1993, Dans 1993) because these databases are designed for nancial . In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. True to our expectation the data had a significant number of missing values. 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. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. for example). The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. A tag already exists with the provided branch name. According to Kitchens (2009), further research and investigation is warranted in this area. effective Management. for the project. Model performance was compared using k-fold cross validation. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Approach : Pre . The data has been imported from kaggle website. Removing such attributes not only help in improving accuracy but also the overall performance and speed. 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). 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. Interestingly, there was no difference in performance for both encoding methodologies. "Health Insurance Claim Prediction Using Artificial Neural Networks." 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. Required fields are marked *. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. An inpatient claim may cost up to 20 times more than an outpatient claim. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. In the next part of this blog well finally get to the modeling process! However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. This article explores the use of predictive analytics in property insurance. The primary source of data for this project was from Kaggle user Dmarco. At the same time fraud in this industry is turning into a critical problem. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. In, 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, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The authors Motlagh et al. 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. Example, Sangwan et al. 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). 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. Example, Sangwan et al. Dataset is not suited for the regression to take place directly. This may sound like a semantic difference, but its not. The data included some ambiguous values which were needed to be removed. Notebook. 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. It would be interesting to test the two encoding methodologies with variables having more categories. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. How to get started with Application Modernization? Are you sure you want to create this branch? Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The model predicted the accuracy of model by using different algorithms, different features and different train test split size. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Also it can provide an idea about gaining extra benefits from the health insurance. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. How can enterprises effectively Adopt DevSecOps? These decision nodes have two or more branches, each representing values for the attribute tested. Currently utilizing existing or traditional methods of forecasting with variance. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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 . (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 I. . Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Last modified January 29, 2019, Your email address will not be published. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. As a result, the median was chosen to replace the missing values. This is the field you are asked to predict in the test set. And, just as important, to the results and conclusions we got from this POC. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. 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. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. By filtering and various machine learning models accuracy can be improved. A tag already exists with the provided branch name. Numerical data along with categorical data can be handled by decision tress. This amount needs to be included in Dr. Akhilesh Das Gupta Institute of Technology & Management. (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. 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. These inconsistencies must be removed before doing any analysis on data. 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. 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. According to Rizal et al. The real-world data is noisy, incomplete and inconsistent. I like to think of feature engineering as the playground of any data scientist. The larger the train size, the better is the accuracy. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. For some diseases, the inpatient claims are more than expected by the insurance company. Abhigna et al. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. You signed in with another tab or window. DATASET USED The primary source of data for this project was . The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. (2022). 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. 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. The topmost decision node corresponds to the best predictor in the tree called root node. Random Forest Model gave an R^2 score value of 0.83. 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. was the most common category, unfortunately). 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. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. According to Zhang et al. Where a person can ensure that the amount he/she is going to opt is justified. Application and deployment of insurance risk models . In the next blog well explain how we were able to achieve this goal. That predicts business claims are 50%, and users will also get customer satisfaction. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. The website provides with a variety of data and the data used for the project is an insurance amount data. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. 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. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Insurance companies are extremely interested in the prediction of the future. 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). Take for example the, feature. The train set has 7,160 observations while the test data has 3,069 observations. The models can be applied to the data collected in coming years to predict the premium. Where a person can ensure that the amount he/she is going to opt is justified. ). ). Claim rate is 5%, meaning 5,000 claims. 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. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. This fact underscores the importance of adopting machine learning for any insurance company. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. The dataset is comprised of 1338 records with 6 attributes. According to Kitchens (2009), further research and investigation is warranted in this area. Logs. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Your email address will not be published. The network was trained using immediate past 12 years of medical yearly claims data. Dong et al. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. (2011) and El-said et al. There are many techniques to handle imbalanced data sets. However, this could be attributed to the fact that most of the categorical variables were binary in nature. The model used the relation between the features and the label to predict the amount. 2 shows various machine learning types along with their properties. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Leverage the True potential of AI-driven implementation to streamline the development of applications. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Fig. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. Data. Early health insurance amount prediction can help in better contemplation of the amount needed. 1. The network was trained using immediate past 12 years of medical yearly claims data. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. ), Goundar, Sam, et al. Implementing a Kubernetes Strategy in Your Organization? 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. Using the final model, the test set was run and a prediction set obtained. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. A decision tree with decision nodes and leaf nodes is obtained as a final result. J. Syst. Machine Learning approach is also used for predicting high-cost expenditures in health care. Description. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. (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. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Using this approach, a best model was derived with an accuracy of 0.79. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. 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%. All Rights Reserved. One of the issues is the misuse of the medical insurance systems. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. The models can be applied to the data collected in coming years to predict the premium. 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 sounds like a straight forward regression task!. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. That predicts business claims are 50%, and users will also get customer satisfaction. https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification.