Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. 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. Dyn. 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. The models can be applied to the data collected in coming years to predict the premium. (2016), neural network is very similar to biological neural networks. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Application and deployment of insurance risk models . Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Here, our Machine Learning dashboard shows the claims types status. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. 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. Take for example the, feature. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. Claim rate, however, is lower standing on just 3.04%. Removing such attributes not only help in improving accuracy but also the overall performance and speed. 1993, Dans 1993) because these databases are designed for nancial . Logs. These claim amounts are usually high in millions of dollars every year. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. II. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. 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 comparison in performance will be provided and the best model will be selected for building the final model. Attributes which had no effect on the prediction were removed from the features. (2022). Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Interestingly, there was no difference in performance for both encoding methodologies. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 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. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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). 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. 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. 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. And those are good metrics to evaluate models with. (2011) and El-said et al. 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. The data was in structured format and was stores in a csv file format. It would be interesting to see how deep learning models would perform against the classic ensemble methods. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. (2011) and El-said et al. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Abhigna et al. (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. This amount needs to be included in the yearly financial budgets. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. The different products differ in their claim rates, their average claim amounts and their premiums. 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. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The attributes also in combination were checked for better accuracy results. Also it can provide an idea about gaining extra benefits from the health insurance. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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). (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. The data was imported using pandas library. Dataset is not suited for the regression to take place directly. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Appl. Coders Packet . Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Decision on the numerical target is represented by leaf node. Logs. This sounds like a straight forward regression task!. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Various factors were used and their effect on predicted amount was examined. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 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. License. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. This amount needs to be included in Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Management Association (Ed. We see that the accuracy of predicted amount was seen best. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. These inconsistencies must be removed before doing any analysis on data. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Health Insurance Claim Prediction Using Artificial Neural Networks. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. 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. How can enterprises effectively Adopt DevSecOps? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Required fields are marked *. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. The main application of unsupervised learning is density estimation in statistics. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Health Insurance Claim Prediction Using Artificial Neural Networks. 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