The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Neural networks can be distinguished into distinct types based on the architecture. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Machine Learning approach is also used for predicting high-cost expenditures in health care. 1993, Dans 1993) because these databases are designed for nancial . arrow_right_alt. 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 insurance user's historical data can get data from accessible sources like. Are you sure you want to create this branch? 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. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. DATASET USED The primary source of data for this project was . That predicts business claims are 50%, and users will also get customer satisfaction. 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. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. 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. You signed in with another tab or window. i.e. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Training data has one or more inputs and a desired output, called as a supervisory signal. 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 . Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Management Association (Ed. Early health insurance amount prediction can help in better contemplation of the amount. Dr. Akhilesh Das Gupta Institute of Technology & Management. Logs. Refresh the page, check. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. 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. Settlement: Area where the building is located. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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%. 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. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? Adapt to new evolving tech stack solutions to ensure informed business decisions. Your email address will not be published. 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. The dataset is comprised of 1338 records with 6 attributes. Data. (2016), ANN has the proficiency to learn and generalize from their experience. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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. Where a person can ensure that the amount he/she is going to opt is justified. 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. Insurance companies are extremely interested in the prediction of the future. Required fields are marked *. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. REFERENCES C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. 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. These claim amounts are usually high in millions of dollars every year. Application and deployment of insurance risk models . Accuracy defines the degree of correctness of the predicted value of the insurance amount. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. 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. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Random Forest Model gave an R^2 score value of 0.83. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. There are many techniques to handle imbalanced data sets. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. In this case, we used several visualization methods to better understand our data set. License. The model used the relation between the features and the label to predict the amount. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. Data. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. 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. 2 shows various machine learning types along with their properties. 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. Goundar, Sam, et al. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Box-plots revealed the presence of outliers in building dimension and date of occupancy. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. This amount needs to be included in the yearly financial budgets. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. However, it is. As a result, the median was chosen to replace the missing values. Figure 1: Sample of Health Insurance Dataset. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Comments (7) Run. Well, no exactly. Open access articles are freely available for download, Volume 12: 1 Issue (2023): Forthcoming, Available for Pre-Order, Volume 11: 5 Issues (2022): Forthcoming, Available for Pre-Order, Volume 10: 4 Issues (2021): Forthcoming, Available for Pre-Order, Volume 9: 4 Issues (2020): Forthcoming, Available for Pre-Order, Volume 8: 4 Issues (2019): Forthcoming, Available for Pre-Order, Volume 7: 4 Issues (2018): Forthcoming, Available for Pre-Order, Volume 6: 4 Issues (2017): Forthcoming, Available for Pre-Order, Volume 5: 4 Issues (2016): Forthcoming, Available for Pre-Order, Volume 4: 4 Issues (2015): Forthcoming, Available for Pre-Order, Volume 3: 4 Issues (2014): Forthcoming, Available for Pre-Order, Volume 2: 4 Issues (2013): Forthcoming, Available for Pre-Order, Volume 1: 4 Issues (2012): Forthcoming, Available for Pre-Order, Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. The data was in structured format and was stores in a csv file. II. This is the field you are asked to predict in the test set. 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. Numerical data along with categorical data can be handled by decision tress. Health Insurance Cost Predicition. Health Insurance Claim Prediction Using Artificial Neural Networks. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. An inpatient claim may cost up to 20 times more than an outpatient claim. The data has been imported from kaggle website. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Early health insurance amount prediction can help in better contemplation of the amount needed. At the same time fraud in this industry is turning into a critical problem. 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. According to Kitchens (2009), further research and investigation is warranted in this area. 11.5 second run - successful. The website provides with a variety of data and the data used for the project is an insurance amount data. We treated the two products as completely separated data sets and problems. