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 Save my name, email, and website in this browser for the next time I comment. Description. 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. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. The different products differ in their claim rates, their average claim amounts and their premiums. 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. 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. We already say how a. model can achieve 97% accuracy on our data. Model performance was compared using k-fold cross validation. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 2 shows various machine learning types along with their properties. The main application of unsupervised learning is density estimation in statistics. . During the training phase, the primary concern is the model selection. 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. Take for example the, feature. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. (2019) proposed a novel neural network model for health-related . Appl. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Insurance Claims Risk Predictive Analytics and Software Tools. You signed in with another tab or window. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. DATASET USED The primary source of data for this project was . The x-axis represent age groups and the y-axis represent the claim rate in each age group. II. A matrix is used for the representation of training data. As a result, the median was chosen to replace the missing values. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. So, without any further ado lets dive in to part I ! And, just as important, to the results and conclusions we got from this POC. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. In the below graph we can see how well it is reflected on the ambulatory insurance data. 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. A decision tree with decision nodes and leaf nodes is obtained as a final result. Your email address will not be published. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Fig. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. can Streamline Data Operations and enable This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. ). Comments (7) Run. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Dr. Akhilesh Das Gupta Institute of Technology & Management. Going back to my original point getting good classification metric values is not enough in our case! In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. 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 . (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. In the next part of this blog well finally get to the modeling process! According to Rizal et al. 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 can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. At the same time fraud in this industry is turning into a critical problem. Training data has one or more inputs and a desired output, called as a supervisory signal. The models can be applied to the data collected in coming years to predict the premium. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. Insurance companies are extremely interested in the prediction of the future. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Logs. The website provides with a variety of data and the data used for the project is an insurance amount data. Those setting fit a Poisson regression problem. (2011) and El-said et al. The insurance user's historical data can get data from accessible sources like. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Notebook. effective Management. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. The models can be applied to the data collected in coming years to predict the premium. HEALTH_INSURANCE_CLAIM_PREDICTION. 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. 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. And those are good metrics to evaluate models with. 1. (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. Fig. Regression or classification models in decision tree regression builds in the form of a tree structure. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. A tag already exists with the provided branch name. 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. (2022). Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. The data was imported using pandas library. Are you sure you want to create this branch? These inconsistencies must be removed before doing any analysis on data. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Where a person can ensure that the amount he/she is going to opt is justified. In a dataset not every attribute has an impact on the prediction. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. And its also not even the main issue. (2011) and El-said et al. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. The effect of various independent variables on the premium amount was also checked. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Health Insurance Claim Prediction Using Artificial Neural Networks. 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! An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. 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. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Coders Packet . Key Elements for a Successful Cloud Migration? Also with the characteristics we have to identify if the person will make a health insurance claim. 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