We already say how a. model can achieve 97% accuracy on our data. Required fields are marked *. 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. 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. A major cause of increased costs are payment errors made by the insurance companies while processing claims. 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. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. License. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. This is the field you are asked to predict in the test set. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. 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. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Training data has one or more inputs and a desired output, called as a supervisory signal. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Dataset is not suited for the regression to take place directly. 2 shows various machine learning types along with their properties. Dong et al. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). (2019) proposed a novel neural network model for health-related . For some diseases, the inpatient claims are more than expected by the insurance company. 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Insurance Claims Risk Predictive Analytics and Software Tools. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? A matrix is used for the representation of training data. HEALTH_INSURANCE_CLAIM_PREDICTION. The data was in structured format and was stores in a csv file format. In a dataset not every attribute has an impact on the prediction. Using this approach, a best model was derived with an accuracy of 0.79. Regression or classification models in decision tree regression builds in the form of a tree structure. 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. Each plan has its own predefined . Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. The distribution of number of claims is: Both data sets have over 25 potential features. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Neural networks can be distinguished into distinct types based on the architecture. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. 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. (2022). 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 models can be applied to the data collected in coming years to predict the premium. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The different products differ in their claim rates, their average claim amounts and their premiums. To do this we used box plots. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. Your email address will not be published. Key Elements for a Successful Cloud Migration? In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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 . 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 Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. 1993, Dans 1993) because these databases are designed for nancial . 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. 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. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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? According to Zhang et al. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. (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. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Description. 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). Factors determining the amount of insurance vary from company to company. Health Insurance Cost Predicition. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The data has been imported from kaggle website. 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. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. 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. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Management Association (Ed. 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. Where a person can ensure that the amount he/she is going to opt is justified. The test set major cause of increased costs are payment errors made by the insurance business, things! 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