Accurate prediction gives a chance to reduce financial loss for the company. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. These claim amounts are usually high in millions of dollars every year. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Machine Learning approach is also used for predicting high-cost expenditures in health care. 99.5% in gradient boosting decision tree regression. needed. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Required fields are marked *. We treated the two products as completely separated data sets and problems. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. All Rights Reserved. Then the predicted amount was compared with the actual data to test and verify the model. These actions must be in a way so they maximize some notion of cumulative reward. Fig. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. A tag already exists with the provided branch name. 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. This fact underscores the importance of adopting machine learning for any insurance company. Dataset is not suited for the regression to take place directly. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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 commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Abhigna et al. 1993, Dans 1993) because these databases are designed for nancial . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In the past, research by Mahmoud et al. A tag already exists with the provided branch name. The models can be applied to the data collected in coming years to predict the premium. HEALTH_INSURANCE_CLAIM_PREDICTION. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. This amount needs to be included in the yearly financial budgets. However, it is. 2 shows various machine learning types along with their properties. trend was observed for the surgery data). 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. Where a person can ensure that the amount he/she is going to opt is justified. 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. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 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. Goundar, Sam, et al. 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. During the training phase, the primary concern is the model selection. 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. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. Comments (7) Run. 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. 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. Here, our Machine Learning dashboard shows the claims types status. And here, users will get information about the predicted customer satisfaction and claim status. Decision on the numerical target is represented by leaf node. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. J. Syst. Example, Sangwan et al. These decision nodes have two or more branches, each representing values for the attribute tested. 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. According to Kitchens (2009), further research and investigation is warranted in this area. 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. can Streamline Data Operations and enable Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. In this case, we used several visualization methods to better understand our data set. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. ), Goundar, Sam, et al. 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. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Insurance companies are extremely interested in the prediction of the future. 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. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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? 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. Example, Sangwan et al. 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. 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. Various factors were used and their effect on predicted amount was examined. for the project. Logs. According to Rizal et al. Dyn. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? Approach : Pre . Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. So, without any further ado lets dive in to part I ! Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . 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. 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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Key Elements for a Successful Cloud Migration? 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). I like to think of feature engineering as the playground of any data scientist. (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 insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Appl. 11.5 second run - successful. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Data. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Currently utilizing existing or traditional methods of forecasting with variance. The distribution of number of claims is: Both data sets have over 25 potential features. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The authors Motlagh et al. Numerical data along with categorical data can be handled by decision tress. 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. 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 . Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. Your email address will not be published. From the box-plots we could tell that both variables had a skewed distribution. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. 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. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. 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 data included some ambiguous values which were needed to be removed. (2011) and El-said et al. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. "Health Insurance Claim Prediction Using Artificial Neural Networks." Regression or classification models in decision tree regression builds in the form of a tree structure. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. The models can be applied to the data collected in coming years to predict the premium. These claim amounts are usually high in millions of dollars every year. To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. A matrix is used for the representation of training data. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. 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 attributes also in combination were checked for better accuracy results. 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