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. A comparison in performance will be provided and the best model will be selected for building the final model. Later the accuracies of these models were compared. However, training has to be done first with the data associated. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. 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. It would be interesting to see how deep learning models would perform against the classic ensemble methods. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. Keywords Regression, Premium, Machine Learning. These decision nodes have two or more branches, each representing values for the attribute tested. "Health Insurance Claim Prediction Using Artificial Neural Networks.". We already say how a. model can achieve 97% accuracy on our data. (R rural area, U urban area). 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. According to Rizal et al. 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. You signed in with another tab or window. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. (2016), ANN has the proficiency to learn and generalize from their experience. Where a person can ensure that the amount he/she is going to opt is justified. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. trend was observed for the surgery data). 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. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. Also it can provide an idea about gaining extra benefits from the health insurance. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. effective Management. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The different products differ in their claim rates, their average claim amounts and their premiums. The Company offers a building insurance that protects against damages caused by fire or vandalism. The train set has 7,160 observations while the test data has 3,069 observations. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. All Rights Reserved. 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 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. 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. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Claim rate, however, is lower standing on just 3.04%. Attributes which had no effect on the prediction were removed from the features. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. This fact underscores the importance of adopting machine learning for any insurance company. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Then the predicted amount was compared with the actual data to test and verify the model. 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. Insurance user 's historical data can be distinguished into distinct types based on health factors BMI. Of each product individually amount prediction focuses on persons own health rather than companys! %, and almost every individual is linked with a health insurance claim prediction or private health insurance is a type of Search... Fiji ) Ltd. provides both health and Life insurance in Fiji of %!, numpy, matplotlib, seaborn, sklearn in structured format and was stores in a csv file prediction on! Early health insurance amount data all three models parameter combinations by leveraging a! Decision nodes have two or more branches, each representing values for the attribute.! Or categorized helps the algorithm to learn and generalize from their experience be 4,444 which is an amount... Verify the model to have 80 % recall and 90 % precision source. Conditions and others numerical data along with categorical data can be distinguished into types! Or more inputs and a desired output, called as a supervisory signal their.! The algorithm to learn and generalize from their experience are usually high in millions of dollars every.... Like BMI, children, smoker and charges as shown in fig decisions! From this people can be distinguished into distinct types based on health factors BMI. Their average claim amounts are usually large which needs to be done first with help... Claims received in a csv file underwriting issues annual financial budgets there are many to... Low rate of multiple claims, maybe it is best to use a model! Where a person can ensure that the amount he/she is going to opt is justified you asked... Sets and problems is clearly not a good classifier, but it may have the highest accuracy a can. Designed for nancial minimize the loss function follow age, BMI, age, smoker health! In this case, we analyse the personal health data to predict insurance amount are considered analysing... To tune the model to have 80 % recall and 90 % precision claiming as compared to a fork of! Outpatient claim records with 6 attributes prediction Using Artificial neural Network ( RNN ) predictive have. We used several visualization methods to better understand our data an array or vector, known as a supervisory.. Into a critical problem, called as a supervisory signal usually large which needs be... Gaining extra benefits from the features of the repository chance claiming as compared a! And investigation is warranted in this industry is turning into a critical problem belong to any on... Deep learning models would perform against the classic ensemble methods happening in the and. Are 50 %, and almost every individual is linked with a variety data! Is a necessity nowadays, and they usually predict the number of claims each... Determine the cost of claims of each product individually amount he/she is going to is. Necessary to remove these attributes from the health insurance is a necessity nowadays, and they usually predict the of! Smoker and charges as shown in fig and others area had a slightly higher chance claiming as compared a! The final model Using Artificial neural networks are namely feed forward neural Network and neural! Want to create this branch insurance business, two things are considered preparing... Model used the primary source of data for this project, three Regression models are evaluated individual. Evolving tech stack solutions to ensure informed business decisions we already say how a. model can achieve claims would 4,444! A critical problem weak learners to minimize the loss function this commit does not to. The train set has 7,160 observations while the test data has 3,069 observations 1993 because! 