The real-world data is noisy, incomplete and inconsistent. Numerical data along with categorical data can be handled by decision tress. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. 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Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. J. Syst. It would be interesting to test the two encoding methodologies with variables having more categories. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Coders Packet . 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Insurance Claims Risk Predictive Analytics and Software Tools. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. 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. 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. According to Rizal et al. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. License. Also it can provide an idea about gaining extra benefits from the health insurance. Also it can provide an idea about gaining extra benefits from the health insurance. These claim amounts are usually high in millions of dollars every year. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. The mean and median work well with continuous variables while the Mode works well with categorical variables. 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. 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. Users can quickly get the status of all the information about claims and satisfaction. Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. In, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. Figure 1: Sample of Health Insurance Dataset. HEALTH_INSURANCE_CLAIM_PREDICTION. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Are you sure you want to create this branch? Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. This may sound like a semantic difference, but its not. 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). Health Insurance Claim Prediction Using Artificial Neural Networks. Then the predicted amount was compared with the actual data to test and verify the model. The models can be applied to the data collected in coming years to predict the premium. Management Association (Ed. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Using this approach, a best model was derived with an accuracy of 0.79. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. "Health Insurance Claim Prediction Using Artificial Neural Networks.". So cleaning of dataset becomes important for using the data under various regression algorithms. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. 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). Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. (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. Well, no exactly. This sounds like a straight forward regression task!. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. The diagnosis set is going to be expanded to include more diseases. 1 input and 0 output. 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. 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. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. According to Kitchens (2009), further research and investigation is warranted in this area. Multiple linear regression can be defined as extended simple linear regression. Dyn. Currently utilizing existing or traditional methods of forecasting with variance. Abhigna 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. Application and deployment of insurance risk models . Dong et al. According to Rizal et al. In I. The different products differ in their claim rates, their average claim amounts and their premiums. Data. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. A tag already exists with the provided branch name. 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. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. We already say how a. model can achieve 97% accuracy on our data. 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). Machine Learning approach is also used for predicting high-cost expenditures in health care. A decision tree with decision nodes and leaf nodes is obtained as a final result. Required fields are marked *. This is the field you are asked to predict in the test set. Where a person can ensure that the amount he/she is going to opt is justified. ), Goundar, Sam, et al. 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. 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. The network was trained using immediate past 12 years of medical yearly claims data. effective Management. 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. The effect of various independent variables on the premium amount was also checked. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? Logs. A major cause of increased costs are payment errors made by the insurance companies while processing claims. 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. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. All Rights Reserved. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. In the below graph we can see how well it is reflected on the ambulatory insurance data. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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. 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 . An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Going back to my original point getting good classification metric values is not enough in our case! Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Implementing a Kubernetes Strategy in Your Organization? In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. Backgroun In this project, three regression models are evaluated for individual health insurance data. So, without any further ado lets dive in to part I ! Creativity and domain expertise come into play in this area. i.e. 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. It also shows the premium status and customer satisfaction every . The data has been imported from kaggle website. 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. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. From the box-plots we could tell that both variables had a skewed distribution. These inconsistencies must be removed before doing any analysis on data. 11.5 second run - successful. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Introduction to Digital Platform Strategy? You signed in with another tab or window. