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). (R rural area, U urban area). Users can develop insurance claims prediction models with the help of intuitive model visualization tools. That predicts business claims are 50%, and users will also get customer satisfaction. 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. 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. 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%. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Factors determining the amount of insurance vary from company to company. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Also it can provide an idea about gaining extra benefits from the health insurance. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Required fields are marked *. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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 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. 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 included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. (2016), neural network is very similar to biological neural networks. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. The diagnosis set is going to be expanded to include more diseases. (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. Settlement: Area where the building is located. 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. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. You signed in with another tab or window. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. Keywords Regression, Premium, Machine Learning. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. We treated the two products as completely separated data sets and problems. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. Here, our Machine Learning dashboard shows the claims types status. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Currently utilizing existing or traditional methods of forecasting with variance. The larger the train size, the better is the accuracy. Multiple linear regression can be defined as extended simple linear regression. Later the accuracies of these models were compared. 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. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Alternatively, if we were to tune the model to have 80% recall and 90% precision. All Rights Reserved. A tag already exists with the provided branch name. 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Comments (7) Run. Insurance Claims Risk Predictive Analytics and Software Tools. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Here, our Machine Learning dashboard shows the claims types status. 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. The models can be applied to the data collected in coming years to predict the premium. Abhigna et al. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Other two regression models also gave good accuracies about 80% In their prediction. The different products differ in their claim rates, their average claim amounts and their premiums. Dyn. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. In I. Dong et al. All Rights Reserved. Data. The effect of various independent variables on the premium amount was also checked. 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. Machine Learning for Insurance Claim Prediction | Complete ML Model. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. Logs. 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. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. 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. 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. 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 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. Neural networks can be distinguished into distinct types based on the architecture. So, without any further ado lets dive in to part I ! 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 . And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. The authors Motlagh et al. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Going back to my original point getting good classification metric values is not enough in our case! 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. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. In the next part of this blog well finally get to the modeling process! (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. Dataset was used for training the models and that training helped to come up with some predictions. The real-world data is noisy, incomplete and inconsistent. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. The model was used to predict the insurance amount which would be spent on their health. ), Goundar, Sam, et al. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. 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! (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 topmost decision node corresponds to the best predictor in the tree called root node. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Various factors were used and their effect on predicted amount was examined. Continue exploring. DATASET USED The primary source of data for this project was . This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The primary source of data for this project was from Kaggle user Dmarco. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Abhigna et al. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Where a person can ensure that the amount he/she is going to opt is justified. According to Rizal et al. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. You signed in with another tab or window. Fig. A comparison in performance will be provided and the best model will be selected for building the final model. This is the field you are asked to predict in the test set. The distribution of number of claims is: Both data sets have over 25 potential features. The attributes also in combination were checked for better accuracy results. License. This may sound like a semantic difference, but its not. A matrix is used for the representation of training data. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Accurate prediction gives a chance to reduce financial loss for the company. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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? Introduction to Digital Platform Strategy? CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Attributes which had no effect on the prediction were removed from the features. 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. 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). Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. 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. Random Forest Model gave an R^2 score value of 0.83. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. for the project. trend was observed for the surgery data). The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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