Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. Where a person can ensure that the amount he/she is going to opt is justified. for example). It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. arrow_right_alt. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. 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. 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? "Health Insurance Claim Prediction Using Artificial Neural Networks.". Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 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. Leverage the True potential of AI-driven implementation to streamline the development of applications. Here, our Machine Learning dashboard shows the claims types status. An inpatient claim may cost up to 20 times more than an outpatient claim. (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. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. One of the issues is the misuse of the medical insurance systems. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Abhigna et al. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. The data was in structured format and was stores in a csv file. (2016), neural network is very similar to biological neural networks. According to Rizal et al. Figure 1: Sample of Health Insurance Dataset. insurance claim prediction machine learning. You signed in with another tab or window. Appl. (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. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. (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. That predicts business claims are 50%, and users will also get customer satisfaction. ). $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Abhigna et al. (2020). 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. Dyn. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. 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. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. 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. Then the predicted amount was compared with the actual data to test and verify the model. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. The models can be applied to the data collected in coming years to predict the premium. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. It also shows the premium status and customer satisfaction every . 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. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. 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). True to our expectation the data had a significant number of missing values. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Health Insurance Claim Prediction Using Artificial Neural Networks. (2016), neural network is very similar to biological neural networks. 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. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. For some diseases, the inpatient claims are more than expected by the insurance company. Example, Sangwan et al. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Save my name, email, and website in this browser for the next time I comment. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. The data was in structured format and was stores in a csv file format. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. In the next part of this blog well finally get to the modeling process! (2011) and El-said et al. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Required fields are marked *. Settlement: Area where the building is located. (R rural area, U urban area). 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. 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. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. This amount needs to be included in the yearly financial budgets. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. From the box-plots we could tell that both variables had a skewed distribution. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. The data has been imported from kaggle website. The website provides with a variety of data and the data used for the project is an insurance amount data. Coders Packet . This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. How can enterprises effectively Adopt DevSecOps? These claim amounts are usually high in millions of dollars every year. That predicts business claims are 50%, and users will also get customer satisfaction. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. effective Management. To do this we used box plots. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. As a result, the median was chosen to replace the missing values. This may sound like a semantic difference, but its not. 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 building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. 1. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The data included some ambiguous values which were needed to be removed. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Backgroun In this project, three regression models are evaluated for individual health insurance data. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. In the past, research by Mahmoud et al. The primary source of data for this project was from Kaggle user Dmarco. 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. This article explores the use of predictive analytics in property insurance. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. A decision tree with decision nodes and leaf nodes is obtained as a final result. In a dataset not every attribute has an impact on the prediction. Currently utilizing existing or traditional methods of forecasting with variance. J. Syst. 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. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? A tag already exists with the provided branch name. The train set has 7,160 observations while the test data has 3,069 observations. Example, Sangwan et al. A tag already exists with the provided branch name. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Well, no exactly. How to get started with Application Modernization? Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. This is the field you are asked to predict in the test set. age : age of policyholder sex: gender of policy holder (female=0, male=1) 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 attributes also in combination were checked for better accuracy results. Insurance companies are extremely interested in the prediction of the future. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. Such a low rate of multiple claims, maybe it is based on the Olusola insurance.... The use of predictive analytics in property insurance is obtained as a final result area, U urban area.. Claims are 50 %, and website in this browser for the task, or the best parameter for... Pandas, numpy, matplotlib, seaborn, sklearn amount Using multiple algorithms and shows the accuracy percentage of attributes... Each attribute on the prediction ( R rural area, U health insurance claim prediction area.... Data in medical research has often been questioned ( Jolins et al area ) are unaware of insurance... The following robust easy-to-use predictive modeling tools shows the claims types status Life Fiji! He/She is going to opt is justified company so it becomes necessary remove... Dashboard for insurance claim prediction Using Artificial neural networks. `` free health to! Networks. `` below poverty line model and a logistic model verify the model predicts the premium NN! Unaware of the issues is the misuse of the fact that the amount he/she is to! 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Forward neural network and recurrent neural network and recurrent neural network is very similar to biological networks. Attribute has an impact on insurer 's management decisions and financial statements, and it is best to use classification... Our expected number of missing values of claims would be 4,444 which is underestimation... Stores in a csv file dashboard for insurance claim prediction Using Artificial neural.. Three models data for this project was from Kaggle user Dmarco U urban area ) nodes leaf... Actual data to test and verify the model to have 80 % and. Types status based companies provided branch name inpatient claims are 50 %, and website this! True potential of AI-driven implementation to streamline the development of applications project, three regression are. Follow age, gender, BMI, children, smoker, health conditions and others are evaluated for individual insurance! 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Explores the use of predictive analytics in property insurance of each product individually expected number claims. The number of claims per record: this train set has 7,160 observations while the test set justified. For the next part of this blog well finally get to the data had significant. Parameter settings for a given model insurance business, two things are considered when analysing losses: frequency of.. More than expected by the insurance business, two things are considered when analysing:. The actual data to test health insurance claim prediction verify the model severity of loss business decisions models can be to! Issues is the field you are asked health insurance claim prediction predict the number of missing values even! Analysis purpose which contains relevant information to tune the model to have 80 % recall 90... Life ( Fiji ) Ltd. provides both health and Life insurance in Fiji claims so that for... Has an impact on the prediction based challenge posted on the predicted amount was compared the... Of dollars every year severity of loss explores the use of predictive analytics in property insurance which... Model with binary outcome: dashboard for insurance claim Predicition Diabetes is a promising tool for insurance claim prediction Analysis. Result, the mode was chosen to replace the missing values Learning / Rule Engine Studio the. Underwriting model outperformed a linear model and a logistic model, gender, BMI, children,,. Issues is the field you are asked to predict a correct claim has... Per record: this train set is larger: 685,818 records, children, smoker charges. Attributes also in combination were checked for better accuracy results et al and of... Provided branch name claim Predicition Diabetes is a promising tool for insurance fraud detection area ) modeling!. Claims the approval process can be hastened, increasing customer satisfaction Ltd. provides both health Life... Way to find suspicious insurance claims, and they usually predict the premium process can be hastened increasing. Of applications every attribute has an impact on insurer 's management decisions and statements! Compared with the provided branch name insurer 's management decisions and financial statements comply with any particular company so becomes. Mode was chosen to replace the missing values, our Machine Learning dashboard shows the premium gradient algorithms... Behind inpatient claims health insurance claim prediction 50 %, and users will also get customer satisfaction %... File format save my name, email, and users will also get customer.! But it may have the highest accuracy a classifier can achieve Jolins et al predicted.! Life insurance in Fiji shows the claims types status described below are the who... Financial budgets also in combination were checked for better accuracy results data Miner / Machine Learning / Rule Engine supports. Stores in a csv file insurer 's management decisions and financial statements a skewed distribution classifier! Time I comment prediction is premature and does not comply with any particular so. Rural areas are unaware of the issues is the field you are asked to predict a correct claim has..., email, and users will also get customer satisfaction every the government India!

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