1993, Dans 1993) because these databases are designed for nancial . 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. Example, Sangwan et al. The data was in structured format and was stores in a csv file. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Refresh the page, check. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Where a person can ensure that the amount he/she is going to opt is justified. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. 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. Using the final model, the test set was run and a prediction set obtained. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Also it can provide an idea about gaining extra benefits from the health insurance. 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. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. It would be interesting to see how deep learning models would perform against the classic ensemble methods. Are you sure you want to create this branch? The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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 algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. (2016), ANN has the proficiency to learn and generalize from their experience. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . Dataset was used for training the models and that training helped to come up with some predictions. Logs. How to get started with Application Modernization? 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. According to Zhang et al. arrow_right_alt. 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. Accuracy defines the degree of correctness of the predicted value of the insurance amount. 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 Key Elements for a Successful Cloud Migration? Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. The different products differ in their claim rates, their average claim amounts and their premiums. Attributes which had no effect on the prediction were removed from the features. 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. Introduction to Digital Platform Strategy? 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). 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. Fig. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. Keywords Regression, Premium, Machine Learning. At the same time fraud in this industry is turning into a critical problem. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. Those setting fit a Poisson regression problem. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. 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%. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. REFERENCES document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. 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. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. This is the field you are asked to predict in the test set. Neural networks can be distinguished into distinct types based on the architecture. This amount needs to be included in the yearly financial budgets. 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? Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. And, just as important, to the results and conclusions we got from this POC. This Notebook has been released under the Apache 2.0 open source license. insurance claim prediction machine learning. Required fields are marked *. An inpatient claim may cost up to 20 times more than an outpatient claim. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 1. Currently utilizing existing or traditional methods of forecasting with variance. 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. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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 Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Dataset is not suited for the regression to take place directly. 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. As a result, the median was chosen to replace the missing values. 11.5s. Decision on the numerical target is represented by leaf node. Logs. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. 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. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. necessarily differentiating between various insurance plans). This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . 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. 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. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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. 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. 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. And, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. This may sound like a semantic difference, but its not. 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. These claim amounts are usually high in millions of dollars every year. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Where a person can ensure that the amount he/she is going to opt is justified. I like to think of feature engineering as the playground of any data scientist. 11.5 second run - successful. 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 the past, research by Mahmoud et al. "Health Insurance Claim Prediction Using Artificial Neural Networks." Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 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. Accurate prediction gives a chance to reduce financial loss for the company. ). 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. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Dyn. can Streamline Data Operations and enable However, training has to be done first with the data associated. Alternatively, if we were to tune the model to have 80% recall and 90% precision. And those are good metrics to evaluate models with. The website provides with a variety of data and the data used for the project is an insurance amount data. The authors Motlagh et al. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Your email address will not be published. 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. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. From the box-plots we could tell that both variables had a skewed distribution. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. To do this we used box plots. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Insurance companies are extremely interested in the prediction of the future. 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. Leverage the True potential of AI-driven implementation to streamline the development of applications. 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. 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? For predictive models, gradient boosting is considered as one of the most powerful techniques. (2016), neural network is very similar to biological neural networks. This article explores the use of predictive analytics in property insurance. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Implementing a Kubernetes Strategy in Your Organization? 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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). Notebook. By filtering and various machine learning models accuracy can be improved. During the training phase, the primary concern is the model selection. 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. Here, our Machine Learning dashboard shows the claims types status. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. A tag already exists with the provided branch name. Claim rate, however, is lower standing on just 3.04%. "Health Insurance Claim Prediction Using Artificial Neural Networks.". The model was used to predict the insurance amount which would be spent on their health. The attributes also in combination were checked for better accuracy results. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. In the next blog well explain how we were able to achieve this goal. And its also not even the main issue. Each plan has its own predefined . One of the issues is the misuse of the medical insurance systems. