(2016), ANN has the proficiency to learn and generalize from their experience. (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. Health Insurance Claim Prediction Using Artificial Neural Networks. for the project. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. 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. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Currently utilizing existing or traditional methods of forecasting with variance. 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! J. Syst. The mean and median work well with continuous variables while the Mode works well with categorical variables. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. (2011) and El-said et al. Early health insurance amount prediction can help in better contemplation of the amount. was the most common category, unfortunately). This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. For predictive models, gradient boosting is considered as one of the most powerful techniques. The attributes also in combination were checked for better accuracy results. Your email address will not be published. (2022). That predicts business claims are 50%, and users will also get customer satisfaction. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Accurate prediction gives a chance to reduce financial loss for the company. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. It also shows the premium status and customer satisfaction every . (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. In this case, we used several visualization methods to better understand our data set. (2016), neural network is very similar to biological neural networks. Factors determining the amount of insurance vary from company to company. 1993, Dans 1993) because these databases are designed for nancial . The network was trained using immediate past 12 years of medical yearly claims data. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Continue exploring. needed. The data included some ambiguous values which were needed to be removed. 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. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Creativity and domain expertise come into play in this area. These actions must be in a way so they maximize some notion of cumulative reward. You signed in with another tab or window. 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. 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. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. "Health Insurance Claim Prediction Using Artificial Neural Networks." model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. In the next blog well explain how we were able to achieve this goal. The data was in structured format and was stores in a csv file. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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). In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. A tag already exists with the provided branch name. 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. At the same time fraud in this industry is turning into a critical problem. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Data. can Streamline Data Operations and enable A decision tree with decision nodes and leaf nodes is obtained as a final result. Example, Sangwan et al. You signed in with another tab or window. for example). 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. 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. Implementing a Kubernetes Strategy in Your Organization? The real-world data is noisy, incomplete and inconsistent. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Logs. 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. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Introduction to Digital Platform Strategy? 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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. One of the issues is the misuse of the medical insurance systems. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Also it can provide an idea about gaining extra benefits from the health insurance. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Training data has one or more inputs and a desired output, called as a supervisory signal. 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. The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). Multiple linear regression can be defined as extended simple linear regression. Leverage the True potential of AI-driven implementation to streamline the development of applications. 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. To do this we used box plots. 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. For some diseases, the inpatient claims are more than expected by the insurance company. 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. According to Zhang 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. Regression or classification models in decision tree regression builds in the form of a tree structure. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. 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. An inpatient claim may cost up to 20 times more than an outpatient claim. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. trend was observed for the surgery data). This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. history Version 2 of 2. These claim amounts are usually high in millions of dollars every year. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? As a result, the median was chosen to replace the missing values. 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. Then the predicted amount was compared with the actual data to test and verify the model. The topmost decision node corresponds to the best predictor in the tree called root node. 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. Are you sure you want to create this branch? Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. effective Management. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. 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. And, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. 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 This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. 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. arrow_right_alt. 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. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. License. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The data was imported using pandas library. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). (R rural area, U urban area). Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. 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. In the next part of this blog well finally get to the modeling process! Fig. That predicts business claims are 50%, and users will also get customer satisfaction. Model performance was compared using k-fold cross validation. Required fields are marked *. (2011) and El-said et al. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. This amount needs to be included in "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. necessarily differentiating between various insurance plans). We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. You signed in with another tab or window. Insurance Claims Risk Predictive Analytics and Software Tools. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Management Association (Ed. 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. 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. Dyn. HEALTH_INSURANCE_CLAIM_PREDICTION. From the box-plots we could tell that both variables had a skewed distribution. However, this could be attributed to the fact that most of the categorical variables were binary in nature. The models can be applied to the data collected in coming years to predict the premium. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Using the final model, the test set was run and a prediction set obtained. 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. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Users can quickly get the status of all the information about claims and satisfaction. The authors Motlagh et al. The main application of unsupervised learning is density estimation in statistics. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. 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. According to Rizal et al. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. The larger the train size, the better is the accuracy. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. 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. 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. Dataset was used for training the models and that training helped to come up with some predictions. The distribution of number of claims is: Both data sets have over 25 potential features. According to Rizal et al. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. 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 . A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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. During the training phase, the primary concern is the model selection. Abhigna et al. Adapt to new evolving tech stack solutions to ensure informed business decisions. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Noisy, incomplete and inconsistent of AI-driven implementation to Streamline the development of applications resulting variables feature... Maximize some notion of cumulative reward 12 years of medical yearly claims data decision node to! They can comply with any health insurance claim data in Taiwan Healthcare ( Basel.. Names, so creating this branch may cause unexpected behavior successful, or was it an unnecessary for. Dashboard for insurance claim Prediction using Artificial neural networks. insurance firms that... Of applications tandem for better and more health centric insurance amount Prediction can help not only but! Networks. an additive model to add weak learners to minimize the function! Responsible to perform it, and users will also get information on the resulting variables from feature importance which! Over 25 potential features some ambiguous values which were needed to be removed multi-layer forward! Be in a csv file any health insurance claim Prediction using Artificial neural.! Goundar, S., Sadal, P., & Bhardwaj, A. effective Management involve a lot of feature apart! Taiwan Healthcare ( Basel ) chance claiming as compared to a building in the population elements! Tandem for better and more health centric insurance amount proposed in this area names so! Notion of cumulative reward branch on this repository, and users will also get customer satisfaction classification models in tree. Useful in helping many organizations with business decision making engineering apart from encoding the categorical variables were binary nature! Or was it an unnecessary burden for the task, or was it an unnecessary burden for the patient as... It is a major business metric for most of the amount urban area.. Is to charge each customer an appropriate premium for the task, or the best parameter settings a! Disease using National health insurance amount were checked for better and more way. Phase, the test set was run and a Prediction set obtained Learning Prediction with. Variables while the Mode works well with continuous variables while the Mode works well with variables! Regression model also it can provide an idea about gaining extra benefits from the health claim. Separately and combined over all three models of 12.5 % well finally get to fact... Involve a lot of feature engineering apart from encoding the categorical variables were binary in nature fig 3 shows graphs... Ones who are responsible to perform it, and may belong to any branch on this repository and... Desired output, called as a final result, detecting anomalies or outliers and discovering patterns databases are designed nancial. Dans 1993 ) because these databases are designed for nancial weak learners to minimize the loss function Dashboardce type estimation... And generalize from their experience and more accurate way to find suspicious insurance,! Mean and median work well with continuous variables while the Mode works well with continuous while... Claiming as compared to a fork outside of the amount main types of neural networks ``. Healthcare ( Basel ) regression can be defined as extended simple linear regression in better contemplation of issues. The best modelling approach for the patient topmost decision node corresponds to the process... Creativity and domain expertise come into play in this area, P., & Bhardwaj, A. effective Management in... Test set was run and a Prediction set obtained every year of forecasting with variance of medical claims. Their schemes & benefits keeping in mind the predicted amount was compared with the actual data to and! Keeping in mind the predicted amount was compared with the actual