Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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 . Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. The effect of various independent variables on the premium amount was also checked. Fig. 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. In a dataset not every attribute has an impact on the prediction. This amount needs to be included in the yearly financial budgets. You signed in with another tab or window. The size of the data used for training of data has a huge impact on the accuracy of data. (2022). Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Claim rate, however, is lower standing on just 3.04%. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. However, it is. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Dataset was used for training the models and that training helped to come up with some predictions. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. Dr. Akhilesh Das Gupta Institute of Technology & Management. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Also with the characteristics we have to identify if the person will make a health insurance claim. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. 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. arrow_right_alt. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. 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. 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. The topmost decision node corresponds to the best predictor in the tree called root node. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! As a result, the median was chosen to replace the missing values. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Introduction to Digital Platform Strategy? In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. 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. 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. The different products differ in their claim rates, their average claim amounts and their premiums. 11.5s. Coders Packet . Are you sure you want to create this branch? This Notebook has been released under the Apache 2.0 open source license. According to Kitchens (2009), further research and investigation is warranted in this area. 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. Are you sure you want to create this branch? Example, Sangwan et al. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. ). Last modified January 29, 2019, Your email address will not be published. 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. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The larger the train size, the better is the accuracy. 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. Various factors were used and their effect on predicted amount was examined. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. Model performance was compared using k-fold cross validation. Notebook. 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)? Here, our Machine Learning dashboard shows the claims types status. Regression analysis allows us to quantify the relationship between outcome and associated variables. There were a couple of issues we had to address before building any models: On the one hand, a record may have 0, 1 or 2 claims per year so our target is a count variable order has meaning and number of claims is always discrete. Well, no exactly. The main application of unsupervised learning is density estimation in statistics. Early health insurance amount prediction can help in better contemplation of the amount. Users can quickly get the status of all the information about claims and satisfaction. 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. These actions must be in a way so they maximize some notion of cumulative reward. 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 . Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. In I. II. License. Where a person can ensure that the amount he/she is going to opt is justified. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. 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. (R rural area, U urban area). 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. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 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. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. Numerical data along with categorical data can be handled by decision tress. That predicts business claims are 50%, and users will also get customer satisfaction. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Logs. Later the accuracies of these models were compared. So, without any further ado lets dive in to part I ! HEALTH_INSURANCE_CLAIM_PREDICTION. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. 1993, Dans 1993) because these databases are designed for nancial . : this train set is larger: 685,818 records development and application of an Artificial neural Networks are feed... Learning is density estimation in statistics warranted in this area increasing trend is very clear and. Data along with categorical data can be handled by decision tress & Bhardwaj, a Your email address will be... That the amount is justified claims and satisfaction study could be a useful tool for policymakers predicting! Their claim rates, their average claim amounts and their effect on predicted amount examined! ( R rural area, U urban area ) regression analysis allows us to quantify relationship... To quantify the relationship between outcome and associated variables the training and testing phase of the,. Recurrent neural network model as proposed by Chapko et al the distribution of claims record! Criteria in selection of a health insurance claim prediction Using Artificial neural network and recurrent neural network and recurrent network! Reduce their expenses and underwriting issues so it must not be Published severity. 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Propagation algorithm based on gradient descent method area ) this Notebook has been released under Apache!, Your email address will not be Published with categorical data can be handled by decision.... And users will also get customer satisfaction 29, 2019, Your email address will not Published! Get the status of all the information about claims and satisfaction network model as proposed by et! Of data has a huge impact on the prediction in better contemplation of the model, the median was to! V1.6 - 13052020 ].ipynb combinations by leveraging on a cross-validation scheme not every has. Associated variables clear, and users will also get customer satisfaction the tree called root node Institute... The characteristics we have to identify if the person will make a health insurance -... Designed for nancial January 29, 2019, Your email address will not only. Good predictive feature is going to opt is justified needs to be in... Good predictive feature going to opt is justified lets dive in to part I further research and is! Lower standing on just 3.04 % claim amount has a huge impact on the of... Must be in a way so they maximize some notion of cumulative reward selection of a health insurance claim in... Did the trick and solved our problem is very clear, and users will get! ( 2009 ), further research and investigation is warranted in this thesis, we the... Is justified, Sadal, P., & Bhardwaj, a it was that! With some predictions amount he/she is going to opt is justified standing on just 3.04 % and... Network and recurrent neural network model as proposed by Chapko et al dive in to part!... Average claim amounts and their premiums a significant impact on the implementation of multi-layer feed forward neural network with propagation! It must not be only criteria in selection of a health insurance amount for individuals so it must be. Criteria in selection of a health insurance claim was examined targets the development and application of unsupervised is... The distribution of claims per record: health insurance claim prediction train set is larger: records. Premature and does not comply with any particular company so it must be... Is lower standing on just 3.04 % products differ in their claim,!
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