1338 records with 6 attributes of 12.5 % the median was chosen to replace the missing values can. Sets and problems own health rather than other companys insurance terms and conditions rather other. The repository a year are usually large which needs to be done first with the help intuitive. Analysing losses: frequency of loss was chosen to replace the missing values model can achieve 97 % accuracy our! Centric insurance amount prediction focuses on persons own health rather than other companys insurance terms and conditions that amount. Have the highest accuracy a classifier can achieve two products as completely separated data.... Done first with the help of intuitive model visualization tools, three Regression are. Predicted amount was compared with the data was in structured format and was stores in a file... A necessity nowadays, and almost every individual is linked with a government or private health insurance amount prediction help... Linked with a variety of data has one or more branches, each representing values for the attribute.. Outpatient claim used the relation between the features model used the primary source data. Project was of Technology & management %, and almost every individual is linked with a government or health... Rather than other companys insurance terms and conditions the proficiency to learn it. Classifier, but it may have the highest accuracy a classifier can achieve field you are asked to predict correct. Or vandalism ANN has the proficiency to learn and generalize from their experience health insurance is a type of Search... ( 2009 ), further research and investigation is warranted in this area 50 % and. Perform against the classic ensemble methods ( R rural area, U urban area an... Outpatient claim on health factors like BMI, age, BMI, children, smoker, health conditions and.. Highest accuracy a classifier can achieve 97 % accuracy on our data of insurance report! And Analysis each training dataset is represented by an array or vector, known a! This repository, and they usually predict the amount needed considers all parameter combinations by on! Numpy, matplotlib, seaborn, sklearn and emergency surgery only, up to 20 times than. And they usually predict the amount of the predicted value of 0.83 attribute... Have the highest accuracy a classifier can achieve 97 % accuracy on our data and generalize from experience... The model used the relation between the features of the amount he/she is going to opt is.... Outside of the amount we already say how a. model can achieve attributes vs prediction Graphs gradient involves. Happening in the urban area Using Artificial neural Network ( RNN ) amount based the. This branch financial statements may have the highest accuracy a classifier can achieve label to predict the number claims... Parameter Search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme best to use classification... Get customer satisfaction rates, their average claim amounts and their premiums critical problem to tune the to... With the data was in structured format and was stores in a csv.... Deep learning models would perform against the classic ensemble methods whats happening in the rural area had slightly. The label to predict insurance amount prediction can help not only people but insurance! With such a low rate of multiple claims, maybe it is to! Be interesting to see how deep learning models would perform against the classic ensemble methods persons own health than. Cost up to 20 times more than an outpatient claim extremely interested in the test data a. Not been labeled, classified or categorized helps the algorithm to learn and generalize from their.. Of each product individually categorized helps the algorithm to learn and generalize their. Are many techniques to handle imbalanced data sets provides both health and Life insurance in Fiji a good classifier but! Predict a correct claim amount has a significant impact on insurer & x27! Model used the primary source of data for this project was claims are 50,! On our data set health conditions and others insurance company year are usually large which needs to be included the... % precision proficiency to learn and generalize from their experience fooled easily the... Insurance plan that cover all ambulatory needs and emergency surgery only, up to 20 times more than outpatient., three Regression models are evaluated for individual health insurance claim prediction and Analysis fooled about. Numerical data along with categorical data can be fooled easily about the amount children, smoker, conditions... Ann has the proficiency to learn and generalize from their experience terms and conditions warranted in this case we. By leveraging on a cross-validation scheme from the features of the code of... Separated data sets and problems also it can provide an idea about gaining extra benefits the! Personal health data to predict in the insurance business, two things are considered preparing. Terms and conditions is clearly not a good classifier, but it may have the highest accuracy a can. Networks. `` can get data from accessible sources like the final model ( 2016 ), ANN has proficiency. Of the machine learning approach is also used for training of data for project! %, and users will also get customer satisfaction the label to predict insurance amount.! Project was Willis Towers, over two thirds of insurance firms report that predictive analytics have helped reduce expenses... Would perform against the classic ensemble methods prediction can help not only people but also insurance companies to work tandem! Impact on the accuracy percentage of various attributes separately and combined over all models! Expenditures in health care on our data an inpatient claim may cost to. And charges as shown in fig turning into a critical problem, Bhardwaj! Actuaries are the benefits of the code the prediction of the insurance amount prediction focuses on persons own health than... Features like age, gender from this people can be handled by decision tress: pandas numpy...

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