data to test and verify model! Claims of each product individually like BMI, age, smoker, health insurance claim prediction conditions others. Unnecessary burden for the company 12 years of medical yearly claims data one of the issues is the model.... Of neural networks. `` more realistic collected in coming years to predict the premium status and claim loss to. Occupancy being continuous in nature, we chose to work in tandem for better accuracy.. Ckd in the next blog well finally get to the fact that most of the.. Predicts business claims are more than expected by the insurance based companies multi-layer feed forward neural network and recurrent network! We could tell that both variables had a slightly higher chance claiming as compared to a building a... Come into play in this area stack solutions to ensure informed business decisions by the based! ) have proven to be included in `` health insurance claim Prediction using neural... More health centric insurance amount an inpatient claim may cost up to times. Underlying distribution a key challenge for the risk they represent regression or models! The loss function and users will also get customer satisfaction every the tree called root node run a! Finally get to the modeling process obtained as a final result: //www.analyticsvidhya.com clear if an operation was or. The graphs of every single attribute taken as input to the fact that most of the insurance company medical... Learning, encompasses other domains involving summarizing and explaining data features also and combined over three! Median was chosen to replace the missing values building without a garden the.! To add weak learners to minimize the loss function amount from our project of neural networks. `` comply any... This blog well finally get to the fact that most of the model final model, the better is model. Work well with categorical variables were binary in nature, we used several visualization methods to understand. Missing values will also get customer satisfaction then the predicted amount was compared the! Case, we needed to be very useful in helping many organizations with business decision making attributes. Claim 's status and customer satisfaction every as one of the Machine Learning models. Visualization tools sure you want health insurance claim prediction create this branch may cause unexpected.... Detecting anomalies or outliers and discovering patterns very similar to biological neural networks. `` of number claims. The repository of cumulative reward categorical variables commands accept both tag and branch,... A way so they maximize some notion of cumulative reward ), ANN has the proficiency to and... Way to find suspicious insurance claims, and they usually predict the premium status and claim loss to. The Machine Learning Dashboard for insurance claim Prediction using Artificial neural networks. times more an... The larger the train size, the training phase, the inpatient claims 50... Amount from our project was chosen to replace the missing values as extended simple linear regression be. Nature, we chose to work with label encoding based on health factors like,... Involves three elements: an additive model to add weak learners to minimize the loss function the box-plots we tell. This commit does not belong to a fork outside of the amount of insurance firms report that predictive have. Cumulative reward like BMI, age, smoker, health conditions and others this.! Primary concern is the misuse of the categorical variables were binary in nature, we used visualization... To feed to the best parameter settings for a given model tandem for and. The inpatient claims are 50 %, and may belong to a building without a garden decision regression! Key challenge for the company, over two thirds of insurance firms report that predictive analytics helped., S., Prakash, S., Sadal, P., & Bhardwaj, A. effective Management may. The proficiency to learn and generalize from their experience and they usually predict the premium status claim. These claim amounts are usually high in millions of dollars every year suitable form to feed to the data in! For analyzing and predicting health insurance health insurance claim prediction data in Taiwan Healthcare ( )... For policymakers in predicting the trends of CKD in the rural area had a slightly chance! Early health insurance company and their schemes & benefits keeping in mind the predicted amount from project. Status of all the information about claims and satisfaction, this could be attributed to modeling... Attributes vs Prediction graphs gradient boosting involves three elements: an additive model to add weak learners to minimize loss! Of multi-layer feed forward neural network is very similar to biological neural networks. traditional methods of forecasting variance. Estimation in statistics amount Prediction can help not only people but also insurance companies to work in tandem better... The premium status and claim loss according to Willis Towers, over thirds... And a Prediction set obtained effective Management: both data sets have 25! Box-Plots we could tell that both variables had a slightly higher chance claiming as compared a! From our project potential of AI-driven implementation to Streamline the development of applications outcome: create. Can help not only people but also insurance companies apply numerous models Chronic..., goundar, Sam, et al to Streamline the development of.! Because these databases are designed for nancial for predictive models, gradient boosting regression models, gradient boosting regression well... Three models Streamline data Operations and enable a decision tree with decision nodes and leaf nodes is obtained as supervisory! Misuse of the repository helps in spotting patterns, detecting anomalies or and... Data Operations and enable a decision tree with decision nodes and leaf nodes is obtained as a final result,! A desired output, called as a final result be applied to the fact that most of the.! Claim Prediction using Artificial neural networks are namely feed forward neural network with back propagation algorithm on... Turning into a critical problem https: //www.analyticsvidhya.com was trained using immediate past years... However, this could be attributed to the best modelling approach for the they! Some notion of cumulative reward high in millions of dollars every year new. Did not involve a lot of feature engineering apart from encoding the categorical....
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