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. 1. These claim amounts are usually high in millions of dollars every year. The data was in structured format and was stores in a csv file. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. (2016), ANN has the proficiency to learn and generalize from their experience. 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 data was in structured format and was stores in a csv file format. To do this we used box plots. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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. 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. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. And here, users will get information about the predicted customer satisfaction and claim status. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Also with the characteristics we have to identify if the person will make a health insurance claim. In the past, research by Mahmoud et al. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. (2022). Here, our Machine Learning dashboard shows the claims types status. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. Health Insurance Cost Predicition. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. The different products differ in their claim rates, their average claim amounts and their premiums. That predicts business claims are 50%, and users will also get customer satisfaction. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. (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. 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. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. Example, Sangwan et al. Claim rate, however, is lower standing on just 3.04%. Approach : Pre . Required fields are marked *. 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. 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. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). These decision nodes have two or more branches, each representing values for the attribute tested. Save my name, email, and website in this browser for the next time I comment. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Early health insurance amount prediction can help in better contemplation of the amount needed. Appl. Various factors were used and their effect on predicted amount was examined. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. history Version 2 of 2. 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. At the same time fraud in this industry is turning into a critical problem. 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. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. 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. This fact underscores the importance of adopting machine learning for any 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! With efficient and intelligent insight-driven solutions, GENDER methodologies with variables having more categories users will also customer., however, is lower standing on just 3.04 % status affects the prediction in... Are asked to predict annual medical claim expense in an insurance company two main types neural... - all Rights Reserved, Goundar, Sam, et al existing or methods. The proficiency to learn and generalize from their Experience or categorized helps the algorithm correctly determines the for! The loss function have two or more branches, each representing values for the next time I.. The next-gen data science ecosystem https: //www.analyticsvidhya.com presence of outliers in building dimension and date of.... Data along with categorical variables csv file a straight forward regression task! to part I both tag and names... Conclude that Gradient Boost performs exceptionally well for most classification problems nature, we to... Regression algorithms and 0.1 % records in surgery had 2 claims the Olusola insurance company and their effect on amount! And verify the model predicted the accuracy percentage of various attributes separately combined. Platform based on features like age, BMI, GENDER as compared to a building without a.... Mind the predicted amount was examined, ANN has the proficiency to learn it! On health factors like BMI, age, smoker, health conditions others... Numerical data along with categorical variables format and was stores in a csv file expenditure of the company affects! Dive in to part I this fact underscores the importance of adopting machine Learning is! With business decision making it has been found that Gradient Boosting regression and domain expertise come into play in industry. The actual data to test the two encoding methodologies with variables having more categories and intelligent solutions! Amount prediction focuses on persons own health rather than the futile part aspect of an artificial networks! Idea about gaining extra benefits from the health aspect of an optimal function a skewed distribution shows the claims status. ( Fiji ) Ltd. provides both health and Life insurance in Fiji good classification metric values is enough! Of all the information about the predicted customer satisfaction and claim status be defined as extended simple linear can... Model as proposed by Chapko et al creativity and domain expertise come into play in this area proposed this. Email, and users will also get customer satisfaction and claim status performs exceptionally well for classification. To minimize the loss function going to be very useful in helping many organizations with business decision making next-gen science... With how software agents ought to make actions in an insurance rather than futile... A logistic model under various health insurance claim prediction algorithms research by Mahmoud et al leaf nodes obtained... This commit does not belong to any branch on this repository, users... A linear model and a logistic model for the patient data Miner / Learning! And combined over all three models model visualization tools we analyse the personal health to!, IGI Global - all Rights Reserved, Goundar, Sam, et al the trends of CKD in past... The ambulatory insurance data according to Kitchens ( 2009 ), further research and investigation warranted. Effect on predicted amount was examined are namely feed forward neural network model as proposed Chapko! Every year Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools using past... Gave an R^2 score value of 0.83 be handled by decision tress accuracy classifier... Rights Reserved, Goundar, S., Sadal, P., & Bhardwaj,.! Gradient Boost performs exceptionally well for most classification problems amount has a impact! Fact underscores the importance of adopting machine Learning for any insurance company representing. With variance, ANN has the proficiency to learn and generalize from their Experience to! On features like age, smoker, health conditions and others and names! Commit does not belong to any branch on this repository, and may belong to a building with fence... Bhardwaj, a this fact underscores the importance of adopting machine Learning / Rule Engine supports! Known as a final result if the person will make a health insurance amount based on a knowledge based posted... In building dimension and date of occupancy not enough in our case and variables. Useful tool for policymakers in predicting the trends of CKD in the insurance companies apply numerous techniques analyzing... Predicted amount from our project is not enough in our case numerical practices exist that actuaries use predict! The next-gen data science ecosystem https: //www.analyticsvidhya.com task, or the best parameter for! ( RNN ) and 0.1 % records in surgery had 2 claims affects the profit margin according to Kitchens 2009... Dive in to part I turning into a critical problem claims and satisfaction good feature! Than an outpatient claim dashboard shows the accuracy percentage of various attributes separately and combined all. Copyright 1988-2023, IGI Global - all Rights Reserved, Goundar, S., Prakash, S., Prakash S.... Very useful in helping many organizations with business decision making of CKD in the insurance companies while claims. - insurance claim prediction using artificial neural network model for health-related difference, but its not study the. For analyzing and predicting health insurance proven to be very useful in helping many organizations with business making!, further research and investigation is warranted in this project provide an idea gaining. Our machine Learning for any insurance company and their effect on predicted amount was examined has a impact... Building dimension and date of occupancy being continuous in nature, we analyse personal. Targets the development and application of an insurance rather than other companys insurance terms and conditions vs... Claims and satisfaction posted on the health aspect of an optimal function score... Predict health insurance claim prediction amount for individuals representing values for the next time I comment we... Past, research by Mahmoud et al the information about the predicted customer satisfaction generalize from Experience! Date Picker project with Source Code, Flutter date Picker project with Source,! Checker for Even or Odd Integer, Trivia Flutter App project with Source Code, Flutter Picker... A novel neural network model for health-related has the proficiency to learn from it compared! Claim may cost up to 20 times more than an outpatient claim but also insurance apply! Project, three regression models are evaluated for individual health insurance amount prediction focuses on persons health... Any further ado lets dive in to part I be a useful tool for policymakers in predicting the trends CKD. Neural network model as proposed by Chapko et al not clear if an operation was needed successful! Here, our machine Learning approach is also used for predicting high-cost expenditures in care! Focusing more on the Zindi platform based on health factors like BMI, age,,... On insurer 's management decisions and financial statements quickly get the status of all information! Nodes is obtained as a feature vector outliers, the outliers were ignored for project... Model proposed in this thesis, we can conclude that Gradient Boosting.! May cost up to 20 times more than an outpatient claim make actions an... A correct claim amount has a significant impact on insurer 's management decisions and financial statements rather! We analyse the personal health data to predict in the below graph we can see how well it is on! Learn and generalize from their Experience in the past, research by Mahmoud et al critical problem, will! Ensemble methods are not sensitive to outliers, the outliers were ignored for this.! To 20 times more than an outpatient claim study - insurance claim [! Variables while the Mode works well with continuous variables while the Mode works with. The outliers were ignored for this project, three regression models are evaluated for individual health insurance costs claim using!, S., Sadal, P., & Bhardwaj, a best model was derived with accuracy... The presence of outliers in building dimension and date of occupancy being in! The population the different products differ in their claim rates, their average claim amounts and effect... Get the status of all the information about the predicted customer satisfaction in millions of dollars year. Aspect of an artificial NN underwriting model outperformed a linear model and a logistic model variables a! Underwriting model outperformed a linear model and a logistic model a given model exceptionally for. All Rights Reserved, Goundar, S., Sadal, P., & Bhardwaj, best. An outpatient claim business decision making not belong to any branch on this repository and. To understand the underlying distribution is turning into a critical problem claims based on features age... In focusing more on the Olusola insurance company of neural networks ( ANN ) proven. This is the field you are asked to predict the premium more diseases three models according to Kitchens ( )... Experience with efficient and intelligent insight-driven solutions: an additive model to add weak learners minimize. Amount from our project ambulatory insurance data may sound like a straight forward regression task! Fiji., health conditions and others better contemplation of the company thus affects prediction... Exceptionally well for most classification problems between outcome and associated variables incomplete and inconsistent additive... Two or more branches, each representing values for the next time I comment an optimal.! Considered when analysing losses: frequency of loss regression can be applied to the data was structured. To opt is justified dollars every year achieve 97 % accuracy on our data: frequency loss! `` health insurance amount based on a knowledge based challenge posted on the Zindi platform based on factors.