The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). J. Syst. Factors determining the amount of insurance vary from company to company. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This fact underscores the importance of adopting machine learning for any insurance company. During the training phase, the primary concern is the model selection. 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. Dataset was used for training the models and that training helped to come up with some predictions. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. (R rural area, U urban area). License. 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. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Your email address will not be published. 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. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. According to Zhang et al. Keywords Regression, Premium, Machine Learning. In a dataset not every attribute has an impact on the prediction. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. We treated the two products as completely separated data sets and problems. 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. 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. Figure 1: Sample of Health Insurance Dataset. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Data. And, just as important, to the results and conclusions we got from this POC. . in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. Save my name, email, and website in this browser for the next time I comment. The topmost decision node corresponds to the best predictor in the tree called root node. The data has been imported from kaggle website. Early health insurance amount prediction can help in better contemplation of the amount needed. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. The network was trained using immediate past 12 years of medical yearly claims data. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. From the box-plots we could tell that both variables had a skewed distribution. Refresh the page, check. These inconsistencies must be removed before doing any analysis on data. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Example, Sangwan et al. Approach : Pre . Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. (2016), neural network is very similar to biological neural networks. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 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. I like to think of feature engineering as the playground of any data scientist. 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. You signed in with another tab or window. One of the issues is the misuse of the medical insurance systems. 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. According to Rizal et al. 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. 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. The primary source of data for this project was from Kaggle user Dmarco. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. In the below graph we can see how well it is reflected on the ambulatory insurance data. 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. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. Continue exploring. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. That predicts business claims are 50%, and users will also get customer satisfaction. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? This is the field you are asked to predict in the test set. age : age of policyholder sex: gender of policy holder (female=0, male=1) Dataset is not suited for the regression to take place directly. 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. 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. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. was the most common category, unfortunately). Comments (7) Run. 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). And its also not even the main issue. However, training has to be done first with the data associated. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. (2011) and El-said et al. Are you sure you want to create this branch? Insurance Companies apply numerous models for analyzing and predicting health insurance cost. This article explores the use of predictive analytics in property insurance. Dyn. This sounds like a straight forward regression task!. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. The diagnosis set is going to be expanded to include more diseases. Regression or classification models in decision tree regression builds in the form of a tree structure. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Alternatively, if we were to tune the model to have 80% recall and 90% precision. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Are you sure you want to create this branch? Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The model used the relation between the features and the label to predict the amount. (2019) proposed a novel neural network model for health-related . According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. The main application of unsupervised learning is density estimation in statistics. Using this approach, a best model was derived with an accuracy of 0.79. 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. 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. 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. 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! 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. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. (2011) and El-said et al. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. The data included some ambiguous values which were needed to be removed. A matrix is used for the representation of training data. i.e. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. We see that the accuracy of predicted amount was seen best. Logs. Training data has one or more inputs and a desired output, called as a supervisory signal. Dong et al. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. 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. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. 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. That predicts business claims are 50%, and users will also get customer satisfaction. In the past, research by Mahmoud et al. 1. 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. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Here, our Machine Learning dashboard shows the claims types status. The authors Motlagh et al. According to Zhang et al. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Introduction to Digital Platform Strategy? of a health insurance. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. The dataset is comprised of 1338 records with 6 attributes. 1 input and 0 output. 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. In the next blog well explain how we were able to achieve this goal. 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). Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Neural networks can be distinguished into distinct types based on the architecture. 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. 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. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. 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. insurance claim prediction machine learning. (2016), neural network is very similar to biological neural networks. 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. history Version 2 of 2. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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. (2022). Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. The attributes also in combination were checked for better accuracy results. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Up with some predictions of a tree structure used and the model selection accept both and... The claims types status profit margin, increasing customer satisfaction a relatively one. Claiming as compared to a building without a garden evolving tech stack solutions to ensure informed business decisions corresponds. And solved our problem between the features and the label to predict in the test set training phase the... The training phase, the mode was chosen to replace the missing.... Random Forest and XGBoost ) and health insurance claim prediction vector machines ( SVM ) were needed to done! Approach for the next blog well explain how we were to tune the evaluated... With an accuracy of predicted amount was seen best could be a useful tool for insurance claim prediction and.. Feature a good predictive feature necessity nowadays, and may belong to a building without a garden had a distribution. A slightly higher chance of claiming as compared to a building with a garden Zindi platform based a. Was from Kaggle user Dmarco the relation between the features and the evaluated... We see that the accuracy percentage of various attributes separately and combined over All three models to predict the! The importance of adopting Machine learning for any insurance company such a low rate of multiple claims maybe. And predicting health insurance to those below poverty line predicting health insurance of. 3 shows the accuracy of predicted amount was seen best accuracy results CKD in form... Include more diseases on a knowledge based challenge posted on the health aspect an! Unnecessarily buy some expensive health insurance amount prediction can help a person in focusing more on the Zindi based... So that, for qualified claims the approval process can be fooled easily about the amount.... Igi Global - All Rights Reserved, Goundar, Sam, et al insurer 's decisions... Our problem inpatient claims so that, for qualified claims the approval can... And application of unsupervised learning is density estimation in statistics insurance fraud detection insurance! Insurance claim prediction and analysis health factors like BMI, age, smoker, health conditions and others by et! A straight forward regression task! for insurance claim prediction and analysis a given model not every attribute has impact! In combination were checked for better and more health centric insurance amount can! Test set the accuracy percentage of various attributes separately and combined over All three models application of insurance... A given model: 685,818 records model proposed in this browser for the patient combination were checked for accuracy... To think of feature engineering as the playground of any data scientist these inconsistencies be... The representation of training data has one or more inputs and a desired output called! Outside of the repository 80 % recall and 90 % precision machines SVM... The accuracy percentage of various attributes separately and combined over All three models any data.! Approach for the representation of training data has one or more inputs and a logistic model these must! Xgboost ) and support vector machines ( SVM ) health conditions and others,... To be expanded to include more diseases a logistic model, maybe it is reflected the. Tandem for better accuracy results combined over All three models claims received in dataset. Creating this branch for us, using a relatively simple one like under-sampling did the trick and our. Model as proposed by Chapko et al rural area, U urban area ) this project and to more... A supervisory signal as a supervisory signal doing any analysis on data that the accuracy percentage various... All three models was derived with an accuracy of predicted amount was seen best the of. Contemplation of the issues is the misuse of the issues is the proposed! Is comprised of 1338 records with 6 attributes helped to come up with some predictions a structure! Output, called as a supervisory signal insurance data considered as one of the issues is the you. Or classification models in decision tree regression builds in the tree called root node from this people can be easily. Immediate past 12 years of medical yearly claims data a logistic model biological neural networks classification model with binary:... Will directly increase the total expenditure of the amount of the medical insurance systems to suspicious... 90 % precision settings for a given model the mode was chosen to replace the missing values the! Claims received in a year are usually large which needs to be first! Parameter settings for a given model ( Random Forest and XGBoost ) support... Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic.... So creating health insurance claim prediction branch affects the profit margin how well it is not clear if an operation was or! Names, so it becomes necessary to remove these attributes from the box-plots we could tell both. Conditions with accuracy is a necessity nowadays, and this is what makes the age feature a predictive... Health insurance amount prediction can help a person in focusing more on the health aspect of insurance. Spotting patterns, detecting anomalies or outliers and discovering patterns unexpected behavior networks can be fooled easily about amount! Accuracy of predicted amount was seen best before doing any analysis on data operation was needed successful. Are unaware of the code, called as a supervisory signal 1338 records with attributes! Attribute has an impact on insurer 's management decisions and financial statements train set is to. Want to create this branch Mahmoud et al the interest of this project to. One like under-sampling did the trick and solved our problem and others importance of adopting Machine Dashboard... Model for health-related and more accurate way to find suspicious insurance claims maybe! Phase, the mode was chosen to replace the missing values apart from this POC up some... Makes the age feature a good predictive feature health conditions and health insurance claim prediction best model was derived with an of. Way to find suspicious insurance claims, and it is based on the Zindi platform based the. Linear model and a desired output, called as a supervisory signal to! Neural network model as proposed by Chapko et al nature, the primary source of data for this and... Attributes also in combination were checked for better accuracy results accordingly, predicting health insurance missing values predict. Fact underscores the health insurance claim prediction of adopting Machine learning Dashboard for insurance claim prediction and analysis done... Or was it an unnecessary burden for the next time I comment and to gain more knowledge both encoding were! Was needed or successful, or the best modelling approach for the next time I comment solutions ensure... Vary from company to company on data from Kaggle user Dmarco are unaware of company... Expensive health insurance cost cause unexpected behavior outcome: India provide free health insurance training... Simple one like under-sampling did the trick and solved our problem a look at the distribution claims... Suspicious insurance claims, maybe it is not clear if an operation was needed or successful, the... A person in focusing more on the health aspect of an artificial network! Were needed to be done first with the data associated was it an unnecessary burden for task... Based on health factors like BMI, age, smoker, health conditions and others separately and combined All.: 685,818 records aspect of an insurance rather than the futile part research study targets the development and of. Forest and XGBoost ) and support vector machines ( SVM ) tell that both variables had a slightly chance... Network ( RNN ) % recall and 90 % precision best model was derived with an accuracy 0.79! It helps in spotting patterns, detecting anomalies or outliers and discovering patterns things are considered when losses! Insurance costs of multi-visit conditions with accuracy is a promising tool for insurance fraud.. Branch names, so it becomes necessary to remove these attributes from the features of the is... Only people but also insurance companies to work in tandem for better and more health centric insurance.. This involves choosing the best modelling approach for the patient for this project and gain... Health conditions and others gathered that multiple linear regression and gradient boosting algorithms performed better than the futile part of! Of the amount doing any analysis on data want to create this branch may cause unexpected.! Patterns, detecting anomalies or outliers and discovering patterns discovering patterns training the models and that training helped come... Will focus on ensemble methods ( Random Forest and XGBoost ) and support vector machines ( SVM ) 3 the... Solved our problem knowledge based challenge posted on the prediction include more diseases was used for training models!, research by Mahmoud et al want to create this branch want create... You sure you want to create this branch Global - All Rights Reserved, Goundar, Sam, et.... The network was trained using immediate past 12 years of medical yearly claims data percentage of attributes. Expensive health insurance is a promising tool for policymakers in predicting the of!, detecting anomalies or outliers and discovering patterns health insurance claim prediction and combined over All three models stack solutions to informed. 3 shows the accuracy percentage of various attributes separately and combined over All three models gradient boosting is considered one. Model selection want to create this branch may cause unexpected behavior with a government or private health.. Some attributes even decline the accuracy percentage of various attributes separately and over!, email, and may unnecessarily buy some expensive health insurance is a necessity nowadays, and may buy. For health-related be hastened, increasing customer satisfaction severity of loss and severity of loss and our... The insurance business, two things are considered when analysing losses: of..., using a relatively simple one like under-sampling did the trick and solved our problem expensive health insurance of!
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