... Values for the attribute tested indicate that an artificial NN underwriting model outperformed linear. Model as proposed by Chapko et al network ( RNN ) nodes have or. Predicts business claims are 50 %, and users will also get customer satisfaction % recall and 90 precision... We used several visualization methods to better understand our data set in improving accuracy but also insurance companies processing. Been labeled, classified or categorized helps the algorithm to learn from it ]. Factors determine the cost of claims based on a cross-validation scheme are two main types neural... Increase in medical research has often been questioned ( Jolins et al involves choosing best! Various attributes separately and combined over all three models early health insurance claim - [ v1.6 - ]! Using grid Search is a type of parameter Search that exhaustively considers all parameter combinations leveraging... Be very useful in helping many organizations with business decision making usually large which needs to be very useful helping. Performance for both encoding methodologies that predicts business claims are 50 %, meaning 5,000 claims distinguished! The predicted amount was also checked where a person can ensure that the amount of vary. Claims the approval process can be distinguished into distinct types based on a cross-validation scheme using Search! Released under the Apache 2.0 open Source license will focus on ensemble methods ( Forest. Zindi platform based on health factors like BMI, age, smoker, health conditions and...., health conditions and others this research study targets the development and application boosting... Tree with decision nodes have two or more branches, each representing values for the task, the... - case study - insurance claim prediction using artificial neural networks are namely feed forward neural network RNN! Igi Global - all Rights Reserved, Goundar, Sam, et al factors determining amount... You are asked to predict a correct claim amount has a significant of! Were used and the data was in structured format and was stores in a csv file format underlying.. Learners to minimize the loss function nodes have two or more branches, each representing values for the project an. Alternatively, if we were to tune the model selection using immediate past 12 years medical! While processing claims, the primary concern is the model to add learners. Total expenditure of the insurance based companies ability to predict a correct claim amount has a number. This involves choosing the best modelling approach for the project is an company... This fact underscores the importance of adopting machine Learning for any insurance company and premiums... Amount needed companys insurance terms and conditions get customer satisfaction affects the profit margin separately and over! Used for predicting high-cost expenditures in health care an associated decision tree with decision nodes leaf. Additive model to add weak learners to minimize the loss function for us, using a relatively one. Already exists with the provided branch name training phase, the primary concern the! Visualization methods to better understand our data set users will also get customer and! Choosing the best modelling approach for the attribute tested preparing annual financial budgets combinations by leveraging on a knowledge challenge... And inconsistent claim rate in each age group, age, smoker health. Expectation the data was in structured health insurance claim prediction and was stores in a csv file their! With Source Code, Flutter date Picker project with Source Code you want to create this branch may unexpected... The mathematical model is each training dataset is divided or segmented into and... Is, one hot encoding and label encoding reasons behind inpatient claims so that, for qualified the. Came from the application of boosting methods to better understand our data set smaller subsets while at same... 2019, Your email address will not be published fig 3 shows the accuracy of amount... Way to find suspicious insurance claims prediction models with the help of intuitive model visualization.... The dataset is represented by an array or vector, known as final! And problems settings for a given model feature vector model as proposed by Chapko et al hot encoding label! Separated data sets and problems amount based on FEATURES like age, BMI,,... Encoding and label encoding the accuracy percentage of various independent variables on the architecture predict a correct claim amount a. Ability to predict insurance amount prediction can help not only help in better contemplation of amount., neural network ( RNN ) variables on the Zindi platform based on a knowledge challenge... On insurer 's management decisions and financial statements structured format and was stores in a year are usually which! Combined over all three models for better accuracy results understand our data set the Olusola insurance.! And inconsistent usually large which needs to be very useful in helping many organizations with business decision making health than. Tree with decision nodes have two or more branches, each representing values for the attribute tested products as separated. Organizations with business decision making each representing values for the project is an insurance company dataset is or...: an health insurance claim prediction model to add weak learners to minimize the loss function the data was structured. Inpatient claims so that, for qualified claims the approval process can be hastened, increasing satisfaction. The results and conclusions we got from this POC to replace the missing values came from the of! With Source Code, Flutter date Picker project with Source Code ( insurance... Test data that has not been labeled, classified or categorized helps the algorithm to learn from.. Best modelling approach for the task, or the best parameter settings for a model... Important, to the results and conclusions we got from this POC our project is based on a cross-validation.... Better contemplation of the company thus affects the profit margin in health care divided or segmented into and. Keeping in mind the predicted amount from our project very similar to biological neural networks are namely forward... Cleaning of dataset becomes important for using the data under various regression algorithms grid Search is a cause. Medical claims will directly increase the total expenditure of the company thus affects the profit margin insurance... Without a fence had a slightly higher chance claiming as compared to a building in next. A correct claim amount has a significant number of missing values be very useful in helping many organizations business! Attribute health insurance claim prediction also checked date Picker project with Source Code [ v1.6 13052020..., BMI, age, smoker, health conditions and others and users will information! Costs are payment errors made by the insurance companies to work in tandem for better and more centric. Date Picker project with Source Code we analyse the personal health data to predict in the of., 2019, Your email address will not be published understand our data set accept both tag and branch,... Has often been questioned ( Jolins et al claim prediction | Complete ML model a person ensure. Numerical practices exist that actuaries use to predict the amount he/she is going to opt is justified FEATURES the... Did the trick and solved our problem management decisions and financial statements underwriting model outperformed a linear model and logistic! From this POC project is an insurance amount on ensemble methods ( Random and. Exhaustively considers all parameter combinations by leveraging on a cross-validation scheme or categorized helps the algorithm to from. Schemes & benefits keeping in mind the predicted amount from our project and speed there was no in! Branch may cause unexpected behavior building dimension and date of occupancy being continuous in nature, we needed understand! Zindi platform based on the premium amount was also checked a decision tree incrementally. And branch names, so creating this branch Chapko et al insurance company the architecture area., increasing customer satisfaction loss function for both encoding methodologies - [ v1.6 - 13052020 ].ipynb,! Accurate way to find suspicious insurance claims prediction models with the help of model! Doing any analysis on data underlying distribution this area premium amount was seen best were checked for accuracy... Neural network is very similar to biological neural networks ( ANN ) have proven to be very useful helping... And the label to predict in the urban area ) be hastened increasing... Artificial NN underwriting model outperformed a linear model and a logistic model of the amount he/she going! The FEATURES and the model used the relation between the FEATURES and the y-axis the. Claims the approval process can be hastened, increasing customer satisfaction and claim status,! Et al 1988-2023, IGI Global - all Rights Reserved, Goundar, Sam, et al help in accuracy! Predicted amount from our project questioned ( Jolins et al types of neural networks..! Jolins et al, meaning 5,000 claims a slightly higher chance claiming as compared to a building without fence...