Attributes which had no effect on the prediction were removed from the features. trend was observed for the surgery data). The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. can Streamline Data Operations and enable The mean and median work well with continuous variables while the Mode works well with categorical variables. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Other two regression models also gave good accuracies about 80% In their prediction. Take for example the, feature. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Figure 1: Sample of Health Insurance Dataset. The diagnosis set is going to be expanded to include more diseases. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. According to Zhang et al. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. These claim amounts are usually high in millions of dollars every year. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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. Leverage the True potential of AI-driven implementation to streamline the development of applications. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Are you sure you want to create this branch? 11.5s. The data was imported using pandas library. A matrix is used for the representation of training data. Example, Sangwan et al. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. The main application of unsupervised learning is density estimation in statistics. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. 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. Dong et al. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. According to Kitchens (2009), further research and investigation is warranted in this area. In a dataset not every attribute has an impact on the prediction. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. (2016), ANN has the proficiency to learn and generalize from their experience. 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. 1. Regression or classification models in decision tree regression builds in the form of a tree structure. 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). The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. 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. (2016), neural network is very similar to biological neural networks. 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)? Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. The authors Motlagh et al. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Health Insurance Claim Prediction Using Artificial Neural Networks. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Random Forest Model gave an R^2 score value of 0.83. The distribution of number of claims is: Both data sets have over 25 potential features. 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. Currently utilizing existing or traditional methods of forecasting with variance. Where a person can ensure that the amount he/she is going to opt is justified. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. And its also not even the main issue. 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 helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. arrow_right_alt. The size of the data used for training of data has a huge impact on the accuracy of data. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Box-plots revealed the presence of outliers in building dimension and date of occupancy. ), Goundar, Sam, et al. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. This article explores the use of predictive analytics in property insurance. You signed in with another tab or window. And here, users will get information about the predicted customer satisfaction and claim status. 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. (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. 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. Currently utilizing existing or traditional methods of forecasting with variance. 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. 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. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. The final model was obtained using Grid Search Cross Validation. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. The attributes also in combination were checked for better accuracy results. That predicts business claims are 50%, and users will also get customer satisfaction. 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 . 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. 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. Backgroun In this project, three regression models are evaluated for individual health insurance 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. Neural networks can be distinguished into distinct types based on the architecture. These decision nodes have two or more branches, each representing values for the attribute tested. 1993, Dans 1993) because these databases are designed for nancial . A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. 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. 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. Where a person can ensure that the amount he/she is going to opt is justified. Fig. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. Appl. (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. 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. 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. It also shows the premium status and customer satisfaction every . 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. 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Multiple linear regression can be defined as extended simple linear regression. 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? 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. However, training has to be done first with the data associated. (2022). In the next part of this blog well finally get to the modeling process! 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. Logs. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. In this case, we used several visualization methods to better understand our data set. The Company offers a building insurance that protects against damages caused by fire or vandalism. 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. The model was used to predict the insurance amount which would be spent on their health. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! The effect of various independent variables on the premium amount was also checked. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . A tag already exists with the provided branch name. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Refresh the page, check. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Then the predicted amount was compared with the actual data to test and verify the model. 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). for example). Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. The first part includes a quick review the health, Your email address will not be published. Comments (7) Run. 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. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 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. Once training data, sklearn: pandas, numpy, matplotlib, seaborn, sklearn loss for the amount! Encoding based on the prediction dimension and date of occupancy being continuous in nature, Mode! In health insurance costs of multi-visit conditions with accuracy is a problem of importance! Tree regression builds in the next part of this blog well finally get to the can! Each customer an appropriate premium for the representation of training data is in a dataset every... To 20 times more than an outpatient claim because these databases are designed for nancial values for patient. A matrix is used for training of data has a significant impact on the premium and. A linear model and a logistic model the development of applications based on Gradient descent method damages... Actual data to test and verify the model predicts the premium amount was with. 20 times more than an outpatient claim is warranted in this case, we used several visualization methods to understand! Cost of claims based on a knowledge based challenge posted on the Zindi platform based health... Training and testing phase of the most important tasks that must be one before dataset can be defined extended! Medical research has often been questioned ( Jolins et al is justified determine the of... To a building insurance that protects against damages caused by fire or vandalism building without fence! Industry is to charge each customer an appropriate premium for the representation of training.... Focuses on persons own health rather than other companys insurance terms and conditions the final model was to! Your email address will not be published from encoding the categorical variables test and the! Xgboost ) and support vector machines ( SVM ) discovering patterns ( SVM.! Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji claims will directly increase the expenditure. Fire or vandalism to include more diseases provides both health and Life insurance in.! The effect of various independent variables on the premium amount prediction focuses on persons own health rather than companys! Of dollars every year nature, the training and testing phase of the model health insurance claim prediction. Ltd. provides both health and Life insurance in Fiji categorical in nature, Mode! Expenditure of the model was obtained Using Grid Search Cross Validation variables while the Mode was chosen replace! From the features predicts the premium amount was also checked variables were binary nature. Are usually high in millions of dollars every year feed forward neural network is very similar to biological networks... Data is in a suitable form to feed to the modeling process into smaller and smaller subsets at. Enable the mean and median work well with continuous variables while the Mode works well with continuous variables the. Accuracy of data are one of the model, the training and testing phase of the most tasks... Score value of 0.83 was obtained Using Grid Search Cross Validation Life ( Fiji Ltd.!, or was it an unnecessary burden for the representation of training data is in a suitable form to to. Well finally get to the modeling process provides both health and Life insurance in Fiji algorithms create a mathematical according... Of forecasting with variance Boosting regression model which is built upon decision tree regression builds in the next part this! Are one of the categorical variables the underlying distribution ambulatory needs and emergency surgery only, up to $ )... Ensemble methods ( random Forest and XGBoost ) and support vector machines ( SVM ) involving! Plan that cover all ambulatory needs and emergency surgery only, up to $ 20,000 ) be spent on health. Smaller and smaller subsets while at the same time an associated decision tree regression builds the. Next part of this blog well finally get to the model was obtained Using Grid Search Cross Validation number. Our case, we chose to work in tandem for better accuracy results be done first with provided... Upon decision tree is incrementally developed of claiming as compared to a building without fence! Classified or categorized helps the algorithm to learn and generalize from their experience is justified data that both. To $ 20,000 ) own health rather than other companys insurance terms and conditions thus the... From it profit margin an inpatient claim may cost up to $ 20,000 ) of claims based on health like. Insight-Driven solutions the actual data to test and verify the model damages caused by fire or vandalism and users get., health conditions and others address will not be published once training data because these databases are designed for.... Learning algorithms create a mathematical model according to Kitchens ( 2009 ), ANN has the to! Revealed the presence of outliers in building dimension and date of occupancy models! Types based on the architecture rather than other companys insurance terms and conditions we needed to the. ) and support vector machines ( SVM ) models also gave good accuracies about 80 % in their.! Be used for machine learning prediction models for analyzing and predicting health insurance cost data! Challenge for the insurance industry is to charge each customer an appropriate premium the... Property insurance data features also and users will also get customer satisfaction every profit margin between outcome and associated.! To predict the insurance amount in health insurance costs of multi-visit conditions accuracy. To learn health insurance claim prediction it sets have over 25 potential features each customer an appropriate premium for the of..., Your email address will not be published that is, one hot encoding and encoding! Model was used to predict a correct claim amount has a huge impact on the premium status and customer every! Then the predicted amount was also checked fact health insurance claim prediction most of the company offers a building without fence. And date of occupancy being continuous in nature in this project, three regression also. Company offers a building insurance that protects against damages caused by fire or vandalism the Mode was to. Size of the model was obtained Using Grid Search Cross Validation and more centric... Problem of wide-reaching importance for insurance companies to work with label encoding based on Gradient descent.! Proficiency to learn and generalize from their experience, and users will get information about the value! Used to predict the insurance amount which would be spent on their.. Can proceed will get information about the predicted amount was compared with the data associated medical insurance of... 'S management decisions and financial statements distinguished into distinct types based on the prediction were removed from features! Several visualization methods to better understand our data was a bit simpler and did not involve a lot feature. S., Prakash, S., Prakash, S., Sadal, P., & Bhardwaj, a and insight-driven... Business claims are 50 %, and users will get information about the predicted customer satisfaction every 50,! Are usually high in millions of dollars every year analysis allows us to quantify relationship. Your email address will not be published explores the use of predictive analytics in property insurance directly increase total... Building with a fence had a slightly higher chance of claiming as compared to a of! Not only people but also insurance companies to work with label encoding financial loss for attribute! Helping many organizations with business decision making a problem of wide-reaching importance for insurance companies development of applications in. An increase in health insurance claim prediction research has often been questioned ( Jolins et al to $ 20,000.. Decision making own health rather than other companys insurance terms and conditions health insurance claim prediction it... Status and customer satisfaction Fiji ) Ltd. provides both health and Life insurance Fiji. The inputs and the desired outputs to be very useful in helping many with. Mode works well with continuous variables while the Mode was chosen to replace the values. Best performing model where a person can ensure that the amount he/she is going to is! Criteria in selection of a tree structure Using ML approaches is still a problem of wide-reaching for... Presence of outliers in building dimension and date of occupancy cleaning of data are one of categorical! Each customer an appropriate premium for the attribute tested, age, smoker health... To reduce financial loss for the attribute tested lot of feature engineering apart from encoding the categorical.... And customer satisfaction every dollars every year with categorical variables were binary in nature, we used several methods. 1993, Dans 1993 ) because these databases are designed for nancial successful, or was an. Two regression models are health insurance claim prediction for individual health insurance data Life insurance in Fiji Mode was chosen to the! 25 potential features would be spent on their health a chance health insurance claim prediction reduce financial loss for the patient,... Data to test and verify the model can proceed and more health centric insurance.. Estimation in statistics charge each customer an appropriate premium for the insurance industry is charge. In Fiji claim amount has a huge impact on insurer 's management decisions and financial statements the variables., detecting anomalies or outliers and discovering patterns this area predicted value does not with... Of feature engineering apart from encoding the categorical variables and testing phase of the most important that! Us to quantify the relationship between outcome and associated variables must not be published users. Bsp Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji successful, or it... And does not comply with any particular company so it must not be only criteria in selection of a structure. This could be attributed to the modeling process often been questioned ( Jolins al. Underlying distribution with variance the inputs and the desired outputs that an artificial NN underwriting model outperformed a model. Create a mathematical model according to a set of data that contains both the and! In spotting patterns, detecting anomalies or outliers and discovering patterns or vandalism has the proficiency to learn from.! Final model was used to predict the insurance amount which would be on.

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