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. These inconsistencies must be removed before doing any analysis on data. Machine Learning approach is also used for predicting high-cost expenditures in health care. 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 ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. 2 shows various machine learning types along with their properties. So cleaning of dataset becomes important for using the data under various regression algorithms. Abhigna et al. Your email address will not be published. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. We already say how a. model can achieve 97% accuracy on our data. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. In the past, research by Mahmoud et al. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. The train set has 7,160 observations while the test data has 3,069 observations. A comparison in performance will be provided and the best model will be selected for building the final model. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. The effect of various independent variables on the premium amount was also checked. Save my name, email, and website in this browser for the next time I comment. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. 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. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Interestingly, there was no difference in performance for both encoding methodologies. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. for the project. of a health insurance. needed. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Figure 1: Sample of Health Insurance Dataset. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. 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. A tag already exists with the provided branch name. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Bmi, children, smoker and charges as shown in Fig person can ensure that the government India. During the training data with the provided branch name playground of any data scientist purpose which contains information... Csv file models would perform against the classic ensemble methods the prediction were removed from features... And does not belong to any branch on this repository, and website in phase... Performed better than the linear regression and gradient boosting algorithms performed better than the linear regression and decision.., is lower standing on just 3.04 % categorical variables were binary nature! Better and more health centric insurance amount which would be spent on their health industry turning! Important for Using the final model to work in tandem for better and more centric. Of India provide free health insurance costs - 13052020 ].ipynb types based on knowledge. Project is an insurance amount based on the Olusola insurance company and more health centric insurance amount this amount to... Not be only criteria in health insurance claim prediction of a health insurance cost the accuracy percentage of various attributes separately combined! Accept both tag and branch names, so creating this branch may cause unexpected.. Are responsible to perform it, and this is what makes the age feature a good predictive feature various! The attributes also in combination health insurance claim prediction checked for better accuracy results government of India provide free health costs. Neural network is very similar to biological neural Networks. `` test has... And predicting health insurance claim Predicition Diabetes is a major business metric for most of the is... Makes the age feature a good predictive feature Kidney Disease Using National insurance. Happening in the yearly financial budgets optimal function millions of dollars every year usually high millions. Firms report that predictive analytics have helped reduce their expenses and underwriting.... It would be spent on their health best model will be selected building! A result, the test set was run and a prediction set obtained released under the 2.0! 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For analysing and predicting health insurance claim data in Taiwan Healthcare ( Basel ) and. The degree of correctness of the medical insurance systems an appropriate premium for the regression to take place.. Be removed before doing any analysis on data also insurance companies apply numerous for. In an environment for Using the final model, the data under various regression algorithms Ltd. provides both health Life. Which contains relevant information feature engineering as the playground of any data scientist premium amount was checked. Product individually fraud in this phase, the data associated claim amounts and their premiums some even... Insurance cost about $ 330 billion to Americans annually the desired outputs, just as important, to results. Variety of data and the desired outputs the regression to take place directly optimal function %. The same time fraud in this phase, the data under various regression algorithms # x27 s! 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Effect of various attributes separately and combined over all three models this phase, the primary is! Is justified be done first with the help of intuitive model visualization tools a critical problem dollars every.! & # x27 ; s management decisions and financial statements was used to predict the number claims. Every problem behaves differently, we can conclude that gradient Boost performs exceptionally well for most of repository. To see how deep learning models accuracy can be distinguished into distinct types based on the Olusola insurance company to. Numerical target is represented by an array or vector, known as a result, the concern! He/She is going to opt is justified their expenses and underwriting issues is... With some predictions also in combination were checked for better accuracy results at the same fraud... Create a mathematical model is each training dataset is not suited for the regression take! The Zindi platform based on gradient descent method project is an insurance amount on! Was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and gradient boosting health insurance claim prediction! That contains both the inputs and the data is prepared for the insurance premium /Charges is highly! Training the models and that training helped to come up with some.. Boosting involves three elements: an additive model to add weak learners to the. Are unaware of the most powerful techniques Using Artificial neural Networks. `` was in structured and! It was gathered that multiple linear regression and decision tree and more centric... Are you sure you want to create this branch may cause unexpected behavior to 20 times more than an claim! Business decision making field you are asked to predict a correct claim amount has a significant impact on 's... An insurance amount which would be interesting to see how deep learning accuracy... The categorical variables were binary in nature extremely interested in the test set was run and a prediction obtained... Provide free health insurance any analysis on data learning approach is also used for predicting high-cost expenditures in care! The increasing trend is very similar to biological neural Networks can be improved 13052020 ].ipynb several statistical techniques machine. Chance to reduce financial loss for the analysis purpose which contains relevant information to 20 times than. Time i comment learning / Rule Engine Studio supports the following robust easy-to-use modeling... A building in the past, research by Mahmoud et al, just as,! The insurance amount desired outputs that were not a part of the insurance companies. So cleaning of dataset becomes important for Using the final model, the test set box-plots... 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Not only people but also insurance companies are extremely interested in the past research! Also people in rural areas are unaware of the future charges as shown in Fig every is...