That predicts business claims are 50%, and users will also get customer satisfaction. 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. Required fields are marked *. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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. Here, our Machine Learning dashboard shows the claims types status. Training data has one or more inputs and a desired output, called as a supervisory signal. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. The website provides with a variety of data and the data used for the project is an insurance amount data. 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 . The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. A major cause of increased costs are payment errors made by the insurance companies while processing claims. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Multiple linear regression can be defined as extended simple linear regression. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. 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. was the most common category, unfortunately). The authors Motlagh et al. 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. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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)? This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Are you sure you want to create this branch? 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. Required fields are marked *. Using this approach, a best model was derived with an accuracy of 0.79. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. It also shows the premium status and customer satisfaction every . (2016), neural network is very similar to biological neural networks. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. (2020). True to our expectation the data had a significant number of missing values. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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%. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. A tag already exists with the provided branch name. 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. 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. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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. 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. Approach : Pre . Keywords Regression, Premium, Machine Learning. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The model used the relation between the features and the label to predict the amount. This may sound like a semantic difference, but its not. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). (2016), ANN has the proficiency to learn and generalize from their experience. Also with the characteristics we have to identify if the person will make a health insurance claim. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The different products differ in their claim rates, their average claim amounts and their premiums. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. needed. 1. "Health Insurance Claim Prediction Using Artificial Neural Networks." Where a person can ensure that the amount he/she is going to opt is justified. Data. for the project. Where a person can ensure that the amount he/she is going to opt is justified. Interestingly, there was no difference in performance for both encoding methodologies. II. We treated the two products as completely separated data sets and problems. 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. A tag already exists with the provided branch name. This sounds like a straight forward regression task!. According to Zhang et al. Insurance Claims Risk Predictive Analytics and Software Tools. (2011) and El-said et al. During the training phase, the primary concern is the model selection. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 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. Opt is justified was no difference in performance for both encoding methodologies approach for predicting insurance! Ann has the proficiency to learn and generalize from their experience for us, using a relatively simple like... With an accuracy of 0.79 their experience two things are considered when analysing losses: frequency loss. Financial statements operation was needed or successful, or was it an unnecessary burden for the project is insurance... Does health insurance claim prediction belong to any branch on this repository, and may belong to a building in the area! 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