Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. In the below graph we can see how well it is reflected on the ambulatory insurance data. Fig. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. (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 also shows the premium status and customer satisfaction every . (2020). Application and deployment of insurance risk models . for the project. 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. 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. Using this approach, a best model was derived with an accuracy of 0.79. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Figure 1: Sample of Health Insurance Dataset. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). True to our expectation the data had a significant number of missing values. Also it can provide an idea about gaining extra benefits from the health insurance. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. 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. 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. 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 model was used to predict the insurance amount which would be spent on their health. However, this could be attributed to the fact that most of the categorical variables were binary in nature. A decision tree with decision nodes and leaf nodes is obtained as a final result. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). The model used the relation between the features and the label to predict the amount. The models can be applied to the data collected in coming years to predict the premium. Take for example the, feature. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. And those are good metrics to evaluate models with. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Here, our Machine Learning dashboard shows the claims types status. The mean and median work well with continuous variables while the Mode works well with categorical variables. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. REFERENCES 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). 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 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Keywords Regression, Premium, Machine Learning. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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 ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. These decision nodes have two or more branches, each representing values for the attribute tested. The models can be applied to the data collected in coming years to predict the premium. age : age of policyholder sex: gender of policy holder (female=0, male=1) The larger the train size, the better is the accuracy. You signed in with another tab or window. These inconsistencies must be removed before doing any analysis on data. 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. A comparison in performance will be provided and the best model will be selected for building the final model. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Dataset was used for training the models and that training helped to come up with some predictions. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Settlement: Area where the building is located. 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. In I. 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)? 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. Backgroun In this project, three regression models are evaluated for individual health insurance data. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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Management Association (Ed. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Data. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. It would be interesting to see how deep learning models would perform against the classic ensemble methods. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). 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. A tag already exists with the provided branch name. The dataset is comprised of 1338 records with 6 attributes. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. The data was in structured format and was stores in a csv file format. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. These claim amounts are usually high in millions of dollars every year. . We see that the accuracy of predicted amount was seen best. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. 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. Training data has one or more inputs and a desired output, called as a supervisory signal. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 1993, Dans 1993) because these databases are designed for nancial . Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. HEALTH_INSURANCE_CLAIM_PREDICTION. Are you sure you want to create this branch? 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. was the most common category, unfortunately). 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Example, Sangwan et al. 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. 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. Accurate prediction gives a chance to reduce financial loss for the 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. The attributes also in combination were checked for better accuracy results. Health Insurance Cost Predicition. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. I like to think of feature engineering as the playground of any data scientist. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. 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! BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Health Insurance Claim Prediction Using Artificial Neural Networks. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. Approach : Pre . (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. The real-world data is noisy, incomplete and inconsistent. 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. Continue exploring. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Well, no exactly. 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. 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. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. The data was in structured format and was stores in a csv file. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. Currently utilizing existing or traditional methods of forecasting with variance. Other two regression models also gave good accuracies about 80% In their prediction. 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. (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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 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